02 Risk Management Class Notes Spring 2024 (1)
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Intro to Risk Management
Class introduction
Professor’s goals
o
Cover the primary financial risk management tools and risks
o
Push students to skepticism right up to (but not beyond) cynicism
o
Create critical thinkers in risk management
“Teaching students to think critically
is the principal aim of undergraduate education.” Academically Adrift
“In the world of modern finance, a love of numbers has replaced a desire for critical thinking
. As long as something has a number attached to it, then it is taken as gospel truth.” James Montier
“There’s too much math and not enough thought
about what risk is really about. . . We have to make decisions with unknown outcomes. So we have to think about consequences as well as probabilities.” Peter Bernstein
Professor’s expectations of students
o
Come to class (every class)
o
Be prepared (read class notes, Internet research to read others’ definitions of key term)
o
No use of computer or iphone except for class purposes
o
Don’t leave in the middle of class unless you are dying
o
Produce exceptional papers
o
Be engaged in class (and even look engaged in class)
o
Trust me that what we are covering is of critical importance to your career
Student expectations of professor
Paper Assessment
1.
Coverage (40%)
2.
Clarity (40%): Technical writing (purposeful writing: goal is to convince the reader)
a.
Well organized (visually organized)
b.
Titles, sub titles
c.
Paragraphs (at least 4 a page)
i.
Limit your ideas in a paragraph
d.
If you quote in the paper, I don’t need a full bibliography (can be “According to . . . “
i.
If quote is more than one line long, indent, separate and italicize
ii.
Sources: dates are important
e.
Bold key terms (if really important, bring it out)
f.
No fluff (if it does not serve your purpose, take it out)
g.
Avoid unnecessary repetition
3.
Critical Thinking (passion) (20%). Good critical thinking can be set up with phrases like:
a.
I find it interesting that
b.
I was surprised to learn that
c.
I wonder if
d.
I was curious to see if
e.
I was amazed that
f.
I question if
g.
I think they should also consider
h.
I think this is a unique strength in their process because (for Paper 1)
i.
This is similar to what we discussed in class because j.
This contradicts what we discussed in class because
k.
Draw on other items from class not in readings
l.
Reference your own outside reading
m.
Reword in your own words
n.
I was not sure the meaning of _____ but I think it means
Some helpful websites:
https://www.glynholton.com/
http://vlab.stern.nyu.edu/welcome/risk/
(especially useful for Paper 1)
www.CMRA.com
https://hubbardresearch.com/
Great books in Risk Management
Books by Peter Bernstein:
o
Against the gods:
http://www.amazon.com/Against-Gods-Remarkable-Story-Risk/dp/0471295639/
ref=sr_1_1?s=books&ie=UTF8&qid=1368712798&sr=1-
1&keywords=peter+bernstein
o
Capital Ideas:
http://www.amazon.com/Capital-Ideas-Improbable-Origins-Modern/dp/
0471731749/ref=sr_1_4?s=books&ie=UTF8&qid=1368712798&sr=1-
4&keywords=peter+bernstein
Books by Nassim Taleb:
o
Fooled by Randomness:
http://www.amazon.com/Fooled-Randomness-Hidden-Chance-Markets/dp/
1400067936/ref=sr_1_3?ie=UTF8&qid=1368712704&sr=8-3&keywords=taleb
o
Black Swan:
2
http://www.amazon.com/The-Black-Swan-Improbable-Robustness/dp/
081297381X/ref=sr_1_2?ie=UTF8&qid=1368712704&sr=8-2&keywords=taleb
o
Antifragile:
http://www.amazon.com/Antifragile-Things-That-Gain-Disorder/dp/1400067820/
ref=sr_1_1?ie=UTF8&qid=1368712704&sr=8-1&keywords=taleb
Books by Daniel Kahneman
o
Thinking, Fast and Slow
http://www.amazon.com/Thinking-Fast-Slow-Daniel-Kahneman/dp/0374533555/
ref=sr_1_1?s=books&ie=UTF8&qid=1368712885&sr=1-
1&keywords=kahneman+thinking+fast+and+slow
o
Noise
https://www.amazon.com/Noise-Flaw-Human-Judgment/dp/B08LNYM39M/
ref=sr_1_1?
crid=219KBHXBV856W&keywords=Kahneman+Noise&qid=1642432418&sprefix=ka
hneman+noise%2Caps%2C137&sr=8-1
The Undoing Project: A Friendship That Changed Our Minds by Michael Lewis: o
https://www.amazon.com/Undoing-Project-Friendship-Changed-Minds/dp/B01KBM82M4/
ref=sr_1_1?keywords=the+undoing+project&qid=1576941108&sr=8-1
Thinking in Bets: Making Smarter Decisions When You Don't Have All the Facts by Annie Duke
o
https://www.amazon.com/Thinking-Bets-Making-Smarter-Decisions/dp/B078SBSBW3/
ref=sr_1_1?keywords=thinking+in+bets&qid=1576941187&s=audible&sr=1-1
Books by Douglas Hubbard
o
The Failure of Risk Management: Why It's Broken and How to Fix It
http://www.amazon.com/The-Failure-Risk-Management-Broken/dp/0470387955/
ref=sr_1_1?ie=UTF8&qid=1377879103&sr=8-
1&keywords=the+failure+of+risk+management
o
How to Measure Anything: Finding the Value of 'Intangibles' in Business
https://www.amazon.com/How-Measure-Anything-Intangibles-Business/dp/B005O5JR4G/
ref=sr_1_1?keywords=how+to+measure+anything&qid=1576941249&s=audible&sr=1-1
You're About to Make a Terrible Mistake!: How Biases Distort Decision-Making and What You Can
Do to Fight Them by Olivier Sibony
o
https://www.amazon.com/Youre-About-Terrible-Mistake-Decision-Making/dp/
B08D3WTDFG/ref=sr_1_1?crid=1F4IRA6PLSBEW&keywords=you
%27re+about+to+make+a+terrible+mistake&qid=1642432515&sprefix=You
%27re+about+to+mak%2Caps%2C103&sr=8-1
Regulating Wall Street: The Dodd-Frank Act and the New Architecture of Global Finance by Viral V.
Acharya, Thomas F. Cooley, Matthew P. Richardson, Ingo Walter, New York University Stern School
of Business
3
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o
http://www.amazon.com/Regulating-Wall-Street-Dodd-Frank-Architecture/dp/
0470768770/ref=sr_1_1?ie=UTF8&qid=1388326931&sr=8-
1&keywords=regulating+wall+street
4
Class Introduction
Let’s start with an example of a risk management decision from the TV show Deal or no Deal
In this show, a contestant chooses one briefcase from a group of 26. Each briefcase contains a cash
value from $5 to $4,000,000. Over the course of the game, the contestant eliminates cases from the
game, periodically being presented with a "deal" from The Banker to take a cash amount to quit the
game. Should the contestant refuse every deal, they are given the chance to trade the first case –
chosen before play – for the only other one left in play, and win whatever money was in the chosen
case.
Is there a way to make an “optimal” decision based on good risk management techniques?
Would the “optimal” answer be the same for everyone?
Does it matter to this decision
that she started with $0?
Expected value (average) = $839,218; Median: $17,500, so this is a skewed distribution. Bank’s offer:
$701,000
Probability next case selected is $25,000 or less: 67%
We also need to be willing to ask, do conventional risk management techniques always work? See this
scene from the movie, “The Duel” (2:04).
https://www.youtube.com/watch?v=BJ0wP1R9jqw
5
Risk Management: A general management function
that seeks to assess and address
the causes and
effects
of all uncertainty and risk
on an organization.
Goal of Risk Management: Risk optimization
versus “risk ignorance” or “risk minimization”. We want to
receive adequate payment for all risks we take on. We want to be “paid for taking risk.”
The Risk Management Process can be expressed at a high level as: Define, Measure, Mitigate. More
detailed expressions of the process of risk management are usually broken down into steps similar to
the following (
Define, Measure, Mitigate
):
1.
Determine Mission of Organization: identify relationship between risk management and the mission
of the organization 2.
Risk Assessment a.
Risk identification: 1) identify activities or conditions that create or increase the likelihood of
loss (perils) and 2) identify what in the organization is exposed to loss due to the perils
(exposures). See an example “Universe of Risks” below.
b.
Risk analysis: how perils and exposures come to exist and how they interact to produce a
loss or gain
c.
Risk measurement: evaluate the likelihood of loss or gain (frequency) and the value of the
loss or gain (severity). Of the two, severity is the more easily calculated.
3.
Risk Control: internal activities focused on
a.
Risk avoidance: eliminate the risk b.
Risk prevention: activities focused on reducing the frequency of losses c.
Risk reduction: activities focused on reducing severity of losses 4.
Risk Financing/Transfer: Seek the help of an external entity. It can be the purchase of insurance, self
insuring through a captive insurance subsidiary, risk securitization, or use of letters of credit
5.
Risk Retention (the risk that is left after we have mitigated the risks we can)
Step 2. a. above (risk identification) is often accomplished by a corporate Universe of Risks. There are
many examples of risk universes. Here is one from Capital Market Risk Advisors:
6
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The list we will be using is drawn from the The Group of Thirty
, often abbreviated as G30. The g30 is an
international body of leading financiers and academics which aims to deepen understanding of
economic and financial issues and to examine consequences of decisions made in the public and private
sectors. 1.
Interest Rate Risk 2.
Liquidity Risk 3.
Market/Price Risk 4.
Credit Risk 5.
Basis/Tracking Risk 6.
Modeling Risk 7.
Legal Risk 8.
Disclosure Risk 9.
Tax Risk
10.
Operational Risk 11.
Purchasing power risk
12.
Currency risk
Before getting into detail on each of these risks, let’s step back and consider the longer-term history of
risk management
as presented in “Against the gods: The Remarkable History of Risk” by Peter
Bernstein.
Key concepts from the book that are important to consider
o
Statistical inference
o
Law of Large Numbers
o
Reversion to mean
o
Correlation and Diversification
7
For each of these, you should focus on
o
Their definitions
o
How they are applied in finance
o
How they are misapplied in finance
Statistical inference:
Take an adequate sample size of a given population and apply the statistics of that
sample to the population to infer the characteristics of the population.
In finance, we apply statistical inference
by taking history as our sample to determine the
characteristics of the “population” which is the future. That is, historical data is our sample and our
population is the future. But is the last 50 years a sample size of 50 or a sample size of 1? How large of a
sample from the past do we need to have confidence predicting the characteristics of the future?
Statistical inference can be misapplied
in the following ways:
Sampling error.
We are not taking our sample from the actual population (the future) but from
something we think is very similar to the population (the past). In essence, we assume the past
predicts the future.
The world (its means, standard deviations and correlations) changes with time. Technology,
demographics, markets, culture, the environment, etc. change over time such that history becomes
a weak predictor of the future.
History changes the future.
We learn from our mistakes and we are conditioned by our experiences
to behave differently “the next time” such that the next time looks very different than the last time.
Law of Large Numbers:
The larger the sample, the more closely your results will reflect the true
characteristics of the population. “Moral certainty” is the belief that if one obtains a large enough
sample size, their forecast will be as good as with a game of chance (like throwing dice).
We apply the law of large numbers
in finance by obtaining as much data as possible. This is the idea
behind “big data”, artificial intelligence and machine learning, the belief that if we unleash fast and
powerful computers to search for relationships in the huge databases we are now creating throughout
the world, we can better manage businesses and risk. We will find truly statistically significant
relationships unknown before the age of machine learning.
The law of large numbers can be misapplied
when we overly rely on statistical relationships that could
just be random noise. That is, we uncover relationships via what is known as “data mining” which are
simply random noise one would expect to find in any huge data set. And then once we find those
relationships, we have the confidence to make a decision we did not have the confidence to make
before without the data. We should wait for some out-of-sample confirmation of the relationships, but
the desire to be ahead of the competition gets us to act before we should.
8
Reversion to the Mean:
There is a natural tendency for data to move back toward the mean rather than
away from it. If you have had several years of above average stock returns, less than average is more
likely going forward. If this were not true, things would get out of whack after several years. Successful
companies would take over the world if they could sustain exceptionally high growth rates. But
reversion to the mean states that there are natural limits to any data set which causes them to
eventually move back to their means. The fast growing data set becomes too big to be sustained.
Reversion to the mean is applied
in finance by recognizing it and making it part of our models and
forecasts. If interest rates have been unusually low recently, our models assume they are more likely to
rise than fall. If a portfolio manager has had recent very strong performance, they are more likely than
not to have below average performance going forward. Pension plans are currently being accused of
ignoring reversion to the mean as they assume very high expected returns on assets based on historical
returns.
Reversion to the mean is misapplied
when we are too confident in its timing. It can take a very long
time for a data series to revert to its mean. Also, there are times when the mean itself is changing,
making reversion to the mean very difficult to forecast. For example, global warming may be changing
temperatures and the size and strength of hurricanes. A period of above average temperatures or
hurricane activity may not revert to the historical mean as the mean itself is rising.
