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Accelerating FHS Option Pricing Under Linear GARCH

Author

Listed:
  • Haibin Xie

    (University of International Business and Economics)

  • Xinyu Wu

    (Anhui University of Finance and Economics)

  • Pengying Fan

    (Beijing Technology and Business University)

Abstract

We propose an analytical approximation technique to accelerate the filtered historical simulation (FHS) option pricing method. The analytical approximation technique has at least two advantages over the FHS method: first, it does not suffer from random sampling error as it needs no simulation; second, it is fast in calculating option price as it is analytical. Simulation results indicate our technique approximates the FHS method quite well, and empirical results show that our technique has very good option pricing performance.

Suggested Citation

  • Haibin Xie & Xinyu Wu & Pengying Fan, 2021. "Accelerating FHS Option Pricing Under Linear GARCH," Computational Economics, Springer;Society for Computational Economics, vol. 58(2), pages 395-411, August.
    • Handle: RePEc:kap:compec:v:58:y:2021:i:2:d:10.1007_s10614-020-10033-1
    DOI: 10.1007/s10614-020-10033-1
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    References listed on IDEAS

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    1. Michael H. Breitner & Christian Dunis & Hans-Jörg Mettenheim & Christopher Neely & Georgios Sermpinis & Christian Spreckelsen & Hans‐Jörg Mettenheim & Michael H. Breitner, 2014. "Real‐Time Pricing and Hedging of Options on Currency Futures with Artificial Neural Networks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(6), pages 419-432, September.
    2. Christoffersen, Peter & Heston, Steve & Jacobs, Kris, 2006. "Option valuation with conditional skewness," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 253-284.
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    5. Suk Joon Byun & Byungsun Min, 2013. "Conditional Volatility and the GARCH Option Pricing Model with Non‐Normal Innovations," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 33(1), pages 1-28, January.
    6. Robert JARROW & Andrew RUDD, 2008. "Approximate Option Valuation For Arbitrary Stochastic Processes," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 1, pages 9-31, World Scientific Publishing Co. Pte. Ltd..
    7. Giovanni Barone Adesi & Robert F. Engle & Loriano Mancini, 2014. "A GARCH Option Pricing Model with Filtered Historical Simulation," Palgrave Macmillan Books, in: Giovanni Barone Adesi (ed.), Simulating Security Returns: A Filtered Historical Simulation Approach, chapter 4, pages 66-108, Palgrave Macmillan.
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    9. Fabio Bellini & Lorenzo Mercuri, 2014. "Option pricing in a conditional Bilateral Gamma model," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(2), pages 373-390, June.
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    Cited by:

    1. Liu, Bingqi & Pang, Tianxiao & Cheng, Siang, 2024. "Estimation for generalized linear cointegration regression models through composite quantile regression approach," Finance Research Letters, Elsevier, vol. 65(C).

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