多分布假设下的AR-EGARCH气温预测模型研究——基于气温衍生品定价视角
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摘要
如何实现准确定价是目前气温衍生品研究领域的热点问题。已有研究表明:合适的气温预测是气温衍生品精确定价的关键,因此创新性地提出在正态分布、t分布与GED分布这三种分布假设下,构建AR-EGARCH气温预测模型,从而抓住气温波动率的非对称动态特征,并与AR-GARCH气温预测模型进行对比分析。结果显示,无论是样本内拟合,还是样本外预测,AR-EGARCH模型都具有显著的优越性,且不同城市的日平均气温数据具有不同的残差分布形态,因此得出用传统单一分布假设对不同城市进行气温预测会降低预测精度。
Accurate pricing is a hot issue in the field of temperature derivatives research.Existing literature indicates that proper temperature forecasting is the key to accurate pricing of temperature derivatives.So,AR-EGARCH temperature forecasting model is established to capture the asymmetrical dynamic characteristics of temperature volatility under three distribution hypotheses,i.e.normal distribution,t-distribution and GED distribution.Then,it is compared with the AR-GARCH temperature prediction model.As a result,AR-EGARCH model shows significant advantage both in inside sample fitness and outside sample forecasting,and the average daily temperature data of different cities have different residual distribution patterns,so it is concluded that the traditional single distribution hypothesis will reduce the accuracy of temperature forecasting in different cities.
Accurate pricing is a hot issue in the field of temperature derivatives research.Existing literature indicates that proper temperature forecasting is the key to accurate pricing of temperature derivatives.So,AR-EGARCH temperature forecasting model is established to capture the asymmetrical dynamic characteristics of temperature volatility under three distribution hypotheses,i.e.normal distribution,t-distribution and GED distribution.Then,it is compared with the AR-GARCH temperature prediction model.As a result,AR-EGARCH model shows significant advantage both in inside sample fitness and outside sample forecasting,and the average daily temperature data of different cities have different residual distribution patterns,so it is concluded that the traditional single distribution hypothesis will reduce the accuracy of temperature forecasting in different cities.
引文
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[13]Jewson S,Caballero R.The use of weather forecasts in the pricing of weather derivatives[J].Meteorological Applications,2003,10(4):377-389.
[14]Tol R S J.Autoregressive conditional heteroscedasticity in daily temperature measurements[J].Environmetrics,1996,7(1):67-75.
[15]Campbell S D,Diebold F X.Weather forecasting for weather derivatives[J].Publications of the American Statistical Association,2005,100(469):6-16.
[16]Iqelan B M.Time series modeling of monthly temperature data of Jerusalem/Palestine[J].UTM Centre for Industrial and Applied Mathematics,2015,31(2):159-176.
[17]Caporin M,Pre?J,Torro H.Model based Monte Carlo pricing of energy and temperature Quanto options[J].Energy Economics,2012,34(5):1700-1712.
[18]Franses P H,Neele J,Dijk D V.Modeling asymmetric volatility in weekly Dutch temperature data[J].Environmental Modelling&Software,2001,16(2):131-137.
[19]崔海蓉,张京波,何建敏.基于AR-EGARCH的空气气温预测模型[J].统计与信息论坛,2013(10):36-41.
[20]Benth F E,Benth J?.The vlatility of temperature and pricing of weather derivatives[J].Quantitative Finance,2007,7(5):553-561.
[21]Taib C M I C,Benth F E.Pricing of temperature index insurance[J].Review of Development Finance,2012,2(1):22-31.
[2]G?ncüA.Pricing temperature-based weather derivatives in China[J].The Journal of Risk Finance,2011,13(1):32-44.
[3]张波,刘晓倩.基于EGARCH-M模型的沪深300股指期货跨期套利研究--一种修正的协整关系[J].统计与信息论坛,2017(4):34-40.
[4]冷琦琪,王学军.银行间同业拆借利率的波动性研究[J].统计与决策,2018(2):147-151.
[5]Moreno M.Riding the temp[EB/OL].[2018-06-11].http://michael.moreno.free.fr/Documents/Ride.PDF.
[6]Carmona R.Calibrating degree day options.In:3rd Seminar on stochastic analysis,random field and applications[R].Ascona,Switzerland:Ecole Polytechnique de Lausanne,1999.
[7]陈赛霞.基于气温的天气衍生品定价及其仿真实验研究[D].长沙:湖南大学,2011.
[8]Cao M,Wei J.Pricing weather derivatives:An intuitive and practical approach[J].Risk,2000,5(11):67-70.
[9]Caballero R,Jewson S,Brix A.Long memory in surface air temperature:Detection,modeling,and application to weather derivative valuation[J].Climate Research,2002,21(2):127-140.
[10]邢周华.基于气温的天气衍生品及其定价研究[D].成都:西南财经大学,2014.
[11]曾小艳,陶建平.基于ARMA模型的气温衍生品定价研究:以武汉市为例[J].区域金融研究,2014(7):12-17.
[12]牛珊.基于时间序列方法的天气衍生品定价研究[D].哈尔滨:哈尔滨理工大学,2016.
[13]Jewson S,Caballero R.The use of weather forecasts in the pricing of weather derivatives[J].Meteorological Applications,2003,10(4):377-389.
[14]Tol R S J.Autoregressive conditional heteroscedasticity in daily temperature measurements[J].Environmetrics,1996,7(1):67-75.
[15]Campbell S D,Diebold F X.Weather forecasting for weather derivatives[J].Publications of the American Statistical Association,2005,100(469):6-16.
[16]Iqelan B M.Time series modeling of monthly temperature data of Jerusalem/Palestine[J].UTM Centre for Industrial and Applied Mathematics,2015,31(2):159-176.
[17]Caporin M,Pre?J,Torro H.Model based Monte Carlo pricing of energy and temperature Quanto options[J].Energy Economics,2012,34(5):1700-1712.
[18]Franses P H,Neele J,Dijk D V.Modeling asymmetric volatility in weekly Dutch temperature data[J].Environmental Modelling&Software,2001,16(2):131-137.
[19]崔海蓉,张京波,何建敏.基于AR-EGARCH的空气气温预测模型[J].统计与信息论坛,2013(10):36-41.
[20]Benth F E,Benth J?.The vlatility of temperature and pricing of weather derivatives[J].Quantitative Finance,2007,7(5):553-561.
[21]Taib C M I C,Benth F E.Pricing of temperature index insurance[J].Review of Development Finance,2012,2(1):22-31.
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