An option pricing model calibration using algorithmic differentiation

Emmanuel M. Tadjouddine; Yi Cao

doi:10.1007/978-1-4471-2155-8-74

An option pricing model calibration using algorithmic differentiation

Emmanuel M. Tadjouddine^*, Yi Cao

^*Corresponding author for this work

School of Advanced Technology

Xi'an Jiaotong-Liverpool University

Research output: Contribution to conference › Paper › peer-review

2 Citations (Scopus)

Abstract

We study the application of gradient-based optimization methods for calibrating a stochastic volatility model used for option pricing. To this end, we derived and analyzed Monte Carlo estimators for computing the gradient of a certain payoff function using Finite Differencing and Algorithmic Differentiation. We have assessed the accuracy and efficiency of both methods and their impacts into the optimization algorithm. Numerical results are presented and discussed. This work can benefit investors in financial products with the need for fast and more precise predictions of future market data.

Original language	English
Pages	577-581
Number of pages	5
DOIs	https://doi.org/10.1007/978-1-4471-2155-8-74
Publication status	Published - 2012
Event	26th Annual International Symposium on Computer and Information Science, ISCIS 2011 - London, United Kingdom Duration: 26 Sept 2011 → 28 Sept 2011

Conference

Conference	26th Annual International Symposium on Computer and Information Science, ISCIS 2011
Country/Territory	United Kingdom
City	London
Period	26/09/11 → 28/09/11

Keywords

Algorithmic differentiation
Calibration
Monte Carlo simulation
Optimization
Stochastic pricing models

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10.1007/978-1-4471-2155-8-74

Cite this

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abstract = "We study the application of gradient-based optimization methods for calibrating a stochastic volatility model used for option pricing. To this end, we derived and analyzed Monte Carlo estimators for computing the gradient of a certain payoff function using Finite Differencing and Algorithmic Differentiation. We have assessed the accuracy and efficiency of both methods and their impacts into the optimization algorithm. Numerical results are presented and discussed. This work can benefit investors in financial products with the need for fast and more precise predictions of future market data.",

keywords = "Algorithmic differentiation, Calibration, Monte Carlo simulation, Optimization, Stochastic pricing models",

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AU - Tadjouddine, Emmanuel M.

AU - Cao, Yi

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N2 - We study the application of gradient-based optimization methods for calibrating a stochastic volatility model used for option pricing. To this end, we derived and analyzed Monte Carlo estimators for computing the gradient of a certain payoff function using Finite Differencing and Algorithmic Differentiation. We have assessed the accuracy and efficiency of both methods and their impacts into the optimization algorithm. Numerical results are presented and discussed. This work can benefit investors in financial products with the need for fast and more precise predictions of future market data.

AB - We study the application of gradient-based optimization methods for calibrating a stochastic volatility model used for option pricing. To this end, we derived and analyzed Monte Carlo estimators for computing the gradient of a certain payoff function using Finite Differencing and Algorithmic Differentiation. We have assessed the accuracy and efficiency of both methods and their impacts into the optimization algorithm. Numerical results are presented and discussed. This work can benefit investors in financial products with the need for fast and more precise predictions of future market data.

KW - Algorithmic differentiation

KW - Calibration

KW - Monte Carlo simulation

KW - Optimization

KW - Stochastic pricing models

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DO - 10.1007/978-1-4471-2155-8-74

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T2 - 26th Annual International Symposium on Computer and Information Science, ISCIS 2011

Y2 - 26 September 2011 through 28 September 2011

ER -

An option pricing model calibration using algorithmic differentiation

Abstract

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Keywords

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