Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/275093 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 15 [Issue:] 12 [Article No.:] 616 [Year:] 2022 [Pages:] 1-8
Publisher: 
MDPI, Basel
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.
Subjects: 
graphics processing unit (GPU) accelerated computing
implied volatility
Newton-Raphson method
PyTorch
TensorRT
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.