Functional central limit theorems for rough volatility

Blanka Horvath (), Antoine Jacquier (), Aitor Muguruza () and Andreas Søjmark ()
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Blanka Horvath: University of Oxford
Antoine Jacquier: Imperial College London, and the Alan Turing Institute
Aitor Muguruza: Imperial College London
Andreas Søjmark: London School of Economics

Finance and Stochastics, 2024, vol. 28, issue 3, No 1, 615-661

Abstract: Abstract The non-Markovian nature of rough volatility makes Monte Carlo methods challenging, and it is in fact a major challenge to develop fast and accurate simulation algorithms. We provide an efficient one for stochastic Volterra processes, based on an extension of Donsker’s approximation of Brownian motion to the fractional Brownian case with arbitrary Hurst exponent H ∈ ( 0 , 1 ) $H \in (0,1)$ . Some of the most relevant consequences of this ‘rough Donsker (rDonsker) theorem’ are functional weak convergence results in Skorokhod space for discrete approximations of a large class of rough stochastic volatility models. This justifies the validity of simple and easy-to-implement Monte Carlo methods, for which we provide detailed numerical recipes. We test these against the current benchmark hybrid scheme and find remarkable agreement (for a large range of values of H $H$ ). Our rDonsker theorem further provides a weak convergence proof for the hybrid scheme itself and allows constructing binomial trees for rough volatility models, the first available scheme (in the rough volatility context) for early exercise options such as American or Bermudan options.

Keywords: Functional limit theorems; Fractional Brownian motion; Rough volatility; Binomial trees; 60F17; 60F05; 60G15; 60G22; 91G20; 91G60; 91B25 (search for similar items in EconPapers)
JEL-codes: G11 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00780-024-00533-5

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