## All Weeks Introduction to Portfolio Construction and Analysis with Python Quiz Answers

## Table of Contents

### Introduction to Portfolio Construction and Analysis with Python Coursera Quiz Answers

#### Week 1: Introduction to Portfolio Construction and Analysis with Python

Q1. Read in the data in the file “Portfolios_Formed_on_ME_monthly_EW.csv” as we did in the lab sessions.We performed a series of analysis on the ‘Lo 10’ and the ‘Hi 10’ columns which are the returns of the lowest and highest decile portfolios. For purposes of this assignment, we will use the lowest and highest quintile portfolios, which are labelled ‘Lo 20’ and ‘Hi 20’ respectively.

What was the Annualized Return of the Lo 20 portfolio over the entire period?

Enter the answer as a percentage. e.g. if your answer is 23.43% enter the number 23.43

Enter answer here

Q2. What was the Annualized Volatility of the Lo 20 portfolio over the entire period?

Enter the answer as a numeric to one decimal place, as a percentage. e.g. if your answer is 23.43% enter the number 23.4

Enter answer here

Q3. What was the Annualized Return of the Hi 20 portfolio over the entire period?

Enter the answer as percentage. e.g. if your answer is 23.43% enter the number 23.43

Enter answer here

Q4. What was the Annualized Volatility of the Hi 20 portfolio over the entire period ?

Enter the answer as percentage. e.g. if your answer is 23.43% enter the number 23.43

Enter answer here

Q5. What was the Annualized Return of the Lo 20 portfolio over the period 1999 – 2015 (both inclusive)?

Enter the answer as a percentage. e.g. if your answer is 23.43% enter the number 23.43

Enter answer here

Q6. What was the Annualized Volatility of the Lo 20 portfolio over the period 1999 – 2015 (both inclusive)?

Enter the answer as a percentage. e.g. if your answer is 23.43% enter the number 23.43

Enter answer here

Q7. What was the Annualized Return of the Hi 20 portfolio over the period 1999 – 2015 (both inclusive)?

Enter the answer as a percentage. e.g. if your answer is 23.43% enter the number 23.43

Enter answer here

Q8. What was the Annualized Volatility of the Hi 20 portfolio over the period 1999 – 2015 (both inclusive)?

Enter the answer as a percentage. e.g. if your answer is 23.43% enter the number 23.43

Enter answer here

Q9. What was the Max Drawdown (expressed as a positive number) experienced over the 1999-2015 period in the SmallCap (Lo 20) portfolio?

Enter the answer as a percentage. e.g. if your answer is 23.43% enter the number 23.43

Enter answer here

Q10. At the end of which month over the period 1999-2015 did that maximum drawdown on the SmallCap (Lo 20) portfolio occur?

Enter the answer in the format YYYY-MM. Eg for February of 2015 enter 2015-02

Enter answer here

Q11. What was the Max Drawdown (expressed as a positive number) experienced over the 1999-2015 period in the LargeCap (Hi 20) portfolio?

Enter the answer as a percentage. e.g. if your answer is 23.43% enter the number 23.43

Enter answer here

Q12. Over the period 1999-2015, at the end of which month did that maximum drawdown of the LargeCap (Hi 20) portfolio occur?

Enter the answer as YYYY-MM i.e. for February of 2015 you would enter 2015-02

Enter answer here

Q13. For the remaining questions, use the EDHEC Hedge Fund Indices data set that we used in the lab assignment and load them into Python. Looking at the data since 2009 (including all of 2009) through 2018 which Hedge Fund Index has exhibited the highest semideviation?

Enter answer here

Q14. Looking at the data since 2009 (including all of 2009) which Hedge Fund Index has exhibited the lowest semideviation?

Enter answer here

Q15. Looking at the data since 2009 (including all of 2009) which Hedge Fund Index has been most negatively skewed?

Enter answer here

Q16. Looking at the data since 2000 (including all of 2000) through 2018 which Hedge Fund Index has exhibited the highest kurtosis?

Enter answer here

#### Week 2: Introduction to Portfolio Construction and Analysis with Python

A1. Use the EDHEC Hedge Fund Indices data set that we used in the lab assignment as well as in the previous week’s assignments. Load them into Python and perform the following analysis based on data since 2000 (including all of 2000): What was the Monthly Parametric Gaussian VaR at the 1% level (as a +ve number) of the Distressed Securities strategy?

Enter the positive number as a percent .e.g. For 5.32% enter 5.32

NOTE: You may either round or truncate to 1 decimal place, we will accept either answer

Enter answer here

A2. Use the same data set at the previous question. What was the 1% VaR for the same strategy after applying the Cornish-Fisher Adjustment?

