Gas Storage Valuation and Hedging: A Quantification of Model Risk (original) (raw)

Asset Pricing in Incomplete Markets: Valuing Gas Storage Capacity

Social Science Research Network, 2015

We investigate the relationship between the gas spot market and the price of gas storage capacity. Contrary to the common belief, the auction prices for gas storage are mostly affected by the volatility of current market prices rather than by the winter-summer price differences. This paper provides a numerical solution for pricing storage capacity, by taking investor's activities through the spot market and storage service into account. A bivariate Generalized Autoregressive Score (GAS) model is employed for modeling the dynamics of the day-ahead and month-ahead spot market prices, as well as the time-varying volatilities and correlations. Under an incomplete market setting, our model is able to approximate the realized auction prices. Moreover, one interesting implication is that the implied average risk aversion of investor for a storage contract increases with the volatility of the spot market. This is an intuitive result because storage capacity can serve as an effective hedging product for the spot market, and the demand for this product is high when the market becomes risky: more risk averse investors are participating in the auctions. Moreover, a sensitivity analysis on different injection/withdrawal rates is also included, and particularly, contracts with higher capacity rates are priced at a higher level.

Gas Storage Valuation: Price Modelling v. Optimization Methods

2011

In the literature, one approach is to analyse gas storage within a simple one-factor price dynamics framework that is solved to optimality. We follow an alternative approach, where the market is represented by a forward curve with daily granularity, the price uncertainty is represented by six factors, and where we impose a simple and intuitive storage strategy. Based on UK

GAS STORAGE VALUATION UNDER LIMITED MARKET LIQUIDITY: AN APPLICATION IN GERMANY

2009

Natural gas storages may be valuated by applying real options theory. However it is crucial, not to ignore that most evolving gas spot markets, like the German spot market, lack of liquidity. In this context, considering storage operators as price takers does not account for interdependencies of storage operations and market prices. This paper offers a novel approach to storage

Natural gas storage valuation and optimization: A real options application

Naval Research Logistics, 2009

In this paper we present an algorithm for the valuation and optimal operation of natural gas storage facilities. Real options theory is used to derive nonlinear partial-integro-differential equations (PIDEs) for the valuation and optimal operating strategies. The equations are designed to incorporate a wide class of spot price models that can exhibit the same time-dependent, mean-reverting dynamics and price spikes as those observed in most energy markets. Particular attention is paid to the operational characteristics of real storage units, these characteristics include: working gas capacities, variable deliverability and injection rates and cycling limitations. We illustrate the model with a numerical example of a salt cavern storage facility that clearly shows how a gas storage facility is like a financial straddle with both put and call properties. Depending on the amount of gas in storage the relative influence of the put and call components vary.

Pricing an European gas storage facility using a continuous-time spot price model with GARCH diffusion

In this article we present both a theoretical framework and a solved example for pricing an European gas storage facility and computing the optimal strategy for its operation. As a representative price index we choose the Dutch TTF day-ahead gas price. We present statistical evidence that the volatility of this index is time-varying, so we introduce a new continuous-time model by incorporating GARCH diffusion into an Ornstein-Uhlenbeck process. Based on this price process we use dynamic programming methods to derive partial differential equations for pricing a storage facility. As an example we apply our methodology to a storage site located in Epe at the German-Dutch border. In this context we investigate the effects of multiple contract types, and perform a sensitivity analysis for all model parameters. We obtain a value surface displaying the properties of a financial straddle. Both volatility and mean reversion influence the facility value-but only around the long-run mean of the gas price. The terminal condition, which includes information about the contract provisions, is of importance if it contains e.g. penalty terms for low inventory levels. Otherwise its influence is diminishing for increasing lease periods.

Valuation of Storage at a Liquefied Natural Gas Terminal

Operations Research, 2011

The valuation of the real option to store liquefied natural gas (LNG) at the downstream terminal of an LNG value chain is an important problem in practice. As the exact valuation of this real option is computationally intractable, we develop a novel and tractable heuristic model for its strategic valuation that integrates models of LNG shipping, natural gas price evolution, and inventory control and sale into the wholesale natural gas market. We incorporate real and estimated data to quantify the value of this real option and its dependence on the throughput of an LNG chain, the type of price variability, the type of inventory control policy employed, and the level of stochastic variability in both the shipping model and the natural gas price model used. In addition, we develop an imperfect information dual upper bound to assess the effectiveness of our heuristic, and find that our method is near optimal. Our approach also has potential relevance to value the real option to store other commodities in facilities located downstream from a commodity production or transportation stage, such as petroleum and agricultural products, chemicals, and metals, or the real option to store the input used in the production of a commodity, such as electricity.

