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Ornstein-Uhlenbeck process and GARCH model for temperature forecasting in weather derivatives valuation

Author

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  • Žmuk Berislav

    (University of Zagreb, Faculty of Economics and Business, Department of Statistics, Zagreb, Croatia)

  • Kovač Matej

    (Addiko bank, Croatia)

Abstract

An accurate weather forecast is the basis for the valuation of weather derivatives, securities that partially compensate for financial losses to holders in case of, from their perspective, adverse outside temperature. The paper analyses precision of two forecast models of average daily temperature, the Ornstein-Uhlenbeck process (O-U process) and the generalized autoregressive conditional heteroskedastic model (GARCH model) and presumes for the GARCH model to be the more accurate one. Temperature data for the period 2000-2017 were taken from the DHMZ database for the Maksimir station and used as the basis for the 2018 forecast. Forecasted values were compared to the available actual data for 2018 using MAPE and RMSE methods. The GARCH model provides more accurate forecasts than the O-U process by both methods. RMSE stands at 3.75 °C versus 4.53 °C for the O-U process and MAPE is 140.66 % versus 144.55 %. Artificial intelligence and supercomputers can be used for possible improvements in forecasting accuracy to allow for additional data to be included in the forecasting process, such as up-to-date temperatures and more complex calculations.

Suggested Citation

  • Žmuk Berislav & Kovač Matej, 2020. "Ornstein-Uhlenbeck process and GARCH model for temperature forecasting in weather derivatives valuation," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 6(1), pages 27-42, May.
    • Handle: RePEc:vrs:crebss:v:6:y:2020:i:1:p:27-42:n:3
    DOI: 10.2478/crebss-2020-0003
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    References listed on IDEAS

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    1. Frank Schiller & Gerold Seidler & Maximilian Wimmer, 2012. "Temperature models for pricing weather derivatives," Quantitative Finance, Taylor & Francis Journals, vol. 12(3), pages 489-500, March.
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    4. Roberto Buizza & James W. Taylor, 2004. "A comparison of temperature density forecasts from GARCH and atmospheric models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(5), pages 337-355.
    5. Fred ESPEN Benth & Jurate saltyte Benth, 2007. "The volatility of temperature and pricing of weather derivatives," Quantitative Finance, Taylor & Francis Journals, vol. 7(5), pages 553-561.
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    More about this item

    Keywords

    GARCH model; MAPE; Ornstein-Uhlenbeck process; RMSE; temperature forecasting; weather derivatives;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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