PURPOSE

The Individual Assignment relates to the following Learning Outcomes:

•   Apply econometric methods to modelling, analysing and forecasting financial data

•   Demonstrate and explain different estimation methodologies.

•   Critically evaluate empirical econometric work.

These learning outcomes support development of the following Graduate Capabilities:

•   Discipline Specific Knowledge and Skills

•   Critical, Analytical and Integrative Thinking

•   Effective Communication

SUBMISSION

•   The  assignment  must  be  submitted  via  Turnitin  by  11.55pm  on  Wednesday  12 October 2022. The submission link is available on iLearn under the “Assignment” tab from 5pm on Monday 10 October .

•   No  extensions  will  be  granted,  except  in  cases where  an  application for  “Special Consideration” has been made and approved.

•   Late submission will incur a penalty of 5% per day of total available marks.

•   Submissions will not be accepted beyond 96 hours past submission deadline.

•   You can only submit ONCE to Turnitin.  See the “After-submission checklist” (on page

2) for what you need to check after submitting .

•   Note: there is no “expected” range for similarity reports. You may get a high or low   number . The issue is this: Have you produced your own work? The similarity report  allows the lecturer to quickly check your submission against others for originality. A  high similarity report will be a problem if you have not quoted your sources, or          included more quotes than your own work, or your work is substantially the same as another’s. Check thislinkfor more information:                                                                  https://www.plagiarism.org/article/quoting-material

•   Marks will be assigned based on the quality of the responses to each question and in line with the rubric at the end of this document.

PLAGIARISM

•   Avoid plagiarism. The consequence of plagiarism is a zero mark.

•   You may work with other students at the preparatory stage. However, the final version of the assignment should be written in your own words.

•   Note that Turnitin will compare your submission against others’ as well as internet sources and any submission you have made in previous sessions in any unit.

•    Get familiarised with the academic honesty policy:

https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies- and-procedures/policies/academic-honesty

DOCUMENT PREPARATION  CHECKLIST

   Submit   your   assignment   with  the   file   name:   FamilyName_GiveName_Student

ID_Econ3034Assignment.

For example: Smith_Mary_12345678_Econ3034Assignment

   All questions  involving calculations must  show  all steps  involved to  arrive at your

answer .

   You have used Equation Editor . In MS Word go to: “Insert” then “Equation” .       All questions requiring a written response must include all relevant reasoning .

   All written answers  are typed  and then  saved as a  pdf. You cannot  submit a hand

written document.

   You can NOT submit a pages (Mac) document.

   You  can  NOT submit  photos  of your  assignment.  It  must  be  written  using  word

processing software and then saved and submitted as a pdf.

   All answers are presented in the same sequence as the questions in the assignment.    All answers are written in full sentences and in clear language.

   All answers are proofread and grammatical errors and typos are minimised.    Your submission should be in size 12 font.

   The master document is saved as a pdf file of less than 40MB.

   If you try to submit, and get a message that reads “Error M14:11” or similar, then you

have tried to upload a Word document.  Save as a pdf and re -submit.

   Include page numbers.

   Do not include appendices.

   Do not go beyond 15 pages.

AFTER-SUBMISSION  CHECKLIST

   I have double-checked whether the document was properly uploaded.

   I have seen my originality report.

   I have received the Turnitin receipt via email .

•    cma(Conservative minus Aggressive . Average monthly return on a portfolio of    stocks of companies which invest conservatively less the average monthly return on a portfolio of stocks of companies which invest aggressively).

To read about the factors,  mkt _ rf ,   hml and smb, search under Fama- French three factor model and to read about the factors  rmwand cma search under Fama-French five factor    model .

Note: All of the returns are expressed in percent, e.g ., 2.65% is represented by 2.65, not by 0.0265. Also, please see the document ‘How to save images from AppStream’ on our iLearn site under ‘Assignment’ in order to obtain an image of a table or graph in EViews .

Answer the following questions based on this dataset:

1.          Estimate the following regression for the excess return on the portfolio of food industry stocks for the full sample 1980m01 to 2020m02 and include a table of results from EViews.

food _ rft  = 1 + 2mkt _ rft  + 3hmlt  + 4smbt  + 5rmwt  + 6cmat  + ut                         Are the estimated coefficients on the five factors jointly statistically significant at the 5% level?  (2 marks)

2.          For the food industry stocks regression, perform a test of the hypothesis that

3  = 4   against the alternative that  3      4   using EViews. What do you conclude? (2 marks)

3.          Estimate the following regression for the excess return on the portfolio of                  transportation industry stocks for the full sample 1980M01 to 2020M02 and include a table of results from EViews.

transp _ rft  = 1 + 2mkt _ rft  + 3hmlt  + 4smbt  + 5rmwt  + 6cmat  + ut                      Are the estimated coefficients on the five factors jointly statistically significant at the 5% level? (2 marks)

