Asset Allocation Flashcards
Higher transaction cost
Wider corridor
Higher risk tolerance
Wider corridor
Higher correlation of asset class with rest of the portfolio
Wider the corridor
Higher volatility
Narrower the corridor
pretax returns
lower corridors because more volatility
mean variance optimization
- most common asset only allocation based approach
- identifies the portfolio allocations that maximize return for every level of risk
criticism of mvo
- GIGO (highly sensitive inputs)
- Concentrated asset class allocations (Identifies efficient portfolios that are highly concentrated in a subset of asset class)
- Ignores Skewness and kurtosis (asymmetry and tail risk)
- risk diversification (sources of risk not diversified)
- Ignores liabilities (for LDI)
- single period framework (does not take into account interim cash flows or serial correlation of asset returns from one period to next)
reverse optimization
instead of starting with expected returns and deriving portfolio weights, start with what should be the ideal portfolio weights and drive the expected return consistent with those weights. then use this answer for the traditional MVO. These implied returns can then be used in a forward-looking optimization.
black litterman model
extension of the reverse optimisation model where the implied returns (or rather implied excess returns) from a reverse optimization are subsequently adjusted to reflect the investor’s unique views of future returns
resampled MVO
resampling can also be used to address the GIGO and highly concentrated issues by applying Monte Carlo simulation to the MVO and then running multiple scenarios and then doing an average weight of each. also creates more diversified portfolios
absolute contribution to total risk
asset classes contribution to total volatility
= MCTR x Asset weight
marginal contribution to total risk
Measures how a small change in a specific asset affects the total portfolio risk.
Formula: MCTR = (Beta of asset class relative to portfolio) × (Portfolio return volatility).
It is a bottom up risk attribution analysis
surplus optimization
Surplus optimization applies the mean-variance optimization (MVO) framework to the surplus (the difference between assets and liabilities) rather than focusing directly on the assets themselves.
two portfolio approach
we separate the asset portfolio into 2 sub portfolios: a hedging portfolio and a risk seeking portfolio
Return seeking portfolio: The remaining assets are allocated to a return-seeking portfolio, which aims to generate higher returns but with higher risk. This portfolio typically includes assets such as equities or other riskier investments that provide greater potential for growth but do not directly align with liabilities.
integrated asset-liability approach
this approach integrates both the assets and the liabilities in a joint optimization method
Liability driven approach
generally encompasses asset allocation that is focused on funding an investor’s liabilities.
Objective of asset only and liabilities only approach
Maximize Sharpe ratio
for acceptable level of
volatility
Fund liabilities and invest
excess assets for growth
Risk measurements in asset only approach
- standard deviation
- tracking error (towards benchmark)
- tail risk (downside risk)
Asset classes criteria
- Assets within an asset class should be relatively homogeneous
- Asset classes should be mutually exclusive
- Asset classes should be diversifying
- asset classes as a group should be made up of world investable wealth
- Asset classes selected for investment should have the capacity to absorb a
meaningful proportion of an investor’s portfolio.
Dynamic asset allocation
A strategy incorporating deviations from the strategic asset
allocation that are motivated by longer-term valuation signals or economic views
active risk budgeting
addresses the question of how much benchmark-relative risk an investor is willing to take in seeking to outperform a benchmark.
Belief in momentum
Wider corridor
belief in mean reversion
narrower corridors
taxable portfolio
wider and more asymmetric
The efficient mix at the far
left of the frontier with the lowest risk is referred to as the global minimum variance
portfolio, while the portfolio at the far right of the frontier is the maximum expected
return portfolio. In the absence of constraints beyond the budget and non-negativity
constraints, the maximum expected return portfolio consists of a 100% allocation to
the single asset with the highest expected return (which is not necessarily the asset
with the highest level of risk)
how do solve for single period framework issue of MVO
Monte carlo simulation. It is particularly useful when an investor’s risk tolerance is uncertain or needs validation.
Key features of MCS
Enhanced Modeling: Can incorporate non-normal returns, correlations, tax rates, evolving asset allocation, non-traditional investments, and human capital.