Enter the answer as a positive number, in percent terms. e.g. for 5.32% enter 5.32

Enter answer here

Q3. Use the same dataset as the previous question. What was the Monthly Historic VaR at the 1% level (as a +ve number) of the Distressed Securities strategy?

Enter the answer as a positive number, in percent terms. e.g. for 5.32% enter 5.32

Enter answer here

Q4. Next, load the 30 industry return data using the erk.get_ind_returns() function that we developed during the lab sessions. For purposes of the remaining questions, use data during the 5 year period 2013-2017 (both inclusive) to estimate the expected returns as well as the covariance matrix. To be able to respond to the questions, you will need to build the MSR, EW and GMV portfolios consisting of the “Books”, “Steel”, “Oil”, and “Mines” industries. Assume the risk free rate over the 5 year period is 10%.

What is the weight of Steel in the EW Portfolio?

Enter the answer in percent terms rounded to one decimal place. e.g. for 5.32% enter 5.3

Enter answer here

Q5. What is the weight of the largest component of the MSR portfolio?

Enter the answer in percent terms rounded to one decimal place. e.g. for 5.32% enter 5.3

Enter answer here

Q6. Which of the 4 components has the largest weight in the MSR portfolio?

Enter answer here

Q7. How many of the components of the MSR portfolio have non-zero weights?

Enter answer here

Q8. What is the weight of the largest component of the GMV portfolio?

Enter the answer in percent. e.g. for 5.32% enter 5.32

Enter answer here

Q9. Which of the 4 components has the largest weight in the GMV portfolio?

Enter answer here

Q10. How many of the components of the GMV portfolio have non-zero weights?

Enter answer here

Q11. Assume two different investors invested in the GMV and MSR portfolios at the start of 2018 using the weights we just computed. Compute the annualized volatility of these two portfolios over the next 12 months of 2018? (Hint: Use the portfolio_vol code we developed in the lab and use ind[“2018”][l].cov() to compute the covariance matrix for 2018, assuming that the variable ind holds the industry returns and the variable l holds the list of industry portfolios you are willing to hold. Don’t forget to annualize the volatility)

What would be the annualized volatility over 2018 using the weights of the MSR portfolio?

Enter the answer in percent. e.g. for 5.32% enter 5.32

Enter answer here

Q12. What would be the annualized volatility over 2018 using the weights of the GMV portfolio? (Reminder and Hint: Use the portfolio_vol code we developed in the lab and use ind[“2018”][l].cov() to compute the covariance matrix for 2018, assuming that the variable ind holds the industry returns and the variable l holds the list of industry portfolios you are willing to hold. Don’t forget to annualize the volatility)

Enter the answer in percent terms. e.g. for 5.32% enter 5.32

Enter answer here

#### Week 3: Introduction to Portfolio Construction and Analysis with Python

Q1. Consider the Monte Carlo Simulation we ran for CPPI. Assume there is no floor set (i.e. Floor is set to

Zero) As you increase the number of scenarios, which of the following would you expect:

- The difference in terminal wealth between the Worst Scenario and the Best Scenario will INCREASE
- The difference in terminal wealth between the Worst Scenario and the Best Scenario will DECREASE
- The difference in terminal wealth between the Worst Scenario and the Best Scenario will DEPEND ON

THE RISK FREE RATE - The difference in terminal wealth between the Worst Scenario and the Best Scenario will STAY THE

SAME

Q2. As you increase the FLOOR, the WORST CASE scenario will:

- REMAIN UNCHANGED
- DECREASE
- INCREASE

Q3. Assume a non-zero floor that is less than the starting wealth. As you increase mu and keep other

parameters fixed, you would expect that the terminal wealth:

- DECREASES
- INCREASES OR DECREASES – IT DEPENDS ON multiplier ‘m’
- INCREASES OR DECREASES – IT DEPENDS ON SIGMA
- INCREASES

Q4. All other things being equal, which of these changes will cause an INCREASE in floor violations

- Decreasing “m” but increasing “sigma”
- Increasing “m” but decreasing “sigma”
- Increasing both “m” and “sigma”

Q5. All other things being equal, which of these changes will cause an INCREASE in floor violations

- Increasing both “m” and “rebals per year”
- Increasing “m” but decreasing “rebals per year”
- Decreasing “m” but increasing “rebals per year”

Q6. All other things being equal, which of these changes will cause in INCREASE in Expected Shortfall

- Decreasing “m” but increasing “rebals per year”
- Increasing “m” but decreasing “rebals per year”
- Increasing both “m” and “rebals per year”

