Option Pricing Models Driven by the Space-Time Fractional Diffusion: Series Representation and Applications

TL;DR

This paper develops space-time fractional diffusion models for option pricing, providing series representations and applying them to real market data to estimate parameters and implied volatility.

Contribution

It introduces a novel series representation for fractional diffusion-based option pricing models and demonstrates their application to real market data.

Findings

Series representation converges rapidly for practical computation

Model parameters can be effectively estimated from market data

Implied volatility can be derived within the fractional framework

Abstract

In this paper, we focus on option pricing models based on space-time fractional diffusion. We briefly revise recent results which show that the option price can be represented in the terms of rapidly converging double-series and apply these results to the data from real markets. We focus on estimation of model parameters from the market data and estimation of implied volatility within the space-time fractional option pricing models.