Option pricing with non-Gaussian scaling and infinite-state switching volatility

TL;DR

This paper introduces a new option pricing model that incorporates non-Gaussian scaling and infinite-state switching volatility, capturing stylized facts of financial markets to improve pricing accuracy.

Contribution

It develops a closed-form option pricing formula based on a model that accounts for volatility clustering and long-range dependence, extending the Black & Scholes framework.

Findings

Pricing accuracy improved by ~30% compared to market prices

Model captures stylized facts like volatility clustering

Reduces reliance on calibrating to derivative prices

Abstract

Volatility clustering, long-range dependence, and non-Gaussian scaling are stylized facts of financial assets dynamics. They are ignored in the Black & Scholes framework, but have a relevant impact on the pricing of options written on financial assets. Using a recent model for market dynamics which adequately captures the above stylized facts, we derive closed form equations for option pricing, obtaining the Black & Scholes as a special case. By applying our pricing equations to a major equity index option dataset, we show that inclusion of stylized features in financial modeling moves derivative prices about 30% closer to the market values without the need of calibrating models parameters on available derivative prices.