Gas storage valuation using a multifactor price process

Abstract

In this paper we discuss an extension to a popular gas storage valuation method called the spot approach. Least-Squares Monte Carlo, which is the basis for the spot approach, allows for multi-factor price processes. Such price processes can capture more realistically the actual price behavior present in energy markets. In this paper we demonstrate the application of multi-factor Least-Squares Monte Carlo to gas storage valuation. We study the impact of using multi-factor price processes on different aspects of the valuation such as convergence, average storage value and distribution of storage values in a numerical example. We find a counter example to the idea that an increase in market volatility leads to an increase in storage value. As well, we find a counter example to the idea that the natural hedging strategy of the spot approach is no hedge: a simple static financial hedge can reduce the inherent risk of the spot approach. Finally, we study the impact of model error related to the price process.

Modeling the Price of Natural Gas with Temperature and Oil Price as Exogenous Factors

“…In this model the variance of the gas prices increases over the winter depending on the actual weather conditions and has a maximum at the end of winter. This is much more in line with the observations than the behavior of the model of Boogert and de Jong [6] where the variance of the gas price has a minimum at the end of winter as there is no effect of the winter-summer spread used there. Another major difference is the use of exogenous variables that can be observed and thus the optimal exercising decision for American-style options depends on these variables and therefore also the price of these real options will be different.…”

“…Therefore, we use a temperature model closely related to the one proposed by Benth and Benth [2]. (6) with X (T ) t being an AR(3) process. The model fit with respect to the deterministic part (ordinary least squares regression) and the AR(3) process is shown in Fig.…”

“…Longstaff and Schwartz [9] introduced the least squares Monte Carlo method for the valuation of American options, Meinshausen and Hambly [10] extended the idea to swing options and Boogert and de Jong [5] applied it to the valuation of gas storages. Furthermore, Boogert and de Jong [6] found that the different components of the gas price process should be included into the regression of the least squares Monte Carlo method for the valuation of gas storages as this increases the value. While they included components that are not observable but virtual components of their price process, the price process introduced in Sect.…”

“…An important application of gas price models is the valuation of gas storage facilities. Within this context, Chen and Forsyth [7] and Boogert and de Jong [6] propose gas price models. Chen and Forsyth [7] analyze regime-switching approaches incorporating mean-reverting processes and random walks.…”

“…Chen and Forsyth [7] analyze regime-switching approaches incorporating mean-reverting processes and random walks. The class of factor models is extended by Boogert and de Jong [6]. The three factors in their model represent shortand long-term fluctuations as well as the behavior of the winter-summer spread.…”