代做08 33938 Financial Econometrics Computer Project代做R编程

Assignment Remit

Programme Title	Economics
Module Title	LM Financial Econometrics
Module Code	08 33938
Assignment Title	Financial Econometrics Computer Project
Level	Postgraduate
Weighting	25%
Hand Out Date	02/02/2024
Due Date & Time	20/03/2024	12pm
Feedback Post Date	24/04/2024
Assignment Format	Report
Assignment Length	1,500 words
Submission Format	Online	Individual

General information

•           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.

How to dowell

•           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 1st  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%] 

Module Learning Outcomes

In this assessment the following learning outcomes will be covered:

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.

Plagiarism

It is your responsibility to ensure that you understand correct referencing practices. You are expected to use appropriate references and keep carefully detailed notes of all your  information sources, including any material downloaded from the Internet. It is your responsibility to ensure that you are not vulnerable to any alleged breaches of the assessment regulations. More information is available at

https://intranet.birmingham.ac.uk/as/registry/policy/conduct/plagiarism/index.aspx

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

Wellbeing Extenuating Circumstances

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