Robust Optimization

MoneyBestPal Team
A method that aims to find an asset allocation strategy that performs well under the worst possible scenarios of uncertainty in the inputs.

Robust portfolio optimization is a method that aims to find an asset allocation strategy that performs well under the worst possible scenarios of uncertainty in the inputs, such as returns and covariances of the assets. In contrast to the traditional method, where the inputs are taken as fixed and precise estimates based on prior data, this approach uses actual data.

Why is robust portfolio optimization important?

Because in reality, the inputs to the portfolio optimization problem are not known with certainty, but only estimated with errors. These mistakes may significantly affect the performance of the ideal portfolio. For instance, a slight inaccuracy in estimating an asset's projected return can result in a significant departure from the ideal allocation. Moreover, historical statistics could not accurately depict the distribution of future returns, particularly during periods of market volatility or regime transitions. As a result, it is wise to consider the inputs' degree of uncertainty and look for a portfolio that can withstand various contingencies.

How can we implement robust portfolio optimization?

Building robust portfolios can be done in a variety of ways by modeling the input uncertainty. One method is to employ "uncertainty sets," which are collections of potential input values that include point estimates and some variance around them. Statistical techniques, like confidence intervals, or professional judgment can be used to derive the uncertainty sets. Finding a portfolio that minimizes the worst-case objective function over all potential values in the uncertainty sets is the goal. A portfolio that minimizes the worst-case variance for a specific level of expected return, for instance, can be found using the mean-variance model.

Another way to implement robust portfolio optimization is to use "risk measures", which are functions that quantify the risk of a portfolio under uncertainty. Risk can be quantified using a variety of risk metrics, including value-at-risk (VaR), conditional value-at-risk (CVaR), and omega ratio. Finding a portfolio that maximizes the expected return for a given level of risk or minimizes a risk measure for a given level of return is the goal. For instance, by using CVaR, we can identify a portfolio that reduces the average loss that exceeds a specific threshold.

For asset managers who seek to safeguard their portfolios from negative situations and achieve consistent performance, robust portfolio optimization is a potent instrument. Robust portfolio optimization can offer more trustworthy and practical answers than the traditional method by taking uncertainty into account during the optimization process. However, there are significant difficulties and restrictions with robust portfolio optimization, including computational complexity, sensitivity to the selection of uncertainty sets or risk measures, and difficulty in understanding and expressing the outcomes. Asset managers should therefore carefully choose and use reliable portfolio optimization techniques that match their needs and preferences.