CS计算机代考程序代写 python database finance case study Excel Microsoft Word - Group Project T2 2020.docx

Microsoft Word – Group Project T2 2020.docx

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Financial Econometrics, 2021 T2
Case Study Part I

(30% of the course mark)

1. This Part I of the Case Study must be completed in a group of 3 or 4 students. Each
group is identified by a class time (e.g. Wed12) and a group number (e.g. Group 2).

2. Each group will be assigned a dataset for the project.

3. Each group must submit one copy of the assignment via Turnitin. Only one
submission per group. Each group must select one person to submit the assignment.

4. The online submission deadline is 11:55 pm on Monday 12 July, (WEEK 7).

Each group MUST submit the online copy to Turnitin.

The project Cover Sheet must be properly filled, which includes tutorial/group id,
names and student numbers of the group members.

5. A late-submission penalty of 20% will apply for each 24 hours late.

6. All submissions will be checked for plagiarism. The University regards plagiarism as a
form of academic misconduct, and has very strict rules regarding plagiarism. Where it
can be established that individual students are responsible for the plagiarism, those
individual students will be penalised. However, where it is judged that the plagiarism
should have been clear to the other group members, the penalty will apply to all
members of the group.

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material.

UNSW Business School
School of Economics

Financial Econometrics, 2021 T2

Case Study Part I

Group Cover Sheet

Check-list
1. Choose one member to submit the assignment: one soft-copy to be submitted online.
2. Class number, group number, all names and student numbers of the group must be

filled in on this Cover Sheet.

Class/Group ID: ________

Full Name Student No.
1. (person for
submission)

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material.

Software
You MUST use Python to complete this assignment. You must attach a copy of your codes
with your submission.

Topics
This assessment requires you to use material covered in the lectures of Week 1 to Week 4
inclusive.

Data

Select the company corresponding to your group (all these are large companies significant for
superannuation funds, international share portfolios likely to hold most of these companies).
The data allocation (company name) to groups can be seen in the Excel sheet:
https://docs.google.com/spreadsheets/d/1shPrDAbxrNuGEuz9Qp_SHRLKGj66rl9eDtG0aLO
yFJU/edit?usp=sharing

Download open/close stock prices and volume information from http://finance.yahoo.com as
described below.

Enter the company name in the window near GET QUOTES button.

Please, make sure that you download the information for the “main” stock of the company (not
its derivative, or a non-US quote). See appendix for some example screen shots.

Once you are on the company page download historical prices by clicking on the corresponding
link on the left-side menu.

Select the following data-period for your historical quotes:
1 September 2015 till the day of download

Enter the date range and click on “download to spreadsheet” on the bottom of the page (see
Appendix). You will receive a comma-delimited file opened by Excel. The data are sorted in
descending date order. For the purpose of the analysis you need to re-sort the data in the
ascending date order.

Note: Some companies may have shorter periods of data available. Possible reason: company
changed its name or merged with another company. Investigate if this is the case and add the
data on the predecessor. Talk to me if you have difficulties.

REPORT
Write a report on the tasks below. Keep your answers short and to the point. Show the necessary
information (the null of a test and the decision rule, derivation of analytical results, and only
necessary Python output that is needed to answer the question). Make sure to provide comments
on all of your results. Please keep the page numbers limited to 7 pages maximum.
Python codes are added as an appendix and are not counted in the pages limit.

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material.

Case Study (I) Description

Suppose that you work as a financial advisor. Your client (bank) is interested in investing in a
given share.
1. Give a short profile of the company, its business and recent history (2-4 sentences).
(2 Points)
2. Transform daily close adj. prices to (log) returns. Plot returns over time. Plot the histogram
and obtain basic descriptive statistics. Discuss your findings.
(3 Points)
3. CAPM Model

a) Estimate CAPM model for your company. Use S&P500 index returns as a proxy for
the market portfolio. The data on the index are available from yahoo (you still need to
transform them into log returns). As a proxy for interest rates use the rate of return on
a three-month U.S. Treasury bill (short-term government-issued securities have
virtually zero risk of default). The data are available from
https://fred.stlouisfed.org/series/DGS3MO

(3 Points)
Notes:
Dates when shares were traded may not fully coincide with the dates when TBills were traded.
To match the dates use Excel’s VLOOKUP function (make sure to use false option for
range_lookup to disable approximate date match). Alternatively, you may use MS Access
database.

b) Test whether CAPM holds and interpret an estimate of β. If CAPM does not hold
explain possible reasons and suggest possible solutions (you do not have to implement
them). Assume that CAPM hold (at least approximately) and construct replication
portfolio with S&P500 index and T-Bill. Compute the historical expected return and
the variance of the portfolio and compare it to the variance of the share. Based on these
findings advise your client whether investment in portfolio is a better option.

(3 Points)

c) Check whether the CAPM model specification is stable under the COVID-19 period.
You May use parameters/model stability tests like the CUSUM test. If you reject
stability, re-estimate a post COVID-19 CAPM and comment on the COVID-19 market
beta.

(3 Points)
4. APT Model

a) Estimate and test an APT type pricing model using the Fama & French three factor
model for your company stock. Data on the size/book-to-market factors can be
downloaded from:
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html#Research
In your report interpret the meaning of the factors beta, and what they represent in
addition to the CAPM premium.

(3 Points)

b) Investigate whether unexpected changes in oil prices, and currency exchange rate are
sources of risk that pay premium on your company stock.

Hint: Data on oil price and currency exchange rates are also available from Yahoo.Finance. Whether you
include an exchange rate and which currency you want to consider depends on your company. If your
company is domiciled in Australia and buys parts from China, changes in the AUD/YUAN exchange rate
may impact profitability of the company and the return on investment.
(3 Points)

5. Model Selection:

a) Test for Unit root in the series. Describe the Dickey-Fuller test (the null hypothesis and
the alternative) and comment on the conclusion of the test.

(2 Points)

b) If you do not reject the null of unit root, describe your strategy for transforming the
series into stationarity. Note that you may have deterministic source of non-stationarity.
For example a structural break around COVID-19 pandemic!

(2 Points)

c) Fit an ARIMA(1,1) model to your company returns data. Obtain the standardized

residuals from your model and investigate if there is any dependence structure left in
the residuals.

Hint: To do this you can use BDS test (Brock, Dechert and Schienkman [1987](*) ).
Explain why this test is appropriate, and why this test is more informative that a test
for zero correlation in the residuals series. Shortly explain the test, the null hypothesis
and the outcomes. What conclusions do you reach?

(3 Points)

• In Python, you can find codes for implementing the BDS test in:

https://programtalk.com/vs2/python/12423/statsmodels/statsmodels/tsa/tests/test_bds.py

(*)Brock, Dechert, Scheinkman, and LeBaron, A test for independence based
on the correlation dimension, Econometric Reviews, 15, 1996: 197-235.

d) Derive and plot the impulse response function of the ARIMA model you have estimated in (c)
(3 Points)

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