Sample path generation of the stochastic volatility CGMY process and its application to path-dependent option pricing

TL;DR

This paper introduces a new Monte Carlo-based method for generating sample paths of the stochastic volatility CGMY process, enabling improved pricing of European, American, and path-dependent options like Asian and Barrier options.

Contribution

It develops a novel sample path generation technique for the stochastic volatility CGMY process and applies it to calibrate and price various types of options.

Findings

Effective Monte Carlo method for path generation

Successful calibration to S&P 100 options market

Accurate pricing of path-dependent options

Abstract

This paper proposes the sample path generation method for the stochastic volatility version of CGMY process. We present the Monte-Carlo method for European and American option pricing with the sample path generation and calibrate model parameters to the American style S\&P 100 index options market, using the least square regression method. Moreover, we discuss path-dependent options such as Asian and Barrier options.