Pricing and Calibration in the 4-Factor Path-Dependent Volatility Model

Guido Gazzani and Julien Guyon
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Guido Gazzani: UNIVR | DSE - Department of Economics [Verona] - UNIVR - Università degli studi di Verona = University of Verona
Julien Guyon: CERMICS - Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique - ENPC - École nationale des ponts et chaussées, MATHRISK - Mathematical Risk Handling - UPEM - Université Paris-Est Marne-la-Vallée - ENPC - École nationale des ponts et chaussées - Centre Inria de Paris - Inria - Institut National de Recherche en Informatique et en Automatique, NYU Tandon School of Engineering

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Abstract: We consider the path-dependent volatility (PDV) model of Guyon and Lekeufack (2023), where the instantaneous volatility is a linear combination of a weighted sum of past returns and the square root of a weighted sum of past squared returns. We discuss the influence of an additional parameter that unlocks enough volatility on the upside to reproduce the implied volatility smiles of S&P 500 and VIX options. This PDV model, motivated by empirical studies, comes with computational challenges, especially in relation to VIX options pricing and calibration. We propose an accurate neural network approximation of the VIX which leverages on the Markovianity of the 4-factor version of the model. The VIX is learned as a function of the Markovian factors and the model parameters. We use this approximation to tackle the joint calibration of S&P 500 and VIX options.

Keywords: S&; amp; P 500/VIX joint calibration; Neural networks; Calibration of financial models; Path-dependent volatility (search for similar items in EconPapers)
Date: 2025
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Published in Quantitative Finance, 2025, 25 (3), pp.471-489. ⟨10.2139/ssrn.4853419⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04855305

DOI: 10.2139/ssrn.4853419

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