Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/323392 
Year of Publication: 
2020
Citation: 
[Journal:] Croatian Review of Economic, Business and Social Statistics (CREBSS) [ISSN:] 2459-5616 [Volume:] 6 [Issue:] 1 [Year:] 2020 [Pages:] 27-42
Publisher: 
Sciendo, Warsaw
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 êC versus 4.53 êC 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.
Subjects: 
GARCH model
MAPE
Ornstein-Uhlenbeck process
RMSE
temperature forecasting
weather derivatives
JEL: 
C22
C53
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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