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Newton–Raphson Emulation Network for Highly Efficient Computation of Numerous Implied Volatilities

Author

Listed:
  • Geon Lee

    (Department of Mathematics, Chonnam National University & Statistics, Gwangju 61186, Republic of Korea)

  • Tae-Kyoung Kim

    (Asset Management Department, KB Kookmin Bank, Seoul 07328, Republic of Korea)

  • Hyun-Gyoon Kim

    (Department of Mathematics, Yonsei University, Seoul 03722, Republic of Korea)

  • Jeonggyu Huh

    (Department of Statistics, Chonnam National University, Gwangju 61186, Republic of Korea)

Abstract

In finance, implied volatility is an important indicator that reflects the market situation immediately. Many practitioners estimate volatility by using iteration methods, such as the Newton–Raphson (NR) method. However, if numerous implied volatilities must be computed frequently, the iteration methods easily reach the processing speed limit. Therefore, we emulate the NR method as a network by using PyTorch, a well-known deep learning package, and optimize the network further by using TensorRT, a package for optimizing deep learning models. Comparing the optimized emulation method with the benchmarks, implemented in two popular Python packages, we demonstrate that the emulation network is up to 1000 times faster than the benchmark functions.

Suggested Citation

  • Geon Lee & Tae-Kyoung Kim & Hyun-Gyoon Kim & Jeonggyu Huh, 2022. "Newton–Raphson Emulation Network for Highly Efficient Computation of Numerous Implied Volatilities," JRFM, MDPI, vol. 15(12), pages 1-8, December.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:12:p:616-:d:1007258
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    References listed on IDEAS

    as
    1. Corrado, Charles J. & Miller, Thomas Jr., 1996. "A note on a simple, accurate formula to compute implied standard deviations," Journal of Banking & Finance, Elsevier, vol. 20(3), pages 595-603, April.
    2. Shuaiqiang Liu & Cornelis W. Oosterlee & Sander M. Bohte, 2019. "Pricing Options and Computing Implied Volatilities using Neural Networks," Risks, MDPI, vol. 7(1), pages 1-22, February.
    3. Higham,Desmond J., 2004. "An Introduction to Financial Option Valuation," Cambridge Books, Cambridge University Press, number 9780521547574, October.
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