中文摘要：

本文对近十五年多达17万笔高频交易数据研究发现，早晨9：30股市开盘期间收益回报显著为负值，而在下午3：00收盘前的5分钟集合竞价阶段的收益回报显著为正值，称这种现象为“首尾5分钟现象”。并且日内收益数据具有较为显著的季节效应或周期效应，本文首次提出利用具有季节效应的SVJt—S模型对上证综合指数的5分钟高频交易数据进行建模，并给出模型的两步估计方法。由于高频随机波动建模时的数据量巨大、计算负荷严重，模型的估计、评价以及预测评价方法都需进行相应的改进，本文主要通过APF方法计算边际似然和BF进行模型比较，并从模型的预测能力发现本文给出的具有季节效应的SVJt-s模型，优于通常的GARCH模型和基本随机波动模型，最后给出了模型在风险管理中的应用。

英文摘要：

This paper analyzed more than 170,000 high frequency Chinese stock-market transaction data for the recent 15 years, our study finds that during morning 9：30 open return is significantly negative, while five minutes before the close call auction 3：00 PM stage significantly positive returns which is named as the phenomenon of fore and after 5 minutes. As the data has significant seasonal effect （ cyelicality effect）, we consider the effect of season SVJt modeling and provide the two-step estimation method of this model. The high frequency stochastic volatility model has the feature of huge data and serious computational load etc. , the method of estimating, fitting and prediction evaluation the model should be made corresponding improvement. We calculate the marginal likelihood by the APF and make comparison with Bayesian factor methods. In the aspect of forecasting ability, we find that the seasonal effect SVJt-s model is better than the GARCH models and basic stochastic volatility models.