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Detecting the Structural Breaks in GARCH Models Based on Bayesian Method: The Case of China Share Index Rate of Return

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  • Li Qiang
  • Wang Liming
  • Qiu Fei

    (School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai200433, China)

Abstract

This paper investigates the detection for structural breaks in GARCH models based on Bayesian method. The authors firstly introduce the background and significance of this problem, then present the current situation and recent developments in this field. Because the rates of return have heavy tails, the authors present GARCH models. In this paper, the authors innovatively suppose that the error term follows standard student t distribution with degree of freedom v instead of standard normal distribution. The authors give the specific description of estimation using Bayesian method, including a single structural break situation and multiple structural breaks situation when the number of breaks is unknown. In an application, the authors empirically research the volatility of stock market in China. The authors estimate GARCH models with structural breaks for the Shanghai Α-share index and Shenzhen Α-share index rate of return over the period of January 4, 2000–September 30, 2011. The authors explain the breaks together with the nearby big political and economic events. Empirical results show that the detecting method used in this paper is feasible.

Suggested Citation

  • Li Qiang & Wang Liming & Qiu Fei, 2015. "Detecting the Structural Breaks in GARCH Models Based on Bayesian Method: The Case of China Share Index Rate of Return," Journal of Systems Science and Information, De Gruyter, vol. 3(4), pages 321-333, August.
    • Handle: RePEc:bpj:jossai:v:3:y:2015:i:4:p:321-333:n:3
    DOI: 10.1515/JSSI-2015-0321
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    References listed on IDEAS

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