Asset Allocation Analytics

Capital Market Assumptions
The asset allocation decision is correctly recognized as having the greatest impact on long-term return and risk.  There is no simple formula to determine the optimal asset mix.  Asset allocation decisions should be based on a careful examination of a number of factors including the current outlook for the financial markets, fund objectives, time horizon and tolerance for risk.  The Asset Allocation module in the Wilshire Compass combines fund specific factors and advanced optimization technology to assist users in evaluating and establishing asset allocation and rebalancing policies

The Asset Allocation module provides access to Wilshire’s database of index returns including over 1,400 capital market and economic time series.  The index database supports ongoing capital market research and the development of long-term capital market return, risk and diversification expectations, which are then used as the inputs for asset allocation optimization.

Optimization
The Wilshire Compass asset allocation model creates an “efficient frontier” of asset mixes based on selected asset classes, capital market assumptions and user-defined basic (single asset class) and advanced (multiple asset class) constraints.  In fact, up to three sets of constraints can be modeled simultaneously resulting in three efficient frontiers being plotted.  Uses include showing the curve in a totally unconstrained environment and then the shift in the curve as constraints are placed on the optimization. 

The Wilshire Compass also allows the user to incorporate non-funded asset classes such as foreign currencies into asset allocation work.  Depending on the fund’s objectives and asset mix, there may be a desire to reduce currency risk by converting or hedging foreign currency positions back to U.S. dollars.  From an asset allocation perspective, the critical issue is balancing the potentially positive diversification benefits of foreign currencies, against the increased risk that foreign currencies introduce and the costs of implementing a strategic hedging program.  With the Wilshire Compass users can easily incorporate their own assumptions as they relate to currency returns and risks and the expected costs of hedging in order to determine a fund's optimal hedge ratio.

The Asset Allocation module also includes tools which help funds select the most appropriate asset mix on the efficient frontier given the financial condition and time horizon of the fund.  The utility optimizer in the Wilshire Compass takes into account the fund’s specific liability structure and calculates return hurdles for each liability funding objective.  The portfolio with the greatest probability of satisfying the funding objective, subject to a risk penalty related to the time horizon, is identified as maximizing investor utility.

The flexibility of the Asset Allocation analytic allows users that are concerned about minimizing changes in their asset/liability ratio to perform surplus optimization. Compass has several time series that were designed as proxies for a pension plan's liabilities which can be used to model the impact different asset mixes have on surplus volatility.

Rebalancing
Because asset allocation policy is the primary determinant of long-term performance, the magnitude and timing of portfolio rebalancing decisions can significantly impact portfolio results.  A systematic and disciplined rebalancing program is an essential component in total fund risk management. 

The “optimal” rebalancing strategy is a function of a fund’s philosophy, investment objectives, and risk tolerance.  The Wilshire Compass offers tools to weigh these factors against back-tested results of passive and active rebalancing strategies. To test the effectiveness of alternative passive rebalancing methods in various market environments, the sponsor can select either Calendar or Normal Mix strategies.

The Calendar rebalancing option will rebalance assets back to policy targets in specified intervals (i.e., monthly, annually, etc.). The Normal Mix rebalancing option rebalances assets back to policy targets when asset class weights exceed or fall below pre-determined ranges.  Users can assign specific ranges for each asset class and measure the impact of alternative ranges on the risk/return profile of the portfolio.  Both rebalancing analytics allow the user to reflect transaction costs in the analysis.  An example of a “Normal Mix” rebalancing over the last 20-year period is shown below.

 

 

Simulation
The Simulation Analytic uses a statistical simulation technique called Monte Carlo simulations which allow us to “pre-experience” the impact of capital market returns based on the input parameters.  Specifically, the multivariate simulation engine generates simulated returns for each asset class and then combines those asset class returns based upon the user defined portfolios to be modeled.  The simulation process can be controlled using “Sobol sequences” which allows for convergence to true values with fewer trials.  Alternatively users may choose to evaluate the potential range of outcomes using a parametric or cumulative density function model. Distributions of Total Return and Market Value for three portfolio mixes (based on 1,000 trials) are shown below.

These simulation tools can also be used with the Endowment Spending Policy Analytic to forecast the probability of maintaining the funds inflation adjusted purchasing power.

Portfolios may also be evaluated based upon the probability of earning a specific hurdle rate of return, such as the return necessary to fund the plan's liabilities.

 

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