Pathwise large deviations for the rough Bergomi model

DOI10.1017/jpr.2018.72zbMath1405.60037arXiv1706.05291OpenAlexW2625974289WikidataQ128508468 ScholiaQ128508468MaRDI QIDQ4611271

Henry Stone, Mikko S. Pakkanen, Antoine Jacquier

Publication date: 17 January 2019

Published in: Journal of Applied Probability (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1706.05291

zbMATH Keywords

small-time asymptoticslarge deviationsreproducing kernel Hilbert spaceGaussian measurerough volatility

Mathematics Subject Classification ID

Gaussian processes (60G15) Fractional processes, including fractional Brownian motion (60G22) Large deviations (60F10) Derivative securities (option pricing, hedging, etc.) (91G20)

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Cites Work

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• Asymptotic analysis for stochastic volatility: martingale expansion
• Large deviations and asymptotic methods in finance
• A large deviation principle for stochastic integrals
• Brownian moving averages have conditional full support
• Large deviations techniques and applications.
• Convergence en loi des suites d'integrales stochastiques sur l'espace \({\mathbb{D}}^ 1\) de Skorokhod. (Convergence in law of sequences of stochastic integrals on the Skorokhod space \({\mathbb{D}}^ 1)\)
• A function space large deviation principle for certain stochastic integrals
• Averaging principle of SDE with small diffusion: Moderate deviations
• Large deviations and importance sampling for systems of slow-fast motion
• On the short-time behavior of the implied volatility for jump-diffusion models with stochastic volatility
• Interest rate models: an infinite dimensional stochastic analysis perspective
• Sample path large deviations and optimal importance sampling for stochastic volatility models
• Asymptotics for Rough Stochastic Volatility Models
• SMALL-TIME ASYMPTOTICS FOR IMPLIED VOLATILITY UNDER THE HESTON MODEL
• On VIX futures in the rough Bergomi model
• Volatility is rough
• Short-time at-the-money skew and rough fractional volatility
• The Small-Maturity Smile for Exponential Lévy Models
• Pricing under rough volatility
• Asymptotics of Forward Implied Volatility
• Random Fields and Geometry
• Large deviations and stochastic volatility with jumps: asymptotic implied volatility for affine models
• Stochastic Calculus for Fractional Brownian Motion and Applications
• Fractional Brownian Motions, Fractional Noises and Applications
• Marginal Density Expansions for Diffusions and Stochastic Volatility I: Theoretical Foundations
• Hybrid scheme for Brownian semistationary processes