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Forecasting Equity Index Volatility by Measuring the Linkage among Component Stocks

Author

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  • Qiu, Yue

    (WISE and School of Economics, Xiamen University)

  • Xie, Tian

    (School of Economics, Singapore Management University)

  • Yu, Jun

    (School of Economics and Lee Kong Chian School of Business, Singapore Management University)

  • Zhou, Qiankun

    (Department of Economics, Louisiana State University)

Abstract

The linkage among the realized volatilities across component stocks are important when modeling and forecasting the relevant index volatility. In this paper, the linkage is measured via an extended Common Correlated Effects (CCE) approach under a panel heterogeneous autoregression model where unobserved common factors in errors are assumed. Consistency of the CCE estimator is obtained. The common factors are extracted using the principal component analysis. Empirical studies show that realized volatility models exploiting the linkage effects lead to significantly better out-of-sample forecast performance, for example, an up to 32% increase in the pseudo R2. We also conduct various forecasting exercises on the the linkage variables that compare conventional regression methods with popular machine learning techniques.

Suggested Citation

  • Qiu, Yue & Xie, Tian & Yu, Jun & Zhou, Qiankun, 2019. "Forecasting Equity Index Volatility by Measuring the Linkage among Component Stocks," Economics and Statistics Working Papers 7-2019, Singapore Management University, School of Economics.
    • Handle: RePEc:ris:smuesw:2019_007
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    Cited by:

    1. Chao Liang & Yongan Xu & Zhonglu Chen & Xiafei Li, 2023. "Forecasting China's stock market volatility with shrinkage method: Can Adaptive Lasso select stronger predictors from numerous predictors?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 3689-3699, October.

    More about this item

    Keywords

    Volatility Forecasting; Heterogeneous autoregression; Common correlated effect; Factor analysis; Random forest;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • 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
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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