A Cvar Scenario-Based Framework For Minimizing Downside Risk In Multi-Asset Class Portfolio
In this research paper published in The Journal of Portfolio Management, we consider a scenario-based conditional value at risk (CVaR) approach for minimizing the downside risk of an existing portfolio with multi-asset class (MAC) overlays.
MAC portfolios can be composed of investments in equities, fixed income, commodities, foreign exchange, credit, derivatives, and alternatives such as real estate and private equity.
The return for such nonlinear portfolios is asymmetric with significant tail risk.
The traditional Markowitz mean–variance optimization (MVO) framework, which linearizes all the assets in the portfolio and uses the standard deviation of return as a measure of risk, does not always accurately measure the risk for such portfolios.
We compare the CVaR approach with parametric MVO approaches that linearize all the instruments in the MAC portfolio on two examples involving the hedging of an equity portfolio with index puts and the hedging of a callable bond portfolio with interest rate caps, and show that the CVaR approach
generates portfolios with better downside risk statistics; and further, it selects hedges that produce more attractive risk decompositions and stress test numbers—tools commonly used by risk managers to evaluate the quality of hedges.