Dynamic Optimization of Volatility Risk Premium Strategies with Short Dated Options

Abstract

This thesis studies if variance risk premium harvesting with short dated options can be improved with dynamic portfolio allocation. Options selling tends to have an asymmetric return where gains are steady during normal market conditions and losses are extreme during market stress. Because the use of short dated options is involved, other risk factors have to be accounted for. To approach this problem, this thesis creates a state dependent exposure rule. Portfolio exposure is then chosen using a Conditional Value at Risk (CVaR) optimization. The performance of this approach will be compared to a benchmark that will have a static constant exposure. The findings suggest that dynamic allocation can improve the management of variance risk premium strategies by balancing premium collection with downside risk.