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Computer and Information Sciences II pp 577–581Cite as

An Option Pricing Model Calibration Using Algorithmic Differentiation

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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.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Software Engineering, Xian Jiaotong-Liverpool University, SIP, Suzhou, Jiangsu Province, China

    Emmanuel M. Tadjouddine & Yi Cao

Authors
  1. Emmanuel M. Tadjouddine
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  2. Yi Cao
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Corresponding author

Correspondence to Emmanuel M. Tadjouddine .

Editor information

Editors and Affiliations

  1. , Dept of Electrical and Electronics Eng'g, Imperial College, London, SW7 2BT, United Kingdom

    Erol Gelenbe

  2. Imperial College, London, United Kingdom

    Ricardo Lent

  3. University of East London, London, United Kingdom

    Georgia Sakellari

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© 2011 Springer-Verlag London Limited

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Tadjouddine, E.M., Cao, Y. (2011). An Option Pricing Model Calibration Using Algorithmic Differentiation. In: Gelenbe, E., Lent, R., Sakellari, G. (eds) Computer and Information Sciences II. Springer, London. https://doi.org/10.1007/978-1-4471-2155-8_74

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  • DOI: https://doi.org/10.1007/978-1-4471-2155-8_74

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-2154-1

  • Online ISBN: 978-1-4471-2155-8

  • eBook Packages: EngineeringEngineering (R0)

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