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Hot money and China’s stock market volatility: Further evidence using the GARCH–MIDAS model

Author

Listed:
  • Wei, Yu
  • Yu, Qianwen
  • Liu, Jing
  • Cao, Yang

Abstract

This paper investigates the influence of hot money on the return and volatility of the Chinese stock market using a nonlinear Granger causality test and a new GARCH-class model based on mixed data sampling regression (GARCH–MIDAS). The empirical results suggest that no linear or nonlinear causality exists between the growth rate of hot money and the Chinese stock market return, implying that the Chinese stock market is not driven by hot money and vice versa. However, hot money has a significant positive impact on the long-term volatility of the Chinese stock market. Furthermore, the dependence between the long-term volatility caused by hot money and the total volatility of the Chinese stock market is time-variant, indicating that huge volatilities in the stock market are not always triggered by international speculation capital flow and that Chinese authorities should further focus on more systemic reforms in the trading rules and on effectively regulating the stock market.

Suggested Citation

  • Wei, Yu & Yu, Qianwen & Liu, Jing & Cao, Yang, 2018. "Hot money and China’s stock market volatility: Further evidence using the GARCH–MIDAS model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 923-930.
    • Handle: RePEc:eee:phsmap:v:492:y:2018:i:c:p:923-930
    DOI: 10.1016/j.physa.2017.11.022
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    Cited by:

    1. Zhang, Li & Liang, Chao & Huynh, Luu Duc Toan & Wang, Lu & Damette, Olivier, 2024. "Measuring the impact of climate risk on renewable energy stock volatility: A case study of G20 economies," Journal of Economic Behavior & Organization, Elsevier, vol. 223(C), pages 168-184.
    2. Mei Tang & Jie Wang & Jianping Lu & Guiwu Wei & Cun Wei & Yu Wei, 2019. "Dual Hesitant Pythagorean Fuzzy Heronian Mean Operators in Multiple Attribute Decision Making," Mathematics, MDPI, vol. 7(4), pages 1-27, April.
    3. Zhu, Sha & Liu, Qiuhong & Wang, Yan & Wei, Yu & Wei, Guiwu, 2019. "Which fear index matters for predicting US stock market volatilities: Text-counts or option based measurement?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    4. Zeng, Sheng & Liu, Xinchun & Li, Xiafei & Wei, Qi & Shang, Yue, 2019. "Information dominance among hedging assets: Evidence from return and volatility directional spillovers in time and frequency domains," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    5. D’Amato, Valeria & Levantesi, Susanna & Piscopo, Gabriella, 2022. "Deep learning in predicting cryptocurrency volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    6. Wang, Yilin & Zhang, Zeming & Li, Xiafei & Chen, Xiaodan & Wei, Yu, 2020. "Dynamic return connectedness across global commodity futures markets: Evidence from time and frequency domains," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).

    More about this item

    Keywords

    Hot money; Chinese stock market; Nonlinear granger causality test; GARCH–MIDAS; Long-term volatility; Real estate market;
    All these keywords.

    JEL classification:

    • F32 - International Economics - - International Finance - - - Current Account Adjustment; Short-term Capital Movements
    • F65 - International Economics - - Economic Impacts of Globalization - - - Finance
    • G01 - Financial Economics - - General - - - Financial Crises
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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