# Decomposition formula for rough Volterra stochastic volatility models

## TL;DR

This paper introduces a new decomposition formula for pricing European options under rough fractional stochastic volatility models, providing an efficient approximation method that enhances calibration speed compared to traditional Monte Carlo simulations.

## Contribution

It offers a novel decomposition formula and explicit approximation for option pricing in rough volatility models, improving computational efficiency and calibration speed.

## Key findings

- The approximation formula performs well for the rBergomi model.
- The hybrid calibration scheme accelerates market calibration.
- Numerical tests show good accuracy of the approximation.

## Abstract

The research presented in this article provides an alternative option pricing approach for a class of rough fractional stochastic volatility models. These models are increasingly popular between academics and practitioners due to their surprising consistency with financial markets. However, they bring several challenges alongside. Most noticeably, even simple non-linear financial derivatives as vanilla European options are typically priced by means of Monte-Carlo (MC) simulations which are more computationally demanding than similar MC schemes for standard stochastic volatility models. In this paper, we provide a proof of the prediction law for general Gaussian Volterra processes. The prediction law is then utilized to obtain an adapted projection of the future squared volatility -- a cornerstone of the proposed pricing approximation. Firstly, a decomposition formula for European option prices under general Volterra volatility models is introduced. Then we focus on particular models with rough fractional volatility and we derive an explicit semi-closed approximation formula. Numerical properties of the approximation for a popular model -- the rBergomi model -- are studied and we propose a hybrid calibration scheme which combines the approximation formula alongside MC simulations. This scheme can significantly speed up the calibration to financial markets as illustrated on a set of AAPL options.