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Volatility risk and stock return predictability on global financial crises

Author

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  • Worawuth Kongsilp
  • Cesario Mateus

Abstract

Purpose - The purpose of this paper is to investigate the role of volatility risk on stock return predictability specified on two global financial crises: the dot-com bubble and recent financial crisis. Design/methodology/approach - Using a broad sample of stock options traded on the American Stock Exchange and the Chicago Board Options Exchange from January 2001 to December 2010, the effect of different idiosyncratic volatility forecasting measures are examined on future stock returns in four different periods (Bear and Bull markets). Findings - First, the authors find clear and robust empirical evidence that the implied idiosyncratic volatility is the best stock return predictor for every sub-period both in Bear and Bull markets. Second, the cross-section firm-specific characteristics are important when it comes to stock returns forecasts, as the latter have mixed positive and negative effects on Bear and Bull markets. Third, the authors provide evidence that short selling constraints impact negatively on stock returns for only a Bull market and that liquidity is meaningless for both Bear and Bull markets after the recent financial crisis. Practical implications - These results would be helpful to disclose more information on the best idiosyncratic volatility measure to be implemented in global financial crises. Originality/value - This study empirically analyses the effect of different idiosyncratic volatility measures for a period that involves both the dotcom bubble and the recent financial crisis in four different periods (Bear and Bull markets) and contributes the existing literature on volatility measures, volatility risk and stock return predictability in global financial crises.

Suggested Citation

  • Worawuth Kongsilp & Cesario Mateus, 2017. "Volatility risk and stock return predictability on global financial crises," China Finance Review International, Emerald Group Publishing Limited, vol. 7(1), pages 33-66, February.
  • Handle: RePEc:eme:cfripp:cfri-04-2016-0021
    DOI: 10.1108/CFRI-04-2016-0021
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    Citations

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    Cited by:

    1. Kashif Abbass & Abdul Aziz Khan Niazi & Abdul Basit & Tehmina Fiaz Qazi & Huaming Song & Halima Begum, 2021. "Uncovering Effects of Hot Potatoes in Banking System: Arresting Die-Hard Issues," SAGE Open, , vol. 11(4), pages 21582440211, December.
    2. Lv, Wendai & Qi, Jipeng, 2022. "Stock market return predictability: A combination forecast perspective," International Review of Financial Analysis, Elsevier, vol. 84(C).
    3. Abdul Aziz Khan Niazi & Suleman Aziz Lodhi & Abdul Basit & Tehmina Fiaz Qazi, 2020. "Tacit Knowledge Sharing Model For Banks: Remedial Measure Of Likelihood Of Default," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 9(1), pages 32-50, March.
    4. Lu, Fei & Ma, Feng & Guo, Qiang, 2023. "Less is more? New evidence from stock market volatility predictability," International Review of Financial Analysis, Elsevier, vol. 89(C).
    5. Wang, Yudong & Pan, Zhiyuan & Wu, Chongfeng & Wu, Wenfeng, 2020. "Industry equi-correlation: A powerful predictor of stock returns," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 1-24.
    6. Suripto & Supriyanto, 2021. "The Effect of the COVID-19 Pandemic on Stock Prices with the Event Window Approach: A Case Study of State Gas Companies, in the Energy Sector," International Journal of Energy Economics and Policy, Econjournals, vol. 11(3), pages 155-162.
    7. Tissaoui, Kais, 2019. "Forecasting implied volatility risk indexes: International evidence using Hammerstein-ARX approach," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 232-249.
    8. Tissaoui, Kais & Zaghdoudi, Taha, 2021. "Dynamic connectedness between the U.S. financial market and Euro-Asian financial markets: Testing transmission of uncertainty through spatial regressions models," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 481-492.
    9. Qiu, Rui & Liu, Jing & Li, Yan, 2023. "Long-term adjusted volatility: Powerful capability in forecasting stock market returns," International Review of Financial Analysis, Elsevier, vol. 86(C).
    10. Gong, Xu & Lin, Boqiang, 2018. "Structural changes and out-of-sample prediction of realized range-based variance in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 27-39.
    11. Xie, Nan & Wang, Zongrun & Chen, Sicen & Gong, Xu, 2019. "Forecasting downside risk in China’s stock market based on high-frequency data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 530-541.
    12. Yongsheng Yi & Feng Ma & Dengshi Huang & Yaojie Zhang, 2019. "Interest rate level and stock return predictability," Review of Financial Economics, John Wiley & Sons, vol. 37(4), pages 506-522, October.
    13. Zhuang, Chunjuan, 2018. "Improving performance of exchange rate momentum strategy using volatility information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 741-753.
    14. Hassan Zada & Arshad Hassan & Wing-Keung Wong, 2021. "Do Jumps Matter in Both Equity Market Returns and Integrated Volatility: A Comparison of Asian Developed and Emerging Markets," Economies, MDPI, vol. 9(2), pages 1-26, June.
    15. Diaz, Juan & Duarte, Diogo & Galindo, Hamilton & Montecinos, Alexis & Truffa, Santiago, 2021. "The importance of large shocks to return predictability," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    16. Zhang, Chuanhai & Liu, Zhi & Liu, Qiang, 2021. "Jumps at ultra-high frequency: Evidence from the Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    17. Rialdi Azhar & Fajrin Satria Dwi Kesumah & Ambya Ambya & Febryan Kusuma Wisnu & Edwin Russel, 2020. "Application of Short-term Forecasting Models for Energy Entity Stock Price (Study on Indika Energi Tbk, JII)," International Journal of Energy Economics and Policy, Econjournals, vol. 10(1), pages 294-301.

    More about this item

    Keywords

    Options; Stock; Volatility; Risk premium; G10; G12; C53;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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