Please briefly explain the results with explanation and clear points.
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Question - List the main results, and explain the intuition behind these results?
Transcribed Image Text: 46
Table 1
Descriptive statistics and sample distribution.
Panel A: Pooled sample statistics
Variable
Price
Price+bp
Scrap
Weight
Spread
Age
Order
Q1
02
prov
arm
assay_off
2009
2010
2011
2012
2013
2014
Quarter
Mean
1st
2nd
3rd
4th
880.74
1073.17
295.16
25.93
0.39
166.06
Obs.
551
667
671
539
447
332
0.67
0.08
489
1134
424
1160
0.19
0.02
0.40
0.62
Std. dev,
1906.84
2311.57
441.80
37.03
0.13
77.75
0.25
0.26
0.39
0.15
0.49
0.48
Price
Scrap
708.85
220.07
698.21 278.90
899.24 538.90
1089.38 645.23
1062.86 417.61
1386.42
485.82
P. Draper et al. / Economics Letters 162 (2018) 45-48
892.68
391.53
928.36
424.30
895.67
393.99
959.65 452.56
Min.
20.00
24.40
0.89
Panel B: Sample distribution of lots sold, average lot hammer price (£), and average lot scrap value (£)
Year
Bonhams
Knightsbridge
0.10
0.05
1.00
0.00
Max.
85,00000
101,5700
6,211.80
553.00
1.00
510.00
1.00
1.00
1.00
1.00
Knowle
Obs. Price
Scrap
179.87
402 382.44
476 458.25 298.34
472 596.60 459.47
1.00
1.00
291 465.10 275.94
394 488.48
296 487.53
324.64
306.71
358.25
369 491.45
the bullion dealer at the prices quoted and with speedy or even
immediate payment. The possibility, therefore, of selling for scrap
provides a minimum guaranteed price, allowing sellers to realise
cash with a minimum of restrictions.
Both sales for scrap, and to antique dealers provide immediacy
to the seller. They also provide certainty and low transaction costs.
egener. They also provide
They do not however provide exposure to a wide range of buyers.
The significant transactions costs associated with selling by auction
suggests that sellers of silver goods that are of low quality and
plentiful supply should sell direct to bullion or antique dealers to
sential supplying fras
antig
maximise their receipts from sale. Despite this, low quality silver
in cold at aucti Collion for
is sold at auction. Selling for scrap removes any chance of receiving
is sold at auction sering for
a premium for artistic merit or scarcity and there may be some
sellers of low quality items who prefer, perhaps through ignorance,
to sell by auction. High quality items should not be scrapped but
sold to dealers or by auction.
Mani
Many silver goods have some worth relating to their scarcity,
usefulness, artistic merit and quality of workmanship. A number
of items, however, have no such merit. They may have been mass
produced and of poor quality originally, or may be battered and
broken. The demand for such items is slight. They are of limited
interest to collectors or dealers. They do however retain value,
a value that depends on their silver content. They may be easily
melted and reduced to pure silver. In an efficient market, we would
expect any silver item to have a minimum value directly related to
its scrap value.
2. Sample and data
To examine the role that the scrap value plays in determining
the price of silver items sold at auction, along with any possible
opportunities for riskless arbitrage, we construct a unique database
from the catalogues of 88 silver auctions from two major En-
glish auction houses, Bonhams, and Woolley and Wallis, between
Description
Hammer price of lot (£)
Hammer price of lot including the buyer's premium (£)
Scrap value of lot (£)
Weight of lot (ounces)
Auctioneer's spread (High estimate - Low estimate)/Low estimate)
Age of lot (years)
Order of lot in the auction
Dummy (= 1 if notable 20th century maker; 0 otherwise)
Dummy (= 1 if notable pre-20th century makers; 0 otherwise)
Dummy (= 1 if provenance; 0 otherwise)
Dummy (= 1 if armorial; 0 otherwise)
Dummy (= 1 if London assay office; 0 otherwise)
New Bond Street
Obs. Price
148 3245.54
132
146
61
93
42
Scrap
283.95
3614.77 377.16
3987.33 741.78
3179.48 674.79
4074.73 798.47
5035.71 685.61
274 3748.17 635.91
72 2750.69 285.49
276 3981.84 541.78
Woolley and Wallis
Salisbury
Obs.