A key challenge with reversion to the mean is to consider the two most dangerous statements one can
make in finance:
“It is different this time.”
“It is not different this time.”
Correlation and Diversification:
Harry Markowitz is famous for creating the “Efficient Frontier”, which is
a display of all combinations of portfolios which have the highest expected return at each level of risk.
The key to a portfolio being optimal is how the different asset classes correlate with each other. Asset
classes that have low or even negative correlation can be combined in a portfolio in a way that reduces
risk without impacting the expected return. Correlation is one of the most used tools in risk
management.
Correlation and diversification are applied
in finance when construct portfolios of assets and liabilities
looking for “optimal” strategies (highest expected return at a desired level of expected risk).
Correlation and diversification are misapplied
when we fail to recognize that correlations are not
stable. They are not constant, so their usefulness in managing risk is unreliable. In fact, when we most
need the benefits of diversification (during a financial crisis like we saw in 2008), correlations tend to rise
significantly such that portfolios lose much more money than expected.
9
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Let’s do a quick review of the risk measurement tools you have already learned in your coursework. We
will be using all of these in our discussions in the class.
1.
Standard Deviation
2.
Value at Risk
3.
Correlation
4.
Beta
5.
Alpha, Tracking Error, Information Ratio, and Sharpe Ratio (and other related ratios)
6.
Duration
7.
Convexity
8.
Credit Ratings
We will now work through our universe of risks. With each risk, we will follow the format of Define-
Measure-Mitigate
, using real-life examples from finance to understand the power and weaknesses of
our main tools in finance.
Universe of Risks: Interest Rate Risk
Define:
When rates change, the impact on assets and liabilities is inconsistent causing a loss in net
worth, especially for highly leveraged entities like banks, life insurance companies and pension plans
where interest rates impact both assets and liabilities.
How do interest rates change? Consider the US Treasury Yield Curve, which has 11 points over different
maturities: 1 month, 3 months, 6 months, 1 year, 2 years, 3 years, 5 years, 7 years, 10 years, 15 years, 30
years.
10
We can understand the yield curve in the following ways.
1.
Its Steepness, which is usually determined by comparing the 2 year and 10 year yields.
2.
How it shifts through time
a.
Parallel shifts. All maturities move up or down together by the same amount.
b.
Non-parallel shifts. Short, medium and long rates move differently.
3.
When preparing scenarios (“stress tests”), we must consider different possibilities:
a.
Rates rising in parallel
b.
Rates falling in parallel
c.
Yield curve flattening
d.
Yield curve steeping
Some great resources for historical yields:
https://www.treasury.gov/resource-center/data-chart-center/interest-rates/Pages/TextView.aspx?
data=yield
http://www.federalreserve.gov/releases/h15/data.htm
What assets and liabilities are most sensitive to changes in interest rates?
1.
Bonds: have the most direct tie to interest rates changes of any asset, as we will see below.
2.
Stocks: have an implied tie to interest rates from the Dividend Discount Model where Price =
Dividend Next Year / (
risk free rate
+ beta * market risk premium – growth)
This implies stocks move like bonds in relation to interest rates. However, this relationship is
unreliable because there are strong countervailing relationships in the Dividend Discount Model.
Stocks rise when economy is strong, but so do interest rates, and stocks fall when economy is weak,
but so do interest rates. The economic impacts on earnings tend to drive stocks more than changes
in interest rates.
3.
Real estate: Like stocks, there is an implied relationship between real estate values and interest
rates based on how we value real estate as the present value of future rents. But how strong the
relationship is depends on the type of real estate. A 20-year office lease with a high credit quality
tenant will behave very much like a bond. But a hotel property where rents can change every day
will be much less sensitive to interest rates. 4.
Loans: Much like bonds, loans are very sensitive to changes in interest rates. You must also consider
the options provided in the loans which can create prepayment risk and extension risk.
5.
Debt: These are bonds but as held by the borrower, not lender. Thus a firm’s debt is sensitive to
changes in interest rates, but now on the liability side of the balance sheet.
6.
Deposit accounts: bank and life insurance accounts like checking and savings accounts, certificates
of deposits, annuities and cash value life insurance have varying degrees of sensitivity to interest
rates.
7.
Other liabilities: many other liabilities such as pension plan liabilities can be very sensitive to interest
rates.
11
Banks, Life Insurance Companies, Pension Plans, REITs (Real Estate Investment Trusts), Pension Benefit
Guarantee Corporation (U.S. federal agency that takes over pension plans for bankrupt companies) are
unique entities in that they are sensitive to changes in interest rates on both sides of their balance
sheets.
Measure
The two key ways we measure interest rate risk are Duration and Convexity.
Duration
is the weighted average time to maturity of a bond using the discounted cash flows as the
weights. Once we have duration calculated, we use it in the following formula to assess interest rate
sensitivity of an asset or liability: Percentage change in price of asset or liability = - Duration * change in yields
For example, if a bond portfolio has a duration of 5 years and rates rise 1%, what do you expect the price
(market value) of the portfolio to do? Percentage change in price = - 5 * (+.01) = -5%, so we would
expect the bond price to fall 5% if interest rates rose 1%. If rates rise 2%, bond price would be expected
to fall 10%. If rates fall 1.5%, we would expect the bond price rise to 7.5%.
For amortizing loans that pay both interest and principle (for example, a home mortgage), the duration
is shorter than for non-amortizing bonds because more of the cash flow is received earlier.
There are different ways to calculate duration, and you should do your own reading on the following
terms: Macaulay Duration. Modified Duration. Effective Duration. https://en.wikipedia.org/wiki/Bond_duration
Convexity
is second derivative of price change versus interest rate changes (how duration changes as
interest rates change). Convexity describes the curvature of the bond price change. When interest
change a lot, bond prices move in a more convex way as described in the following formula:
% change in price of bond = - D * change in yield + C * (change in yield squared)/2
On Blackboard, there is an Excel File called Duration and Convexity Calculator in Excel which works
through the math using duration and convexity together to assess the interest rate sensitivity of bonds.
Mitigate:
We mitigate interest rate risk by seeking to get assets and liabilities to move in a consistent
way when rates change to buffer the impact on net worth. This is known as “Balance Sheet
Immunization”
where we manage the duration of net worth to an acceptable level by managing the
duration of assets and liabilities. 12
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In the examples that follow, we will be using Balance Sheet Immunization and Dollar Duration Gap
Analysis
to mitigate a bank’s interest rate risk. Dollar Duration Gap Analysis is mainly used with pension
plans (not banks), but we can use it with banks to get some exposure to the concept before discussing
pension plans. We use the term “immunize” rather than “defease” because immunization reduces but
does not completely eliminate a risk while defeasing a risk completely eliminates it. To immunize a
balance sheet from changes in interest rates, we move the duration of net worth closer to zero. Many bank boards of directors will have policies on how exposed their bank’s balance sheets can be to
changes in interest rates. Below might be a typical board policy at a bank:
Change in
Rates
Allowed Change in Net Worth
-300
-15%
-200
-10%
-100
-5%
+100
-5%
+200
-10%
+300
-15%
Note that we express the change in interest rates in basis points
. One basis point is .0001. To get from
an interest rate to basis points, multiple by 10,000. So interest rates falling 1% is the same as interest
rates falling 100 basis points (.01 * 10,000).
In the table above, we can see that the bank’s board permits a duration of net worth of no more than 5
years and no less than negative 5 years. You can determine this by looking at the allowed change in net
worth for a 100 basis points change in interest rates where the net worth is allowed to fall by no more
than 5%. Given the formula from above (% change in price = - Duration times change in interest rates),
the duration of the bank’s net worth must be between negative 5 years and positive 5 years for the bank
to be in compliance.
Balance Sheet Immunization Example Problems
Key Formula to learn:
Duration of Net Worth = (D
A
– $L/$A * D
L
) * Leverage
Leverage = $A / $NW
D
A
: Duration of Assets
D
L
: Duration of Liabilities
$L: Dollar amount in Liabilities
$A: Dollar amount in Assets
$NW: Dollar amount in Net Worth
13
Example 1:
Assets
$ in Millions
Duration
Cash
$150
0.00
Loans
850
5.20
Total Assets
$1,000
Liabilities
Debt
$750
3.75
Net Worth
$250
?
1.
Calculate the duration of Company X’s assets.
Duration of Assets = (150 / 1000) * 0 + (850 / 1000) * 5.20 = 4.42 years
2.
Calculate the duration of Company X’s net worth.
Duration of Net Worth = (DA – $L/$A * DL) * Leverage
Duration of NW = (4.42 – (750 / 1000) * 3.75) * (1000 / 250) = 6.43 years
3.
Calculate Company X’s expected net worth if interest rates were to fall 150 basis points.
150 basis points = 150 / 10,000 = .015
% change in Net Worth = Minus Duration * Change in Yields
Minus (+6.43) * (-.015) = +.09645
Net Worth Started at 250, so 250 * (1 + 0.09645) = $274
Check this:
Duration of Assets 4.42; - (+4.42) * (-.015) = so assets should rise to $1,066 Duration Liabilities 3.75; -(+3.75) * (-.015) = so liabilities should rise to 792
Net Worth after rates fall 150 bps = 1,066 – 792 = $274
4.
How did we do versus board policy?
Change in Rates
Allowed Change in Net Worth
Bank in the example (D
nw
= +6.43)
-300
-15%
+19.29%
-200
-10%
+12.86%
-100
-5%
+6.43%
+100
-5%
-6.43%
+200
-10%
-12.86%
14
+300
-15%
-19.29%
Notice that this bank violates the Board Policy when interest rates rise because the duration of
their net worth is 6.43 years where the board policy implicitly restricts the duration of net worth
to just 5 years. There duration of net worth needs to be reduced by reducing the duration of
assets or increasing the duration of liabilities
.
If duration of net worth is negative 6 years, when it needs to be between negative 5 to positive 5 years,
what does the bank need to do?
1.
Duration of Assets needs to be
increased
by make more auto and home equity loans with longer
durations. However, this disrupts the banking operations.
2.
Duration of Liabilities needs to be
decreased
by moving to shorter duration deposit accounts (that
is, push shorter term certificates of deposit). However, this also would be disruptive to banking
operations. If the bank is borrowing money, they could switch from long term debt to short term
debt. However, they may not be well known in shorter term debt markets, and thus will not receive
good rates to borrow.
3.
Use Interest Rate Swaps
to reduce the duration of liabilities. This will be our approach as shown
below. Interest Rate Swaps are Over-the-Counter (OTC) derivatives similar to forwards. They are
extremely flexible because they are OTC (versus futures that are standardized, exchange traded
derivatives).
Swaps can be done on almost any index or asset class, including interest rates, stock market indices,
currencies, commodity prices and even weather. They can take time to get set up with all the complex
legal and accounting review needed (see example swap standardized contract (“ISDA”) on Blackboard).
A key to great risk management is having the infrastructure for risk management in place and tested
before you need it. That is, get your swap contracts approved and implemented well before you intend
to use them because it can take several months to get everything set up. When the environment
changes and you suddenly need a swap contract to manage risk, it will be too late to start working on
setting up the swap.
There are four parties to a swap contract: The customer (firm immunizing its balance sheet), the
counterparty (investment bank setting up the swap), the custodial banks that holds the assets (one for
the customer and one for the investment bank), and the third party administrator (the firm that does
the accounting for the customer (may not exist for banks, but will be a party to the contract for a
pension plan).
Reduce the Duration of Assets
o
Stop doing 5 year loans and instead do more credit card loans
o
Stop selling home equity loans and sell only short term auto loans
15
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However, these moves would disrupt bank operations
Increase the duration of liabilities
o
Push more long term certificates of deposits by reducing rates on savings accounts and
increasing rates on longer term certificates of deposit
However, this would disrupt bank operations
o
Switch from short term debt to longer term debt
However, the short-term market might not be a good or familiar debt market for them
o
Use derivatives (futures, forwards and interest rate swaps). If use interest rate swaps, swap
from floating to fixed rate
This approach does not impact bank operations or force them to borrow in unfamiliar debt
markets.
Use the information on the current cost of financing for Company AAA and Company BBB in the
following table to answer the questions below. BP stands for basis points.
AAA
BBB
Difference
5-Year Fixed-Rate Financing
Treas +125BP
Treas+185BP
60 AAA cheaper
Floating Rate Financing
LIBOR + 10BP
LIBOR + 20BP
10 AAA cheaper
1.
Determine which company has the comparative advantage in fixed-rate debt. AAA
Take the difference in the cost of debt for AAA and BBB. For fixed rate debt, the difference is 60BP
(185 – 125) and for floating rate debt the difference is 10BP (20 – 10). Wherever that difference is
greater (that is, where AAA has the biggest advantage), that is where AAA has the comparative
advantage. BBB has the comparative advantage where the difference is the smallest. In this case, AAA has the comparative advantage in fixed rate debt and BBB has the comparative
advantage in floating rate debt. That is the normal relationship due to the “term structure of credit
spreads” and the difference in credit spreads increases between high quality and low quality debt as
one moves out in maturity.
