市道轮换下的高频数据参数估计

Parameter estimation via regime switching model for high frequency data

在高频金融数据研究分析中引入市道轮换模型,结合自回归模型和波动率替代模型,对高频数据进行建模分析,运用滤波算法以及Kim平滑算法等进行参数估计和预测.利用2017-01-03至2017-08-02上证综指每5 min的收盘价格,用Matlab实现马氏市道轮换自回归模型的预测和推测,并构建波动率替代模型.结果表明,马氏市道轮换高频数据模型是一种具有模型创新且理论性强的分析方式.

To analyze high-frequency financial data,we use the regime switching method combined with the autoregressive model and volatility replacement model. We perform modelling analysis for high frequency data and use filtering algorithm and Kim smoothing algorithm to perform parameter estimation and prediction. By using the closing prices of each 5 minutes of the 2017-01-03 to 2017-08-02 Shanghai Composite Index,we achieve regime-switching autoregressive model of prediction and speculation,and construct the volatility replacement model. The results show that the Markov high frequency data model is an innovation model and an analysis method with strong theoretical background.