Reinforcement learning for options on target volatility funds
This addresses funding cost challenges for financial institutions managing target volatility strategies, but it is incremental as it extends existing methods to a more complex volatility model.
The paper tackles the problem of evaluating option prices for target volatility funds under uncertain portfolio composition and varying hedging costs, deriving an analytical solution for the Black-Scholes scenario and using reinforcement learning to find conservative prices under the local volatility model. The results show that RL agents perform comparably to applying the analytical strategy from Black-Scholes to the local volatility dynamics.
In this work we deal with the funding costs rising from hedging the risky securities underlying a target volatility strategy (TVS), a portfolio of risky assets and a risk-free one dynamically rebalanced in order to keep the realized volatility of the portfolio on a certain level. The uncertainty in the TVS risky portfolio composition along with the difference in hedging costs for each component requires to solve a control problem to evaluate the option prices. We derive an analytical solution of the problem in the Black and Scholes (BS) scenario. Then we use Reinforcement Learning (RL) techniques to determine the fund composition leading to the most conservative price under the local volatility (LV) model, for which an a priori solution is not available. We show how the performances of the RL agents are compatible with those obtained by applying path-wise the BS analytical strategy to the TVS dynamics, which therefore appears competitive also in the LV scenario.