摘要
为检测我国商品期货隔夜信息对日间交易的预测能力,在构建不同分布随机波动模型的基础上,本文采用贝叶斯MCMC模拟技术对我国铜、铝、大豆和小麦市场进行了实证分析,研究结果显示:与正态分布、学生分布和广义误差分布相比,基于混合正态分布的随机波动模型能更好地刻画隔夜信息对日间交易的影响.从实证结果看,总隔夜收益对日间收益及其波动均具有显著的预测能力;对不同交易品种而言,其预测方向及其程度均存在一定差异.更具体地,交易当晚、周末假日和中长假日收益对日间收益及其波动均具有显著的预测能力,且比总隔夜收益的预测能力明显增强.并且,交易当晚、周末假日和中长假日收益对日间收益及其波动的预测能力呈现出不同程度的非对称性,即除大豆和小麦市场的中长假日收益对日间收益具有一定程度的反杠杆效应外,其它市场的交易当晚、周末假日和中长假日收益日间收益及其波动的影响均具有杠杆效应.
To investigate the predictive ability of overnight information on daytime trading in Chinese commodity futures market, this paper gives the stochastic volatility models based on normal, student-t, generalized error and the mixture of normal distributions respectively. Then, using Bayesian Markov Chain Monte Carlo （MCMC） estimation techniques, the empirical analyses are given for copper, aluminum, soybean and wheat futures markets. The results show that the stochastic volatility models with the mixture of normal distributions can better fit the stochastic volatility with normal, student-t, generalized error distributions in describing the impact of overnight information on daytime trading prices. Total overnight information shows significant predictive ability for daytime returns and volatility. Moreover, the aspects and degrees of prediction are different for different futures contracts. Furthermore, weeknight returns, weekend returns and medium-long holiday returns show prominent predictive abilities for daytime returns and volatility. Their predictive abilities are evidently stronger than those of the total overnight information, and show different degrees of asymmetry. Concretely, there are leverage effects for the impact of overnight information during the weeknights, weekends and medium-long holidays on the daytime returns and volatility in most futures contracts, except for inverse leverage effects of medium-long holiday in both soybean and wheat futures contracts.
出处
《管理科学学报》
CSSCI
北大核心
2013年第11期81-94,共14页
Journal of Management Sciences in China
基金
国家自然科学基金资助项目(71073026
71073025)
复旦大学金融研究中心高端学术课题(2012FDFRCGD17)
上海哲学社会科学规划项目(2010BJB015)
关键词
期货市场
隔夜信息
随机波动
贝叶斯MCMC
futures market
overnight information
stochastic volatility
Bayesian MCMC
作者简介
刘庆富（1973～），男，山东临沂人，博士，副教授．Email：liuqf@fudan．edu．cn
