On simulation of rough Volterra stochastic volatility models

Jan Matas and Jan Posp\'i\v{s}il

Papers from arXiv.org

Abstract: Rough Volterra volatility models are a progressive and promising field of research in derivative pricing. Although rough fractional stochastic volatility models already proved to be superior in real market data fitting, techniques used in simulation of these models are still inefficient in terms of speed and accuracy. This paper aims to present accurate and efficient tools and techniques for Monte-Carlo simulations for a wide range of rough volatility models. In particular, we compare three commonly used simulation methods: the Cholesky method, the Hybrid scheme, and the rDonsker scheme. We also comment on the implementation of variance reduction techniques. In particular, we show the obstacles of the so-called turbocharging technique whose performance is sometimes counter-productive. To overcome these obstacles, we suggest several modifications.

Date: 2021-07, Revised 2022-08
New Economics Papers: this item is included in nep-cmp, nep-isf and nep-rmg
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