ECON334: Financial Econometrics 计量经济 assignment代写

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  • ECON334: Financial Econometrics 计量经济 assignment代写

     
    ECON334: Financial Econometrics
    ASSIGNMENT – S1, 2017 
    Due date and time: 4pm on Friday of Week 10
    19 May 
    Instructions
    -  This is an individual assignment. It should reflect your individual effort
    -  The assignment should be typed, with the main tables, charts and results presented
    throughout the assignment to highlight your responses to the questions
    -  There should be no appendices (appendices will not be marked) 
    -  Marks will be awarded for neatness, conciseness and clarity of answers 
    -  Where answers call for explanation, a simple reporting of numerically correct results
    will yield few (if any) marks
    -  When conducting hypothesis tests, outline all steps in your answer
    -  Maximum number of pages allowed: 12  (additional pages will not be marked)
    -  Pages should be numbered
    -  Be as concise as you can, while clearly addressing each question 
    -  Total marks: 30
    Submission instructions   
    -  You are required to submit the assignment in both print and electronic copies 
    -  Electronic submission is via a submission link on iLearn
    -  Print copy (with a signed assignment coversheet) must be submitted at BESS (E4B) –
    ensure you know your tutor’s name, as there will be separate submission boxes for
    each tutor
    -  A link to the FBE cover sheet is provided under the “Assignment” heading on iLearn
    Fill in the details of the cover sheet and staple it to the front of your assignment 
      
    2
    Part 1
    Part 1 ‐ Total number of marks: 15
    The Eviews workfile “Part1_Assignment_Workfile.wf1” located under “Assignment” heading
    on iLearn contains four series for the period November 1999 – February 2017 (208
    observations). The following variables are included:
    1. Monthly prices for  
      agt   (Agilent stock. Agilent Technologies is a U.S. technology company listed on
    the NYSE) 
    2. Returns on three pricing factors from Fama and French (1993) in percent.
      _ mkt rf ?the U.S. Market Risk Premium?
      hml ?High minus Low?
      smb ?Small minus Big?
    3.   rf  (the U.S. risk free rate in percent)
    Answer the following questions based on this dataset:
    A. Explain what the factors  hml and  smb are? (Hint: Search for Fama and French (1993)
    three factor model. Their paper is on iLearn but you may want to search for text‐book
    type expositions). Briefly describe the major company characteristics of Agilent
    Technologies. (2 marks) 
    B. Create log returns (in percent, e.g. 3.25%) and name them  _ . r agt  Calculate the excess
    return on  agt  stock as  _ r agt rf   and name it  _ . er agt    Consider the following
    model:
    1 2 3 4
    _ _
    t t t t t
    er agt mkt rf hml smb u           
    What signs (positive or negative) would you expect to estimate for each of the factors?
    Why? (3 marks)
    C. Estimate the model in part B and present the fitted equation. Interpret the fitted
    coefficients. Which coefficients are statistically significant at the 5% level? Are the
    estimated coefficients of the same sign as you expected in part B? (4 marks)
    D. Conduct a test for the joint significance of the estimated coefficients on the hml and
    smb factors? What do you conclude? (2 marks)
    E. 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,
    misspecification of functional form (only quadratic term). Comment on your results.
    Explain how using White or Newey‐West robust standard errors would address some
    of the issues identified in the diagnostic tests above?  (4 marks)
      
    3
    Part 2
    Part 2 ‐ Total number of marks: 15
    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 to December 2016, comprising a total of 444 observations. It is designated
    “divyield” in the workfile.
    A. 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. Comment on your findings from both tests. 
    (3 marks)
    B. Report the autocorrelations and partial autocorrelations of “divyield” (in levels) out to
    20 lags. Comment on your findings. (2 marks)
    C. Use the Eviews procedure (Automatic ARIMA forecasting, which is in Version 9 of
    Eviews)  to search over all ARMA models up to and including eight (8) AR lags and two
    (2) MA lags, and present the comparison table produced by Eviews. 
    (i)  What is the preferred model on the basis of the AIC criterion? (Hint: Refer to
    the Eviews instructions in the Week 7 tutorial).
    (ii)  What is the preferred model on the basis of 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).
    (iii)  Do both information criteria select the same model? If not, why not?
    (3 marks)
    D. Estimate in Eviews the ARMA model selected by the AIC criterion (in part C) for the
    sample January 1980 to December 2015 (i.e. 1980M01 to 2015M12) and present the
    fitted equation with t‐statistics. (Hint: In the equation estimation box change the end
    date of the sample from 2016M12 to 2015M12 as we keep the last twelve monthly
    observations for a dynamic out‐of‐estimation period forecast in Part E of the
    question). Briefly comment on the significance of the fitted coefficients.  (1 mark)
    E. Generate a dynamic forecast using the model you estimated in part D for the period
    January 2016 to December 2016 (i.e. for 2016M01 to 2016M12) 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
    4
    ECON334: Financial Econometrics 计量经济 assignment代写
    forecast and set the forecast sample to 2016M01 to 2016M12). Explain what is meant
    by a dynamic forecast.
    (i)  Comment on the convergence or otherwise of the forecast values and on the
    behaviour of the two‐standard error band.
    (ii)  Plot the divyield series and the dynamic forecast of the divyield series on the
    same graph for the period 2016M01 to 2016M12. Comment on the graph. 
    (6 marks)
    Reference
    Fama, E. F. and French, K. R. (1993). "Common risk factors in the returns on stocks
    and bonds". Journal of Financial Economics 33(3), 3‐56.
    ECON334: Financial Econometrics 计量经济 assignment代写