主管：中国科学院
主办：中国优选法统筹法与经济数学研究会
　　　中国科学院科技战略咨询研究院
中国管理科学

中国管理科学 ›› 2009, Vol. 17 ›› Issue (4): 21-29.

• 论文 • 上一篇    下一篇

套期保值, 估计风险与贝叶斯统计——基于中国铜期货市场的经验研究

付剑茹1, 张宗成2   

  1. 1. 云南财经大学统计与数学学院, 云南昆明650221;
    2. 华中科技大学经济学院, 湖北武汉430074
  • 收稿日期:2008-12-05 修回日期:2009-07-08 出版日期:2009-08-30 发布日期:2009-08-30
  • 作者简介:付剑茹(1974- ),男(汉族),江西高安人,华中科技大学经济学院金融工程研究所,数量经济学博士研究生,副教授,研究方向:金融计量及金融工程.

Futures Hedging, Risk and Estimation Bayesian Statistics——Based on Empirical Research In China’s Copper Futures Market

FU Jian-ru1, ZHANG Zong-cheng2   

  1. 1. School of Statistics&Mathematics, Yunnan University of Fiuance and Economics, Qunming 650221, China;
    2. School of Economic, Huazhong University of Science and Technology, Wuhan 430074, China
  • Received:2008-12-05 Revised:2009-07-08 Online:2009-08-30 Published:2009-08-30

PDF (PC)

1565

摘要： 针对期货最优套期保值策略估计中可能存在的估计风险问题,本文对单变量线性回归模型(OLS模型)和多变量线性回归模型(VAR模型和EC-VAR模型)进行贝叶斯分析,并采用Gibbs抽样方法对中国铜期货市场的最优套期保值策略进行了实证分析。本文还同时估计了基于频率统计方法的最优套期保值策略,并对贝叶斯统计下和频率统计下的最优套期保值策略进行了分析比较。实证结果清楚表明,估计风险对模型结果有重要影响。在处理估计风险方面,贝叶斯统计较频率统计方法有明显优势。

关键词: 套期保值, 估计风险, 贝叶斯统计, Gibbs抽样, 频率统计

Abstract: The risk of econometric models includes model-misspecification risk and estimation risk.Backward-looking econometric models based on frequentist statistics doesn't account for the existence of estimation risk. The Bayesian approach provides a general framework where estimation risk is naturally accounted for when considering the parameters as random variable. This article uses Bayesian approach based on MCMC simulation to estimate the optimal hedge ratio of China's copper futures market. The performance of the Bayesian hedge ratios is compared to that of alternative frequentist statistics approach.The Bayesian empirical result indicates EC-VAR model performs best and the hedging performance of VAR model significantly surpasses that of simple OLS model.On the contrary,if not accounting for estimation risk,EC-VAR model performs worst and the hedging performance of VAR model down't significantly surpass that of OLS model.

Key words: futures hedging,estimation risk, bayesian statistics, gibbs sampler frequentist statistics

中图分类号: