煤炭类期货收益率分布拟合与风险度量

Distribution fitting and risk metrics of coal futures returns

近年来,受煤价下跌的影响,煤炭类期货价格波动剧烈,随之而来的风险也不断加大。在这样的市场环境下,原有的正态分布的VaR模型已很难准确度量煤炭类期货的价格波动风险。因此,如何更好地更准确地度量煤炭类期货价格风险成为当前亟待解决的问题。本文在利用VaR模型的基础上,借助K-S检验逐类筛选其他可替换正态分布的假设,以更好、更准确地提高风险度量精度。K-S检验结果显示,煤炭类期货收益率服从双曲线分布;概率密度曲线图和Q-Q图显示,双曲分布比正态分布拟合效果更优;VaR计算与比较结果显示,双曲线分布VaR比正态分布VaR更接近历史VaR,且焦煤VaR大于动力煤VaR。因此,基于双曲线分布的VaR模型更适于投资者度量煤炭类期货风险,投资焦煤期货的风险大于投资动力煤。

In recent years,the influence of falling coal prices makes coal futures＇ price volatility risk increases.The market environment leads the VaR model which based on the normal distribution could not measure coal futures price risk accurately.Therefore,how to measure coal futures price risk accurately becomes a serious problem.This paper based on the VaR model,tries using K-S tests to find other distributions,which can improve the risk metrics accuracy,to replace the normal distribution assumptions.The K-S test results show that coal futures returns comply with the hyperbolic distribution;probability density curves and Q-Q figures show that the hyperbolic distribution fits the reture series better than the normal distribution;VaR calculation and comparison shows that the hyperbolic VaRs are closer to history VaRs than the normal VaRs,and coking coal futures＇ VaRs are greater than power coal futures＇ VaRs.Therefore,the VaR model based on the hyperbolic distribution is more suitable for coal futures investors to measure risk;investment risk of coking coal futures is bigger than that of power coal futures.