时变最优套期保值比估计及比较研究——基于卡尔曼滤波在状态空间模型中的应用

Evaluation and comparison of time-variant optimal hedging ratio:Based on the use of Kalman filter in state space model

运用状态空间模型并基于卡尔曼滤波方法对中国铜期货市场时变最优套期保值比进行估计.对OLS、VAR、VECM、CC-GARCH及SSPACE等模型的套期保值效率进行了比较.套期保值效率分别用方差下降百分比和夏普比下降百分比来测度.两种测度方法都表明,基于卡尔曼滤波的状态空间模型明显优于其他模型.该结论对于套期保值期限是稳定的.GARCH模型并不确定优于非时变模型.非时变模型中,VECM模型的表现最差.而VAR模型也并不明显优于简单的OLS模型.计量经济模型预测总风险由模型(误设)风险和估计风险构成.高级计量经济模型的模型(误设)风险较小,估计风险增大,总效应则不确定.卡尔曼滤波获得贝叶斯规则最优解,因而在处理估计风险方面较其他模型占优.

Based on Kalman filter, this article use state space model to estimate time varying optimal hedge ratio of china＇ s copper futures market and compare hedge performance of it with that of CC-GARCH model, VECM model, VAR model and OLS model. Hedging effectiveness is measured using the percentage of variance reduction and the percentage of sharp ratio reduction. We find that in terms of the two different measurement of hedging effectiveness, state space model based on Kalman filter perform significantly better than other models. The conclusion is robust to hedge periods. The results of the comparison of dynamic CC-GARCH model with static models depend on the duration of the hedge. VECM model perform worst and the hedging performance of VAR model does not significantly surpass that of simple OLS model. The risk of econometric models includes model-misspeeification risk and estimation risk. Although the model-misspecification risk of advanced econometric model may be smaller than simple models, its estimation risk is greater and the total risk is uncertain. We find the solution of Kalman filter agrees with Bayesian rules, as suggestes Kalman filter approach outperform other models in dealing with estimation risk.