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

Geon Lee, Tae-Kyoung Kim, Hyun-Gyoon Kim and Jeonggyu Huh ()
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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

JRFM, 2022, vol. 15, issue 12, 1-8

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.

Keywords: graphics processing unit (GPU) accelerated computing; implied volatility; Newton–Raphson method; PyTorch; TensorRT (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2022
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