16
2.
Calculate the ultimate borrowing costs for AAA and for BBB if these two entities borrow in the debt
market in which they have a comparative advantage and then enter into a fixed-for-floating interest
rate Swap agreement where AAA pays LIBOR to BBB and BBB pays Treas +140 fixed to AAA.
3.
The above transaction is sometimes referred to as “interest-rate Swap arbitrage”. Explain why this is
not truly an arbitrage transaction. Not arbitrage because while you reduce interest rate risk with the
swap, you are picking up credit risk (counterparty risk). Counterparty risk is the risk that when the
swap is paying you, the counterparty (investment bank) is bankrupt. This is discussed in more detail
below. 4.
Refer back to Company X in previous section. Determine which side of the swap transaction
described above (AAA’s or BBB’s) Company X should take in order to reduce its interest rate risk.
Explain your answer. BBB, because they started with floating rate and ended with fixed rate thus
increasing the duration of their liabilities, and thus reducing the duration of their net worth
In summary, if D
NW
is too low (too negative):
Either increase D
A
or decrease D
L
For the swap, assume you are on the liability side
o
Decrease D
L
o
Swap from fixed rate to floating rate (AAA)
If D
NW
is too high:
Either decrease D
A
or increase D
L
For the swap, assume you are on the liability side
o
Increase D
L
o
Swap from floating rate to fixed rate (BBB)
5.
Using Dollar Duration Gap analysis, if an investment bank were to offer a 5-year duration interest
rate swap, what notional amount of this swap should Company X obtain to fully immunize its
balance sheet?
Remember:
o
Duration of Assets: 4.42 (assets = $1,000)
o
Duration of Liabilities: 3.75 (liabilities = $750)
o
Duration of Net Worth: 6.43 years
Dollar Duration Gap Analysis
o
D
A
* Value of Assets – D
L
* Value of Liabilities = Dollar Duration Gap
o
4.42 * 1,000 – 3.75 * 750 = 1,607.50
o
Assume we are going to enter into an interest rate swap with a 5-year duration
Notional Amount of Swap Needed = 1,607.50 / 5 (duration of swap) = 321.50 which
is the notional amount of a floating to fixed rate swap needed to increase duration
of liabilities and set D
NW
to zero
17
Example 2:
Assets
$ in Millions
Duration
Cash
$200
0.00
Loans
900
3.30
Total Assets
$1,100
Liabilities
Debt
$950
3.90
Net Worth
$150
?
1.
Calculate the duration of Company X’s net worth.
D
A
= 900 / 1,100 * 3.3 = 2.7 D
NW
= (Da – ($L/$A) * Dl) * ($A / $NW) = (2.7 – (950/1,100)*3.9)*(1,100 / 150) = -4.91
2.
Calculate Company X’s expected net worth if interest rates were to rise 200 basis point.
% change in Net Worth = - D * change in yields = - (-4.91) * (+.02) = +.0982
150 * (1 + .0982) = $164.7
If we want to immunize the balance sheet, we need to raise the duration of net worth by decreasing
the duration of liabilities (start fixed rate and swap to floating)
Use the information on the current cost of financing for Company AAA and Company BBB in the
following table to answer the questions below. BP stands for basis points.
AAA
BBB
5-Year Fixed-Rate Financing
Treas + 50BP
Treas + 150 BP
Floating Rate Financing
LIBOR + 10BP
LIBOR + 160BP
1.
Determine which company has the comparative advantage in fixed-rate debt.
BBB has a comparative advantage in fixed rate (that is, less of a disadvantage)
2.
Calculate the ultimate borrowing costs
for AAA and for BBB if these two entities borrow in the debt
market in which they have a comparative advantage and then enter into a fixed-for-floating interest
rate Swap agreement where AAA pays Treasury plus 15 basis points to BBB and BBB pays LIBOR to
AAA.
AAA borrows normally at +L+10
BBB Borrows normally at +T+150
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Swap
Swap
AAA pays +T+15
BBB pays +L
AAA receives –L
BBB receives –T- 15
Cost: +T +25
Cost: +L + 135
3.
Refer back to Company X in the previous question. Determine which side of the swap transaction
described above (AAA’s or BBB’s) Company X should take in order to reduce its interest rate risk.
Explain your answer.
BBB because we need to decrease duration of liabilities to increase the duration of net worth, so
swapping from fixed to floating will decrease the duration of liabilities
4.
Using Dollar Duration Gap analysis, if an investment bank were to offer a 10-year duration interest
rate swap, what notional amount of this swap should Company X obtain to fully immunize its
balance sheet?
Dollar Duration Gap = 2.7 * 1,100 – 3.9 * 950 = 735 absolute value
735 / 10 = 73.5 notional needed in a fixed to floating swap
This transaction is often referred to as “Interest rate Swap arbitrage”. But is this really arbitrage?
Arbitrage is a gain with no risk and no capital invested (that is, a “free lunch”). No it is not arbitrage
because it creates a new risk known as “Counterparty Risk”. Counterparty risk is a special type of credit
risk. One’s exposure changes over time as interest rates change. Sometimes the counterparty (the
investment bank setting up the swap) owes you on the swap, which creates credit risk to the
counterparty. Sometimes you owe the counterparty on the swap.
If you owe the counterparty on the swap, does that mean there is no counterparty risk? No because
interest rates can change dramatically and quickly, more than the collateral the counterparty has set
aside, such that you go quickly from a net owe to net receive position.
How should we measure counterparty risk, then? We use scenario analysis. We run scenarios of
different interest rate movements to see the most the counterparty could owe us at some reasonable
probability. This is known as “Potential Exposure”:
what could be owed to use in a short period of time
under some extreme but reasonable scenario. This is very similar to a Value at Risk analysis we will
discuss later. Potential exposure is essentially a Value at Risk analysis of your counterparty risk on an
interest rate swap.
Balance Sheet Immunization for banks is just one of many examples of this in finance. A few other
examples that create even more difficult challenges are pension plans, life insurance deferred annuities
and endowments.
19
Pension Plan Balance Sheet Immunization.
A pension plan is a firm’s promise to pay their retiring
employees a monthly amount (sometimes adjusted for inflation) for the rest of their lives. These are
liabilities with very “long tails” as they can go on for decades (until every participant dies).
The liability is a fixed cash flow whose value today is the present value of those promised cash flows at
some discount rate. According to current GAAP accounting, the discount rate is the current rate on
investment-grade corporate bonds matching the duration of the pension plan’s cash flows. This is a
liability with a very long duration, so it is very sensitive to interest rates. Its duration can be 18 years or
longer, much longer than almost any bonds in the U.S.
We will address the complexity and appropriate investment strategy for dealing with pension plans via
the article by Robert Arnott titled “Can We Keep Our Promises?” (also see your professor’s rewritten,
simplified version of this article) and your professor’s blog (and PowerPoint) on the issue:
https://profesweet.wordpress.com/2017/07/13/borrowing-to-invest-the-risk-management-
considerations/
Life Insurance Balance Sheet Immunization.
This is a very different animal than banks or pension plans,
and is one of the most complex applications of balance sheet immunization (known as “Asset-Liability
Management” in life insurance companies) because of Disintermediation Risk and Embedded Options
.
Disintermediation risk is the risk money goes from one financial intermediary to another. In this case, it
is when money flows from life insurers to banks because rates rose sharply. Embedded options refers to
the options made part of insurance contracts that allow the customer to make changes to the policy
(including surrendering it early to find a better deal elsewhere).
Life insurance actuaries have to be more precise with their balance sheet immunization by matching not
just duration of assets and liabilities but also by matching key rate durations
and key rate convexity. The
term “key rate” means they have to match different buckets of cash flows for assets and liabilities.
Endowment Balance Sheet Immunization.
Endowments are charities that raise money for a particular
group or cause. Often the group or cause is people facing financial hardship and are in need of financial
assistance. There are three components to an endowment: The donors, the portfolio, and the clients/
recipients of endowment funds. The donors are usually wealthy individuals with large security portfolios
and the clients are people in need.
Most endowments are allocated heavily to stocks (usually 60%) with the remainder in intermediate-
term bonds (with a duration around 5 years). In a strong economy, donors are doing well and donate
more and the portfolio is experiencing large gains. The clients have smaller needs because in a strong
economy they are doing better. However, in a weak economy, donor giving will drop because the donors
are losing money on their personal portfolios and have less need for the tax break from donations, the
endowment’s portfolio is experiencing large losses as stocks fall but the 5-year duration bonds provide
20
very little offsetting gains, and the clients have much greater needs. Is a large allocation to stocks really
the best strategy for an endowment given the extreme downside of an eventual weak economy?
Endowments need something other than 5-year duration bonds to offset the risk of stocks in a weak
economy. Five-year duration bonds will simply not rise much in a weak economy, especially given that
they are usually corporate bonds that have some credit risk. Endowments need something that will do
extremely well when donors are hurting and client need are rising.
Two approaches that might work better than what endowments are currently doing are shown below.
Professor Sweet refers to these strategies as: “When everyone is laughing, I want to be smiling; when
everyone is crying, I want to be laughing.” That is, find a strategy that does slightly below average in
most situations but does great in a major downturn.
A 100% allocation to equities but with those equities hedged with options.
A high allocation to equities (less than 100% but 60% or higher) and partially hedged with the
remaining portfolio in something that will do much better in a weak economy than 5-year duration
bonds.
Why does the investment profession not focus on balance sheet immunization and instead focuses on
peer risk? Because this profession’s biggest risk is Career Risk
. The Prudent Expert Rule
requires the
fiduciary of a portfolio to use "care, skill, prudence and diligence", and to act in the same way that
someone "familiar with such matters" would act
. The "familiar with such matters" language has been
interpreted to mean how an "expert" in the field would typically behave. This language creates an
important distinction from the prudent person guideline in that it holds fiduciaries to a stricter standard
than the average person on the street. It also tends to argue against unconventional approaches. Your
career is safe as long as you fail when everyone else is failing. Never fail by yourself even if there is a
better strategy out there for your client. The prudent expert rule means most in the industry play it safe
by following only conventional approaches. These leads to herding (all are doing the same thing) and
limited innovation.
Universe of Risks: Liquidity Risk
Define:
Prior to 2008, liquidity risk was defined as the inability to obtain cash quickly when desperately
needed absent a “fire sale”. The act of selling an asset causes that asset’s price to fall dramatically due
to supply and demand forces in the market. If one does not need cash, prior to 2008 one would say they
did not have liquidity risk as they could simply wait out short-term gyrations in asset prices. According to
this definition of liquidity,
Illiquid Assets include real estate and corporate bonds. Corporate bonds are usually sold over-the-
counter (that is, not on exchanges but between sophisticated buyers and sellers). They are
especially illiquid in a “credit crisis”.
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Liquid Assets: Stocks, Treasuries, agency bonds and most asset-backed securities, and exchanged
traded derivatives like options and futures.
After 2008, the definition of liquidity risk has changed. Illiquid asset prices fell so much due to their
illiquidity and the extreme state of the crisis that everyone had to take notice, even those not needing to
sell assets. Prices fell 80 percent on some fixed income securities that appeared to have a high
probability of paying off. The extreme lack of liquidity caused the asset prices to behave very differently
than we were accustomed to. No one knew who owned what, and we are shocked at the concentrations
of ownership in some very large financial institutions.
The main issue for the Federal Reserve was trying to distinguish between a liquidity crisis
which they
can address very easily by providing funding and a solvency crisis
for which they have few tools to
address and might make worse by funding institutions that should be allowed to fail.
Measure:
There are no good ways to measure liquidity, but similar analysis as we do for credit risk using
fundamental analysis and understanding the companies we are buying, what their exposures are and
what they are guaranteeing is a good technique for assessing liquidity. One should also perform stress
tests, running extreme scenarios and looking for unexpected correlations between entities and assets.
A good way to highlight an area needing stress testing is to look at where the hot money is flowing
today. The next liquidity crisis would likely involve today’s most crowded trade
. One should consider
what will happen to that asset’s price if liquidity were to suddenly disappear.
Mitigate: The most common approaches to mitigating liquidity risk are:
Establishing concentration limits, the amount of exposure allowed to one entity, sector or asset type
or even to a particular macro factor, such as subprime mortgages in 2008.
Perform scenario analysis and ask each entity to provide their liquidity plan.
Hold a cash reserve.
Create a list of liquidity reserves and test them.
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Universe of Risks: Price/Market Risk
Define:
Price of an asset falls or liability rises and you lose money. Interest rate risk is a special case of
price risk.
Measure:
There are several measures of price risk you have seen in previous classes. Let’s review them
quickly.
Standard deviation, a measure of “total risk”. It assumes symmetry and utilizes the normal curve.
Beta, a measure of systematic risk of an individual stock.
Value at Risk (VaR) is the measure of the amount of loss expected at a given probability or risk
tolerance. VaR can be calculated as Parametric VaR, Historical VaR or Stochastic VaR, discussed
below.
Stress tests, which is the amount off loss under different scenarios.
