N1611 Financial Econometrics – Coursework #1
2023-24 (Resit)
Coursework Guidelines
· This coursework is worth 30% of the total mark for the module.
· The coursework consists of an understanding of theoretical models, along with data manipulation, analysis and interpretation. Please note that this is NOT a group assignment. Although you may discuss the project with others, the analysis and discussion of the coursework must be undertaken and written individually. You may receive reduced or no marks if there are strong similarities between the work submitted 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 may provide the references at the end of your answers and discussion for all questions.
· Information on where to find the material: The material to be used in answering 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 they would appear in published academic research papers. (Take a look at some published journal articles --sometimes the output is presented in tables, sometimes presented as estimated equations with s.e./t-statistics/p-values in parentheses 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., this word count would be split across the questions. Tables, references and appendices are not included in the word count.
· Note that your coursework must be submitted electronically via Canvas. Please check the deadline for submitting your work on the Canvas module site (under the "Assignments" section). For more information about the deadline or any issues about submitting your work, please contact the UG School Office (at this email:
· Upload your word (or pdf) document via 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 dealt with in accordance with university regulations. More information on assessment regulations can be found at the following link: http://www.sussex.ac.uk/adqe/standards/examsandassessment/esubmission
Coursework Questions
1. Explain two types of seasonality that exist in financial markets. Explain how each type of seasonality you discuss can be tested empirically. Support your discussion with appropriate mathematical equations and academic references in this area of research.
[30%]
2. You are given the monthly UK House Price Index (HPI) over the period 1990M1 to 2023M8. The data file name is "HPI.xlsx", which is uploaded to Canvas along with this file. First, calculate the UK house price change series, i.e., ∆hpit= hpit - hpit-1, where hpit is the natural logarithm of the HPI at time t and ∆ is the first difference operator. Then:
a) Follow the Box-Jenkins approach to build an ARMA(p, q) model for the house price change series, specifically,
i) Obtain the autocorrelation function (ACF) and the partial autocorrelation function (PACF) for the series (specify the number of lags to 6) using data from 1990M1 to 2021M12 (note that this is not the full sample). Discuss the significance of the ACF and PACF coefficients and identify the appropriate models that you would estimate.
[15%]
ii) Estimate all ARMA models of order (0, 0) to (6, 6) for the series over the sample period 1990M1 to 2021M12. From your estimates, what is the appropriate model order? Explain why. (You would also need to report in a table all relevant information for the models you estimate, including the value of the AIC and SBIC and other relevant required criteria).
[20%]
iii) Re-estimate the appropriate model(s) you identified in question a(ii). Again, use only the 1990M1 to 2021M12 sample. 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?
[15%]
b) Use the model(s) estimated in question a(iii) to generate one-step ahead (static) forecasts of the series for the period 2022M1-2023M8. Plot the actual series and the forecasts that you have generated over the specified out-of-sample period. Comment on the results.
[20%]
Conduct all your statistical tests at the 5% level.
End of Paper