Symbolic regression-based adaptive generation of implied volatility

Joseph Yen, Yuan Yuan Qi (), Seng Fat Wong () and Jiantao Zhou ()
Additional contact information
Joseph Yen: Electrical-Mechanical Engineering, University of Macau, Macau, P. R. China
Yuan Yuan Qi: Computer and Information Science, University of Macau, Macau, P. R. China
Seng Fat Wong: Electrical-Mechanical Engineering, University of Macau, Macau, P. R. China
Jiantao Zhou: Computer and Information Science, University of Macau, Macau, P. R. China

International Journal of Financial Engineering (IJFE), 2022, vol. 09, issue 03, 1-27

Abstract: This research paper introduces a new form of Implied Volatility calculation with Symbolic Regression suited for high-frequency trading. The solutions are easily migratable to hardware accelerators like Field Programmable Gate Arrays. This machine learning approach is flexible, and configurable for either high precision, lower latency, or energy efficiency. The model evaluates each mathematical operator in terms of cycles, which then generates highly parallel yet low depth formulas. From testing with C++, the formulas achieved higher accuracy and less than a sixth the time of traditional Implied Volatility models. The data were tested on the SPX dataset to validate accuracy.

Keywords: Implied volatility; FPGA; finance; machine learning; symbolic regression (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S2424786322500189
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijfexx:v:09:y:2022:i:03:n:s2424786322500189

Ordering information: This journal article can be ordered from

DOI: 10.1142/S2424786322500189

Access Statistics for this article

International Journal of Financial Engineering (IJFE) is currently edited by George Yuan

More articles in International Journal of Financial Engineering (IJFE) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().