Price
567
478.66
920.42
684.69
828
986
1018 580.38
1080
559.84
956 506.69
1180.00 549.56
1376.00 691.92
1415.00 607.24
1464.00 634.33
Scrap
81.93
192.16
288.25
211.68
162.78
116.80
170.31
186.19
186.37
185.68
January 2009 and December 2014. Although no statistics are
available for the proportion of antique silver sold by dealers in
the UK, informed sources have suggested that, while dealers are
responsible for about half of annual sales, auction houses have
become more dominant in the market. Bonhams is the third major
international UK auction house and the largest seller of UK antique
silver by volume over our sample period, and Woolley and Wallis
is the UK's largest regional auction house and the second largest
seller of UK antique silver by volume.
112
Since it is common for auctioneers to exclude some of the
com
available pre-sale information from the online data (Campos and
Barbosa, 2009), copies of the physical catalogues are used. Whilst
attempting to be as comprehensive as possible we exclude unsold
lots, mixed collections (since it is impossible to assign detailed
information to the individual pieces), and lots without a pre-
sale estimate. Our final sample contains information on 10,614
sold lots spread across three Bonhams locations, Knightsbridge
(30%), Knowle (13%), New Bond Street (6%), and Woolley and
Wallis' house in Salisbury (51%). In terms of the timing of the
silver auctions, Woolley and Wallis hold auctions quarterly, while
Bonhams have departed from a fixed schedule in recent years,
and closed their Knowle salesroom in 2011. Table 1 reports the
main descriptive statistics and sample distribution of the dataset
described above.
3. A hedonic price model for silver goods
To examine the relation between the hammer price and the
scrap value of the auction lot, we use the following hedonic pricing
1 Unlike other studies, we do not concentrate on the international auction
houses, Christies and Sotheby's, since both rarely hold specialist silver auctions in
London over the sample period.
Transcribed Image Text: Table 2
Hedonic regression of natural log of hammer price on the natural log of scrap value and other characteristics.
Variable
Predicted sign
Full sample
Bonhams
Coeff.
(3)
0.485***
-0.239***
0.317***
-0.100**
In (scrap)
Spread
In (age)
Order
Q1
Q2
prov
arm
assay_off
Constant
Observations
Description dummies
+ve
-ve
+ve
?
+ve
+ve
+ve
+ve
-ve
N.A
P. Draper et al. / Economics Letters 162 (2018) 45-48
Table 3
Identifying arbitrage opportunities.
Arbitrage category
Price > Scrap
Price < Scrap
Price & buyer's premium < Scrap
No. lots sold
9924
690
56
Coeff.
(1)
0.457***
-0.248***
0.411***
0.129***
0.240***
0.056***
0.602***
0.050***
-0.071***
(11)
Place dummies
(3)
Year dummies
(5)
Quarter dummies
(3)
Adj. R-squared
0.547
See Table 1 for variable definitions. *** p < 0.01, ** p < 0.05, * p < 0.1.
1.850***
10,614
Std. err.
(2)
(0.008)
(0.046)
(0.018)
(0.033)
(0.029)
(0.016)
(0.061)
(0.014)
(0.015)
(0.115)
Total weight (oz.)
245,311
29,877
1,608
regression:
In(Price); ao + a,In(Scrap); + Characteristics;
+ Controls + e₁
(1)
Where In(Price); is the natural log of the auction lot's hammer price,
and In(Scrap), is our main variable of interest, the natural log of
the lot's scrap value, for which we expect a significant positive
coefficient if buyers' consider the scrap value of a lot an important
feature when bidding? Characteristics; include characteristics
found in the auction catalogues that may explain the auction price.
