Stochastic Path-Dependent Volatility Models for Price-Storage Dynamics in Natural Gas Markets and Discrete-Time Swing Option Pricing

TL;DR

This paper introduces a novel stochastic path-dependent volatility model for natural gas markets, incorporating path dependence in price volatility and storage, and proposes a deep learning method for swing option pricing with convergence guarantees.

Contribution

It presents a new path-dependent volatility model for natural gas prices and storage, along with a deep learning approach for swing option pricing and its convergence analysis.

Findings

Model effectively captures price-storage dynamics.

Deep learning method provides accurate swing option prices.

Convergence of the numerical approach is theoretically validated.

Abstract

This paper is devoted to the price-storage dynamics in natural gas markets. A novel stochastic path-dependent volatility model is introduced with path-dependence in both price volatility and storage increments. Model calibrations are conducted for both the price and storage dynamics. Further, we discuss the pricing problem of discrete-time swing options using the dynamic programming principle, and a deep learning-based method is proposed for numerical approximations. A numerical algorithm is provided, followed by a convergence analysis result for the deep-learning approach.