ECON334 - Financial Econometric - Economics Assignment Help

Assignment Task:

Assessment Description

The EViews workfile “Assignment_Workfile.wf1” located under “Assignment” heading on iLearn contains five daily return series for the period 3 January 2000 – 29 December 2017 (totaling 4528 observations). (Note: In order to obtain an image of a table or graph in EViews, you need to click on the “freeze” tab. If you are using AppStream to do your assignment, please see the document ‘How to save images from AppStream’ on our iLearn site under Assignment. Alternatively, you may prefer to use iLab which is available until the end of the year in which case “freeze” the table or graph in EViews, then copy and paste the table or graph into your Word document). 

The following daily return series appear in the file: 

A. Daily returns on two portfolios of stocks: 

• small _ hibm (A portfolio consisting of all NYSE, AMEX, and NASDAQ stocks which are characterized as small companies with high book-to-market equity ratios). 

• Mkt ( A-weighted portfolio of all stocks in the U.S. market)

 B. Daily returns on two pricing factors from Fama and French (1996)

• hml (High minus Low) 

• smb (Small minus Big)

C. Daily returns on a risk-free asset (U.S. four week treasury bill rate)

? rf 

Questions

1. What are the pricing factors hml and smb ? (Hint: To read about the factors, hml and smb , search under Fama-French three factor model. You may also want to search for textbook expositions of the Fama-French three factor model, e.g. in the textbook Investments by Bodie, Kane and Marcus of which there are several editions).

2. Open the EViews workfile “Assignment_Workfile.wf1”. Create a new variable ( mkt _ rf ) for excess returns on the market over the risk free rate, also known as the daily market risk premium, i.e. mkt _ rf = mkt − rf . (Hint: If you do this correctly the first value of mkt _ rf will be − 0.710). Next, create a new variable for excess returns on the small _ hibmportfolio, i.e. sh _ rf = small _ hibm − rf . (Hint: If you do this correctly the first value of sh _ rf will be − 0.531).

3. Provide a graph of rf and comment on any interesting features you see in the graph. 

(The horizontal axis of the graph shows the observation number. For example, to determine the date corresponding to observation 2010, click on the date in the workfile and you will see observation 2010 corresponds to date 2007-12-31 i.e. the 31st of December 2007. You may want to use this feature when you comment on this graph and others you may do in the assignment).

4. Provide a graph and descriptive statistics for both sh _ rf and mkt _ rf returns and compare them. Are there any important differences between them over the full sample?

5. Repeat the exercise from part 4 for the shorter sample period: 3 January 2000 – 31 December 2007. (Hint: This sample corresponds to observations 1 to 2010. Click on the “sample” tab and in the sample range pairs box and type 1 2010). Comment on the performance of the sh _ rf portfolio relative to the mkt _ rf portfolio during this period.

6. Estimate the following model for the full sample period: 

sh _ rf t = β 1 + β 2 mkt _ rf t + β 3 hml t + β 4 smb t + u t First, present the results by showing the EViews output. Second, present the fitted equation showing the coefficient estimates, standard errors and t-statistics in a table which you type in your assignment.

7. State what sign (either positive or negative) you would expect on the coefficients β 2, β 3and β 4, respectively, and provide a justification in each case. Then indicate whether the signs you found in Part 6 are what you expect. If not, how do they differ?

8. Conduct a hypothesis test to determine whether β 3and β 4are jointly significantly different from zero. Be sure to specify the null and alternative hypotheses, and to report the test statistic and to state your conclusion. Use a 5% significance level. (Hint: In EViews, click ‘View Coefficient Diagnostics/Wald test’). What does your test result imply about the validity of the Capital Asset Pricing Model (CAPM)?

9. Conduct a test of the hypothesis that β 2, β 3and β 4are all equal to one. Be sure to specify the null and alternative hypotheses, and to report the test statistic and to state your conclusion. Use a 5% significance level. (Hint: In EViews, click ‘View Coefficient Diagnostics/Wald test’). What do you conclude about the responsiveness of sh _ rf to each of the factors?

10. Conduct the basic diagnostic tests on the estimated model, i.e. autocorrelation (use 4 lags of residuals), heteroskedasticity (White with no cross product), non-normality (JB test), and functional form (RESET test with the number of fitted terms equal to 1). Comment on your results. (Note: You do not need to write out all of the steps of the hypothesis tests and you may copy the EViews output of the tests into your assignment. However, you must clearly write out the null and alternative hypotheses in each case, and clearly state the conclusion of each test. Use a 5% significance level).

11. Conduct ADF and KPSS unit-root tests on the series mkt for the full sample (conduct the test in levels, not differences, with an intercept and no time trend, and use default values for the remaining settings). Be careful to properly state the null and alternative hypotheses for the two tests. You may copy in the relevant parts of the EViews output. Do you draw the same conclusion from both tests?

 12. Report graphs of the autocorrelations and partial autocorrelations of mkt (in levels) out to 10 lags. Comment on the magnitude and significance of the correlations. What ARMA (p,q) model would you choose based on these graphs? Why?

13. Estimate the ARMA (1,1) model for the mkt series and comment on the significance of the parameter estimates. Present the ACF and PACF graphs and statistics for the residuals up to 10 lags and comment on them.

14. Now estimate the ARMA (1,1) model for the mkt series for the sample 3 January 2000 to 8 December 2017. (Hint: In the estimation settings box change the end date of the sample from 4528 to 4514, which corresponds 8 December 2017). Generate a dynamic forecast for observations 4515 to 4528 (i.e. for 11 December 2017 to 29 December 2017). (Hint: Click on the Forecast tab, select dynamic forecast, and set the forecast sample to 4515 4528. In the forecast name box, type mktfd which saves the dynamic forecasts on the workfile under this series name). Now generate a static forecast for observations 4515 to 4528. (Hint: Here select static forecasts and in the forecast name box, type mktfs). You are going to graph the static and dynamic forecasts in one graph. First, change the sample to 4515 4528. Then click on Object/New Object/Group and OK. In the list of the series box, type mktfd and mktfs and click OK. Then click on the graph to graph both series. Comment on the graph of the dynamic and static forecasts.

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