Specifically, we control for uncertainty as the spread between the
high and low pre-sale estimate, age as the natural log of the lot
age, the lot position in the auction, two binary quality variables
to indicate if the lot was made by a notable silversmith working
in the twentieth century (Q1) or earlier (Q2), and three binary
variables to indicate provenance, the presence of an armorial or
other inscription, and whether the assay office was other than
London. Finally, we include binary variables ( Controls) to control
for the lot description (e.g. flatware), place of sale, year of sale, and
quarter within the year in which the lot was sold.
4. Empirical analysis and results
Table 2 reports the hedonic regression results of Eq. (1) for
the full sample, lots sold by Bonhams, and lots sold by Woolley
and Wallis. In all three regressions, we use unreported dummy
variables to control for the lot description, place of sale, year of sale
and quarter within the year in which the lot was sold. We report
robust standard errors using the White heteroskedasticity-robust
procedure. For all three regressions, the scrap value of silver has a
2 The lot's scrap value is calculated as the product of the lot's weight in ounces
and the corresponding non-trade scrap price of silver on the auction day from
Cookson Precious Metals Ltd at http://www.cooksongold.com/. This represents the
price at which they are prepared to trade on that day. Over our sample period the
non-trade scrap price of silver per ounce ranges from £5.26 on 15 January 2009 to
£21.38 on 28 April 2011, has a mean (standard deviation) of £11.03 (£3.53), and an
average (standard deviation) daily change of £0.002 (£0.295).
0.187***
0.058***
0.268***
0.047***
0.018
2.255***
5,179
(11)
(2)
(5)
(3)
0.638
Total price (£)
8,976,321
371,875
14.490
Std.err.
(4)
(0.010)
(0.055)
(0.022)
(0.043)
(0.034)
(0.020)
(0.092)
(0.018)
(0.019)
(0.134)
5. Conclusions
Woolley and Wallis
Coeff.
(5)
0.399***
-0.061
0.512***
0.421***
0.240***
0.052**
0.772***
0.061***
-0.104***
1.147***
5,435
(11)
N.A
(5)
(3)
0.389
Total price+bp (£)
10,900,000
454,229
17.679
47
Std. err.
(6)
(0.011)
(0.074)
(0.029)
(0.052)
(0.046)
(0.025)
(0.074)
(0.021)
(0.021)
(0.180)
Total scrap (£)
2,734,583
398,286
20,792
significant positive effect on the hammer price and the remaining
variables, where significant, are all of the predicted sign. In an
auction environment, this implies that bidders are partly setting
their bids based on the lot's scrap value. The evidence does not,
however, confirm whether profitable arbitrage opportunities exist
betw
between the silver auctions and scrap value of the lots sold and
purchased.
If arbitrage opportunities exist, then there is prima facie ev-
dan
idence of market inefficiency. While this is a weak-form test of
market offi
market efficiency, since it only uses price information, the absence
of arbitrage is a very important characteristic of financial markets,
which we test for in the physical silver market. Purchasers
silver goods are faced with a variety of costs, notably the buyer's
premium. Therefore, we expect profitable arbitrage may be pos-
sible before allowing for the buyer's premium but is most likely
impossible once it is added into the price of the good. To test this,
we identify and report summary statistics in Table 3 of all lots sold
above and below their scrap value based on the hammer price paid
to the seller, and all lots sold below the scrap value based on the
hammer price and buyer's premium paid by the bidder.
Our findings in Table 3 show that arbitrage opportunities are
available if the only cost imposed on buyers is the hammer price.
However, once the buyer's premium is included, arbitrage oppor-
tunities almost completely disappear. Specifically, less than one
percent of our sample would yield a profit of, on average, less than
£60 per transaction, ignoring any physical costs of collection and
resale.
The absence of arbitrage opportunities after costs, particularly
the buyer's premium, suggests that bidders are sensitive to the
total price. Moreover, although scrap silver can be sold for cash at
3 The buyer's premium is a fee set by the auction house, and paid to the auction-
eer by the buyer. This is constant for each auction, but varies between houses and
over the sample period from 19.5-25% of the hammer price.
4 Analysis of the 56 lots for which arbitrage is possible indicates that 91% were
sold by Bonhams and more than half in the fourth quarter.