An Approximate Dynamic Programming Approach to Benchmark Practice-Based Heuristics for Natural Gas Storage Valuation

Abstract

The valuation of the real option to store natural gas is a practically important problem that entails dynamic optimization of inventory trading decisions with capacity constraints in the face of uncertain natural gas price dynamics. Stochastic dynamic programming is a natural approach to this valuation problem, but it does not seem to be widely used in practice because it is at odds with the high-dimensional natural gas price evolution models that are widespread among traders. According to the practice-based literature, practitioners typically value natural gas storage heuristically. The effectiveness of the heuristics discussed in this literature is currently unknown because good upper bounds on the value of storage are not available. We develop a novel and tractable approximate dynamic programming method that, coupled with Monte Carlo simulation, computes lower and upper bounds on the value of storage, which we use to benchmark these heuristics on a set of realistic instances. We find that these heuristics are extremely fast to execute but significantly suboptimal compared to our upper bound, which appears to be fairly tight and much tighter than a simpler perfect information upper bound; computing our lower bound takes more time than using these heuristics, but our lower bound substantially outperforms them in terms of valuation. Moreover, with periodic reoptimizations embedded in Monte Carlo simulation, the practice-based heuristics become nearly optimal, with one exception, at the expense of higher computational effort. Our lower bound with reoptimization is also nearly optimal, but exhibits a higher computational requirement than these heuristics. Besides natural gas storage, our results are potentially relevant for the valuation of the real option to store other commodities, such as metals, oil, and petroleum products.

Dynamic Risk Management of Commodity Operations: Model and Analysis

“…These papers, however, mostly deal with a situation where the markets are complete and/or the firm does not face risk costs (cases (NC), (SC) and (NI) in Table 1). Secomandi (2010) and Lai et al (2010) for instance, are papers that consider complete markets and determine the optimal operational policy for a natural gas storage facility. Goel and Gutierrez (2006) and Berling and Martínez-de Albéniz (2011) are papers where the market is incomplete, but the firm faces no significant risk costs.…”

Asset Pricing in Incomplete Markets: Valuing Gas Storage Capacity

“…Boogert and De Jong [2008]), futures term structure models (e.g. Lai et al [2010]), and equilibrium asset pricing models. This is a more general classification than the one given by De Jong [2015] since they included the equilibrium pricing models.…”

Real Option Management of Hydrocarbon Cracking Operations

“…Indeed, HCs are only one example of commodity conversion assets. Other examples include natural gas and oil production facilities (Smith and McCardle, 1999), electricity transmission capacity (Deng et al, 2001), natural gas pipelines (Secomandi, 2010b, Secomandi and, natural gas storage facilities (Thompson et al, 2009, Secomandi, 2010a, Lai et al, 2010, Nadarajah et al, 2012a, Nadarajah et al, 2012b, Thompson, 2012, Wu et al, 2012, liquefied natural gas storage terminals (Lai et al, 2011), various cross-commodity conversion assets (Tseng and Barz, 2002, Tseng and Lin, 2007, Hahn and Dyer, 2008, Boyabatli et al, 2011, Devalkar et al, 2011, Wu and Chen, 2010, Adkins and Paxon, 2011, Dockendorf and Paxon, 2011, and energy swing (acquisition) assets (Jaillet et al, 2004, Nadarajah et al, 2012b. Dixit and Pindyck (1994), Clewlow and Strickland (2000), Ronn (2002), Eydeland and Wolyniec (2003), Kaminski (2004), Geman (2005), provide additional examples and references to the literature.…”