Gas Storage valuation with regime switching

TL;DR

This paper models gas storage valuation as a Markov Decision Process with a regime-switching gas price model, extending existing methods by analyzing policy structure and comparing two numerical algorithms.

Contribution

It introduces a regime-switching model for gas prices into storage valuation and extends the analysis of optimal policies, reducing computational complexity.

Findings

Both algorithms effectively handle the regime-switching model.

The structure of optimal policies is characterized, simplifying computations.

Numerical comparison shows the strengths of each algorithm.

Abstract

In this paper we treat a gas storage valuation problem as a Markov Decision Process. As opposed to existing literature we model the gas price process as a regime-switching model. Such a model has shown to fit market data quite well in Chen and Forsyth (2010). Before we apply a numerical algorithm to solve the problem, we first identify the structure of the optimal injection and withdraw policy. This part extends results in Secomandi (2010). Knowing the structure reduces the complexity of the involved recursion in the algorithms by one variable. We explain the usage and implementation of two algorithms: A Multinomial-Tree Algorithm and a Least-Square Monte Carlo Algorithm. Both algorithms are shown to work for the regime-switching extension. In a numerical study we compare these two algorithms.