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Market momentum amplifies market volatility risk: Evidence from China’s equity market

Author

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  • Liang, Chao
  • Huynh, Luu Duc Toan
  • Li, Yan

Abstract

We examine the role of a belief-based momentum indicator, measured by conditional past returns (CPR), in the realized volatility (RV) predictability of equity markets. Based on the week- and month-horizon CPR, we construct the HAR-CPR and HAR-LCPR models on the basis HAR-RV model. Here, the HAR-LCPR model additionally includes the daily leverage factor in the absence of daily CPR. In China, we find that: 1) week- and month-horizon CPR have significantly positive impacts on one-, five-, and 22-days-ahead RVs; 2) our out-of-sample results further indicate that the HAR-LCPR model performs best in forecasting one- and five-days-ahead RVs, whereas the HAR-CPR model is a more reliable forecasting model for 22-days-ahead volatility; 3) the performance also passes various robustness tests, including sub-period performance testing, alternative training rolling window, and alternative RV estimation. We show the economic mechanism underlying the predictive role of CPR from the perspective of investors’ trading activities.

Suggested Citation

  • Liang, Chao & Huynh, Luu Duc Toan & Li, Yan, 2023. "Market momentum amplifies market volatility risk: Evidence from China’s equity market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
  • Handle: RePEc:eee:intfin:v:88:y:2023:i:c:s1042443123001245
    DOI: 10.1016/j.intfin.2023.101856
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    More about this item

    Keywords

    Volatility forecasting; Realized volatility; Conditional past returns; HAR; Leverage effect; COVID-19; Global financial crisis;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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