Duration and convexity, which are measures of price risk given changes in interest rates.
In this class, we will be focusing on Value at Risk, using standard deviation in calculating a Parametric
VaR. Parametric VaR is calculated as follows: Mean - # of St Dev * St Dev
. This is the loss expected at a
probability associated with the number of standard deviations. For example, VaR
1%
= Mean – 2.33 * St
Dev. That is, the loss that is expected to occur with a frequency of 1% because a 1% event is 2.33
standard deviations from the mean of a normal distribution. That is, 1% of the time we expect this loss
or more
,
or I expect to lose no more than this 99% of the time.
While the formula will always give a negative number, we always express VaR as a positive number
, as
we will see below.
The primary means of mitigating price risk are correlation (that is, Diversification) and risk budgets.
Correlation (diversification). This is the concept of “don’t have all of your eggs in the same basket.”
Make sure you have a variety of assets in your portfolio that are uncorrelated.
The formula to know to understand how correction reduces risk is: Risk of a two asset portfolio =
(W
A
^2 * St Dev
A
^2 + W
B
^2 * St Dev
B
^2 + 2 * W
A
* St Dev
A
* W
B
* St Dev
B
* Correlation
A,B
)^.5
Risk Budgets (also known as “Risk Limits”). This is the maximum amount a firm is allowed to lose by
policy at a given probability or risk tolerance. The Risk Budget for a corporation could be the firm’s
Net Worth, in which case the analysis is known as “Risk of Ruin analysis”. Risk budgets for individuals
are highly subjective for each individual.
In short:
Measurement for Price Risk is VaR: how much we expect to lose
at a given risk tolerance.
Mitigation of Price Risk is the Risk Budget: how much we are allowed to lose
at a given risk tolerance.
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The process of measuring and mitigating Price Risk using Parametric VaR and Risk Budgets.
1.
Select a “
risk tolerance
”, some low probability. The closer you are to 0%, the lower your risk
tolerance. For corporations, risk tolerance is usually tied to the corporation’s desired credit rating.
For example, a 1% risk tolerance might represent BBB/Baa rated firms while a 0.2% risk tolerance
might represent AA/Aa rated firms.
For individuals, financial planners use rather subjective assumptions. Age and time to retirement is
the about the only thing we know that correlates with risk tolerance. For our purposes, we will make
the very general assumptions that the average Americans has a risk tolerance of 1%. Younger
investors might go up to 5%, and older investors might be as low as .1%.
2.
Calculate the VaR and determine our expected loss at that risk tolerance.
3.
Compare your VaR to your “Risk Budget” and take action. If the VaR is less than the Risk Budget, you
can do nothing or even take on more risk. If the VaR exceeds the Risk Budget, you need to reduce
risk somewhere.
Many financial planners use Risk Questionnaires to try to assess an investor’s Risk Tolerance. Here is
USAA’s Risk Tolerance Questionnaire which is typical of those used in practice:
1.
I am willing to lose money in the short-term if it means I might make more long-term.
a.
Yes
b.
No
c.
Maybe
2.
I do not panic when a stock loses money.
a.
Yes
b.
No
c.
Maybe
3.
I am an informed investor, and stay current with economic news.
a.
Yes
b.
No
c.
Maybe
4.
How many years until you expect to retire?
a.
Less than 5
b.
5 – 20
c.
20 or more
Let’s work some VaR Examples:
Type 1 Problems: you’re given the risk tolerance
, use the table to get # of standard deviations, given
mean and standard deviation, and you calculate the VaR
and compare VaR to the Risk Budget.
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Type 2 Problems: you’re given a certain target maximum loss
and asked the probability
it will
occur. Using the formula to determine the # of standard deviations, go to table to get the
probability. To get the probability, you will actually compute it as 1 minus the table value.
1.
If Uncle Todd wants to buy US large cap stocks for some inheritance money. He says he has a risk
tolerance of 1% and does not want to lose more than 20% (his risk budget) at that risk tolerance.
Can he invest 100% in US stocks if the expected return (mean) for stocks is 8% and the expected risk
(standard deviation) is 15%?
a.
Risk tolerance: 1% associates with a 2.33 standard deviation event (“a 2.33 sigma event”)
b.
Risk Budget: 20%
c.
VaR
1%
= .08 – 2.33 * .15 = -.2695, or a VaR
1%
of 26.95% (expressed as a positive number as
VaRs are always a loss)
d.
Result: he cannot invest 100% in stocks as it would exceed his risk budget
2.
Your boss is interested in a new portfolio strategy. You run your analysis and determine it has a
expected standard deviation of 8% and an expected return of 6%. Your boss has a risk budget of
$15,000 and the firm’s risk tolerance is 1.3%. The portfolio is $225,000. Is this strategy acceptable?
a.
Risk Budget: $15,000; Risk Tolerance: 1.3%; # of St Dev = 2.23 (from the table)
b.
VaR
1.3%
as a percent = .06 – 2.23 * .08 = -.1184 or reported as .1184 (state as a positive
number) c.
VaR
1.3%
in dollars = 225,000 * .1184 = $26,640
d.
Result: The strategy is too risky
e.
What risk tolerance could accept this portfolio? How many standard deviations is $15,000
loss from the mean?
f.
Expected return = 225,000 * .06 = 13,500
g.
How far is 15,000 loss from mean gain of 13,500 when the standard deviation is 225,000
* .08 (18,000)
i.
13,500 - - 15,000 = 28,500
ii.
28,500 / 18,000 = 1.58 standard deviations
h.
Mean – 1.58 * standard deviation = 13,500 – 1.58 * 18,000 = - 15,000
i.
What is the risk tolerance? 5.71% (find value in normal table at 1.58 standard deviations) or
use the formula in Excel Normsdist(1.58) = .9429. You must subtract this value from 1, so 1
minus .9429 = .0571.
j.
Another way to determine this: Risk Budget = 15,000; 15,000 / 225,000 = 6.67% (this is
actually a loss of 6.67%).
i.
-.0667 = .06 - # of St Dev * .08
ii.
-.12667 = - # of St Dev * .08; # of St Dev = .12667 / .08 or about 1.58 standard
deviations. From the table, that is a 5.71% probability, which is much higher than
the portfolio’s risk tolerance of 1.3%.
3.
Your boss has a $500,000 currency portfolio and a daily risk budget of $20,000. You want to start
trading a strategy that has an expected return of 0% and a standard deviation of 2% (based on a
trailing 60 day analysis). Your Risk Tolerance is 5%. Can you move to this new strategy?
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a.
Risk Budget: $20,000
b.
Risk Tolerance: 5%
c.
Standard Deviation: 2% (or $10,000, which is $500,000 * .02)
d.
# of St Dev: 1.65 (note that 1.64 is also acceptable)
e.
VaR
5%
= 0 – 1.65 * .02 = -.033 or 3.3%
f.
VaR
5%
= 500,000 * .033 = $16,500
g.
This means that 5% of the time we expect to lose 16,500 or more, and our budget allows us
to lose 20,000 or more 5% of the time. So we could leverage this strategy up a little more to
take on more risk and hopefully get more return (that is, we have unused risk budget).
h.
VaR = 0 - # of St Deviations * 10,000 = -20,000; So the # of st dev = 2, or 2.28% from the
Table is the actual risk tolerance associated with your strategy (versus 5% allowed)
4.
You are a Risk Manager for AIG. AIG has a risk tolerance of 0.5%. The Credit Default Swap
department has a daily risk budget of $100 million. They are trading a CDS portfolio that has a
notional a value of $4 billion. Their daily expected return is 0% and you have determined their daily
standard deviation is 3.0%. You meet with Mr. Greenburg tomorrow and you will tell him what?
a.
Risk Budget: 100 million
b.
Risk Tolerance: 0.5%
c.
Expected Standard Deviation: 3%
d.
# of St Dev = 2.58
e.
VaR
0.5%
= 0 – 2.58 * .03 = .0774, or .0774 * 4 billion = $310 million, which greatly exceeds the
Risk Budget of $100 million.
f.
What risk tolerance would have found this acceptable? 100 million = VaR * 4 billion, or a loss
of 2.5%; -.025 = 0 - # of St Dev * .03, # of St Dev = .83, which equates to a probability of
20.3%, which is much higher than the risk tolerance of 0.5%.
What happens if you have more than one thing to allocate to? In this case, we must now consider
correlation between the different assets (and, when appropriate, the different liabilities). This is where
we bring in the Portfolio Standard Deviation formula. The Standard Deviation of a Portfolio of Two
Assets equals:
(W
A
^2 * St Dev
A
^2 + W
B
^2 * St Dev
B
^2 + 2 * W
A
* St Dev
A
* W
B
* St Dev
B
* Correlation
A,B
)^.5
This formula can be used to build an efficient frontier. Let’s try it in Excel.
Now look at the Goldman Sachs Annual Report and consider how they calculate VaR and adjust for this
“diversification effect”. Note that Goldman Sachs uses a Risk Tolerance of 95%, but in our terms we
would state it as 5% (that is, 1 minus 95%).
Now let’s try some of the more complex Value at Risk problems
that incorporate correlation.
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There are some important considerations when using correlation as your mitigation tool for managing
Price Risk:
When correlation is negative, a higher standard deviation can actually be a good thing. For example,
gold has a low expected return and a high standard deviation. But investors allocate to gold because
it usually does very well in a crisis (that is, has a significantly negative correlation to other assets).
Low correlation means risk reduction, but this relies heavily on correlations being stable
over time
and predictable. However, during crisis, correlations for all risk assets tend to move to 1.0. This may
be getting worse. Assets around the world may be becoming more correlated to each other due to
globalization. See the Bridgewater article
on correlation in Blackboard on how they handle unstable
correlations.
Let’s walk through Professor Sweet’s views on diversification via his PowerPoint and blog
https://profesweet.wordpress.com/2017/05/30/diversification-everyone-does-it-but-does-it-
actually-work/
.
VaR is used extensively in the financial services industry to measure Price Risk. But it certainly has its
distractors. Here is the view of a Managed Futures manager: “I don’t like VaR. Uncertainty and risk is like
a cliff everyone stays safely away from. VaR is like building a fence along the edge of the cliff. Now, with
VaR, everyone feels comfortable leaning against the fence until the added weight causes the fence to
collapse.” VaR as a measure ignores how many people are all doing the same thing, which can cause
unexpected liquidity crisis when everyone starts selling the same assets in a crisis.
Professor Sweet’s views:
“VaR tells us to ignore risks that have a low probability. I actually am obsessed with just those risks.”
How we manage investment risk is inconsistent with how we manage other risk. Almost everyone
owning a house purchases homeowners insurance, but few purchase “insurance” for their
investments (insurance could be the purchase of options). But the probability of a fire destroying
over half of house is far lower the risk one’s portfolio could drop in half. Why do we buy insurance
for one risk but not the other?
The cost of insurance for a house rises when the risk of a fire increases. However, this might not be
true for insurance on an investment portfolio. Option prices fall dramatically during periods of low
volatility markets (that is, when the Volatility index (the VIX) is unusually low), but that might be
right when risk is at its highest.
Professor Sweet’s approach to managing investment risk for older investors:
https://profesweet.wordpress.com/2015/09/12/my-approach-to-investing-the-mature-investor/
Professor Sweet’s approach to managing investment risk for young investors:
https://profesweet.wordpress.com/2015/08/18/my-approach-to-investing-putting-it-all-together/
Here is a great example of where a VaR analysis could have prevented a bad decision. The Pension
Benefit Guarantee Corporation (PBGC)
is a U.S. federally chartered corporation created by the
Employee Retirement Income Security Act of 1974 (ERISA) to insure private defined benefit pension
plans. Prior to 2008, their management decided their stock allocation was too low and that they were
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too conservative. Therefore, they greatly increased their allocation to stocks. However, they should have
done a VaR analysis to understand the tail risk they were taking on in allocating to stocks. Their liabilities
soar when there is a rising number of firms going out of business, such as in the 2008 financial crisis. This
is also when stock markets tend to be crashing.
It is at the extremes
that unusual things happen completely out of line with normal distributions and
historical correlations and standard deviations. Seemingly uncorrelated things suddenly become highly
correlated and losses occur that the statistics showed to be impossible. We need to integrate all risks
and take a portfolio approach to risk management (known as “
contextual risk management
”).
As we discussed above, the investment community is more focused on career risk than on client risk.
When the investment community is worried that the stock market is overvalued, they might move their
stock allocation from 60% to 55%. Why don’t they reduce their stock allocation more? Because it creates
too much career risk. They are focused on small risks (from their client’s perspective) and ignoring the
biggest risks.
Here is a related quote from Professor Sweet: “When you have a gun to your head, this is not the time
to be focused on your cholesterol level.”
Let’s look again, but in a little more detail, at the three ways to calculate Value at Risk:
1.
Parametric VaR. VaR = Mean - # of St Dev * St Dev
2.
Historical VaR. Same as Stochastic VaR, except your simulations are selected points from history.
History becomes your simulation model.
3.
