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Asset Pricing in Incomplete Markets: Valuing Gas Storage Capacity

Author

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  • Lin Zhao

    (University of Amsterdam, the Netherlands)

  • Sweder van Wijnbergen

    (University of Amsterdam, the Netherlands)

Abstract

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.

Suggested Citation

  • Lin Zhao & Sweder van Wijnbergen, 2015. "Asset Pricing in Incomplete Markets: Valuing Gas Storage Capacity," Tinbergen Institute Discussion Papers 15-104/VI/DSF95, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20150104
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    File URL: https://papers.tinbergen.nl/15104.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    stochastic volatility; Generalised Autoregressive Score modeling; incomplete markets; real options; utility indifference pricing; gas storage; capacity constraints;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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