代写ECO310 – Econometrics of Time Series 2nd SEMESTER 2023/24代写留学生Matlab程序

2nd SEMESTER 2023/24 Group Assignment

ECO310 – Econometrics of Time Series

This assessment takes the form. of a group research project (7 students in each group, except for one group), with group membership randomly assigned.

The coursework project is part of the assessment for the ECO310 module with a weight of 15% of the final mark for the module.

Project Task

Each project group is randomly assigned time series for the prices of 3 stocks sourced from DataStream, which contains closing prices from the periods of December 30, 2016 to December 30, 2022.

Using appropriate time series econometric techniques, please address these questions in sequential order in your report. You are expected to present your report as if you are a team of investment analysts advising your clients.

1. Evaluate and comment on the trading performance of the three stocks during this period. You should use log-returns.

2. Does any of the series display serial correlation and unit root? If so, what does this mean to a retail investor who has zero knowledge of time series analysis? You should attempt to explain these concepts as intuitively as possible.

3. For all six pairings of the 3 stocks, please estimate a best model autoregressive distributed lags (ARDL) model in each case. You should explain clearly the rationale underpinning the optimal lag lengths selected in each case.

Further, you should also employ appropriate forecast combination technique to develop a combined forecast model in forecasting the returns for each of the 3 stocks. Argue for the best forecasting model for each of the 3 stocks.

4. Based on your analyses in Part (3), which stock is likely to be the main driver of performance, among the three stocks you are assigned? Explain your reasoning.

5. Now, implement formal Granger causality tests instead. Which stock is then likely to be the main driver among the three stocks you are assigned? Is this ordering the same, or different from your choice in Part (4)? Explain.

If further analyses are deemed relevant and can strengthen your arguments, you can add more information that may be peripheral but in support of your arguments.

Submission and deadline

Note that group mark is the same for all group members. However, if the majority of students within a group are in consensus that a particular student has not participated and contributed in the group project, he/she would receive 50% penalty from the group’s project mark.

Each group should submit a report, with no less than 1000, but no more than 1300 words (excluding title page and Appendix), through LMO no later than 11:00, May 13, 2024.

In each group one group member submits the assignment on behalf of all the group members on the LMO. Detailed student names and numbers of all group members should therefore be included in the front title page of the report submitted.

Late submissions policy: Late submissions will be penalized 10 marks for every working day past deadline. Late submissions will be accepted till +5 working days after the deadline.

Backup: If for some reason submission through LMO fails, students can send their coursework to the module leader via e-mail: [email protected]

Assessment

The final mark will be based on the evaluation of the submitted report according to the following criteria (percentages out of total marks in parentheses):

(i) Data & preliminary analysis (15 percent): Your report should have a clearly explained dataset with the indicated time period. The selection of the dataset and specific details such as handling missing observations etc., as well as the preliminary graphical analysis, should be included in the report.

(ii) Methodology (15 percent): Econometrics methodology applied in every step should be correct, and explained clearly and sufficiently. Specifically, the rationale of applying each methodology should be justified given the empirical features observed in the dataset.

(iii) Written report quality, applications, and presentation of results (50 percent): Overall, quality of the written report and the clarity of presentation of the results are very important criteria in the marking. The flows of the analyses implemented should be coherent, easy to follow, and make econometric senses. Any mistake made in terms of results’ interpretations will be penalized accordingly. Note that proper use of English grammar and vocabulary is important.

(iv) Literature and References (10 percent): Literatures referred to should be discussed and relevant, and then properly cited and referred to. References styling must be consistent following the Harvard referencing style. The studies that are utilized should be briefly discussed and referenced.

(v) Technical appendix (10 percent): All the E-Views output (or other computational tools deemed appropriate) involved in guiding the empirical analyses must be included in the Appendix part. These should be clearly labelled and put in order according to the use in the project report.

Please refer to the marking grids for further details on how these criteria are individually assessed.

Plagiarism

Passing off someone else’s work as your own–whether deliberately or inadvertently–amounts to a serious form. of academic misconduct. All submitted group assignments are subject to Turnintin Similarity Scores check. As such, do not copy and paste material from any source such as lecture notes, academic books, journals or websites. You should not copy and paste graphs directly from other sources too. Instead, construct these graphs yourself in either E-Views, MS Excel, or any other equivalent computational programs.

For recurring acronyms, define on first mention. Allow time for editing and proof-reading to ensure your work reads well and has no spelling/grammatical errors.

Usage of generative AI

The use of generative artificial intelligence (AI) and AI-assisted technologies in the data analyses and direct writings of this report are prohibited.

You are allowed to use these technologies only to improve readability and language of your report. The application of these technologies should be done carefully with human oversight and controls, which include proper review and editing works, as any authoritative-sounding writing could potentially be flagged out as involving a direct copying/imitation of AI-produced output and would be penalized accordingly in the marking process.

All usages of generative artificial intelligence (AI) and AI-assisted technologies in the writing process of this report should be disclosed.