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BEAM046 Financial Modelling (2022/3)

Group Assignment

(30% of Total Module Assessment)

Submission Deadline: 11:59AM on 25 November 2022

(Late Submission Penalties Will Apply)

Word Limit: 3,000 (excluding tables, references, and appendices)

You work for a consulting firm. The University of Exeter (UE) has just hired your team to make recommendations on the composition of its endowment fund. UE is considering investing in a broad selection of risky assets, ranging from bonds to cryptocurrencies. As a proxy for the different asset categories, you decide to use appropriate ETFs and investment trusts. These include:

iShares Core FTSE 100 ETF (ISF.L)

iShares Core MSCI Total International Stock ETF (IXUS)

Vanguard Total Bond Market Index Fund ETF Shares (BND)

Vanguard S&P 500 ETF (VOO)

iShares U.S. Real Estate ETF (IYR)

Grayscale Bitcoin Trust (GBTC)

Due to the pandemic, UE has been relying more and more heavily on its endowment to meet its budget’s need in the past two years. After meeting with UE’s Chief Financial Officer (CFO), your manager identified several issues to be addressed and asked you to conduct the analysis. In your analysis, assume the risk-free rate is 0% and ignore the effects of exchange rate fluctuations. Also assume that UE’s position is large enough that trading fees and transaction costs are negligible. Do not include the risk-free asset in your recommendation because UE’s CFO is interested in the construction of its risky portfolio, not the complete portfolio. Use historical monthly return from January 2016 to December 2020 to construct portfolios (note that you need prices on 1/1/2021 to calculate returns for December 2020). Use data from January 2021 to August 2022 to evaluate portfolio performance unless otherwise instructed (e.g. Q1).

1.   From January 2016 to December 2020, UE’s risky portfolio invested 50% in the U.K. ’s domestic equity market (proxied by ISF.L), 30% in the global equity market (proxied by IXUS), and 20% in the bond market (proxied by BND). Refer to this portfolio as P0  and assume that UE’s asset allocation remained the same over this period. How well did P0 perform? Collect monthly historical data, calculate performance summary statistics, and compare these statistics with those of the S&P500 index (proxied by VOO) over the same period.

2.   Do you recommend UE to expand its investment universe to include the U.S. equity (proxied by VOO), real estate (proxied by IYR), and cryptocurrencies (proxied by GBTC)? Plot the efficient frontier using the three asset categories that UE already invested in. Compare it with the efficient frontier generated using all six asset categories UE is considering. Assume there are no trading restrictions.

3.   Assume there are no trading restrictions, and UE uses the historical averages to proxy for expected returns. How should UE form its risky portfolio? Refer to this portfolio as P1 .

4.   Assume that UE is not allowed to short sell. How should UE construct its risky portfolio? Refer to this portfolio as P2 . How costly is the short sale constraint? (Note: Continue to assume that UE uses the historical averages to proxy for expected returns.)

5.   The CFO is also considering managing the risky portfolio passively. After finding out the market capitalisation of each asset category, you determine that the weights for the passive portfolio are as follow:

ISF: 1.23%

IXUS: 32.27%

BND: 40.98%

VOO: 14.02%

IYR: 11.23%

GBTC: 0.27%

Refer to this portfolio as P3 . Use P3 to calculate the expected return of each asset category implied by the market, assuming that the market return is 0.7% per month. After examining the implied expected returns, you and your colleagues believe that some adjustments are necessary. Since central banks around the world are raising interest rates, your team believes that the expected return for BND should be adjusted downward by 0.02% per month. You also  believe  that  blockchain  technology  will  quickly  become  more  important,  so  the expected return for GBTC should be increased by 0.01% per month. Form an active portfolio that incorporate these views and refer to it as P4 . Calculate the weights for P4 .

6.   A nonprofit organisation, UE is very concerned about its investment risk. Suppose UE uses the variance-covariance method (with normality assumption) and data from 2016 to 2020 to estimate VaR. What is the VaR of monthly return on P4?

7.   The CFO wants to ensure that the standard deviation of the risky portfolio’s monthly return does not exceed 0.03, and the one-month 99% VaR does not exceed 6%. Use the expected returns derived in Q5 (i.e., those that incorporate your own view) to construct the optimal risky portfolio under these risk management constraints while assuming short sales are not allowed. Refer to this portfolio as P5 and calculate its weights. (Note: When calculating the VaR, use the expected returns derived in Q5 for consistency.)

8.   Compare the performance of P1 ~ P5 using data from January 2021 to August 2022.

Deliverable:

Write up a report that answers all the above questions. Make sure the results are logically presented. You  should begin by briefly  introducing  the purpose  of this  report. Next, provide  detailed explanations  on  your  data,  methods,  numerical  results,  and  result  discussions.  Lastly,  the conclusion section should provide a summary of your recommendations on how UE should invest. Feel free to add additional sections. Your report should not exceed 3,000 words (tables, references, and appendices are not included in the word count). Each group should upload one PDFfile to BART. Other forms of document (e.g., Excel worksheet) will not be accepted.

Optional:

If you wish to earn extra credits, consider incorporating some of the following into your analysis:

9.   Estimate VaR using other methods. Compare the estimation results with those of the variance- covariance method. Are there huge differences? Why or why not? Do you recommend UE to change its VaR estimation method? How do these changes affect portfolio weights?

10. Does the problem of measurement error in expected returns and the covariance matrix affect your results? Provide evidence to support your argument.