Reinforcement learning for options on target volatility funds

TL;DR

This paper combines analytical solutions and reinforcement learning to price options on target volatility funds, addressing funding costs and portfolio rebalancing under different volatility models.

Contribution

It derives an analytical solution in the Black-Scholes model and applies RL techniques to optimize fund composition in local volatility scenarios where no explicit solution exists.

Findings

RL agents perform comparably to analytical strategies in BS model

RL effectively determines conservative fund compositions in LV model

Analytical and RL methods are compatible in pricing TVS options

Abstract

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.