Suppose that the index model for two Canadian stocks HD and ML is estimated with the following results: RHD =0.02+0.80RM+eHD R-squared =0.6 RML =-0.03+1.50RM+eML R-squared =0.4 σM =0.20 where M is S&P/TSX Comp Index and RX is the excess return of stock X. What is the standard deviation of each stock? (Hint: bi = (ρiM σi) / σM.)

Suppose that the index model for two Canadian stocks HD and ML is estimated with the following results: RHD =0.02+0.80RM+eHD R-squared =0.6 RML =-0.03+1.50RM+eML R-squared =0.4 σM =0.20 where M is S&P/TSX Comp Index and RX is the excess return of stock X. What is the standard deviation of each stock? (Hint: bi = (ρiM σi) / σM.)

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

A catalog company builds a logistic regression (LR) model to predict the probability that a customer will buy from the catalog during a particular campaign (mailing). The LR model contains 2 independent variables: X1 spend per year in 1000's of dollars (so $2000 will be coded as X1 2) and X2= does customer possess a loyalty card (X2 1 means customer has loyalty card, X2 = 0 means customer does not have the card). Once the model is fitted, the LR coefficients are provided below: = Constant term (BETAO): - 3.5 (negative 3.5) X1 coeff. (BETA1): 0.6 X2 coeff.(BETA2): 1.5 What is the probability that a customer who spends $6000/year and who does NOT have a loyalty card will respond to the campaign? = O About 53% O About 15% About 33% O About 45%
We have measurements from eight companies for the % workforce change in 2010 from 2009 and the % change in profits from 2010 to 2011. % workforce change (x): 1.0 % profit change (y): 1.5 -0.4 -2.6 -3.8 -5.8 -2.5 -0.1 0.4 0.4 0.1 -0.2 -0.5 -7.5 0.2 0.3 We can calculate: Er = -12.7, Ex² = 64.51, Συ--6.8 , Σ 57, Σεy- 46.35. Find the sample correlation between the two variables, and fit a simple linear model to the dataset. Also, plot the residuals, ri, against xi, and comment on the fit of the model.
The least-squares regression equation is y=784.6x+12,431 where y is the median income and x is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7962. In a particular region, 26.5 percent of adults 25 years and older have at least a bachelor's degree. The median income in this region is $29,889. Is this income higher or lower than what you would expect? Why?
2.2 7. pls help
The following table gives the regression results of consumption behaviour between female and male. Consumption (C), in RM Model 1 Model 2 Constant 0.7515 0.6587 (0.024) (0.037) Y 0.8603 0.5442 (0.0301) (0.2881) Gen 0.2001 (0.0004) Y*Gen 0.3105 (0.0076) Adjusted R 0.78 0.78 No. of Observations 47 47 Note: Y=income (RM) Gen= Gender ( It is equal to 1 if person is female; 0 is for male) Figures in the parentheses are p- values a) Identify which are the qualitative, quantitative and interaction variable. Refer to Model 1: b) Write the regression equations for female and male. Draw the regression lines for female and male on the same diagram. What can you c) say? d) Interpret all the estimated slope coefficients. e) Conduct an appropriate test to determine whether variable gender is statistically significant at 5%. Should this variable be dropped from this regression model.
In the following time series linear regression equation, t is time in years and y is sales in millions of dollars: ýt = Interpret the regression coefficient. = 11. 602 + 3.46t For every one year increase in time, sales increase by $3.46 million on average. For every one year increase in time, sales decrease by $3.46 million on average. As time increases, sales fluctuate around a constant mean of $3.46 million. For every one year increase in time, sales decrease by $346 million on average.
In Step 2: Construct an estimated simple linear regression model how did you come up with the column X*X ?
A linear regression analysis reveals a strong, negative linear relationship between x and y. Which of the following could possibly be the results from this analysis? (A) ŷ 13.1 27.4x, r = 0.85 (B) == 27.4+ 13.1x, r = -0.95 (D) == (C) ŷ 13.1+ 27.4x, r = 0.95 542 385x, r = -0.15 (E) 0.85 0.25x, r = -0.85
lakhs) of a certain commodity for the year On the basis of quarterly sales (in 2008-12, the following calculations were made : Trend: Y = 20 + 0.5 t with origin at 1st quarter of 2008. where 1 = time unit = one quarter and y = quarterly sales ( Seasonal variations: Quarter I II III IV Seasonal Index 80 90 120 110 Estimate the quarterly sale for the year 1979 using multiplicative model.
Assume there is a positive linear correlation between the variable R (Return rate in percent of a financial investment) and the variable t (age in years of the investment) given by the regression equation R= 2.3t + 4.8 A. Without further information, can we assume there is a cause-and-effect relationship between the return rate and the age of the investment? B. If the investment continues to grow at a constant rate, what is the expeted return rate when the investment is 7 years old? C. If the investment continues to grow at a constant rate, how old is the investment when the return rate is 30%?
An economist at Nedbank ran a study of the relationship between FTSE/JSE All Shares index return (JALSH) and consumer price index (CPI) from 2006 to 2017, the data collected is shown in the Table 1 below. FTSE/JSE All Shares index return (JALSH) and consumer price index (CPI) from 2006 to 2017. Year JALSH (Y) CPI (X) 2006 0.41 4.7 2007 0.19 7.1 2008 -0.23 11.5 2009 0.32 7.1 2010 0.19 4.3 2011 0.03 5.0 2012 0.27 5.6 2013 0.21 5.7 2014 0.11 6.1 2015 0.05 4.6 2016 0.00 6.4 2017 0.21 5.3 The estimated regression…