Symbolic regression-based adaptive generation of implied volatility

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.