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Regime switching in the presence of endogeneity

Author

Listed:
  • Tingting Cheng
  • Jiti Gao
  • Yayi Yan

Abstract

In this paper, we propose a state-varying endogenous regime switching model (the SERS model), which includes the endogenous regime switching model by Chang et al. (2017), the CCP model, as a special case. To estimate the unknown parameters involved in the SERS model, we propose a maximum likelihood estimation method. Monte Carlo simulation results show that in the absence of state-varying endogeneity, the SERS model and the CCP model have similar performance, while in the presence of state-varying endogeneity, the SERS model performs much better than the CCP model. Finally, we use the SERS model to analyze the China stock market returns and our empirical results show that there exists strongly state-varying endogeneity in volatility switching for the Shanghai Composite Index returns. Moreover, the SERS model can indeed produce a much more realistic assessment for the regime switching process than the one obtained by the CCP model.

Suggested Citation

  • Tingting Cheng & Jiti Gao & Yayi Yan, 2018. "Regime switching in the presence of endogeneity," Monash Econometrics and Business Statistics Working Papers 9/18, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2018-9
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    File URL: https://www.monash.edu/business/ebs/research/publications/wp09-2018.pdf
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    More about this item

    Keywords

    latent factor; maximum likelihood estimation; Markov chain; regime switching models; state-varying endogeneity.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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