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Pathwise large deviations for the rough Bergomi model

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Pathwise large deviations for the rough Bergomi model. / Jacquier, Antoine; Pakkanen, Mikko S.; Stone, Henry.

In: Journal of Applied Probability, Vol. 55, No. 4, 2018, p. 1078-1092.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

Jacquier, A, Pakkanen, MS & Stone, H 2018, 'Pathwise large deviations for the rough Bergomi model', Journal of Applied Probability, vol. 55, no. 4, pp. 1078-1092. https://doi.org/10.1017/jpr.2018.72

APA

Jacquier, A., Pakkanen, M. S., & Stone, H. (2018). Pathwise large deviations for the rough Bergomi model. Journal of Applied Probability, 55(4), 1078-1092. https://doi.org/10.1017/jpr.2018.72

CBE

Jacquier A, Pakkanen MS, Stone H. 2018. Pathwise large deviations for the rough Bergomi model. Journal of Applied Probability. 55(4):1078-1092. https://doi.org/10.1017/jpr.2018.72

MLA

Jacquier, Antoine, Mikko S. Pakkanen and Henry Stone. "Pathwise large deviations for the rough Bergomi model". Journal of Applied Probability. 2018, 55(4). 1078-1092. https://doi.org/10.1017/jpr.2018.72

Vancouver

Jacquier A, Pakkanen MS, Stone H. Pathwise large deviations for the rough Bergomi model. Journal of Applied Probability. 2018;55(4):1078-1092. https://doi.org/10.1017/jpr.2018.72

Author

Jacquier, Antoine ; Pakkanen, Mikko S. ; Stone, Henry. / Pathwise large deviations for the rough Bergomi model. In: Journal of Applied Probability. 2018 ; Vol. 55, No. 4. pp. 1078-1092.

Bibtex

@article{08511bc9a80c43f4b2ad132d819086b3,
title = "Pathwise large deviations for the rough Bergomi model",
abstract = "Introduced recently in mathematical finance by Bayer et al. (2016), the rough Bergomi model has proved particularly efficient to calibrate option markets. We investigate some of its probabilistic properties, in particular proving a pathwise large deviations principle for a small-noise version of the model. The exponential function (continuous but superlinear) as well as the drift appearing in the volatility process fall beyond the scope of existing results, and a dedicated analysis is needed.",
keywords = "Rough volatility, large deviations, small-time asymptotics, Gaussian measure, reproducing kernel Hilbert space, IMPLIED VOLATILITY, STOCHASTIC VOLATILITY, ASYMPTOTICS, DIFFUSION, PRINCIPLE, JUMPS",
author = "Antoine Jacquier and Pakkanen, {Mikko S.} and Henry Stone",
year = "2018",
doi = "10.1017/jpr.2018.72",
language = "English",
volume = "55",
pages = "1078--1092",
journal = "Journal of Applied Probability",
issn = "0021-9002",
publisher = "CAMBRIDGE UNIV PRESS",
number = "4",

}

RIS

TY - JOUR

T1 - Pathwise large deviations for the rough Bergomi model

AU - Jacquier, Antoine

AU - Pakkanen, Mikko S.

AU - Stone, Henry

PY - 2018

Y1 - 2018

N2 - Introduced recently in mathematical finance by Bayer et al. (2016), the rough Bergomi model has proved particularly efficient to calibrate option markets. We investigate some of its probabilistic properties, in particular proving a pathwise large deviations principle for a small-noise version of the model. The exponential function (continuous but superlinear) as well as the drift appearing in the volatility process fall beyond the scope of existing results, and a dedicated analysis is needed.

AB - Introduced recently in mathematical finance by Bayer et al. (2016), the rough Bergomi model has proved particularly efficient to calibrate option markets. We investigate some of its probabilistic properties, in particular proving a pathwise large deviations principle for a small-noise version of the model. The exponential function (continuous but superlinear) as well as the drift appearing in the volatility process fall beyond the scope of existing results, and a dedicated analysis is needed.

KW - Rough volatility

KW - large deviations

KW - small-time asymptotics

KW - Gaussian measure

KW - reproducing kernel Hilbert space

KW - IMPLIED VOLATILITY

KW - STOCHASTIC VOLATILITY

KW - ASYMPTOTICS

KW - DIFFUSION

KW - PRINCIPLE

KW - JUMPS

U2 - 10.1017/jpr.2018.72

DO - 10.1017/jpr.2018.72

M3 - Journal article

VL - 55

SP - 1078

EP - 1092

JO - Journal of Applied Probability

JF - Journal of Applied Probability

SN - 0021-9002

IS - 4

ER -