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Meteorological forecasts and the pricing of weather derivatives

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  • Matthias Ritter
  • Oliver Mußhoff
  • Martin Odening

Abstract

In usual pricing approaches for weather derivatives, forward-looking information such as meteorological weather forecasts is not considered. Thus, important knowledge used by market participants is ignored in theory. By extending a standard model for the daily temperature, this paper allows the incorporation of meteorological forecasts in the framework of weather derivative pricing and is able to estimate the information gain compared to a benchmark model without meteorological forecasts. This approach is applied for temperature futures referring to New York, Minneapolis and Cincinnati with forecast data 13 days in advance. Despite this relatively short forecast horizon, the models using meteorological forecasts outperform the classical approach and more accurately forecast the market prices of the temperature futures traded at the Chicago Mercantile Exchange (CME). Moreover, a concentration on the last two months or on days with actual trading improves the results.

Suggested Citation

  • Matthias Ritter & Oliver Mußhoff & Martin Odening, 2010. "Meteorological forecasts and the pricing of weather derivatives," SFB 649 Discussion Papers SFB649DP2010-043, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    • Handle: RePEc:hum:wpaper:sfb649dp2010-043
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    References listed on IDEAS

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    1. Wolfgang Karl Härdle & Brenda López Cabrera, 2012. "The Implied Market Price of Weather Risk," Applied Mathematical Finance, Taylor & Francis Journals, vol. 19(1), pages 59-95, February.
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    Cited by:

    1. Wolfgang Karl Härdle & Brenda López-Cabrera & Matthias Ritter, 2012. "Forecast based Pricing of Weather Derivatives," SFB 649 Discussion Papers SFB649DP2012-027, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    2. Füss, Roland & Mahringer, Steffen & Prokopczuk, Marcel, 2015. "Electricity derivatives pricing with forward-looking information," Journal of Economic Dynamics and Control, Elsevier, vol. 58(C), pages 34-57.
    3. Matthias Ritter, 2012. "Can the market forecast the weather better than meteorologists?," SFB 649 Discussion Papers SFB649DP2012-067, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. Rui Zhou & Johnny Siu-Hang Li & Jeffrey Pai, 2019. "Pricing temperature derivatives with a filtered historical simulation approach," The European Journal of Finance, Taylor & Francis Journals, vol. 25(15), pages 1462-1484, October.
    5. Brenda López Cabrera & Martin Odening & Matthias Ritter, 2013. "Pricing Rainfall Derivatives at the CME," SFB 649 Discussion Papers SFB649DP2013-005, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    6. Groll, Andreas & López-Cabrera, Brenda & Meyer-Brandis, Thilo, 2016. "A consistent two-factor model for pricing temperature derivatives," Energy Economics, Elsevier, vol. 55(C), pages 112-126.
    7. López Cabrera, Brenda & Odening, Martin & Ritter, Matthias, 2013. "Pricing rainfall futures at the CME," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4286-4298.

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    More about this item

    Keywords

    Weather forecasting; weather risk; price forecasting; nancial markets; temperature futures; CME;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • N23 - Economic History - - Financial Markets and Institutions - - - Europe: Pre-1913

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