Stochastic VaR. Here you create multiple simulations using well-coordinated models in terms pf
means (that is, expected returns), risks and correlations. The advantage of Stochastic VaR is you can
start with the current economic and market data and incorporate reversion to the mean and
unstable correlations and risks. The disadvantage is that it is time consuming to developed and run.
For Historical and Stochastic VaR, the VaR is determined as follows. Take the number of simulations *
your risk tolerance, and select the loss that is that many from the worst case simulation. For example, if
you run 10,000 simulations and your risk tolerance is 0.2%, your VaR will be the 20
th
worst (10,000
* .002).
If your stochastic/historical VaR and parametric VaR are inconsistent, this is most likely because the risk
is not as symmetrical as parametric VaR would assume. That is, there is a fatter tail risk than normal
distributions assume, or there could be a truncated tail because options are being used.
Let’s discuss our project for the class which incorporates all of these concepts. The project involves
completing the following analysis:
1.
Create an Efficient Frontier
for a client and, based on that, make an asset allocation
recommendation. Assess your recommendation using both Parametric and Historical VaR analysis
.
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2.
Perform a Style Analysis
for a client’s mutual fund. A good article to read when doing a style
analysis: http://www.stanford.edu/~wfsharpe/art/sa/sa.htm
3.
Perform an Attribution Analysis
for a client’s mutual fund using concepts such as Alpha
, Tracking
Error
, Sharpe Ratio
, Treynor Ratio
and Jensen's Alpha
.
Universe of Risks: Credit Risk
Define:
Risk that someone who has a contractual relationship with you fails to perform that contract. It
is “default risk” or the bankruptcy of someone who owes you money. “Spread risk”: Risk that spreads
widen because the probability of default goes up (even though there was no default)
As we discussed above with the Federal Reserve, 2008 raised the issue of how can we really tell if an
issue is one of credit/solvency (a permanent failure) or liquidity (a temporary crisis)?
Measure:
1.
Credit ratings (see more details below
).
a.
High Quality Ratings: AAA (S&P) and Aaa (Moody’s)
b.
Medium Quality Ratings: BBB (S&P) and Baa (Moody’s)
c.
Low Quality (Junk) Ratings: BB+ (S&P) and BB1 (Moody’s)
2.
Altman Z score and Bloomberg’s Probability of Default. These are regression analyses that try to
identify firms at risk of bankruptcy.
Let’s look at the historical data from the main rating agencies. From their historical data, we can
determine the Probability of default
(that is, the frequency
of default) at each rating level. We need to
adjust this probability for the amount we expect to lose when a firm goes into default, which is 1 -
Recovery rates
(which is the severity
of default). Expected Loss = Probability of Default TIMES (1 MINUS the expected Recovery Rate)
The spread a bond provides (that is, the extra yield over the risk-free rate) needs to at least cover this
expected loss. It will generally be much higher than this to also cover tail events were defaults are much
higher and recoveries are much lower than normal. The spread also needs to cover your risk of the bond
being downgraded. When bonds are downgraded, their price will usually drop dramatically.
If your probability of default over 5 years is 5% and your expected recovery rate in the case of a default
is 40%, then the minimum spread on that bond should be (.05 / 5 years) * (1 - .4) which is .006 or 60
basis points.
Mitigate
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1.
Risk Limits (also known as Risk Budgets) and Concentration Limits. This is how much you can have
exposure to any one issuer and/or industry
2.
Aggregate limits across entities and asset types at the enterprise level. Could go beyond just bonds
and also include exposures from stocks, real estate, vendor relations and counterparties.
3.
Strong Covenants
4.
Credit Default Swaps
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Source:
QuantumOnline.com What are Credit Ratings? Credit ratings are published by Moody's, Standard & Poor's (S&P), Fitches and
others. Credit ratings for a specific security issuer and their securities represent the rating company's
evaluation of the credit worthiness of the issuing company. The following is what Moody’s has to say
about Credit Ratings: "The first thing you should know is that ratings are not investment
recommendations. A bond rated Aaa is not necessarily "better" or "worse" than a bond rated Baa1, for
example. Ratings are only one input you might consider in making your investment decision". The ratings are based, in varying degrees, on the following considerations:
1.
Likelihood of default - capacity and willingness of the obligor as to the timely payment of interest
and repayment of principal in accordance with the terms of the obligation;
2.
Nature of and provisions of the obligation; and
3.
Protection afforded by, and relative position of, the obligation in the event of bankruptcy,
reorganization, or other arrangement under the laws of bankruptcy and other laws affecting
creditors' rights. S&P’s Credit Rating Definitions
INVESTMENT GRADE
AAA
- Debt rated "AAA" has the highest rating assigned by S&P. Capacity to pay interest and repay
principal is extremely strong
.
AA
- Debt rated "AA" has a very strong
capacity to pay interest and repay principal and differs from the
highest rated issues only in small degree.
A
- Debt rated "A" has a strong
capacity to pay interest and repay principal although it is somewhat
more susceptible to the adverse effects of changes in circumstances and economic conditions than debt
in higher rated categories.
BBB
- Debt rated "BBB" is regarded as having an adequate
capacity to pay interest and repay principal.
Whereas it normally exhibits adequate protection parameters, adverse economic conditions or changing
circumstances are more likely to lead to a weakened capacity to pay interest and repay principal for
debt in this category than in higher rated categories.
SPECULATIVE GRADE (“Junk Bonds” or “High Yield Bonds”)
Debt rated "BB", "B", "CCC", "CC" and "C" is regarded as having predominantly speculative
characteristics with respect to capacity to pay interest and repay principal. "BB" indicates the least
degree of speculation and "C" the highest. While such debt will likely have some quality and protective
characteristics these are outweighed by major uncertainties or major exposures to adverse conditions.
BB
- Debt rated "BB" has less near-term vulnerability
to default than other speculative issues. However,
it faces major ongoing uncertainties or exposure to adverse business, financial, or economic conditions
which could lead to inadequate capacity to meet timely interest and principal payments.
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B
- Debt rated "B" has a greater vulnerability
to default but currently has the capacity
to meet interest
payments and principal repayments. Adverse business, financial, or economic conditions will likely
impair capacity or willingness to pay interest and repay principal.
CCC
- Debt rated "CCC" has a currently identifiable vulnerability
to default, and is dependent upon
favorable business, financial, and economic conditions
to meet timely payment of interest and
repayment of principal. In the event of adverse business, financial, or economic conditions, it is not likely
to have the capacity to pay interest and repay principal.
CC
- The rating "CC" typically is applied to debt subordinated to senior debt that is assigned an actual or
implied "CCC" debt rating.
C
- The rating "C" typically is applied to debt subordinated to senior debt which is assigned an actual or
implied "CCC" debt rating. The "C" rating may be used to cover a situation where a bankruptcy petition
has been filed, but debt service payments are continued.
D
- Debt rated "D" is in payment default
. The "D" rating category is used when interest payments or
principal payments are not made on the date due even if the applicable grace period has not expired,
unless S&P believes that such payments will be made during such grace period. The "D" rating also will
be used upon the filing of a bankruptcy petition if debt service payments are jeopardized.
Plus (+) or Minus (-): The ratings from "AA" to "CCC" may be modified by the addition of a plus or minus
sign to show relative standing within the major rating categories.
NR - Indicates no rating has been requested, that there is insufficient information on which to base a
rating, or that S&P does not rate a particular type of obligation as a matter of policy.
Moody’s Credit Rating Definitions
INVESTMENT GRADE
Aaa
- Bonds are judged to be of the best quality
. They carry the smallest degree of investment risk and
are generally referred to as "gilt edged." Interest payments are protected by a large or by an
exceptionally stable margin and principal is secure. While the various protective elements are likely to
change, such changes as can be visualized are most unlikely to impair the fundamentally strong position
of such issuer.
Aa
- Bonds are judged to be of high quality
by all standards. Together with the "Aaa" group they
comprise what are generally known as high-grade bonds. They are rated lower than the best bonds
because margins of protection may not be as large as in "Aaa" securities or fluctuation of protective
elements may be of greater amplitude or there may be other elements present which make the long-
term risks appear somewhat larger than in "Aaa" securities.
A
- Bonds possess many favorable investment attributes and are to be considered as upper medium-
grade
obligations. Factors giving security to principal and interest are considered adequate but elements
may be present which suggest a susceptibility to impairment sometime in the future.
Baa
- Bonds considered medium-grade
obligations, i.e., they are neither highly protected nor poorly
secured. Interest payments and principal security appear adequate for the present but certain
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protective elements may be lacking or may be characteristically unreliable over any great length of time.
Such bonds lack outstanding investment characteristics and in fact have speculative characteristics as
well.
SPECULATIVE GRADE
Ba, B, Caa, Ca, and C:
Bonds that possess one of these ratings provide questionable protection of
interest and principal ("Ba" indicates some speculative elements; "B" indicates a general lack of
characteristics of desirable investment; "Caa" represents a poor standing; "Ca" represents obligations
which are speculative in a high degree; and "C" represents the lowest rated class of bonds). "Caa," "Ca"
and "C" bonds may be in default.
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Universe of Risks: Basis/Tracking Risk
Define:
When one is attempting to hedge a risk, the instrument used as a hedging vehicle does not
behave as expected, causing a loss.
Measure:
Tracking error: the standard deviation of the difference between the return of what is being hedged
and the hedge itself.
Value at Risk. Find the largest expected difference between what is being hedged and the hedge at a
given probability. Need to consider how tracking errors change in different environments.
Mitigate
Test and double test under many scenarios (especially the crisis you are most worried about) and
possibly change the hedge ratio in response.
Match what is being hedged as closely as possible to the hedging instrument. This might lead to the
use of Over-the-Counter derivatives rather than exchange-traded derivatives. Universe of Risks: Model Risk
Define:
Create a model to simplify real life but it has little to do with real life. If the model is very
complex but wrong, you can end up with “Garbage in, Gospel out”.
Measure:
Model risk is a function of model complexity and the complexity of what is being modeled.
Mitigation
Test output. Especially, look at the extremes. Model errors become more obvious in extreme
scenarios.
Check and double check your models.
Use clear and logical model design: all assumptions in one place, use a lot of comment boxes, break
up formulas into steps, and clearly document as you go.
Quickly admit you are wrong when you discover error.
Interesting book on model risk: “The Signal and the Noise: Why So Many Predictions Fail — but Some
Don't” by Nate Silver http://www.amazon.com/The-Signal-Noise-Many-Predictions/dp/159420411X/ref=sr_1_1?ie=UTF8&qid=1356638896&sr=8-
1&keywords=signal+and+the+noise
Some of your professor’s favorite quotes related to financial models:
“All models are wrong. Some models are useful.”
“Models are not evidence”
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“It is the mark of an educated person to look for precision only as far as the nature of the subject
allows.” Aristotle
“Ineffective methods are used with great confidence even though they add error to the evaluation.
Perhaps much effort is spent on seemingly sophisticated methods, but there is still no objective,
measureable evidence they improve on intuition
. These sophisticated methods are far worse than
doing nothing or simply wasting money on ineffectual methods. They cause erroneous decisions to
be taken that would not otherwise have been made. Note that in this spectrum, doing nothing
about risk management is not actually the worst case
. It is in the middle of the list. Those firms
invoking the infamous ‘at least I am doing something’ defense of their risk management process are
likely to fair worse. Doing nothing is not as bad as things can get for risk management. The worst
thing to do is to adopt a soft scoring method or an unproven but seemingly sophisticated method,
what some have called ‘crackpot rigor’, and act on it with high confidence.” From “The Failure of
Risk Management: Why It's Broken and How to Fix It” by Douglas W. Hubbard.
Universe of Risks: Legal Risk
Define:
Contract is reinterpreted in an unexpected way or is not enforceable. Contracts document how
we are going to share risk when something bad happens.
Measure:
Number of pages in the contract and complexity of language used.
Mitigate:
Careful use of language. Be sure to have the contract say what you want it to say. Ask many
questions, including asking what will happen in an extreme scenario. Never trust lawyers. They are
trained to sound like they know what they are talking about, whether they do or not, and many have a
weak background in complex financial transactions.
Universe of Risks: Disclosure Risk
Define:
Cannot manage a risk due to the complexity of reporting and other compliance requirements
(legal, regulatory, accounting, etc.). Measure:
Number of pages of regulations and rules and complexity of language (very similar to legal
risk).
Mitigate
Present your ideas to legal and accounting very early in the process. Their reviews often take much
longer than expected.
Hire people and spend a lot of time reading and thinking through scenarios.
Hire lobbyists.
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There will times when great ideas are killed due to onerous disclosure requirements.
Dodd-Frank is a good example of the growing complexity of disclosure requirements. Consider these
quotes from this February 2012 article from The Economist “The Dodd-Frank act; Too big not to fail”
(
http://www.economist.com/node/21547784
)
“There is an ever-more-apparent risk that the harm done by the massive cost and complexity of its
regulations
, and the effects of its internal inconsistencies, will outweigh what good may yet come
from it. The law that set up America's banking system in 1864 ran to 29 pages; the Federal Reserve
Act of 1913 went to 32 pages; the Banking Act that transformed American finance after the Wall
Street Crash, commonly known as the Glass-Steagall act, spread out to 37 pages. Dodd-Frank is 848
pages
long.