4.          For the transportation industry stocks regression, perform a test of the hypothesis that 3  = 4   against the alternative that 3      4   using EViews. What do you

conclude? (2 marks)

5.          Compare the estimated value of the coefficient 2   in the two regressions. Is the      estimated coefficient statistically different from one in each case? (Perform the test in EViews). What do you conclude about the sensitivity of each portfolio to the         market risk premium? (4 marks)

6.          Compare the corresponding estimated coefficients on the other factors in terms of  their sign and significance in both regressions (i .e., compare the estimated                 coefficient on  hml in the food industry regression with that in the transportation     industry regression. Do similarly for the estimated coefficients on smb,  rmwand      cma. What does this comparison suggest about the average character istics of stocks in the food and transportation industries? (Hint: For example, are food industry        stocks value stocks on average, etc?) . (10 marks)

7.          Is the estimate of 1  statistically significant at the 5% level in each of the      regressions? How do you interpret this result?   (4 marks)

8.          Perform White’s test (with no cross-product terms) for heteroscedasticity in the     estimated residuals from each regression. (Write out the null and alternative           hypotheses of the test, explain and provide the EViews output showing the results, and clearly state the conclusion of the test. Use a 5% significance level).          (2 marks)

9.          In view of the results of White’s test, should you be concerned about                         heteroscedasticity and if so, what should you do, and would that change any of the conclusions you reached in earlier questions.   (4 marks)

Part 2

Part 2 - Total number of marks: 28

The ‘Assignment_Question 2 EViews Workfile’ located under the ‘Assignment’ heading on    iLearn contains three monthly yield series for the period June 1993 – August 2022, i .e., for   1993M06 to 2022M08. The series tb _ 3yr is the yield to maturity on 3-year Australian         Treasury Bonds and the series bab _ 3m is the yield to maturity on Australian 3-month Bank Accepted Bills. The series spread is the tb _ 3yr yield less the  bab _ 3myield. The data are   obtained from the Reserve Bank of Australia . The yields are represented, for example, as      5.95 which means 5.95% per year .

10.       Conduct an ADF unit-root test on the spread series. Be sure to 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. Are the results from both tests consistent with each other? (4 marks)

11.       Compute the ACF and PACF for the  spread for the first 16 lags using EViews.             Comment on the pattern of the ACF and PACF and what they may suggest about the ARMA time series model for the spread. (4 marks)

12.       Consider the following two models where yt    denotes the series for spread : Model 1: yt  = c1 +01yt−1 +91ut−1 + ut

Model 2: yt  = c2 +01yt−1 +02yt−2 +91ut−1 +92ut−2 + ut

Estimate each model in EViews, comment of the significance of the coefficients           (apart from the constant) and select the best model using the Akaike Information       Criteria (AIC) and the Schwarz Bayesian Information criteria (SBIC). (Hint: Sample        sizes need to be the same when comparing models with AIC or SBIC . That is,                estimate the models over the sample period 1993M08 to 2022M08 (i .e., beginning in August 1993) because model 2, which has the most AR lags, namely two, uses up        two observations for the lags in the estimation . In the equation estimation settings    box, change 1993M06 to 1993M08). (4 marks)

13.        Using EViews, compute the ACF and PACF of the residuals (out to 16 lags) from   Model 1 and Model 2, respectively. Based on the ACF and PACF of the estimated residuals, do you prefer one model over the other? (4 marks)

14.        Estimate Model 1 for the sample 1993M08 to 2020M12 and generate a dynamic      forecast for the period 2021M01 to 2022M08. (Hint: First estimate Model 1 being    sure to specify 1993M08 2020M12 in the estimation settings box . Having estimated the model, select the forecast tab, select dynamic forecast, set the forecast sample to 2021M01 to 2022M08, and in the forecast name box, type  spread _ df .

(i)         EViews generates a graph for the dynamic forecasts you generated together with the two-standard error band. Present this graph and comment on the   convergence or otherwise of the forecast values and on the behaviour of the two-standard error band. (4 marks)

(ii)        Graph the actual spread series and the dynamic forecast of the  spread

series i .e., spread _ df on the same graph for the period 2021M01 to            2022M08. (Hint: Click on Quick/Sample and specify the sample as 2021M01 2022M08. Click OK. Then, on the main menu bar at the top of the screen,    click on Object/New Object/Group and OK. In the list of series box, type       spread  and spread _ df and click OK. Then click on View/Graph to graph     both series together). Comment on the graph. (Hint: Refer to recent             movements in Australian interest rates). (4 marks)

15.       Now estimate Model 1 for the sample 1993M08 to 2020M12 and generate static forecasts for the period 2021M01 to 2022M08. (Hint: Here select static forecasts, and in the forecast name box, type spread _ sf ).

(i)         EViews generates a graph for the static forecasts . Present this graph and comment on it. (2 marks)

(ii)        Graph the actual spread series and the static forecast of the  spread series

i .e., spread _ sf on the same graph for the period 2021M01 to 2022M08. Comment on the graph. (2 marks)