CAJ | 학술논문

天气衍生品的出现,为天气风险管理提供了一种新的手段.由于大多数天气衍生品都是以气温为标的物,因此对气温变化准确预测是天气衍生品定价的关键.针对此,本文在O-U均值回复模型的基础上,构建了时变均值回复速度的气温预测模型.基于全国代表性城市的1951-2011年日平均气温数据对气温均值回复速度进行了实证分析,研究表明,我国主要代表性城市气温年均值回复速度均能通过ARIMA模型进行拟合估计.利用两种模型对各城市的日平均气温进行预测分析,其误差衡量指标的协方差比表明,时变均值回复模型优于O-U均值回复模型.因此,本文提出的时变均值回复速度的气温预测模型适用于我国的气温预测,有利于我国天气衍生品的进一步研究.
천기연생품적출현,위천기풍험관리제공료일충신적수단.유우대다수천기연생품도시이기온위표적물,인차대기온변화준학예측시천기연생품정개적관건.침대차,본문재O-U균치회복모형적기출상,구건료시변균치회복속도적기온예측모형.기우전국대표성성시적1951-2011년일평균기온수거대기온균치회복속도진행료실증분석,연구표명,아국주요대표성성시기온년균치회복속도균능통과ARIMA모형진행의합고계.이용량충모형대각성시적일평균기온진행예측분석,기오차형량지표적협방차비표명,시변균치회복모형우우O-U균치회복모형.인차,본문제출적시변균치회복속도적기온예측모형괄용우아국적기온예측,유리우아국천기연생품적진일보연구.
The purpose of weather derivatives is to allow businesses and other organizations,which are affected by the weather,to insure themselves against weather changes.Most of researches about weather derivatives mainly focus on temperature derivatives based on HDD and CDD indices.Hence,forecasting the trend of temperature accurately is critical to pricing the weather derivatives.Therefore,this paper mainly studies the forecasting temperature in pricing weather derivatives.In the first part,we give a short overview of weather derivatives including their concepts,the application prospects and the research trend.We further summarize recent studies using the O-U mean-reverting model to price temperature derivatives.The previous studies based on the O-U mean-reverting model considered the mean-reverting speed to be a fixed constant.However,in real life human activity,solar activity,atmospheric changes and other factors will have impact on the climate,thereby affecting the temperature mean-reverting effect.To forecast the change of the temperature more accurately,we considered the mean-reverting speed to change over time.Therefore,this paper proposes a new forecasting temperature model which is called Time-varying mean-reverting model based on the O-U process.The unknown parameters in the model are estimated using historical temperature data.Since we only have discrete observations,the estimation of mean-reverting speed in the model is based on the use of martingale estimation functions.The quadratic variation of the temperature＇s volatility varies across different months of the year,but nearly constant within each month.In the second part,to obtain the characteristic of the mean-reverting speed we analyzed the data in each year from 1951 to 2011 collected from China＇s main cities,including Nanjing,Hangzhou,Wuhan and Beijing.The ADF test of Wuhan and Hangzhou shows that their annual mean-reverting speed sequences are stationary series,and can be further treated as white noise sequence.After firstorder difference processing,Beijing and Wuhan＇s annual mean-reverting sequence turns out to be stationary series,further modeled by AR （2） and ARMA （1,2） according to the AIC criterion of Schwarz criteria,parameters significantly test,and the white noise test for residuals.With the same analysis method,the annual mean-reverting speed in Chinese main cities can be modeled by ARIMA timeseries with different orders.In the third part,we compared our model with the O-U mean-reverting process to forecast the temperature of the main cities in 2011 based on the data from 1951 to 2010.According to the measure error covariance which measures the non-systematic errors,timevarying mean-reverting model is superior to the O-U mean-reverting process.According to the correlation coefficient of the meanreverting sequence between two related cities in Nanjing,Hangzhou,Wuhan and Beijing,we can conclude that the trend of meanreverting within the same latitude is similar.Therefore,we confirm that the time-varying mean-reverting model is applicable in most areas of China.In summary,the temperature prediction model in this paper can reflect Chinese actual situations.Therefore,it can provide suggestions for the development of weather derivatives in China.Furthermore,the time-varying mean-reverting model could be used to forecast the temperature more accurately so that the pricing model about the temperature derivatives can better reflect the reality.