Characteristics of asset classes
- Asset classes should have high within-group correlations but low correlations with other classes.
- Asset classes should be diversifying, low pairwise correlations with other asset classes is not sufficient. An asset class may be highly correlated with some linear combination of the other asset classes even when pairwise correlations are not high.
- If liquidity and transaction costs are unfavorable for an investment of a size meaningful for an investor, an asset class may not be a suitable investment for that investor.
- Asset classes offer a non-skill-based ex-ante expected return premium. Asset classes should have a return premium based on an underlying market risk factor and not any underlying skill of the investor.
Consideration in Rebalancing
- Higher transaction costs for an asset class imply wider rebalancing ranges.
- More risk-averse investors will have tighter rebalancing ranges.
- Less correlated assets also have tighter rebalancing ranges - to preserve its contribution to risk reduction.
- Beliefs in momentum favor wider rebalancing ranges, whereas mean reversion encourages tighter ranges.
- Illiquid investments complicate rebalancing.
- Derivatives create the possibility of synthetic rebalancing.
- Taxes, which are a cost, discourage rebalancing and encourage asymmetric and wider rebalancing ranges.
- Higher volatility asset class 1) with narrow range require frequent rebalancing; 2) with wide range leads to larger divergence
Black Litterman Model
Black–Litterman starts with the excess returns produced from reverse optimization, which commonly uses the observed market-capitalization value of the assets or asset classes of the global opportunity set. It then alters the reverse-optimized expected returns that reflect an investor’s own distinctive views yet still behaves well in an optimizer.
Resample MVO
- Resample MVO uses Monte Carlo simulation to estimate a large number of market assumptions. It generates an efficient frontier for each set. The resulting asset allocations are averaged
- Pro: Leading to a more diversified portfolio
- Cons:
- Riskierasset allocations tend to be over-diversified
- Asset allocations inherit the estimation errors in the original inputs
1/N Rule
The 1/N rule asset allocation heuristic involves equally weighting allocations to assets; 1/N of wealth is allocated to each of N assets available for investment at each rebalancing date. All assets are treated as indistinguishable in terms of mean returns, volatility, and correlations.
60/40
The 60/40 stock/bond heuristic allocates 60% of assets to equities, supplying a long-term growth foundation, and 40% to fixed income, supplying risk reduction benefits. It is not an optimization model.
Norway Model
The Norway model passively invests in publicly traded securities subject to environmental, social, and governance concerns. In comparison, the endowment model asset allocation emphasizes active management of large allocations to non-traditional investments, seeking to earn illiquidity premiums.
Criteria for specifying asset classes
- Assets within a class should be relatively homogenous
- Asset classes should be mutually exclusive
- Asset classes should be diversifying
- Asset class as a group should make up a preponderance of world investable wealth (large enough to be widely investable)
- Asset classes should have the capacity to absorb a meaningful proportion of an investors portfolio (liquidity and transaction costs)
Risk Parity
- Each asset should contribute equally to the total risk of the portfolio for a portfolio to be well-diversified
- Tend to ignore return, and allocate entirely on risk
Factor-Based Asset Allocation
- The factors commonly used in the factor-based approach generally have low correlations with the market and with each other. This results from the fact that the factors typically represent what is referred to as a zero (dollar) investment or self-financing investment, in which the underperforming attribute is sold short to finance an offsetting long position in the better-performing attribute. Constructing factors in this manner removes most market exposure from the factors (because of the offsetting short and long positions.
- The factors commonly used in the factor-based approach are typically similar to the fundamental or structural factors used in multifactor models.
Reverse Optimization vs MVO
- Reverse Optimization Approach
- The asset allocation weights are inputs and are determined by the market capitalization weights of the global market portfolio
- The expected returns of asset classes are the outputs of optimization with the market capitalization weights, covariances, and the risk aversion coefficient used as inputs.
- MVO Approach
- The asset allocation weights are outputs with the expected returns, covariances, and risk aversion coefficient used as inputs
- The expected returns of asset classes are inputs to the optimization, with the expected returns generally estimated using historical data.
