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Pricing Chinese warrants using artificial neural networks coupled with Markov regime switching model

Author

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  • David Liu
  • Lei Zhang

Abstract

A non-parametric valuation framework (ANN-MRS) using artificial neural networks for pricing financial derivatives has been developed whilst the volatility of underlying asset return dynamics are modelled by Markov regime switching model. Its immediate application is on pricing of the Chinese warrants. To access the potential of neural network pricing with volatility in regime switching, weekly data of Jiangtong Stock returns are used to calculate the volatilities by using the maximum likelihood estimation. The ability of neural network for predicting the warrant prices is compared to the Black-Scholes model. Comparisons reveal that the mean squared error for the neural network is less than that of the Black-Scholes model in both in sample and out of sample estimations. The result indicates the neural network model coupled with Markov regime switching (for volatility estimation) has a superior performance comparing the warrant pricing by using the Black-Scholes model with historical volatility.

Suggested Citation

  • David Liu & Lei Zhang, 2011. "Pricing Chinese warrants using artificial neural networks coupled with Markov regime switching model," International Journal of Financial Markets and Derivatives, Inderscience Enterprises Ltd, vol. 2(4), pages 314-330.
    • Handle: RePEc:ids:ijfmkd:v:2:y:2011:i:4:p:314-330
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    Cited by:

    1. Johannes Ruf & Weiguan Wang, 2019. "Neural networks for option pricing and hedging: a literature review," Papers 1911.05620, arXiv.org, revised May 2020.
    2. Maciej Wysocki & Robert Ślepaczuk, 2020. "Artificial Neural Networks Performance in WIG20 Index Options Pricing," Working Papers 2020-19, Faculty of Economic Sciences, University of Warsaw.

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