doi:

DOI: 10.3724/SP.J.1249.2018.04432

Journal of Shenzhen University Science and Engineering (深圳大学学报理工版) 2018/35:4 PP.432-440

Parameter estimation via regime switching model for high frequency data


Abstract:
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.

Key words:application of statistical mathematics,high frequency data,regime switching,filtering algorithm,Kim smoothing algorithm,parameter estimation

ReleaseDate:2018-07-26 10:51:04



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