The scope and structure of Dodd-Frank are fundamentally different to those of its precursor laws,
notes Jonathan Macey of Yale Law School: “Laws classically provide people with rules. Dodd-Frank is
not directed at people. It is an outline directed at bureaucrats and it instructs them to make still
more regulations and to create more bureaucracies
.”
In November four of the five federal agencies charged with enacting this rule jointly put forward a
298-page proposal which is, in the words of a banker publicly supportive of Dodd-Frank,
“unintelligible any way you read it”. It includes 383 explicit questions for firms which, if read
closely, break down into 1,420 sub-questions
, according to Davis Polk, a law firm. The interactive
Volcker “rule map” Davis Polk has produced for its clients has 355 distinct steps.
Along with requiring oodles of contestable rules, Dodd-Frank mandates 87 studies
on big and small
issues, ranging from the impact of drywall on mortgage defaults to the causes of the financial crisis.
The problem is not that the reports are necessarily wrong, but that no one is scrutinizing them
.
But the really big issue that Dodd-Frank raises isn't about the institutions it creates, how they
operate, how much they cost or how they are funded. It is the risk that they and other parts of the
Dodd-Frank apparatus will smother financial institutions in so much red tape that innovation is
stifled
and America's economy suffers. Officials are being given the power to regulate more
intrusively and to make arbitrary or capricious rulings. The lack of clarity which follows from the
sheer complexity of the scheme will sometimes, perhaps often, provide cover for such
capriciousness.
Another problem with complexity is that it encourages efforts to game the system
by exploiting the
loopholes it inevitably creates.
The overall cost
of all this—both directly to public and private institutions and indirectly to the
markets—
is staggering
. At the same time as banks are sacking employees in operating roles, they
are adding swarms to cope with various requests from government agencies and other new filings,
all to avoid violating rules that may never come into existence and temporary measures that may be
rescinded. That is without looking at losses in terms of business not done. Loans that might not fit
into a category favored by regulators are being trimmed or withdrawn.
Dodd-Frank imposes the following types of risk management requirements on banks
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Risk-based capital requirements
Leverage limits and short-term debt limits
Liquidity requirements
Resolution plan and credit exposure requirements
Concentration limits
A contingent capital requirement
Enhanced public disclosures
Some overall risk management requirements
It does little to address government guarantees and “too big to fail” government implied support
Obvious question: would Dodd Frank have prevented 2008? Universe of Risks: Tax Risk
Define:
Treatment of an investment or hedging idea works well before tax but falls apart after tax based
on issues of timing, character and sourcing. The Internal Revenue Service (IRS) likes to move gains
forward and defer losses (
timing risk
). If you hedge a risk, the gain may be taxed immediately while the
loss being hedged might be deferred for tax purposes. If you sell the hedged asset to create a loss to
offset the hedge instrument’s gain, the loss might be disallowed as a “wash sale.” The IRS taxes some
things as normal income and others as capital gains (
character risk
). Some items are taxed differently
outside of US versus in the US, so sourcing
is important.
Measure:
Must look at all possible scenarios before and after tax to make sure the hedge works before
and after tax.
Mitigate:
Work with the Tax Department early in the process to assess all scenarios and have a plan in place
for the most extreme downside scenarios.
Get an IRS opinion ruling in advance if there is uncertainty on tax treatment.
Universe of Risks: Operational Risk
Define:
Risk that a procedure or policy is not followed or is poorly designed such that a major loss
occurs. This is essentially everything else that can go wrong in a firm. Includes:
The firm’s internal controls against theft, rouge traders or a system failure.
Hiring risk and processes in human resources.
Reputational risk such as a major negative headline.
Lawsuits.
Operational disasters that are not adequately insured.
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Measurement:
Operational risks are extremely difficult to measure. One could look at incident reports
and “close calls” from the past and redesign policies and procedures to prevent those close calls from
becoming huge future losses. Process is more important than looking at past performance, especially for
extreme tail events (low frequency and high severity).
Mitigate:
Operational risks are mitigated through well-constructed and tested procedures that are
constantly monitored. Professor Sweet’s approach was to think about how he could single-handedly
destroy his company and then set up procedures to limit his ability to do that. It is also important to
over communicate with top management, including on extreme scenarios. Top management, the board,
and auditors, and in some cases investors, regulators and credit rating agencies need to be reminded of
the downsides of current operations and past decisions.
Accounting perspectives on internal controls:
Internal control activities are the policies and procedures as well as the daily activities that occur
within an internal control system. A good internal control system should include the control
activities listed below. These activities generally fit into two types of activities.
o
Preventive (reduce frequency):
Preventive control activities aim to deter the instance of
errors or fraud. Preventive activities include thorough documentation and authorization
practices.
o
Detective (reduce severity):
Detective control activities identify undesirable occurrences
after the fact. The most obvious detective control activity is reconciliation.
Common Internal Controls
o
Authorization
is the basis by which the authority to complete the various stages of a
transaction is delegated. These stages include the processes of Recording (initiate, submit,
process), Approving (pre-approval, post entry review), and Reconciling. All transactions and
activities should be carried out and approved by employees acting within their range of
knowledge and proper span of control. Proper authorization practices serve as a proactive
approach for preventing invalid transactions from occurring.
o
Documentation:
In the context of internal controls, paper or electronic communication
which supports the completion of the lifecycle of a transaction. Anything that provides
evidence for a transaction, who has performed each action pertaining to a transaction, and
the authority to perform such activities are all considered within the realm of
documentation for these purposes. Documents provide a financial record of each event or
activity, and therefore ensure the accuracy and completeness of transactions. This includes
expenses, revenues, inventories, personnel and other types of transactions. Proper
documentation provides evidence of what has transpired as well as provides information for
researching discrepancies.
o
Reconciliation
is the process of comparing transactions and activity to supporting
documentation. Further, reconciliation involves resolving any discrepancies that may have
been discovered. The process of reconciliation ensures the accuracy and validity of financial
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information. Also, a proper reconciliation process ensures that unauthorized changes have
not occurred to transactions during processing.
o
Security:
The security of assets and records includes three types of safeguards;
Administrative, Physical and Technical:
Administrative security: This focuses on the processes put in place to protect assets
and records. This includes the above mentioned processes of authorization and
reconciliation.
Physical security: This is the protection of physical records and assets from loss by
theft or damage.
Technical security: This is the protection of electronic records from loss by theft,
damage, or loss in transport.
o
Separation of Duties
is the means by which no one person has sole control over the lifespan
of a transaction. Ideally, no one person should be able to initiate, record, authorize and
reconcile a transaction. The separation of duties assures that mistakes, intentional or
unintentional, cannot be made without being discovered by another person.
Glyn Holton’s article “Enterprise Risk Management” discusses how operational risks are becoming much
more significant today and how firms should seek to address them. He says operational risk
management is about culture
, procedures
and technology
. In the 1990s, organizations started suffering
spectacular losses not seen in previous times. For examples, Orange County (1994) - $1.7 billion, Barings
Bank (1995) - $1.5 billion, Daiwa Bank (1995) - $1.1 billion and Sumitomo Corporation (1996) - $1.8
billion.
The world is changing in relation to risk management. The actions of one or very few individuals
can do
much more damage to a firm today than prior to 1980. One person can create very large losses due to
leverage
of derivative
instruments and these instruments’ complexity. Past “bad employees” exposed
company to small losses. Today, people are no more or less wicked, but the tools they have are far more
potentially devastating.
These large losses can easily be prevented with appropriate oversight, but firms often allow them to go
unchecked because no one wants to admit they do not understand the complexity of the trades
.
Regulators and rating agencies are demanding better risk management tools, but they tend to be
focused on the past big risk, not the next one. Ultimately, in a crisis, only survival matters
.
Glyn Holton’s elements of good Enterprise Risk Management:
Culture
o
“The fundamental problem was culture”: Their corporate culture was inadequate for
confronting irresponsible behavior.”
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o
Behavior that reduces organizational risk entails significant personal risk (lose job, appear to
be alarmist if not correct, inability to acknowledge fallibility (e.g. traders), alienate
colleagues, etc.).
o
“Risk management is about rocking the boat, asking questions, and challenging the
establishment.”
o
Corporate culture defines what behavior is condoned and what behavior is shunned.
o
Corporate culture defines what personal risks an individual must take if they are going to
help manage organizational risks.
o
Characteristics of a culture that supports individual responsibility and personal risk taking.
Individuals making decisions (versus group decisions where no one is accountable)
Questioning
Admissions of ignorance
Procedures
o
“The purpose of procedures is to empower people.”
o
“The success of procedures depends critically upon a positive risk culture.”
o
“A lack of procedures increases the personal risk that individuals must take if they are going
to manage organizational risk.”
o
By reducing uncertainty, procedures reduce individual risk, and thus promote action.
o
Organizations also need a process for continually monitoring and changing procedures.
o
Most organizations start with procedures to meet regulatory/rating agencies requirements,
but regulators/rating agencies are focused on minimizing risk, not optimizing it.
Technology
o
The risk of the “cart before the horse”: technology becomes the focus of risk management
rather than the tool.
o
Mistake: launch risk management program by first allocating money to a new information
system rather than first asking what the technology will be measuring and managing.
o
Most difficult part of technology: getting the needed data and in a timely manner.
o
There are so many functional areas with the same risk that getting the information on a
consistent basis is very difficult.
o
Measurement of risk must be prospective, not historical.
o
Tools are powerful, but can be computer intensive and, if not calibrated correctly, can cause
more harm than good (“garbage in, gospel out”).
Let’s consider a few examples of major Operational Losses.
After the April 2010 oil spill, British Petroleum
(BP) stated that they had learned from their mistakes and
could be trusted to be better risk managers going forward. Here is a list of lessons learned from BP:
Spill was “perfect storm of aging infrastructure.”
“Overzealous cost cutting” and “Risk blindness”
“We concentrated on the form and not substance of regulation” (going through the motions)
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“Ignoring warning signs”
“Putting profits before safety”
“Insist there is no over-arching failing of management or corporate culture”
Now they will be much better at risk management?
That all sounds very good. However, a
ll the above quotes were from November 2006
prior to the 2010
oil spill in response to other large operational losses BP had.
Who said this in September 2008
? “It is hard for us without being flippant to even see a scenario within
any kind of realm of reason that would see us losing one dollar in any of those transactions. We're sitting
on a great balance sheet, a strong investment portfolio, and a global trading platform where we can take
advantage of the market in any variety of places.” Joseph Cassano, founding member and head of AIG's
financial-products unit.
A huge component of operational risk is the low quality of planning for corporations, governments and
other entities. We are very bad at forecasting, and we repeat the same mistakes. Let’s walk through Paul
Saffo’s Six Rules for Effective Forecasting in his article, “Cone of Uncertainty”, and the article on
overcoming biases in forecasting, “The Big Idea: Before You Make That Big Decision” by Daniel
Kahneman, Dan Lovallo, and Olivier Sibony.
Some other interesting quotes and ideas on this issue:
“Why leave something out because it is uncertain? The whole point of building a Monte Carlo model
is to deal with uncertainties in a system. Leaving out a variable because it is too uncertain makes
about as much sense as not drinking because you are too thirsty.” From “The Failure of Risk
Management: Why It's Broken and How to Fix It” by Douglas W. Hubbard
Some address this with a “pre-mortem” analysis. Before the project is approved, have the research
team pretend the project failed and discuss why it failed.
Another approach is the use of internal “prediction markets”, where subject matter experts within
the firm place bets on certain outcomes. The results of their bets are used to assign probabilities in
the Monte Carlo model. It has been shown that the average of experts when there is money on the
line is one of the most accurate ways to calibrate probabilities.
We need good statistics in order to prioritize which risks to address first and/or spend the most
money on. See this Ted Talk, Podcast and Internet Link on one economist’s (Bjorn Lomborg) views
on how to prioritize global resources.
https://www.ted.com/talks/bjorn_lomborg_global_priorities_bigger_than_climate_change
https://www.copenhagenconsensus.com/
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Universe of Risks: Purchasing Power Risk
Define:
risk of loss due to inflation pressures that increase payouts more than inflows.
Measure:
Inflation risk can be addressed through a Value-at-Risk analysis, often using Stochastic VaR.
You would model your income, expenses, assets and liabilities and the output is your financial
statements.
Mitigation:
There are assets that tend to do better in inflationary periods such as:
Gold which is negatively correlated to US dollar and positively correlated to U.S. inflation.
Commodities such as oil and timber that are negatively correlated to US Dollar and positively
correlated to inflation.
Real estate might hedge inflation. However, Real Estate Investment Trusts are high yielding stocks
that tend to do poorly when interest rates rise (which will happen if inflation is rising) as REITs are
seen by many as a bond substitute.
Cash and short term savings accounts are good against unexpected inflation, but long term tend to
underperform inflation.
