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Asset Pricing in Incomplete Markets: Valuing Gas Storage Capacity

Listed author(s):
  • Lin Zhao

    (University of Amsterdam, the Netherlands)

  • Sweder van Wijnbergen

    (University of Amsterdam, the Netherlands)

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.

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File URL: http://papers.tinbergen.nl/15104.pdf
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Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 15-104/VI/DSF95.

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Date of creation: 28 Aug 2015
Handle: RePEc:tin:wpaper:20150104
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  1. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 113-147.
  2. Creal, Drew & Koopman, Siem Jan & Lucas, André, 2011. "A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(4), pages 552-563.
  3. Rene Carmona & Michael Ludkovski, 2008. "Pricing Asset Scheduling Flexibility using Optimal Switching," Applied Mathematical Finance, Taylor & Francis Journals, vol. 15(5-6), pages 405-447.
  4. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024, March.
  5. Siem Jan Koopman & André Lucas & Marcel Scharth, 2016. "Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models," The Review of Economics and Statistics, MIT Press, vol. 98(1), pages 97-110, March.
  6. Patrick Henaff & Ismail Laachir & Francesco Russo, 2013. "Gas storage valuation and hedging. A quantification of the model risk," Papers 1312.3789, arXiv.org.
  7. Nicola Secomandi, 2010. "Optimal Commodity Trading with a Capacitated Storage Asset," Management Science, INFORMS, vol. 56(3), pages 449-467, March.
  8. Guoming Lai & François Margot & Nicola Secomandi, 2010. "An Approximate Dynamic Programming Approach to Benchmark Practice-Based Heuristics for Natural Gas Storage Valuation," Operations Research, INFORMS, vol. 58(3), pages 564-582, June.
  9. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," University of California at Los Angeles, Anderson Graduate School of Management qt43n1k4jb, Anderson Graduate School of Management, UCLA.
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