Can limits‐to‐arbitrage from bounded storage improve commodity term‐structure modeling?

Abstract

This paper develops and estimates a two‐factor competitive storage model for the purpose of pricing commodity futures. The empirical relevance of the model is evaluated for US natural gas and crude oil futures by comparing the pricing performance to reduced form models. Results suggest jump models, both reduced form and economic, improve modeling due to incorporating pricing discontinuities. Furthermore, the economic model precludes carry arbitrage, which appears relevant for pricing natural gas futures. For crude oil, the reduced form models produce superior pricing under nonstationary market conditions, and the economic model produces superior long‐dated futures pricing under stationarity.

Estimating the Competitive Storage Model with Stochastic Trends in Commodity Prices

“…In order to solve the functional equation for the equilibrium price function f (x) as defined by Equations ( 4)-( 6), we use a numerical algorithm which is based on the method of Kleppe and Oglend (2019), detailed in Appendix A.1. This algorithm takes advantage of the fact that the storage space is completely bounded by the non-negative constraint and the capacity limit C, thus providing numerically robust and computationally fast solutions.…”

“…The numerical algorithm we use to solve the functional equation for the price function f (x) as defined by Equations ( 4)-( 6) is based on that used by Kleppe and Oglend (2019) for a model with autocorrelated supply shocks. The algorithm is based on solving the storage policy function σ(x) and then recover f (x) via Equation ( 6), which implies that f (x) = P (x − σ(x)).…”