Log-modulated rough stochastic volatility models
- Bayer, Christian
- Harang, Fabian
- Pigato, Paolo
2010 Mathematics Subject Classification
- 91G30 60G22
- Rough volatility models, stochastic volatility, rough Bergomi model, implied skew, fractional Brownian motion, log Brownian motion
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.