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

Berislav Žmuk orcid id ; University of Zagreb, Faculty of Economics and Business, Department of Statistics, Zagreb, Croatia
Matej Kovač ; Addiko bank, Croatia

Puni tekst: engleski pdf 326 Kb

str. 27-42

preuzimanja: 279



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.

Ključne riječi

GARCH model, MAPE, Ornstein-Uhlenbeck process, RMSE, temperature forecasting, weather derivatives

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