Mean-variance hybrid portfolio optimization with quantile-based risk measure

This paper addresses the importance of incorporating various risk measures in portfolio management and proposes a dynamic hybrid portfolio optimization model that combines the spectral risk measure and the Value-at-Risk in the mean-variance formulation. By utilizing the quantile optimization technique and martingale representation, we offer a solution framework for these issues and also develop a closed-form portfolio policy when all market parameters are deterministic. Our hybrid model outperforms the classical continuous-time mean-variance portfolio policy by allocating a higher position of the risky asset in favorable market states and a less risky asset in unfavorable market states. This desirable property leads to promising numerical experiment results, including improved Sortino ratio and reduced downside risk compared to the benchmark models

Portfolio Choice in the Model of Expected Utility with a Safety-First Component

PublicFinanceWhereas the majority of economists interpret risk as dispersion or variation in an outcome variable, many everyday decision makers tend to associate risk with the outcome failing to meet a certain “safety� level. In this model, a decision maker’s concern about the final wealth distribution per se is captured by the expected utility of the final wealth, and his concern about meeting a safety wealth level is captured by the probability of final wealth exceeding the safety level. The model finds that a positive expected excess return remains sufficient for investing a positive amount in the risky asset except in the special situation where the safety wealth level coincides with the wealth obtained when the entire initial wealth is invested in the riskless asset