08 33938 LM Financial Econometrics

Assignment Remit

• This coursework accounts for 25% of your final mark.

• The coursework is an individual piece of work. You are required to write your own answers and cannot work jointly. All submissions will be routinely checked for plagiarism. [Plagiarism Policy]

• The deadline for the submission of the coursework is 20/03/2024 by 12:00:00 (12 noon).

• The coursework should not be longer than 1,500 words.

• Figures, tables and R codes do not count towards the word limit.

• Please make sure that you fill in the cover page when submitting your report.

• Follow the assignment remit and answer all the questions below.

• Perform the appropriate quantitative analysis and make sure your codes work well. If you receive any error messages, try to correct it. Otherwise, delete or hashtag (#) the code that caused the problem so that you can compile the codes.

• Perform statistical tests to support your results and empirical approach if needed. Explain the tests hypothesis and interpret the results clearly.

• Present your tables and graphs clearly.

• Explain your procedure (of selecting the best fit model) clearly and provide a detailed and thorough analysis on your results (without going over the word limit)

Assignment 

Please read the following requirements carefully before you start your project.

• This is NOT an essay. There is no requirement on the format. But your work needs to be readable and follows the requirements in this remit.

• The coursework MUST be compiled in RStudio and submitted via Canvas in PDF format. You can compile your script to a Word document first and then add the cover page. Please check if your document is clearly readable. If everything looks good to you, you can save it as a PDF file for submission using Microsoft Word.

• The answers to each question MUST be clearly stated. Your comments and answers to the questions could be written following “#” in your R codes, which will be compiled.

You are required to select a listed company in the S&P500 or FTSE100 indices. Download its daily (close) price data from 1 st January 2023 to the date that you start this project (this can be any date between the release of this remit and the deadline) and then perform the following analysis.

1. Write a very brief description on the data you are using (no more than 50 words). Introduce the company and its industry, as well as the data source. Then make a time series plot on the price data and comment on its trends. [10%]
2. Take the log differences on the prices and get the daily returns. Report the summary statistics including mean, median and standard errors. Plot the time series plot and the histogram of the returns. Comment on your results. [10%]
3. Plot the ACF and PACF of the daily returns and comment on your graphs. Is the daily return series stationary? Test it with appropriate ADF and KPSS tests. Explain the null hypothesis and the specific format of the tests. [15%]
4. Estimate an appropriate ARIMA model on your return series. You need to present the estimation results in a well-organised table, explain why you select such a model, and comment on the estimated coefficients. Make forecasts of the next five returns. Based on your forecasts, what’s the expected price in five trading days? [30%]
5. Based on the ARIMA model above, check if there is an ARCH effect. You must explain your procedure and results clearly. If yes, estimate an appropriate ARMA-GARCH model and explain why you select such a model. Otherwise, estimate a GARCH(1,1) model on the return series. Based on your model, what’s the forecasted volatility of the returns in the next trading day? [25%]
6. Reflect on your work. Comment on your work in terms of the strengths and weaknesses of the work, assuming you are reading such a report. Identify the areas that you need to improve. Please be honest in your reflection. You are welcome to tell me your feelings about this assignment (no more than 100 words). [10%]

LO 1. Demonstrate systematic knowledge and understanding of econometric methods and tools and their application to specific problems.

LO 2. Conduct complex empirical econometric analysis and interpret the results.

Grading Criteria

A first-class mark will be awarded to students who:

• Perform the appropriate quantitative analysis in each of the questions.

• Provide a detailed and thorough analysis on the results, without going over the word limit.

• Submit a well organised and detailed report with R code compiled by RStudio.

• Submit a piece of work that is well written, with no typos or grammatical mistakes, organised and well formatted.

Feedback to Students

Both Summative and Formative feedback is given to encourage students to reflect on their learning that feed forward into following assessment tasks. The preparation for all assessment tasks will be supported by formative feedback within the tutorials/seminars. Written feedback is provided as appropriate. Please be aware to use the browser and not the Canvas App as you may not be able to view all comments.

Use of Generative AI

Unless explicitly stated otherwise, students should assume that the use of generative AI within an assessment or assignment is not permitted. Any assessment submitted that is not a student’s own work, including that written by generative AI tools, are in breach of the University’s Code of Practice on Academic Integrity. https://intranet.birmingham.ac.uk/as/registry/policy/conduct/plagiarism/index.aspx

The process for Extenuating Circumstances is to support students who have experienced unforeseen issues that have impacted their ability to engage with their studies and/or complete assessments. Students should notify Wellbeing of any extenuating circumstances as soon as possible via the online form, following the guidance provided. https://intranet.birmingham.ac.uk/social-sciences/college-services/wellbeing/index.aspx