Q7. Parameter changes that increase the probability of floor violations will also tend to increase the Expected

Shortfall. This statement is:

- False
- True only if mu is smaller than sigma
- True
- True if the Risk Free Rate is expected to increase
- True only if mu is greater than sigma

Q8. A CPPI Based Principal Protection Strategy aims to return at least the invested principal by setting the floor

equal to the initial value of the assets. Which of the following is true:

- It is only possible to run a CPPI based Principal Protection Strategy strategy if mu is greater than or equal

to 0 - It is only possible to run a CPPI based Principal Protection Strategy if mu is greater than the risk free rate
- It is only possible to run a CPPI based Principal Protection Strategy if the risk free rate is greater than or

equal to 0 - It is only possible to run a CPPI based Principal Protection Strategy if sigma is equal to zero

Q9. A CPPI based Principal Protection Strategy with 12 rebals per year can have a zero expected shortfall only

if:

- m=1
- mu=1%
- sigma is strictly lower than mu
- m is strictly greater than 1

Q10. All other things being equal, A CPPI based Principal Protection Strategy is more likely to have a final

negative return if:

- mu increases
- the investment horizon increases
- sigma increases
- rebals per year increases

#### Week 4: Introduction to Portfolio Construction and Analysis with Python

Q1. In the following questions, we will be working with three bonds:

- B1 is a 15 Year Bond with a Face Value of $1000 that pays a 5% coupon semi-annually (2 times a year)
- B2 is a 5 Year Bond with a Face value of $1000 that pays a 6% coupon quarterly (4 times a year)
- B3 is a 10 Year Zero-Coupon Bond with a Face Value of $1000 (Hint: you can still use the erk.bond_cash_flows() and erk.bond_price() by setting the coupon amount to 0% and coupons_per_year to 1) Assume the yield curve is flat at 5%. Duration refers to Macaulay Duration

Hint: the macaulay_duration function gives as output the duration expressed in periods and not in years. If you want to get the yearly duration you need to divide the duration for coupons_per_year;

e.g.: duarion_B2 = erk.macaulay_duration(flows_B2, 0.05/4)/4

Which of the three bonds is the most expensive?

- B3
- B2
- B1

Q2. Which of the three bonds is the least expensive?

- B3
- B1
- B2

Q3. What is the price of the 10 Year Zero Coupon Bond B3?

Enter the answer rounded to the nearest Dollar. e.g. if you compute the price as $45.35, enter the number 45.

Enter answer here

Q4. Which of the three bonds has the highest (Macaulay) Duration?

- B1
- B3
- B2

Q5. Which of the three bonds has the lowest (Macaulay) Duration?

- B1
- B3
- B2

Q6. What is the duration of the 5 year bond B2?

Enter the answer as a number – e.g. for 5.43 years, enter 5.43.

Enter answer here

Q7. Assume a sequence of 3 liabilities of $100,000, $200,000 and $300,000 that are 3, 5 and 10 years away,

respectively. What is the Duration of the liabilities?

(Reminder: Assume the yield curve is flat at 5%. Duration refers to Macaulay Duration)

Enter the answer as a number – e.g. for 5.43 years, enter 5.43.

Enter answer here

Q8. Assuming the same set of liabilities as the previous question (i.e. a sequence of 3 liabilities of $100,000, $200,000 and $300,000 that are 3, 5 and 10 years away,

respectively) build a Duration Matched Portfolio of B1 and B2 to match these liabilities. What is the weight of B2 in the portfolio? (Hint: the code

we developed in class erk.match_durations() assumes that all the bonds have the same number of coupons

per year. This is not the case here, so you will either need to enhance the code or compute the weight directly

e.g. by entering the steps in a Jupyter Notebook Cell or at the Python Command Line)

Enter the weight as a percentage. For instance, if you compute the weight as 34.66%, enter 34.66.

Enter answer here

Q9. Assume you can use any of the bonds B1, B2 and B3 to build a duration matched bond portfolio matched

to the liabilities. Which combination of 2 bonds can you NOT use to build a duration matched bond portfolio?

- B2+B3
- B1+B2
- ANY PAIR WILL WORK
- B1+B3

Q10. Assuming the same liabilities as the previous questions (i.e. a sequence of 3 liabilities of $100,000, $200,000 and $300,000 that are 3, 5 and 10 years away,

respectively), build a Duration Matched Portfolio of B2 and B3 to match the liabilities.

What is the weight of B2 in this portfolio?

Enter the answer as a single number. For instance, if you compute the weight as 65.32% enter 65.32.

Enter answer here

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