Inflation linked bonds (TIPs) pay stated yield and adjust the principle for inflation.
Real return funds seek to provide a real rate plus a spread. Sounds like a great deal, but these funds
have not performed as promised in the past.
Derivatives: there are inflation swaps, but they tend to be very thinly traded.
Currencies other than the U.S. dollar would protect you from U.S. inflation but not against a global
inflationary spike. You could hire a currency overlay manager who will be long and short on
currencies based on their views of relative inflation and interest rates between countries, but you
would be relying entirely on their skill to predict currency moves.
Universe of Risks: Currency Risk
Define:
risk that a change in a currency causes a loss due to unequal changes in revenues versus
expenses or assets versus liabilities.
Measure:
Again, Value at Risk is the most common approach to understanding this risk. We run
simulations using history and assumptions based on history to see the impact of different currency
environments on our business. Mitigation:
There are many very liquid derivatives that do an exceptional job managing currency risk,
including currency futures and swaps, options and swaptions. However, one must be aware that
economics does not equal GAAP accounting. You might hedge the economic risk of currency changes
but still show accounting losses.
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Now that we have worked through our universe of risk, we will step back and consider what leading
writers on risk management are saying about the state of risk management and about better ways of
defining, measuring and mitigating risk. The key writers we will be looking at are:
Nassim Taleb
Daniel Kahneman
Annie Duke
Doug Hubbard
Nassim Taleb
is an author of very popular books criticizing traditional approaches to risk management:
Black Swan, Fooled by Randomness, Antifragile and The Bed of Procrustes. We will cover at a very high
level some of his key arguments.
One of his main arguments is that 1) finance over relies on the normal distribution
. Taleb separates the
world into two groups: Mediocrastan verses Extremistan. Statistical inference and a large sample size
help in the first world but are dangerous in second world. In Extremistan, we should always be
suspicious of statistics and doubt the data. History tells what has happened, not what could happen.
“
Sometimes a lot of data can be meaningless; at other times one single piece of information can be very
meaningful. It is true that a thousand days cannot prove you right, but one day can prove you to be
wrong.”
(Black Swam page 57)
“Forget everything you heard in statistics or probability theory. . . One of the most misunderstood
Aspects of a Gaussian (the normal curve/ bell curve) is its fragility and vulnerability in the estimation of
tail events. The odds of a 4 sigma move are twice that of a 4.15 sigma. The odds of a 20 sigma are a
trillion times higher than those of a 21 tail sigma. It means a small measurement error of the sigma will
lead to a massive underestimation of the probability. We can be a trillion times wrong about some
events. . . . Look at the odds of being rich in Europe. If wealth were Gaussian, we would observe the
following divergence away from 1 million Euros: People with a net worth higher than $1 million: 1 in 63,
more than $2 million: 1 in 127,000, more than $3 million: 1 in 14,000,000,000, more than $4 million: 1 in
886,000,000,000,000,000, more than $8 million: 1 in 6,000,000,000,000,000,000,000,000,000,000,000
Higher than 16 million: 1 in . . . none of my computers is capable of handling the computation.”
(Black Swan pages 229 – 233)
“People would find data in which there were no jumps or extreme events, and show me a “proof” that
one could use the Gaussian. This was exactly like my example of the “proof” that O.J. Simpson is not a
killer from Chapter 5 (that is, that he was seen several times not killing). The entire statistical business
confused absence of proof with proof of absence. Furthermore, people did not understand the
elementary asymmetry involved: you need one single observation to reject the Gaussian, but millions of
observations will not fully confirm the validity of its application. Why? Because the Gaussian bell curve
disallows large deviations, but tools of Extremistan, the alternative, do not disallow long quiet stretches.”
(Black Swan page 281)
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“
The correlation measures will be likely to exhibit severe instability; it will depend on the period for which
it was computed. Yet people talk about correlation as if it were something real, making it tangible,
investing it with a physical property, reifying it. . . Once you get a bell curve in your head it is hard to get
it out.”
(Black Swan pages 239 and 241)
Taleb also believes 2) finance gets paid for random luck
. We perform well often entirely due to luck, but
force our clients to pay up for that luck. Taleb says we a playing Russian roulette with our clients’
money, charging them huge fees any time we pull the trigger and no bullet comes out. Of course, each
time we pull the trigger, the probability of a bullet the next time is higher each time. Jack Bogle, the
founder of Vanguard, echoes this sentiment when he said we have moved from an ownership society to
an agency society. That is, we have stopped managing money and have become marketers of random
outcomes. Professor Sweet agrees with this criticism. He believes we add value in finance through
superior risk management and asset-liability management, not through forecasting and trying to beat
the market.
“A successful person will try to convince you that his achievements could not possibly be accidental, just
as a gambler who wins at roulette seven times in a row will explain to you that the odds against such a
streak are one in several million, so you either have to believe some transcendental intervention is in play
or accept his skills and insight in picking winning numbers. The reference point argument is as follows: do
not compute odds from the vantage point of the winning gambler, but from all those who started in the
cohort.”
(Black Swan pages 118-119)
“
At any point in time, the richest traders are often the worst traders. This I will call the cross-sectional
problem: At a given time in the market, the most successful traders are likely to be those that are best fit
to the latest cycle. This does not happen too often with dentists or pianists – because these professions
are more immune to randomness.”
(Fooled by Randomness page 86)
“Let’s construct a population of 10,000 fictional investment managers. Assume they each have a
perfectly fair game; each one has a 50% probability of making $10,000 at the end of the year, and a 50%
probability of losing $10,000. Once a manager has a single bad year, he is thrown out of the sample,
good-bye and have a nice life. We have no patience for low performers. After the 5
th
year, we have now,
simply in a fair game, 313 managers who have made money for 5 years in a row. Out of pure luck.
Meanwhile if we throw one of these successful traders into the real world we would get very interesting
and helpful comments on his remarkable style, his incisive mind, and the influences that helped him
achieve such success. His biographer will dwell on the wonderful role models provided by his parents.
And the following year, should he stop outperforming, they will find something he did before when he
was successful that he has subsequently stopped doing, and attribute his failure to that. The truth will
be, however, that he simply ran out of luck.”
(Fooled by Randomness pages 152-153)
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“Top management is only paid on results-no matter the process. There seems to be no such thing as a
foolish decision if it results in profits. The link between the skill of the CEO and the results of the company
are tenuous. There are so many companies doing all kinds of things that some of them are bound to
make “the right decision.” . . . . What they have is skill in getting promoted within a company rather than
pure skills in making optimal decisions-we call that “corporate political skill.” These are people mostly
trained at using PowerPoint presentations. . . . Shareholders, in the end, are the ones who are fooled by
randomness.” (Fooled by Randomness pages 255-257)
Taleb argues that in our constant search for models that works, 3) finance is too often fooled by the
randomness
of large pools of data. We see patterns in randomness and create a model we can sell to
the public. “The more information you give someone, the more hypotheses they will formulate along the way, and
the worse off they will be. They see random noise and mistake it for information. The problem is that
our ideas are sticky: once we produce a theory, we are not likely to change our minds – so those who
delay developing their theories are better off. When you develop your opinions on the basis of weak
evidence, you will have difficulty interpreting subsequent information that contradicts these opinions,
even if this new information is obviously more accurate.”
(Black Swan page 144)
“It takes considerable effort to see facts while withholding judgment and resisting explanations. Try to
be a skeptic with respect to your interpretations and you will be worn out in no time. Does this suggest
that we are better at explaining than at understanding?”
(Black Swan pages 64-65)
“
We think it is smarter to say “because” than to accept randomness. My biggest problem with the
educational system lies precisely in that it forces students to squeeze explanations out of subject matter
and shames them for withholding judgment, for uttering the “I don’t know.” Have the integrity to deliver
your “because” very sparingly.” (Black Swan page 120)
“Listening to the news on the radio every hour is far worse for you than reading a weekly magazine,
because the longer interval allows information to be filtered a bit.” (Black Swan page 144)
“To be completely cured of newspapers, spend a year reading the previous week’s newspapers.”
(The
Bed of Procrustes)
Taleb argues that 4) experts and elites know far less than we think they do
(or even than they themselves think they do). We spend a great deal of time listening to them and following their advice, but we rarely go back and assess how accurate they have been in the past. They themselves rarely have any idea how accurate what they are saying is.
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“Nobody knew anything, but elite thinkers thought they knew more than the rest because they were elite
thinkers, and if you’re a member of the elite, you automatically know more than the non-elite.” (Black
Swan page 14)
“The ‘expert’ is the closest thing to a fraud, performing no better than a computer using a single metric,
their intuition getting in the way and blinding them. . . . Professions that deal with the future and base
their studies on the non-repeatable past have an expert problem. . . I am not saying that no one who
deals with the future provides any valuable information, but rather that those who provide no tangible
added value are generally dealing with the future. . . The problem with experts is that they do not know
what they do not know. . . . they are quite ashamed to say anything outlandish to their clients – and yet
events, it turns out, are almost always outlandish. . . . economic forecasters tend to fall closer to one
another than to the resulting outcome. Nobody wants to be off the wall. In a study comparing an
investment analyst with weather forecasters, Tadeusz Tyszka and Piotr Aelonka document that analysts
are worse at predicting, while having a greater faith in their own skills. . . .His study exposed an expert
problem: there was no difference in results whether one had a PhD or an undergraduate degree. Tetlock
found the negative effect of reputation on prediction: those who had a big reputation were worse
predictors than those who had none. . . . (These studies) collectively show no convincing evidence that
economists as a community have an ability to predict, and, if they have some ability, their predictions are
at best just slightly better than random ones – not good enough to help with serious decisions.
Makridakis and Hibon reached the sad conclusion that ‘statistically sophisticated or complex methods do
not necessarily provide more accurate forecasts than simpler ones.’ . . . The world is far too complicated
for their discipline.”
(Black Swan pages 146-151 and 154-155)
Taleb argues that it is the 5) massive but rare events that most explain what is going on in the world
.
We ignore these events because they are so rare, and yet they ultimately turn out to be almost all that
matters in our lives.
“
A small number of Black Swans explain almost everything in our world. Ordinary events, the ones we
study and discuss and try to predict from reading the newspapers, have become increasingly
inconsequential. Indeed the normal is often irrelevant. Why do we keep focusing on the minutiae, not the
possible significant large events, in spite of the obvious evidence of their huge influence? Life is the
cumulative effect of a handful of significant shocks. History doesn’t crawl; it jumps.”
(Black Swan pages
xviii, xxiv, xix and 3)
“In this essay, I stick my neck out and make the claim, against many of our habits of thought, that our
world is dominated by the extreme, the unknown, and the very improbable (improbable according to our
current knowledge) – and all the while we spend our time engaged in small talk, focusing on the known,
and the repeated. This implies the need to use the extreme event as a starting point and not treat it as an
exception to be pushed under the rug. . . the future will be increasingly less predictable.” (Black Swan
pages xxvii – xxviii)
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“
How often have your arrive one, three or six hours late on a transatlantic flight as opposed to one, three
are six hours early? This explains why deficits tend to be larger, rarely smaller, than planned.” (The Bed
of Procrustes)
“Risk management professionals look in the past for information on the so called “
worst-case scenario”
and use it to estimate future risk. This method is called “
stress testing.” They take the worst historical
recession, the worst war, the worst historical move in interest rates, or the worst point in unemployment
as an exact estimate for the worst future outcome. But they never notice the following inconsistency.
This so-called worst case event, when it happened, exceeded the worst case at the time.”
(Antifragile)
Taleb says 6) we are easily fooled by the calmness
of markets and economies and actually prefer risks
with low volatility but huge downside risks
. We forget that at the extremes, there is contagion
which
causes losses to be much larger than normal distributions would predict.
“
People are often ashamed of losses, so they engage in strategies that produce very little volatility but
contain the risk of a large loss – like collecting nickels in front of steamrollers. In Japanese culture, which
is ill-adapted to randomness and badly equipped to understand that bad performance can come from
bad luck, losses can severely tarnish someone’s reputation. People hate volatility, thus engage in
strategies exposed to blowups, leading to occasional suicides after a big loss. I learned about this
problem from the finance industry, in which we see ‘conservative’ bankers sitting on a pile of dynamite
but fooling themselves because their operations seem dull and lacking in volatility.” (Black Swan pages
204-205)
“
Every plane crash brings us closer to safety, improves the system and makes the next flight safer. Those
who perish contribute to the overall safety of others. . . These systems learn because they are antifragile
and set up to exploit small error. The same cannot be said of economic crashes since the economic
system is not antifragile the way it is presently built. Why? There are hundreds of thousands of plane
flights every year, and a crash in plane does not involve others, so errors remain confined and highly
epistemic. Whereas global economic systems operate as one. Errors spread and compound. Again,
crucially, we are talking of partial, not general, mistakes; Small, and not severe and terminal ones. This
creates a separation between good and bad systems. Good systems such as airlines are set up to have
small errors independent from each other, or in effect, negatively correlated to each other since mistakes
lower the odds of future mistakes. This is one way to see how one environment can be antifragile
(aviation) and the other fragile (modern economic life with “earth is flat” style interconnectedness). If
every plane crash makes the next one less likely, every bank crash makes the next one more likely. We
need to eliminate the second type of error, the one that produces contagion in our construction of an
ideal socioeconomic system.” (Antifragile)
Instead, Taleb says 7) we should bet on fat tails and unexpected high volatility
so that we benefit from
them rather than being destroyed by them.
