Hello, dear friend, you can consult us at any time if you have any questions, add WeChat: daixieit

N1611 Financial Econometrics – Coursework #1

2022-23

Coursework Guidelines

· The assessment for N1611 Financial Econometrics module is by two pieces of coursework. This is the first coursework and it is worth 30% of the overall assessment of the module.

· The coursework consists of understanding theoretical models, along with data manipulation, analysis and interpretation. Please note that this is NOT a group exercise. Although you may discuss the project with others, the coursework analysis and discussion must be worked out and written up individually. You may receive reduced or no marks if there are strong similarities between the work handed in by two or more people.

· Candidates should attempt ALL questions.

· Your answer to each question should INCLUDE the full references of the articles, books and other sources cited. You can present the references at the end of your answers and discussion to all questions.

· Information on where to find material: The material to be used to answer the questions is on the Canvas site. However, students are expected to do their own research and are encouraged to add other sources.

· STATA output should NOT be copied and pasted directly into the project. You should present your results (e.g. regression output) as it would appear in published academic research papers. (Look at some published journal articles --sometimes the output is in Tables, sometimes presented as estimated equations with s.e./t stats/p-values in brackets under the corresponding coefficient, together with appropriate diagnostic statistics and their p-values).

· You should always comment on your estimation results, i.e. what is the intuition behind your empirical findings.

· The word count of the project must be printed on the first page of the coursework. The maximum word count is 1500, e.g., you could split this word count across the questions. The tables, references and appendices are not included in the word count.

· Note that your coursework is to be submitted electronically via Canvas. Please check for the deadline of submitting your work on Canvas module site (under "Assignments" section). For more information regarding the deadline or issues on submitting your work, please check with the UG School office (at this email: [email protected]).

· Upload your word (or pdf) document through the "E-submissions" link on the module's Canvas site by the deadline. There is no need to upload or submit the data or STATA estimation file.

· Late submissions will be treated according to university regulations. More information on assessment regulations can be found here:

http://www.sussex.ac.uk/adqe/standards/examsandassessment/esubmission

Coursework Questions

You are given monthly data of the U.K. Consumer Price Index (CPI) over the period 1991M1 to 2022M7. The data file name is "CPI.xls", uploaded on Canvas along this file. First calculate the UK inflation rate, i.e., ∆cpi_t= cpi_t - cpi_t-1, where cpi_t is the natural logarithm of the CPI at time t and ∆ is the first difference operator. Then:

a) Explain the Box-Jenkins approach in building an ARMA(p,q) model for ∆cpi_t.      

                                                                     [25%]

b) Use the full sample period to obtain a graph of the ∆cpi_t series. Based on the graph, does the series appear to be stationary? Comment on the behaviour of this series over time.    

                                                                     [10%]

c) Explain the terms autocorrelation function (ACF) and partial autocorrelation function (PACF). What shape would these two functions take for a stationary autoregressive process, a moving average process, and an autoregressive moving average process?        

                                                                     [10%]

d) Obtain the autocorrelation function (ACF) and partial autocorrelation function (PACF) for the ∆cpi_t series (specify the number of lags to be 6) using data from 1991M1 to 2020M12 (Note that this is not the full sample). Discuss the significance of the ACF and PACF coefficients and identify the suitable models that you would estimate.

                                                                      [10%]

e) Estimate all ARMA models from order (0, 0) to (6, 6) for the ∆cpi_t series over the shorter sample period 1991M1 to 2020M12. From your estimations, which is the suitable model order? Explain why? (You would also need to report all relevant information for the models that you estimate, including the value of the AIC and SBIC and other relevant required criteria in a Table).          

                                                                      [20%]

f) Re-estimate only the suitable model(s) identified from Question (e). Again, use only the sample 1991M1 to 2020M12. Report and comment on the results. Perform diagnostic checks on the residuals from these estimated model(s). Do the model(s) fit the data well?

                                                                   [10%]

g) Use the model(s) estimated in Question (f) to generate one step ahead (static) forecasts for the period 2021M1 – 2022M7. Create a graph of the actual ∆cpi_t series and the forecasts that you have generated over the specified out-of-sample period. Comment on the results.

                                                                      [15%]

Conduct all your statistical tests at the 5% level.