Practical Issues: Simulates rebalancing costs, tax impacts, and market interactions that are challenging to calculate analytically.
Path Dependency: Evaluates scenarios where terminal wealth depends on the sequence of returns, which is critical for portfolios with cash inflows or outflows.
Sensitivity to Inputs in MVO
MVO is more sensitive to expected return estimates than to volatility and correlation estimates.
Market portfolio of reverse optimization
To apply reverse optimization, you need a version of the global market portfolio, typically formed using market-capitalization-weighted indexes. These indexes often represent the majority of securities in an asset class. Tey should also be considered of optimal weights.
one issue with reverse optimization?
Reverse optimization relies on market-capitalization-based returns, which may not always align with an investor’s own expectations or views.
2 ways to incorporate Black litterman
The model offers two ways to incorporate views: absolute return forecasts (where an investor specifies a direct forecast for an asset) and relative return forecasts (where the investor expresses a forecast relative to another asset or group of assets).
some of the constraints of MVO
budget constraint (which ensures the total portfolio weight equals 100%)
non-negativity constraint (which prevents negative asset allocations).
Many practical considerations in real-world asset management require constraints to reflect the specific circumstances of an investor’s portfolio or situation. (no alchohol companies)
Overcoming shortcomings of MVO
Solving skewness and kurtosis prblem of MVO
More advanced optimization techniques address these shortcomings by incorporating higher moments (skewness and kurtosis) and using measures like Conditional Value-at-Risk (CVaR) to better reflect risk and investor preferences.
For illiquid assets, how to approach MVO?
Exclude illiquid assets from the asset allocation decision and consider specific funds for implementation.
Include illiquid assets in the asset allocation decision but model inputs based on the specific characteristics of the implementation vehicles.
Include illiquid assets and model inputs to represent the highly diversified characteristics of the asset classes, possibly using listed real estate or infrastructure indexes to approximate private equity and real estate.
Challenges with listed AI
Using listed indexes (e.g., REITs for real estate) can distort asset class separation, leading to higher correlations among asset classes. This can increase sensitivity in mean-variance optimization and lead to less diversified asset allocations.
When is the risk budget considered optimal?
A risk budget is optimal when the ratio of excess return to marginal
contribution to total risk is the same for all assets in the portfolio.
Funding ratio
The funding ratio is calculated as the ratio of assets to liabilities.
basis risk for pension funds
This risk arises when the chosen assets (such as bonds) do not perfectly match the timing of liabilities, which may cause funding issues in the future.
Steps in surplus optimization
- Select asset categories and planning horizon (usually one year).
- Estimate expected returns and volatilities for assets and liabilities.
- Set constraints on asset allocation.
- Estimate the correlation matrix for assets and liabilities.
- Compute the surplus efficient frontier and compare it with the asset-only frontier.
- Choose the optimal portfolio mix based on risk preferences.
The factor-based approach is suited for liabilities dependent on uncertainties like inflation and economic conditions.
Representative bias
Representative bias is when people tend to overweigh the most recent
observations and information compared to a longer-dated or more comprehensive set of long-term observations and information.
Risk parity allocation approach
A risk parity asset allocation is based on the fact that each asset should contribute equally to the total risk of the portfolio for a diversified portfolio. A limitation of risk parity is that it ignores expected returns.
After tax returns
Wider corridors
why derivatives market over cash market
Quick implementation
Flexibility to tactically adjust exposure and quickly reverse decisions Ability to leave external managers in place High levels of liquidity
Risk budgeting
Risk budgeting is a means of making optimal use of risk in the pursuit of return.
2 fundamental parts of asset allocation process
The first centers on the creation of portfolio modules while the second relates to the identification of client goals and the matching of these goals to the appropriate sub-portfolios to which suitable levels of capital are allocated
advantages of electronic trading
The advantages of electronic trading systems include cost and operational efficiencies, lack of human bias, extraordinarily fast speed, and infinite span and scope of attention.
Rebalancing corridor for taxable portfolio
wider
rebalancing for tax exempt portfolio
narrower