Futures hedging and risk management

2021

This dissertation was written as a part of the MSc in Energy and Finance at the International Hellenic University. Based on the recent literature, on this paper we focus on the effectiveness of different hedging strategies, both constant hedge ratio and time-varying hedge ratio, on natural gas prices in the United States. Natural gas prices fluctuate depending on seasons. To examine how these fluctuations affect the hedging ability of the econometric models we use we conduct an analysis regarding seasons (fall-winter, springsummer) and regarding market conditions (contango-backwardation). The analysis is conducted over different various time horizons (weekly-monthly). We complete this study by presenting the economic-financial benefits of using these hedging strategies in order to assess the impact of volatility in monetary values.

Evaluating the Economic Cost of Strategic Storage of Natural Gas

The European Commission wants to implement a single market for gas. One of the components of this market is a regulated provision for "security of supply" which consists of rules for the implementation and use of a given reserve stock of gas. We investigate the impact of this policy on the profitability of a storage operator, using data from Denmark and Italy. Keeping storage capacity constant, the costs of the strategic stock are around 20% of the value of the storage market for Denmark, and 16% for Italy. This cost is due to the inability to extract arbitrage profits from the captive stock.

Weather, storage, and natural gas price dynamics: Fundamentals and volatility

Energy economics, 2007

This paper assesses how market fundamentals affect asset return volatility by drawing on evidence from the U.S. natural gas futures market. One of the novel features of this paper is the use of the deviation of temperatures from normal (weather surprise) as a proxy for demand shocks and a determinant of the conditional volatility of natural gas futures returns. I estimate a GARCH model using daily natural gas futures data from January 1997 to December 2000. The empirical result shows that the weather surprise variable has a significant effect on the conditional volatility of natural gas prices and the inclusion of the weather surprise variable in the conditional variance equation reduces volatility persistence. Combined with the evidence that volatility is considerably higher on Monday and the day when natural gas storage report is released, these results show that information about market fundamentals are important determinants of natural gas price volatility. Aside from these findings, I also document that returns of the first month futures are more volatile than those of the second month futures, which is consistent with Samuelson's (1965) hypothesis that commodity futures price volatility declines with contract horizon. Acknowledgement This paper is based on a chapter of my dissertation. I am grateful to Timothy Dunne, Aaron Smallwood and Daniel Sutter for their guidance. Also I thank Dennis O'Brien and the Institute for Energy Economics and Policy for financial support to acquire natural gas futures trading data, Peter Lamb, Mark Richmond and Reed Timmer for help on weather data. All errors remain mine.

Energy Commodities: A Review of Optimal Hedging Strategies

Energies, 2019

Energy is considered as a commodity nowadays and continuous access along with price stability is of vital importance for every economic agent worldwide. The aim of the current review paper is to present in detail the two dominant hedging strategies relative to energy portfolios, the Minimum-Variance hedge ratio and the expected utility maximization methodology. The Minimum-Variance hedge ratio approach is by far the most popular in literature as it is less time consuming and computationally demanding; nevertheless by applying the appropriate multivariate model Garch family volatility model, it can provide a very reliable estimation of the optimal hedge ratio. However, this becomes possible at the cost of a rather restrictive assumption for infinite hedger's risk aversion. Within an uncertain worldwide economic climate and a highly volatile energy market, energy producers, retailers and consumers had to become more adaptive and develop the necessary energy risk management and optimal hedging strategies. The estimation gap of an optimal hedge ratio that would be subject to the investor's risk preferences through time is filled by the relatively more complex and sophisticated expected utility maximization methodology. Nevertheless, if hedgers share infinite risk aversion or if alternatively the expected futures price is approximately zero the two methodologies become equivalent. The current review shows that when evidence from the energy market during periods of extremely volatile economic climate is considered, both hypotheses can be violated, hence it becomes reasonable that especially for extended hedging horizons it would be wise for potential hedgers to take into consideration both methodologies in order to build a successful and profitable hedging strategy.