Dynamic Hedging using Generated Genetic Programming Implied Volatility Models

TL;DR

This paper enhances dynamic hedging accuracy by using genetic programming to generate implied volatility models, outperforming traditional Black-Scholes models especially for certain option types.

Contribution

It introduces a novel approach of using genetic programming for implied volatility modeling and compares dynamic versus static training methods for improved hedging.

Findings

GP models outperform Black-Scholes in hedging accuracy

Dynamic training of GP yields better results than static training

GP models are more accurate for in-the-money calls and at-the-money puts

Abstract

The purpose of this paper is to improve the accuracy of dynamic hedging using implied volatilities generated by genetic programming. Using real data from S&P500 index options, the genetic programming's ability to forecast Black and Scholes implied volatility is compared between static and dynamic training-subset selection methods. The performance of the best generated GP implied volatilities is tested in dynamic hedging and compared with Black-Scholes model. Based on MSE total, the dynamic training of GP yields better results than those obtained from static training with fixed samples. According to hedging errors, the GP model is more accurate almost in all hedging strategies than the BS model, particularly for in-the-money call options and at-the-money put options.