  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.
