Pricing commodity swing options

Abstract

In commodity and energy markets swing options allow the buyer to hedge against futures price fluctuations and to select its preferred delivery strategy within daily or periodic constraints, possibly fixed by observing quoted futures contracts. In this paper we focus on the natural gas market and we present a dynamical model for commodity futures prices able to calibrate liquid market quotes and to imply the volatility smile for futures contracts with different delivery periods. We implement the numerical problem by means of a least-square Monte Carlo simulation and we investigate alternative approaches based on reinforcement learning algorithms.

Keywords: Commodity, Swing Option, Volatility Smile, Local Volatility, Least-Square Monte Carlo, Reinforcement Learning, Proximal Policy Optimization

JEL Classification: C63, G13