Computer Science ›› 2021, Vol. 48 ›› Issue (6A): 169-177.doi: 10.11896/jsjkx.200900159

• Big Data & Data Science • Previous Articles     Next Articles

Comparison of Temperature Forecasting Model Using in Weather Derivatives Designing

ZHANG Xue, LUO Zhi-hong, JIANG Jing   

  1. Research Center of Geological Resource Economics and Management,East China University of Technology,Nanchang 330013,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:ZHANG Xue,born in 1989,Ph.D,lecturer.Her main interests include risk management and so on.LUO Zhi-hong,born in 1976,Ph.D,professor.Her main interests include risk management and so on.
  • Supported by:
    Research Center of Geological Resource Economics and Management (20JJ03),National Natural Science Foundation of China(71871233),Natural Science Foundation of Beijing,China(9182015),National Social Science Foundation of China (19CJY040),Social Science Foundation of Jiangxi Province,China(20GL16),Educational Science Planning Project During the 13th Five-Year Plan Period of Jiangxi Province,China(20YB076) and Research Foundation for Advanced Talents of East China University of Technology(DHBK2019382).

Abstract: Temperature derivatives is one of the most active contracts in the weather derivatives transactions,so making an appropriate temperature forecasting model is the basis for the design of temperature derivatives.Considering the temperature time series always saccompanied by trend characteristic,seasonality pattern and cycle,this paper uses the continuous time autoregressive model (CAR) based on ornstein-uhlenbeck process,seasonal autoregressive integrated moving average (SARIMA) model and wavelet neural network algorithm these three models to fit the temperature of Mohe,Beijing,Urumqi Wuhu,Kunming and Hai-kou,which are the regional representative cities overall the China.In the study,unbiased absolute percentage error,standard absolute percentage Error and Mean Absolute Scaled Error are used to test forecasting accuracy of these three temperature models.The forecasting accuracy results show that compared with the continuous time autoregressive model and SARIMA model,wavelet neural network has the smallest the values of the unbiased absolute percentage error,standard absolute percentage error and mean absolute scaled error,which shows the best forecasting performance.Wavelet neural network can well fit the changes of temperature's process and provide significance for temperature derivatives pricing.

Key words: Continuous time autoregressive model, Seasonal autoregressive integrated moving average model, Temperature derivatives, Temperature forecasting, Wavelet neural network algorithm

CLC Number: 

  • TP391
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