• Media type: Text; E-Article
  • Title: Log-Modulated Rough Stochastic Volatility Models
  • Contributor: Pigato, Paolo [Author]; Harang, Fabian [Author]; Bayer, Christian [Author]
  • Published: Weierstrass Institute for Applied Analysis and Stochastics publication server, 2020
  • Language: English
  • DOI: https://doi.org/10.2139/ssrn.3668973
  • Keywords: Rough volatility models -- stochastic volatility -- rough Bergomi model -- implied skew -- fractional Brownian motion -- log Brownian motion ; article
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  • Description: We propose a new class of rough stochastic volatility models obtained by modulating the power-law kernel defining the fractional Brownian motion (fBm) by a logarithmic term, such that the kernel retains square integrability even in the limit case of vanishing Hurst index H. The so-obtained log-modulated fractional Brownian motion (log-fBm) is a continuous Gaussian process even for H = 0. As a consequence, the resulting super-rough stochastic volatility models can be analysed over the whole range 0 <= H < 1/2 without the need of further normalization. We obtain skew asymptotics of the form log(1/T)^(-p)T^(H-1/2) as T -> 0, H >= 0, so no flattening of the skew occurs as H -> 0.