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Volatility Options in Rough Volatility Models

Volatility options in rough volatility models
Authors: Blanka Horvath; Antoine Jacquier; Peter Tankov;

Volatility Options in Rough Volatility Models

Abstract

We discuss the pricing and hedging of volatility options in some rough volatility models. First, we develop efficient Monte Carlo methods and asymptotic approximations for computing option prices and hedge ratios in models where log-volatility follows a Gaussian Volterra process. While providing a good fit for European options, these models are unable to reproduce the VIX option smile observed in the market, and are thus not suitable for VIX products. To accommodate these, we introduce the class of modulated Volterra processes, and show that they successfully capture the VIX smile.

52 pages, 33 figures

Keywords

Numerical methods (including Monte Carlo methods), Probability (math.PR), rough volatility, Monte Carlo methods, VIX smile, FOS: Economics and business, Derivative securities (option pricing, hedging, etc.), FOS: Mathematics, Volterra process, Pricing of Securities (q-fin.PR), Monte Carlo, Quantitative Finance - Pricing of Securities, Mathematics - Probability, 60G15, 60G22, 91G20, 91G60, 91B25

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
26
Top 10%
Top 10%
Top 10%
Green
bronze