ECON334 - Financial Econometrics - Eviews Workfile - Finance Assignment Help

Assignment Task:

ECON334: Finance Assignment Help

Part 1 - Total number of marks: 30

The Eviews workfile “Part1_Assignment_Workfile.wf1” located under “Assignment” heading on iLearn contains five monthly return series for the period January 1982 – February 2019 (446 observations). Note: to copy and then insert a table from EViews into your Word document you will need to “freeze” the table in EViews, then copy the table and paste it into your Word document.

The following monthly return series appear in the file:
A. Excess Return on Corning Incorporate stock
• glw rf _ (Monthly excess return on Corning stock. Corning is a US multinational technology company listed on the New York Stock Exchange (NYSE).

B. Returns on three pricing factors from Fama and French.

• mkt rf _ (Excess return on a weighted portfolio of all stocks in the U.S. market. It is the U.S. Market Risk Premium)

• hml (High minus Low)

• smb (Small minus Big)

C. Return on the risk free asset
? rf

Answer the following six questions based on this dataset:
1. Estimate the following model for the full sample period 1982M01 to 2019M02 and include a table of results from EViews.
1 2 3 4 _ _ t t t tt glw rf mkt rf hml smb u =+ + + + ββ β β
Are the coefficients 2 3 β β, and β4 statistically significant at the 5% level? Why or why not? 

2. Do the estimated coefficients on the factors have the signs that you would have expected? (In answering this question, first state what sign you would expect on 2 3 β β, and 4 β , respectively, and provide a justification in each case. Then indicate whether the expected signs are what is obtained in Part A. (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).

3. Is the estimate of β1 statistically significant at the 5% level? How do you interpret this result?

4. Conduct a hypothesis test to determine whether 1 3 β β, and β4 are jointly significantly different to zero, i.e. test the following null hypothesis 0 1 H : 0 β = and
3 β = 0 and 4 β = 0. Set out all of the steps for a formal hypothesis test and state the 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)? Why?

5. Conduct the basic diagnostic tests on the estimated model, i.e. autocorrelation (use 4 lags of residuals), heteroskedasticity (White with no cross product), and non-
normality. 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.

6. In view of the results you found in Part 5, would you recommend that White or Newey- West (HAC) standard errors be utilised in conducting t-tests for the statistical significance of the estimated coefficients in the regression of Part A. Justify your answer.

Part 2

The Eviews workfile “Part2_Assignment_Workfile.wf1” located under “Assignment” heading on iLearn contains monthly data on the Australian Stock Exchange (ASX) dividend yield, from January 1980 (1980M01) to February 2019 (2019M02), comprising a total of 470 observations. It is designated “divyield” in the workfile.

7. Plot a graph of “divyield”, and comment on its salient features. Conduct an ADF unit- root test on the “divyield” series. Be careful to properly state the null and alternative hypothesis for the test. Also conduct a KPSS unit root test and be sure to state the null and alternative hypothesis for the test. Using your findings from both tests, what could be happening?

8. Report the autocorrelations and partial autocorrelations of “divyield” (in levels) out to 20 lags. What can you say about the dividend yield?

9. Use the EViews procedure ‘Automatic ARIMA forecasting’ to search over all ARMA models up to and including eight (8) AR lags and two (2) MA lags.

(i) What is the preferred model based on the AIC criterion? (Hint: Refer to the EViews instructions in the Week 7 tutorial). Present the result produced by
EViews.

(ii) What is the preferred model based on the SBIC criterion? (Hint: On the options tab in the Automatic ARIMA forecasting procedure you see Akaike Info Criterion. This is the default. You can click on it to change the criterion to Schwartz Info Criterion). Present the result produced by EViews.

(iii) Do both information criteria select the same model? If not, why not?

10. Estimate in EViews the ARMA model selected by the AIC criterion (in Q9) for the sample January 1980 to February 2017 (i.e. 1980M01 to 2017M02) and present the
fitted equation with t-statistics. (Hint: In the equation estimation box change the end date of the sample from 2019M02 to 2017M02 as we keep the last twenty-four
monthly observations for a dynamic out-of-estimation period forecast in Part 11 of the question). Are the estimated coefficients significant at the 5% level?

11. Generate a dynamic forecast using the model you estimated in part D for the period March 2017 to February 2019 (i.e. for 2017M03 to 2019M02) and show the dynamic forecasts in a graph together with the 2-standard error confidence band. (Hint: From the window where you estimated the model, select the forecast tab, select dynamic forecast and set the forecast sample to 2017M03 to 2019M02).

(i) Explain what is meant by a dynamic forecast.

(ii) Comment on the convergence or otherwise of the forecast values and on the behaviour of the two-standard error band.

(iii) Plot the “divyield” series and the dynamic forecast of the “divyield” series on the same graph for the period 2017M03 to 2019M02. Comment on the graph.

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