Practice Exam 2 - Theory Flashcards
(8 cards)
What is downside risk and what additional information does the VAR@90% measure provide that is not available through the volatility measure to help better understand the risks of the SAA?
Downside risk is the potential loss in portfolio value (i.e. price decrease or market shocks). Well utility measure treats upside and downside symmetrically and measures overall movement to market conditions (could be increase or decrease in fund value). Higher the volatility the greater the risk as we are unable to reliably estimate the future expected volatility. However we know that the higher volatility the higher chance there is of higher expected return. Value at risk tells us the amount (%) that could be lost with a certain confidence over a period in this instance 90% indicating loss in less likely but more severe events. This provides more Conservative indicator of downside risk, especially if you’re concerned about capital preservation.
To understand if the SAA is able to achieve its investment objectives what additional investment does out of sample testing provide that is not available through in sample testing?
In sample testing uses historical data to develop a strategy and allocation out of sample testing uses new unseen data that the current strategy can be ‘tested’ against an even revised as indicate how it performs or if it can realistically meet risk and return objectives in new or extreme market or economic conditions (that were not used in developing the SAA). Particular useful if we want to test portfolio behaviour against events with magnitudes such as in GC or COVID-19. 
What are the CMA’s inputs necessary for the MV optimisation process?
Inputs:
- Expected returns of each asset: forecasts average return for each asset over a specified investment horizon.
- Expected volatilities (SD) of asset returns: costs the expected risk for each asset class
- Expected correlations: forecast how returns of different asset classes are expected to move in relation with each other helps understand verification benefits within the portfolio
What are the main consequences of using historical CMA’s on the asset allocation?
Main consequences:
- under the assumption that past performance is indicative of future and that is not always true market conditions or structural changes happen all the time
- Historical data used may have captured black Swan events or booms that aren’t necessarily going to truly reflect more stable periods and these events can also distort averages so those values aren’t reliable predicted for the future.
- Allocating assets based on past performance can be detrimental if over/underweighting without consideration of current or future growth this can lead to unnecessary/over performance.
Explain one method to generate appropriate CMAs for the MV optimisation process (not subjective allocation or return normalisation).
The Building Block Approach estimates expected returns by starting with the risk-free rate (e.g. 10-year government bond yield), then adding risk premiums for other asset classes:
• Equities: Bond yield + equity risk premium (typically 3–5%)
• Cash: Short-term rate ≈ bond yield – 0.5 to 1%
• Other assets: Adjusted for specific risk premiums (e.g. illiquidity)
It may also include inflation, earnings growth, dividend yield, and policy expectations.
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a. Black-Litterman model: uses market equilibrium returns box asset classes from broad global market cap weighted portfolios to give risks (reverse optimisation - using market clearing prices at the end of a trading day). b. B-L uses investors views on particuaur asset classes (such as the amount of return they expect) along with the confidence % of that view/guess Investor views are combined with equilibrium expected returns weighting by confidence levels creating new blended expected returns that are more robust for MV optimisation. Reduces estimation error, More stable and combines historical quantitative data and purely subjective qualiatative data.
✅ Pros: Transparent, forward-looking, based on observable data
❌ Cons: Still relies on assumptions about future premiums
Jason is researching Hedge Funds and needs your help to explain these instruments. He has raised two questions below and you are required to provide explanation so he understands the issue well.
A) know that Hedge Funds are less liquid (in terms of entering the fund and leaving the fund) ecause they do not provide liquidity on a daily basis in addition to other restrictions. However, I do not understand why hedge managers make it so difficult for investors to enter and leave these investments.
A) HF managers make it hard to enter/leave because:
a. They invest in long term an complex strategies that require a stable capital. So if investors can pull out their money at anyt time, mangers mighgt be forced to liquidate their assets prematurely and result in potential loss (unwind complex positions) which can disrupt strategies and harm performance of teh remanding investors.
b. Similarly by restricting liquidity, it prevents investors from moving money in and out quickly of funds based on short-term performance and this can also be disruptive forcing managers to constantly adjust their portfolios which can also incur additional transaction costs undermining investment strategy and skills.
c. By limiting cash redemptions managers can focus more on investment decisions rather than focusing on liquidity management and admin which can be a lot of effort and expensive.
d. Look up periods encourage investors to commit to long-term strategies and lining their investment horizon to the funds strategy. This Prevents managers from being pressured to deliver short term strategy decisions that satisfy immediately liquidity demands and allows them to deliver long-term investment ideas.
Jason is researching Hedge Funds and needs your help to explain these instruments. He has raised two questions below and you are required to provide explanation so he understands the issue well.
b) I know that hedge funds are private investments and do not have the same requirement as mutual funds to provide daily holdings. I know that most hedge funds do not even report their performance, and even when they do report their performance, it is only for the period when they performed well.
If I wish to select a good performing fund, what are the two best indicators (not fund’s historical returns) to identify good performing funds. Please make sure why these indicators are reliable in identifying good hedge fund managers.
B) The two best indicators to identify good performing funds and HF managers are:
a. Sharpe ratio (risk adjusted return measure) : Indicates how much access return and manager generates per unit of risk that they undertake indicating skill a higher sharp ratio suggest a manager Is efficiently compensating for risk?.
b. You can also look at alpha: Which measures return that cannot be explained by exposure to non-risk factors such as value size or beta as you mentioned. A statistically significant positive alpha value shows the manager consistently adds value through skill and not just through market exposure or luck.
Explain why top down alpha would not equal the over/under performance of the endowment fund through bottom up analysis.
A) Top down Alpha is derived from regression of excess returns of the portfolio versus the benchmark returns. It captures the performance of the portfolio relative to the benchmark after adjusting for beta.
The bottom up approach split the performance into allocation and selection effect to determine the out/under performance based on portfolio manager skill isolating effects of decision-making. We have negative top down alpha (-1.27%) and a positive bottom-up alpha (-1.1+2.5 = 1.4). Since TopDown uses realised returns and regression estimates and the bottom up uses component based affects the alpha’s are not aligned as components such as transaction costs, unmeasured risks or limitations in bottom up model is not captured but might be implicitly included in top Down regression model. Top down was also calculated on a weekly period whereas bottom up may have used a different methodology..
Beta value for Top down analysis is greater than one which underestimate alpha hence why it’s negative without beta adjusted alpha. The active returns are positive and may have been closer to the bottom up approach.
