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Modelling asset returns in the presence of price limits with Markov-switching mixture of truncated normal GARCH distribution: evidence from China

Author

Listed:
  • Donghua Wang
  • Jin Ding
  • Guoqing Chu
  • Dinghai Xu
  • Tony S. Wirjanto

Abstract

This article proposes a general framework of a Markov Switching GARCH model with a mixture of truncated Gaussian to model asset returns with price limits in China. Theoretically, while retaining many convenient statistical properties of the Gaussian distribution, the proposed model also assumes a flexible time-varying volatility structure to accommodate the feature of the return data under price restrictions in China, such as the clusters near the bounds (due to the ‘bound effect’). Empirically, we apply the model to eight representative stocks from Shanghai and Shenzhen stock markets in China. Lastly, we find that our proposed model dominates the conventional volatility models in terms of Value-at-Risk measures.

Suggested Citation

  • Donghua Wang & Jin Ding & Guoqing Chu & Dinghai Xu & Tony S. Wirjanto, 2021. "Modelling asset returns in the presence of price limits with Markov-switching mixture of truncated normal GARCH distribution: evidence from China," Applied Economics, Taylor & Francis Journals, vol. 53(7), pages 781-804, February.
    • Handle: RePEc:taf:applec:v:53:y:2021:i:7:p:781-804
    DOI: 10.1080/00036846.2020.1814946
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    Cited by:

    1. Amaro, Raphael & Pinho, Carlos & Madaleno, Mara, 2022. "Forecasting the Value-at-Risk of energy commodities: A comparison of models and alternative distribution functions," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 65, pages 77-101.
    2. Kai Zheng & Weidong Xu & Xili Zhang, 2023. "Multivariate Regime Switching Model Estimation and Asset Allocation," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 165-196, January.
    3. Amaro, Raphael & Pinho, Carlos, 2022. "Energy commodities: A study on model selection for estimating Value-at-Risk," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 68, pages 5-27.

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