• Media type: Text; E-Book; Report
  • Title: Log-modulated rough stochastic volatility models
  • Contributor: Bayer, Christian [Author]; Harang, Fabian [Author]; Pigato, Paolo [Author]
  • imprint: Weierstrass Institute for Applied Analysis and Stochastics publication server, 2020
  • Language: English
  • DOI: https://doi.org/10.20347/WIAS.PREPRINT.2752
  • Keywords: Rough volatility models -- stochastic volatility -- rough Bergomi model -- implied skew -- fractional Brownian motion -- log Brownian motion ; 60G22 ; 91G30 ; 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 of Hurst indices between 0 and 1/2, including H = 0, without the need of further normalization. We obtain the usual power law explosion of the skew as maturity T goes to 0, modulated by a logarithmic term, so no flattening of the skew occurs as H goes to 0.
  • Access State: Open Access