Research On Temperature's Forecasting Model Based On The Perspective Of Weather Derivatives Pricing

As a financial instrument to hedge general weather risks,weather derivatives have great market potential.And temperature derivatives that the main body of weather derivatives market have attracted extensive attention on pricing.Studies have shown that the suitable temperature prediction model is the key to the accurate pricing of temperature derivatives.Therefore,from the perspective of the pricing of weather derivatives,this paper makes an in-depth study on how to construct the temperature prediction model scientifically.The details are as follows:(1)Establishment of AR-EGARCH Temperature Forecasting Model with Multi-Distribution Hypothesis.Studies have shown that the residual distribution patterns of daily mean temperature data in different cities are diverse.However,some scholars compared the superiority of the AR-EGARCH model and the AR-GARCH model only under the assumption that the residuals obey the normal distribution.Thus,this paper extends the assumption in previous studies that the residual of temperature is normally distributed,and studies the influence of different distribution assumptions on the fitting and prediction effect of the above two models by taking the daily average temperature data of 10 cities in the United States as samples.The results show that,firstly,unreasonable residual distribution hypothesis will affect the in-sample fitting effect and out-of-sample prediction accuracy of the model.Secondly,the AR-EGARCH model which under the assumption of optimal distribution still has significant advantages and effectively measures the asymmetric effect of temperature volatility.(2)Study on the Applicability of AR-EGARCH Temperature Forecasting Model in China.The above study only fitted the change process of the daily average temperature in the United States.Considering that the climate characteristics of China are different from that of the United States,the paper further investigated the fitting effect of the AR-EGARCH model on the daily average temperature data of seven Chinese cities,and analyzed the applicability of it in China.In addition,based on the parameters of AR-EGARCH model,the feasibility of directly introducing mature temperature derivatives from abroad into China is studied by using the hierarchical clustering method(HCM).The results show that,firstly,the AR-EGARCH model is suitable for temperature prediction in these seven Chinese cities.Secondly,there are similarities in temperature fluctuations between Hangzhou and Europe as well as Japan.So,it is suggested to directly introduce the temperature derivatives from Europe and Japan to Hangzhou.(3)Study on Improvement of AR-EGARCH Temperature Forecasting Model under Multi-distribution Hypothesis.After confirming that AR-EGARCH model is applicable to China and temperature derivatives can be directly introduced into Hangzhou,the AR-EGARCH model is improved by taking the daily average temperature data of Hangzhou as an example in order to improve the prediction accuracy of the model and enhance the application value of the model.Firstly,under the assumption of multiple distributions,the AR-EGARCH model is improved into the ARMA-EGARCH model to better describe the slow decay characteristics of temperature residuals.Secondly,the PCA-ARMA-EGARCH model is constructed by using the principal component analysis(PCA)method to investigate whether the addition of various meteorological information can effectively improve the prediction accuracy of the model.The results show that,compared with the original AR-EGARCH model,the ARMA-EGARCH model can more effectively capture the dynamic change characteristics of daily average temperature.And the prediction accuracy of the PCA-ARMA-EGARCH model built on this basis can be further improved.It is expected that this paper can provide a reference for the research on temperature's forecasting model based on the pricing perspective of weather derivatives.