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ETF5952 QUANTITATIVE METHODS FOR RISK ANALYSIS

Semester 1, 2019

ASSIGNMENT 2

This assignment comprises 25% of the assessment for ETF5952. This is an individual, NOT a

syndicate, assignment. On the Assignment Cover Sheet, read the references to plagiarism and

collusion from University Statute 4.1. Part III – Academic Misconduct.

Deadline: 3PM, May 31 on Friday, 2019

Submission:

1. Your assignment must be typed and you must submit a printed “hard copy” with an

Assignment Cover Sheet (from the “ASSIGNMENTS” section of Moodle). Submit it in

your class/tutorial before the due time, or submit it to your tutor’s mailbox, 5th floor H

Block. For each day that it is late, 10% of Assignment’s allocated marks will be

deducted. Do not submit your assignment in a folder and staple A4 pages.

2. Name your assignment: Surname-Initials_A2.docx or Surname-Initials_A2.pdf (eg.

Trump-DJ_A2.docx) and Upload this file to Moodle (as a backup) – as follows:

Go to the “ASSIGNMENTS” section.

Click on the “ASSIGNMENT 2” link to upload.

The following message will appear momentarily, “File uploaded successfully.”

(To later confirm your upload was successful, go to the “ASSIGNMENTS” section and

click. On the “Assignment 2” uploading link. The uploaded file’s name will be shown.)

Note: DO NOT submit any Excel files. You may upload ONE file only. Retain your

marked assignment until after the publication of final results for this unit.

Your tutor will NOT print or mark your assignment from Moodle.

You are required to

o Answer all questions.

o Write your answer succinctly and include big tables and figures as appendices with

appropriate labels. (If you have trouble pasting figures and tables in your document,

you could print them out separately.)

If you find possible typos or mistakes in this Assignment, please contact a lecturer or tutors

to clarify the questions for you. Also, if you have any other questions, please use

consultation time.

Plagiarism

Intentional plagiarism amounts to cheating in terms of University Statute 4.1. Part III – Academic Misconduct.

Plagiarism: Plagiarism means to take and use another person’s ideas and or manner of expressing them and to

pass these off as one’s own by failing to give appropriate acknowledgement. This includes material from any

source, staff, students or the Internet – published and unpublished works.

Collusion: Collusion is unauthorised collaboration with another person or persons.

Where there are reasonable grounds for believing that intentional plagiarism or collusion has occurred, this

will be reported to the Chief Examiner, who may disallow the work concerned by prohibiting assessment or

refer the matter to the Faculty Manager.

Question 1. Regression Analysis (35 marks = 5+5+5+10+10)

Use the file, house_MEL.csv, which contains data on house sales in Melbourne (since Jan

2016) and the following variables:

Suburb: Suburb

Rooms: Number of rooms

Price: Price in Australian dollars

Type: h (house), u (unit) and t(townhouse).

Distance: Distance from CBD in Kilometres

Car: Number of carspots

Landsize: Land Size in Metres

1. Estimate and report a linear regression model with the dependent variable of Price and

with regressors of Rooms, Distance, Car, and Landsize. Explain an effect of Distance on

Price. (30 words or less)

2. Let LogP be the logarithm of Price. Estimate and report a linear regression model with

the dependent variable of logP and with the same regressors as in 1. Explain an effect of

Distance on Price. (30 words or less)

3. The above analysis does not take into account that housing prices varies significantly

depending on housing type. Create two dummy variables for house and townhouse (i.e.,

a variable taking 1 for type = h and 0 otherwise, and the one taking 1 for type=t and 0

otherwise). Estimate and report the model in 2 by additionally including these two

dummies variables. Interpret estimated coefficients for the dummy variables. (50 words

or less)

4. We considered the effect of distance on housing prices in 1 and 2, but we did not

consider the possibility that the effect is heterogeneous across housing types. Given the

model used in 3, we additionally include two interaction terms: the house dummy *

distance and the townhouse dummy * distance. Estimate and report the model and

interpret the effect of distance on housing prices. (50 words or less)

5. To incorporate the non-linear effect of distance on housing prices, we consider the

model in 2 with squared distance. Estimate and report the model. Do you think that

distance has non-linear effect on price? Explain. (30 words or less)

Question 2. Binary Choice and Classification (40 marks = 5+5+10+10+10)

In this question, use the data set, MEL_weather.csv, on weather in Melbourne to examine

predictability tomorrow weather by using a day-before weather conditions. The data set is

obtain from the Bureau of Meteorology and you find some explanation regarding variables

in the data set from the following link1. The outcome of interest is “RainTomorrow”, which

takes “Yes” for rain and “No” for otherwise.

1. Obtain and report summary statistics of data.

1 http://www.bom.gov.au/climate/dwo/IDCJDW0000.shtml

2. Use a logit model to predict RainTomorrow with the following regressors: MinTemp,

MaxTemp, Rainfall, Evaporation, Sunshine, WindSpeed3pm, Humidity3pm,

Pressure3pm, Cloud3pm, and Temp3pm. First, estimate and report regression

coefficients in the logit model. Next, estimate and report marginal effects of regressors.

3. Explain and interpret three most influential variables, using the results in b. When you

interpret the result, you should clearly quantify the effect of each of the three variables

on the probability of raining tomorrow.

4. Estimate a classification tree with the same dependent variable and regressors as in b,

and report a tree figure. Explain weather conditions that predict tomorrow rain with

more than 80% of chance.

5. Using the result in d, explain weather conditions that predict that tomorrow non-rain

with more than 90% of chance.

Question 3. Simulation (25 marks =5+10+10)

We analyse returns (%) between May, 2015 and April, 2019 from a portfolio consisting of the index

of Australia's top 200 companies (ASX200), the index of 300 companies (ASX300) and Vanguard

Australian Government Bond Index ETF (VGB.AX). The data set, finance_AUS.xls, contains monthly

return rate on ASX200, ASX300 and BOND. Use the data on those three variables to answer

questions.

1. Report means, standard errors and correlation of the monthly returns in the data set.

2. To access financial portfolio based on pairs of ASX200, ASX300 and BOND, obtain simulation

outcomes under the assumption that any pairs follow normal distributions with parameters

estimated in 1. Consider the three cases:

a. 0.5 ASX200 and 0.5 ASX300

b. 0.5 ASX200 and 0.5 VGB.AX

c. 0.2 ASX200 and 0.8 VGB.AX

d. 0.8 ASX200 and 0.2 VGB.AX

To report simulated outcomes (1000 iterations), present a table that includes means, standard

deviations, minimum, maximum and VaR5%. Compare portfolios a-b and explain which portfolio

is the most risky or the least risky (60 words or less).

3. Suppose that you want to maximize the expected return from the portfolio that consists of two

financial products out, while restricting the VaR5% to be -8% or higher. Obtain the optimal portfolio

and report a table including your portfolio choice (weight), its mean return, minimum, maximum and

standard deviation. (You can choose any pair as long as you can satisfy the restriction).

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