摘要
随机条件持续期(SCD)模型能有效刻画超高频时间序列中持续期的变化,但该模型为了确保条件均值的非负性将条件均值函数形式固定为对数形式.文章基于Box-Cox变换,弱化了非负条件限制,提出形式上较为灵活的Box-Cox SCD模型,可以根据数据本身选择最适合的均值函数形式.模型变得更加灵活的同时也给参数估计带来许多困难,文章利用MCMC方法来估计模型参数.最后,基于TEACD(1,1)模型生成的模拟数据以及沪深300指数期货的价格持续期数据,将Box-Cox SCD模型与SCD模型的预测效果进行比较.实证表明,无论是对于模拟数据还是实际数据,价格持续期具有较强的聚集性,Box-Cox SCD模型中的参数δ与0都有很大程度的偏离,这说明SCD模型将条件均值方程设定为固定的对数形式不甚合理.
The SCD model can effectively describe the changes of the durations in the ultra-high time series, but it fixes the logarithmic form for conditional mean function to avoid the negative conditional duration. This paper weakened the restriction of the non-negativity and proposed a Box-Cox SCD model based on the Box-Cox transformation. This new type of SCD model is more flexible, and it can find the most suitable conditional mean function. However, the flexibility is the cost of the complexity of the estimation of the parameters. This paper presented an MCMC estimation and compared the predictions of the Box-Cox SCD model and the SCD model based on the simulated data generated by the TEACD （ 1,1 ） model and by the empirical data of the IF1012 index futures. The empirical study shows that there is obvious clustering in the price durations, and the value of in the Box-Cox SCD model is obviously different from 0, which implies that the logarithmic form of the conditional mean in the SCD model is not reasonable.
出处
《管理科学学报》
CSSCI
北大核心
2014年第1期86-94,共9页
Journal of Management Sciences in China
基金
国家自然科学基金资助项目(71071034)
973计划专题资助项目(2010CB328104-02)
