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A Higher-Order Markov-Switching Autorgerssion Model with It's Application
Chen Guangxi,Rong Tengzhong *
College of Mathematics and Statistics, Chongqing University, Chongqing 401331
*Correspondence author
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Funding: The Fundamental Research Funds for Central University（No.CQDXWL-2013-Z009）
Opened online: 1 March 2017
Accepted by: none
Citation: Chen Guangxi,Rong Tengzhong.A Higher-Order Markov-Switching Autorgerssion Model with It's Application[OL]. [ 1 March 2017] http://en.paper.edu.cn/en_releasepaper/content/4719731
 
 
The property of higher order is introduced into the classic Markov switching autoregression model in this paper. We use the technical of integrated higher order states to obtain small order expression for the newly model. Then we determine the order selection for autoregression part according to AIC criterion, and propose a likelihood function under the assumption of normal distribution to obtain estimation of higher order model parameters. Our model is practiced to analyze the stock return time series, and the numerical results show it can identify the range of periodic fluctuations of return series conveniently, and depict tiny characteristic of fluctuations effectively.
Keywords:Higher order Markov switching; Autoregression model; AIC criterion; Maximum Likelihood Estimation; Return rate time series
 
 
 

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