Log-modulated rough stochastic volatility models

Christian Bayer, Fabian Andsem Harang and Paolo Pigato

Papers from arXiv.org

Abstract: 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 \le H

Date: 2020-08, Revised 2021-05
New Economics Papers: this item is included in nep-rmg
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