Not so Particular about Calibration: Smile Problem Resolved

TL;DR

This paper introduces a Monte Carlo-based calibration algorithm for stochastic volatility models, including rough volatility, that is more robust, exact, and computationally efficient than previous methods.

Contribution

A novel, variance-reducing Monte Carlo calibration method applicable to all stochastic volatility models, removing dependency on kernel functions and bandwidths.

Findings

The algorithm is robust and less prone to errors in production environments.

It provides an exact calibration method with variance reduction.

Effective on various hybrid and rough local stochastic volatility models.

Abstract

We present a novel Monte Carlo based LSV calibration algorithm that applies to all stochastic volatility models, including the non-Markovian rough volatility family. Our framework overcomes the limitations of the particle method proposed by Guyon and Henry-Labord\`ere (2012) and theoretically guarantees a variance reduction without additional computational complexity. Specifically, we obtain a closed-form and exact calibration method that allows us to remove the dependency on both the kernel function and bandwidth parameter. This makes the algorithm more robust and less prone to errors or instabilities in a production environment. We test the efficiency of our algorithm on various hybrid (rough) local stochastic volatility models.