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The IPO Cycles in China's A-share IPO Market: Detection Based on a Three Regimes Markov Switching Model

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  • Zhiqiang HU

    (Economic and Management School of Wuhan University, Wuhan, 430072, China.)

  • Yizhu WANG

    (Economic and Management School of Wuhan University, Wuhan, 430072, China.)

Abstract

This paper expands the IPO market conditions from two states to three states, which include hot periods, cold periods and interim periods, and improves upon measures used to detect IPO market cycles given cycle strength in China’s IPO market. We use a model based on the three Markov regime switching models to conduct regressions with respective proxy variables. By analyzing regression results, filtered probability and smoothed probability, we extract ten differing IPO cycles corresponding to ten different proxy variables. Further, this paper highlights results of various IPO market cycles in China’s A-share market from January 1994 to June 2012. Results confirm the relationship between IPO market cycles and IPO numbers in addition to effects from underpricing, market conditions and government regulation. The aforementioned all enrich the theory of IPO cycles.

Suggested Citation

  • Zhiqiang HU & Yizhu WANG, 2013. "The IPO Cycles in China's A-share IPO Market: Detection Based on a Three Regimes Markov Switching Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 115-131, October.
    • Handle: RePEc:rjr:romjef:v::y:2013:i:3:p:115-131
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    More about this item

    Keywords

    Markov regime switching model; hot/interim/cold periods; IPO market cycles; A-share market; government regulation;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G30 - Financial Economics - - Corporate Finance and Governance - - - General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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