Turbocharging Monte Carlo pricing for the rough Bergomi model
File(s)
Accepted version
Author(s)
McCrickerd, Ryan
Pakkanen, Mikko S
Type
Journal Article
Abstract
The rough Bergomi model, introduced by Bayer, Friz and Gatheral [Quant.
Finance 16(6), 887-904, 2016], is one of the recent rough volatility models
that are consistent with the stylised fact of implied volatility surfaces being
essentially time-invariant, and are able to capture the term structure of skew
observed in equity markets. In the absence of analytical European option
pricing methods for the model, we focus on reducing the runtime-adjusted
variance of Monte Carlo implied volatilities, thereby contributing to the
model's calibration by simulation. We employ a novel composition of variance
reduction methods, immediately applicable to any conditionally log-normal
stochastic volatility model. Assuming one targets implied volatility estimates
with a given degree of confidence, thus calibration RMSE, the results we
demonstrate equate to significant runtime reductions - roughly 20 times on
average, across different correlation regimes.
Finance 16(6), 887-904, 2016], is one of the recent rough volatility models
that are consistent with the stylised fact of implied volatility surfaces being
essentially time-invariant, and are able to capture the term structure of skew
observed in equity markets. In the absence of analytical European option
pricing methods for the model, we focus on reducing the runtime-adjusted
variance of Monte Carlo implied volatilities, thereby contributing to the
model's calibration by simulation. We employ a novel composition of variance
reduction methods, immediately applicable to any conditionally log-normal
stochastic volatility model. Assuming one targets implied volatility estimates
with a given degree of confidence, thus calibration RMSE, the results we
demonstrate equate to significant runtime reductions - roughly 20 times on
average, across different correlation regimes.
Date Issued
2018-11-02
Date Acceptance
2018-03-27
Citation
Quantitative Finance, 2018, 18 (11), pp.1877-1886
ISSN
1469-7688
Publisher
Taylor & Francis (Routledge)
Start Page
1877
End Page
1886
Journal / Book Title
Quantitative Finance
Volume
18
Issue
11
Copyright Statement
© 2018 Informa UK Limited, trading as Taylor & Francis Group. This is an Accepted Manuscript of an article published by Taylor & Francis in Quantitative Finance on 30 April 2018, available online: https://www.tandfonline.com/doi/full/10.1080/14697688.2018.1459812
Identifier
http://arxiv.org/abs/1708.02563v3
Subjects
q-fin.CP
q-fin.CP
q-fin.PR
91G60, 91G20
Notes
16 pages, 10 figures, v3: minor amendments and reformatted
Publication Status
Published
Date Publish Online
2018-04-30