Not so Particular about Calibration: Smile Problem Resolved

18 Pages Posted: 20 Oct 2019 Last revised: 7 May 2020

See all articles by Aitor Muguruza

Aitor Muguruza

Imperial College London; Kaiju Capital Management

Date Written: October 9, 2019

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-Labordere (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.

Keywords: Local Stochastic Volatility, Hybrid Models, Monte Carlo methods, Rough volatility

JEL Classification: C15

Suggested Citation

Muguruza, Aitor, Not so Particular about Calibration: Smile Problem Resolved (October 9, 2019). Available at SSRN: https://ssrn.com/abstract=3461545 or http://dx.doi.org/10.2139/ssrn.3461545

Aitor Muguruza (Contact Author)

Imperial College London ( email )

South Kensington Campus
Exhibition Road
London, Greater London SW7 2AZ
United Kingdom

Kaiju Capital Management ( email )

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