The Riemann-Liouville field and its GMC as \(H \to 0\), and skew flattening for the rough Bergomi model

DOI10.1016/j.spl.2021.109265zbMath1478.60152OpenAlexW3205733273WikidataQ114130494 ScholiaQ114130494MaRDI QIDQ2070629

Yanyan Li

Publication date: 24 January 2022

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2021.109265

zbMATH Keywords

fractional Brownian motionGaussian fieldsGaussian multiplicative chaosrough volatility

Mathematics Subject Classification ID

Gaussian processes (60G15) Random measures (60G57) Fractals (28A80) Convergence of probability measures (60B10)

Related Items

Empirical analysis of rough and classical stochastic volatility models to the SPX and VIX markets ⋮ Optimal trade execution for Gaussian signals with power-law resilience ⋮ MARKOVIAN STOCHASTIC VOLATILITY WITH STOCHASTIC CORRELATION — JOINT CALIBRATION AND CONSISTENCY OF SPX/VIX SHORT-MATURITY SMILES ⋮ Log-Modulated Rough Stochastic Volatility Models

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• On Gaussian multiplicative chaos
• Gaussian multiplicative chaos and applications: a review
• Gaussian multiplicative chaos revisited
• An introduction to the theory of point processes
• Log-infinitely divisible multifractal processes
• Fractional Brownian motion with zero Hurst parameter: a rough volatility viewpoint
• Decompositions of log-correlated fields with applications
• The conditional law of the Bacry-Muzy and Riemann-Liouville log correlated Gaussian fields and their GMC, via Gaussian Hilbert and fractional Sobolev spaces
• OPTION HEDGING AND IMPLIED VOLATILITIES IN A STOCHASTIC VOLATILITY MODEL

This page was built for publication: The Riemann-Liouville field and its GMC as \(H \to 0\), and skew flattening for the rough Bergomi model