ETF5952 QUANTITATIVE METHODS FOR RISK ANALYSIS

Semester 2, 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: 1PM, Oct 21 on Monday, 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source, staff, students or the Internet – published and unpublished works.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 (20 marks = 5+5+10)

XYZ produces the following figure, given the price of oil being equal $1.50.

1. Suppose that XYZ is risk neutral. Obtain the optimal choice and the corresponding 

expected monetary value (EMV). 

2. Suppose that XYZ use the Maximin criterion to choose an area. Obtain the optimal 

choice and the corresponding EMV. 

3. XYZ conducts a sensitivity analysis regarding probabilities of #1 of all three areas. The 

next page presents a spider figure, in which #1(F3), #1(F5) and #1(F7) indicates

probabilities of #1 for Area 1, 2 and 3, respectively. 

a. Explain why the three lines cross at the centre (no more than 25 words). 

b. The red line, #1 (F5), becomes flat if changes are -10% or below. Explain why (no 

more than 25 words). 

Question 2 (30 marks = 5+10+5+10)

Use the file, aribnb_MEL.csv, which contains data on houses/apartment listed in Airbnb in 

Melbourne. The data set contains the following variables: • price: price 

• bathrooms: the number of bathrooms

• accommodates: the number of accommodates

• bedrooms : the number of bedrooms

• review_scores_rating: rating score

• room_type: type of room 

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

with regressors of bathroom, accommodates, bedroom, and review_scores_rating. 

Explain carefully an effect of bedrooms on price (no more than 30 words).

2. Create a dummy variable which takes 1 if room_type is Entire home/apt and zero 

otherwise. Then, Estimate and report the linear regression model in 1 additionally with 

the dummy variable. Explain carefully an estimated coefficient of the dummy variable

(no more than 30 words). (Hint: To create a dummy variable in R, you can use 

(data_name$variable_name == value), which gives you TURE or FALSE, and also if value

is character, you have use double quotation.)

3. From Assignment 1, we know that the price contains outliers and it is better to mitigate 

outliers’ effect. For this end, we consider an application of log transformation. Try to 

estimate the linear regression model with the dependent variable of log price and the 

same regressors as in 3. Explain whether it is possible and why (no more than 30 words). 

4. To implement the idea explained in 3, we decided removing observations causing the 

trouble. Estimate and report a linear regression model. Explain carefully an effect of 

bedrooms on price (no more than 30 words.)

1000

1100

1200

1300

1400

1500

1600

1700

1800

1900

2000

Change in Input (%)

Spider Graph of Decision Tree 'Aucution'

Expected Value of Entire Model 

#1 (F3)

#1 (F5)

#1 (F7)

-30.0%

-20.0%

-10.0%

0.0%

10.0%

20.0%

30.0%

Expected Value

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

We use a data set for analysing chronic kidney disease. The data set comes from the 

Machine Learning Repository1, while the data set, kidney-clean.csv, at Moodle is cleaned up.

The outcome of interest is “class”, which takes “ckd” for chronic kidney disease and 

“notckd” otherwise. For classification, we consider blood pressure (bp) and diabetes 

mellitus (dm). 

1. Obtain and report summary statistics of the three variables: class, bp and dm.

2. Estimate and report a logit model to predict chronic kidney disease with the regressors:

bp and dm. Use “glm” function. Given the estimation result, explain contributions of the 

two factors (regressors) to kidney disease (no more than 15 words). 

3. Estimate and report a CART model for predict chronic kidney disease with the same 

regressors above (you can report only a tree figure). Using all information obtained from 

the estimated tree, explain each end node (leaf) (no more than 30 words for each node). 

Question 4 (25 marks =5+10+10) 

We analyse returns (%) from a portfolio consisting of the index of Australia's top 200 companies 

(ASX200) and a hypothetical safety asset. The data set, asx.csv, contains monthly return rate on 

ASX200. Use the data on “return” to answer questions.

1. Report means, standard deviations and Value-at-Risk 5% of the monthly returns in the data set.

2. Consider a relatively safe financial asset, which has a half of average return of ASX200 but a half 

of its standard deviations. To access financial portfolio based on pairs of ASX200 and the safe 

asset, obtain simulation outcomes under the assumption that the pair follow normal 

distributions with parameters estimated in 1. Let their correlation coefficient is -0.5. Consider 

the three cases:

a. 0.5 ASX200 and 0.5 Safe Asset 

b. 0.2 ASX200 and 0.8 Safe Asset

c. 0.8 ASX200 and 0.2 Safe Asset

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

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

is the most risky or the least risky (50 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 -4% 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). 

1 https://archive.ics.uci.edu/ml/datasets/Chronic_Kidney_Disease