Description
The book by Carmona refers to “Statistical Analysis of Financial Data in R” by Ren´e A. Carmona, 2nd edition;Accessibleat
https://lbdiscover.ust.hk/bib/991003455739703412
- Problem 4.8 on page 273 of the book by Carmona (In both questions 1 and 5, whenconducting LS regressions, check whether the predictors are statistically significant at 5% significance level; as to the residuals, compute the studentized residuals instead of the raw residuals.) The data csv can be downloaded from Canvas.
- Download the dataset “Google” from the course website. The dataset contains: First and second column (rGoog): Date and Alphabet Inc. (GOOG)’s monthly return from 2010.01 to 2021.08.
- Third column(rf): Monthly risk free rate of the same period.
- Fourth to sixth column (rMex, rSmB, rHmL): Fama-French Three-factor monthly returns of the same period.
Conduct the following analysis parallel to what we did in class for Berkeshire Hathaway Inc. (BRK-A). Set the significance level to be 5%.
- Fit a single factor model for the excess return of GOOG with one predictor “rMex” using LS regression. Report the summary of the fit. (cf. Lect 8 p.5)
- Based on the single factor model result, for GOOG stock data during the period tested (cf.Lect 8 p.9-17),
(i). Is the market (excess) return significant in explaining the variation in the return of
GOOG?
(ii). Is α significantly different from 0? If so, in which direction? (iii). Is β significantly different from 1? If so, in which direction?
(iv). Use the standardized/studentized residuals to conduct model diagnostics and comment.
- Fit the Fama-French Three-factor model for GOOG using LS regression. Report the summaryof the fit.(cf. Lect 8 p.21)
- Are the three factors as a whole statistically significant? Is any single one of the factorsstatistically significant?(cf. Lect 8 p.22
- How much can the single factor explain the variation in the GOOG returns? How much canthe three factors explain the variation in the GOOG returns? (cf. Lect 8 p.13)
- Does the 3-factor model explain statistically significantly more variation than the single factormodel (cf. Lect 8 p.24)?