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“You need to put a portion, say 85 to 90 percent, in extremely safe instruments, like Treasury bills – as
safe a class of instruments as you can manage to find on this planet. The remaining 10 to 15% you put in
extremely speculative bets, as leveraged as possible (like options), preferably venture capital-style
portfolios. Or, equivalently, you can have a speculative portfolio and insure it (if possible) against losses
of more than, say, 15%. . . There are both positive and negative Black Swans. . . . The dovetail strategy of
taking maximum exposure to the positive Black Swan while remaining paranoid about the negative ones.
. . . Do not try to predict precise Black Swans: invest in preparedness, not in prediction. . . . All these
recommendations have one point in common: asymmetry. . . .
This idea that in order to make a decision
you need to focus on the consequences (which you can know) rather than the probability (which you
can’t know) is the central idea of uncertainty. Much of my life is based on it.” (Black Swan pages 205-208
and 210-211)
“
I worry less about small failures, more about large, potentially terminal ones. I worry less about
embarrassment than about missing an opportunity. In the end this is a trivial decision making rule: I am
very aggressive when I can gain exposure to positive Black Swans – when a failure would be of a small
moment – and very conservative when I am under threat from a negative Black Swan. I am very
aggressive when an error in a model can benefit me, and paranoid when the error can hurt. This may
not be interesting except that it is exactly what other people do not do.” (Black Swan page 296)
“The best description of my lifelong business in the market is “
skewed bets;” that is, I try to benefit from
rare events, events that do not tend to repeat themselves frequently, but, accordingly, present a large
payoff when they occur. I try to make money infrequently, as infrequently as possible, simply because I
believe that rare events are not fairly valued, and that the rarer the event, the more undervalued it will
be in price.” (Fooled by Randomness page 103)
“My opinion was that the market was more likely to go up (“I would be bullish”), but that it was
preferable to short it (“I would be bearish”), because, in the event of its going down, it could go down a
lot. . . Let us assume that the reader shared my opinion, that the market over the next week had a 70%
chance of going up and a 30% chance of going down. However, let us say that it would go up by 1% on
average, while it could go down an average of 10%. What would the reader do? Is the reader “bullish”
or “bearish?” Accordingly, it is not how likely an event is to happen that matters, it is how much is made
when it happens that should be the consideration.” (Fooled by Randomness pages 101-102)
“
Antifragility is beyond resilience or robustness. The resilient resists shocks and stays the same. The
antifragile gets better. . . The antifragile loves randomness and uncertainty.” (Antifragile)
“
Fragility can be measured. Risk is not measurable outside of casinos or the minds of people who call
themselves “risk experts”. . . . We can almost always detect antifragility and fragility using a simple test
of asymmetry. Anything that has more upside than downside from random events of certain shocks is
antifragile. The reverse is fragile.” (Antifragile)
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Finally, Taleb says 8) we should be careful of forecasting
. Forecasting is necessary but we should not make bit bets based on our confidence in a forecast. Such confidence is never warranted.
“I find it scandalous that in spite of the empirical record we continue to project into the future as if we
were good at it, using tools and methods that exclude rare events. . . . Why on earth do we predict so
much? Why don’t we talk about our record in predicting?” (Black Swan pages 135 and 138)
“What you should avoid is unnecessary dependence on large-scale harmful predictions – those and only
those. Avoid the big subjects that may hurt your future: be fooled in small matters, not in the large. . . By
all means, demand certainty for the next picnic; but avoid government social-security forecasts for the
year 2040. Know how to rank beliefs not according to their plausibility but by the harm they may cause.”
(Black Swan page 203)
“Anyone producing a forecast or making an economic analysis needs to have something to lose from it
given that others rely on those forecasts. To repeat, forecasts induce risk-taking. They are more toxic to
us than any other form of human pollution.” (Antifragile)
Before moving to Daniel Kahneman, the following YouTube discussion between Kahneman and Taleb is
highly recommended: https://www.youtube.com/watch?v=MMBclvY_EMA
Daniel Kahneman
is a psychologist notable for his work on the psychology of judgment and decision-
making, as well as behavioral economics, for which he was awarded the 2002 Nobel Memorial Prize in
Economic Sciences. His empirical findings challenge the assumption of human rationality prevailing in
modern economic theory.
His views are best captured in the book, “Thinking Fast and Slow” and well summarized in the following
lectures:
Nobel Prize Speech: https://www.nobelprize.org/prizes/economic-sciences/2002/kahneman/lecture/
Talk at Google:
https://www.youtube.com/watch?v=CjVQJdIrDJ0&list=FLCYoG-16cnOpfSSEo9EasDg&index=176
Ted Talk on experience and happiness:
https://www.ted.com/talks/daniel_kahneman_the_riddle_of_experience_vs_memory?language=en
Following are some of his main arguments on behavioral economics.
Bounded Rationality
is the idea that human rationality is limited in decision making by the complexity of
the decisions, the cognitive limitations of the mind, and the time available to make the decision.
Decision-makers act as satisficers, seeking a satisfactory solution rather than an optimal one.
There is fast (intuitive) and slow (analytical and rationally consistent) ways of thinking
. With fast
thinking, we commit many systematic biases and errors that lead to bad decisions.
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Our minds perceive changes much better than states
. We make decisions in contrast with what we
have just experienced rather than assessing the absolute values. This causes us to overstate losses
and understate gains
. For example:
o
Suzy went from $100,000 to $105,000 then to $110,000
o
Mike went from $100,000 to $450,000 then to $125,000
o
Who is happier, Suzy or Mike? Who should be happier?
Framing
of the question can radically change how people make a decision, even though the
outcomes are exactly the same but worded differently. Related to the issue above in terms of
changes versus states, stating a gamble in terms of gains and losses produces very different
decisions than when the gamble is stated in terms of ending wealth.
Mental accounting
is the game we play when we segregate pools of money that are perfectly
fungible.
Availability heuristic
states we are more sensitive to a prospective loss than the more probable
outcome that has a gain because we can imagine the loss more easily than the gain.
Hyperbolic discounting
means preferring a smaller payoff now to a larger payoff later. This bias
makes future gains look unattractive because it shifts money from the present into the future.
We are better with averages than with sums
. We use averages as an intuitive substitute when sums
make more sense. He gives four examples in his Nobel Prize lecture: Evaluating a set of goods,
evaluating a set of observations, judging whether an individual belongs to a set, and evaluating an
episode (a set of moments: average of pain versus sum of pain).
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Prospect Theory
challenges the expected utility theory. It is the founding theory of behavioral
economics and of behavioral finance, and constitutes one of the first economic theories built using
experimental methods. Based on results from controlled studies, it describes how individuals assess in
an asymmetric manner their loss and gain perspectives. For example, for some individuals, the pain from
losing $1,000 could only be compensated by the pleasure of earning $2,000 (we are “loss adverse”).
Thus, contrary to the expected utility theory, which models the decision that perfectly rational agents
would make, the prospect theory aims to describe the actual behavior of people. We are risk adverse in
gain situations and risk seeking when facing losses. Loss aversion also produces a strong bias for the
status quo. Where you are should not matter, but it has a strong impact on decision-making.
Loss adverse with what we possess, Risk seeking once we have loss. Consider this scenario:
Airlines: could they make the travel voucher what passengers are losing rather than the perception
the passenger is losing their seat?
Randomly select passengers and say you now have a $600 travel voucher. If you don’t come up in 15
minutes, you lose that travel voucher
Kahneman’s discussion of experience versus memory of experience and the impact on happiness
shows
how important our memories are to our contentment in life and how they influence our decisions more
than our actual experiences. The memory self is “a story teller.” What we get to keep from our
memories is a story. Most of our experiences are completely forgotten. What we remember is
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influenced by time and changes in experiences. A 2-week vacation on the beach will create no more
memories than a 2-day vacation on the beach. Often, what we remember is very different than the
experience itself. The experiencing self has no voice in our decisions. We actually do not choose
between experiences. We choose between memories of experiences. We do not think of our futures are
experiences but rather as anticipated memories. For example, what vacation would you take if your
memory of the vacation is erased at the end? Taleb also addresses this issue:
Your happiness depends far more on the number of instances of positive feelings, what psychologists call
“positive affect”, than on their intensity when they hit. In other words, good news is good news first; how
good matters rather little. So to have a pleasant life you should spread those small “affects” across time
as evenly as possible. Plenty of mildly good news is preferable to one single lump of great news. (Black
Swan page 91)
Annie Duke
was an American professional poker player and is now a very popular author and speaker.
Her experience playing poker has given her great insights on decision making under uncertainty. We will
cover her ideas through a PowerPoint presentation. This blog link is a more detailed version of that
summary, though students are encouraged to read the entire book. https://profesweet.wordpress.com/2018/07/26/behavioral-economics-and-implications-for-decision-
making/
Whom to listen to?
Two criteria
o
Technical knowledge (skilled and knowledgeable)
o
Main Incentive is to be correct: change opinion quickly with new information because they
lose if they don’t, little ego invested
o
(third one for free): they disagree with me
Example Global Warming; Covid 19
o
Politicians (Trump AOC)
o
Advocates (Greta Gore Limbaugh) (CDC and WHO)
o
Swiss Reinsurance and Catastrophe Modeling Companies (RMS and Air)
Douglas Hubbard
is a management consultant, speaker and author in decision sciences and actuarial
science. He is the inventor of the Applied Information Economics (AIE) method and founder of Hubbard
Decision Research. We will cover his ideas through one of his on-line presentations, the PowerPoint of
which is available on Blackboard. As you watch the presentation, answer the 16 questions about it on
Blackboard. https://www.youtube.com/watch?v=w4fHGTsZZD8
Here are some quotes from his “The Failure of Risk Management: Why It's Broken and How to Fix It”:
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If you are rolling a hundred dice or flipping thousand coins, the normal distribution is your best bet for
modeling your uncertainty about the outcome. But financial markets, supply chains, and major IT
projects are complex systems of components where each of the following occurs with characteristics of
power law distributed failures:
The entire system can be stressed in a way that increases the chance of failure of all components.
(“Contagion”)
The failure of one component causes the failure of several other components, that is, a “common
mode failure”.
The failure of those components in a system starts a chain reaction of failures (“cascade failure”).
Although I am a booster for the firms that developed powerful tools like Crystal Ball, the most
popular products seem to have one major omission: of all the wide assortment of distribution types
they include in their models, most still do not include a power law distribution
, but they aren’t hard
to make.
Everybody everywhere
is focusing on the least valuable measurements at the expense of the most
valuable measurements
. Everybody everywhere
is systematically measuring all the wrong things
. It
is so pervasive and impactful that I have to wonder how much this effects the Gross Domestic
Product. Organizations appear to measure what they know how to measure
without wondering
whether they should learn new measurement methods for very high value uncertainties.
"The great investors are like the great sailors: They have the courage to set forth, they know where they
want to go, they have a strong gyroscope to keep them on course, they have appropriate respect for the
dangers of the sea and its potential for radical shifts in weather and currents, and they are not afraid to
be alone for long stretches
." Martin L. Leibowitz, Financial Analyst Journal, 2005
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We will now turn our attention to some case studies all available on Blackboard:
Tylenol.
This is an example of operational risk that is mitigated through public relations. Related
scenarios would include Chipotle, Blue Bell and United Airlines. We may have a speaker on the last
one (airlines) this semester. Another closely related case is whether doctors should admit mistakes
and, if they do, how that impacts malpractice claims (see article on Blackboard). Operational Risk
where focus is on Risk Reduction (reducing severity)
Mettallgellschaft (MG).
This is an example primarily of basis risk/tracking risk, but also includes
price risk, liquidity risk and disclosure risk.
Orange County
is an example of price risk and interest rate risk, and is a great example of when
Value at Risk and Risk Limits could have prevented the entire disaster if they had been in place.
Long-Term Capital Management
is an example of price risk where correlation was the primary
mitigation technique and completely failed. It also covers liquidity risk and credit risk among several
other issues we have hit on in this semester.
Professor Sweet’s Favorite Keys to Great Risk Management:
1.
Have the infrastructure in place and tested regularly well before anyone is thinking about the risk.
2.
One should always consider being tactical, even if “hedging”, especially at the extremes. Hedging
does not mean become complacent.
3.
If you are really going to understand risk, you understand it by going to the extremes.
4.
Think of risks in a portfolio context (assets and liabilities), using integrated risk management
techniques. Do not view risks individually in isolation. 5.
When managing risk, be unconventional. We know the traditional approach fails at the worst
possible time. Be willing to be alone for some time.
6.
Don’t make risk management decisions where the driving force is the protection of ego.
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