Reading 4 Flashcards
MVO
Most common approach to asset allocation. Assumes investors are risk averse, so they prefer more turn for same level of risk.
Given an opportunity set of investable assets, their expected returns and variances, as well as pairwise correlations between them, MVO identifies the portfolio allocations that maximizes the turn for every level of risk
Utility Maximization
Um = E(Rm) - (0.005 x Lambda x variance of m)
- if in % terms eg 8% is 8.0, then use 0.005
if in decimal like 0.08, use 0.5
What is lambda
Captures each individual investor’s preference for trading off risk and return.
It is unique to each individual and is based on the investor’s willingness and capacity to take on risk
risk-neutral: Lamba = 0
typically between 1 and 10
risk average: level of 4
What is MVO budget constraint / unity constraint
asset weights must add up to 100%
MVO non-negativity constraint
all weights in the portfolio should be positive and between 0% and 100%; no short positions
What is the MVO Process
Inputs: expected returns, variances, covariances (Correlations), risk aversion factor, constraints
MVO: maximize utility, subject to constraints
Outputs: asset allocations (portfolio weights), portfolio expected return, portfolio variance
Criticisms of MVO
- GIGO: garbage in, garbage out
- concentrated asset class allocations
- skewness and kurtosis
- risk diversification
- ignores liabilities
- single period framework
What is reverse optimization?
Instead of starting with expected returns (and other inputs) and deriving optimal portfolio weights, start with what we assume to be “optimal” portfolio weights from the global market portfolio and derive the expected returns consistent with those weights.
Then we use these return estimates (Called implied returns) to do a traditional MVO and derive optimal portfolio weights for our particular investor.
What does reverse optimization process look like?
- Inputs: assumed optimal asset allocations, variances, covariances (correlations), risk aversion factor, constraints
- Reverse MVO: maximize utility, subject to constraints
- Outputs: implied returns
- Inputs: implied returns, variances, covariances (correlations), risk aversion factor, constraints
- MVO: maximize utility, subject to constraints
- Outputs: revised asset allocations, portfolio expected return, portfolio variance
What is the benefit of starting with a market portfolio in reverse MVO?
derived returns already reflect a highly diversified portfolio and you avoid the tendency of MVO to come up with highly concentrated asset allocations
Black-Litterman Model
Extension of reverse optimization in which the implied returns (actually implied excess returns) from a reverse optimization are subsequently adjusted to reflect the investor’s unique views of future returns
eg. derives an expected return for EM equities as 6.5%, but you think its too low, you could adjust the expected return by 75 bps to 7.25% and rerun MVO using adjusted return estimates
What is the Black-Litterman Model Process?
Inputs: assumed optimal asset allocations, variances, covariances (correlations), risk aversion factor, constraints
Reverse MVO: maximize utility, subject to constraints
Outputs: implied returns
Black Litterman
Inputs: reverse implied returns, variances, covariances (correlations), risk aversion factor, constraints
MVO: maximize utility, subject to constraints
Outputs: revised asset allocations, portfolio expected return, portfolio variance
How does inclusion of human capital and real estate property impact individual investor’s portfolio?
increases capacity to bear risk
Why are less liquid asset classes difficult to include in MVO?
- there are few indexes available that accurately track these illiquid investments, making it harder to find data to use for estimating return, risk and correlations
- Even where indexes exist to provide return data, they are generally not investable as a passive alternative to active management of these asset classes.
- the risk-return characteristics of a specific real estate, private equity, or infrastructure investment are different from those of its asset class. Eg any one infra fund is not fully diversified, and therefore, its risk and return characteristics reflect both systematic and nonsystematic risk
How do you address illiquidity in MVO?
- Exclude illiquid asset classes when running an MVO, but use them to meet separately set target asset allocations.
- Include the illiquid asset classes in MVO and model the inputs of the specific (not asset class) investments you plan to use (ie the risk estimate will be based on both nonsystematic and systematic)
- include illiquid asset classes in MVO using highly diversified asset class inputs, recognizing that the actual investments made may have different characteristics.
Marginal Contribution to Portfolio Risk
Change in total portfolio risk for a small change in the asset allocation to a specific asset class
Marginal Contribution to Total Risk: Formula
MCTR = (beta of asset class wrt portfolio) x (total portfolio risk measured by sd)
Absolute contribution to total risk
ACTR - weight x MCTR
% of risk contribution by position
ACTR / total portfolio risk