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Tail Risks and Stock Return Predictability: Evidence From Asia-Pacific

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  • Ogbonna, Ahamuefula
  • Olubusoye, Olusanya E

Abstract

Hinging on the recently established relevance of tail thickness information, we examine the predictability of fifteen major stocks in the Asia-Pacific region using conditional autoregressive value at risk (CAViaR) model estimates of tail risks. We used a Westerlund and Narayan–type distributed lag model to examine the nexus between returns and tail risk under controlled global and US stocks spillover effects. Country-specific tail risks induce a near-term rise (completely disappears) in returns on “bad” (“good”) days. Our results are robust.

Suggested Citation

  • Ogbonna, Ahamuefula & Olubusoye, Olusanya E, 2021. "Tail Risks and Stock Return Predictability: Evidence From Asia-Pacific," MPRA Paper 109922, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:109922
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    References listed on IDEAS

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    1. Joakim Westerlund & Paresh Narayan, 2015. "Testing for Predictability in Conditionally Heteroskedastic Stock Returns," Journal of Financial Econometrics, Oxford University Press, vol. 13(2), pages 342-375.
    2. Long, Huaigang & Jiang, Yuexiang & Zhu, Yanjian, 2018. "Idiosyncratic tail risk and expected stock returns: Evidence from the Chinese stock markets," Finance Research Letters, Elsevier, vol. 24(C), pages 129-136.
    3. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    4. Salisu, Afees A. & Vo, Xuan Vinh, 2020. "Predicting stock returns in the presence of COVID-19 pandemic: The role of health news," International Review of Financial Analysis, Elsevier, vol. 71(C).
    5. Salisu, Afees A. & Gupta, Rangan & Ji, Qiang, 2022. "Forecasting oil prices over 150 years: The role of tail risks," Resources Policy, Elsevier, vol. 75(C).
    6. Long, Huaigang & Zhu, Yanjian & Chen, Lifang & Jiang, Yuexiang, 2019. "Tail risk and expected stock returns around the world," Pacific-Basin Finance Journal, Elsevier, vol. 56(C), pages 162-178.
    7. Westerlund, Joakim & Narayan, Paresh Kumar, 2012. "Does the choice of estimator matter when forecasting returns?," Journal of Banking & Finance, Elsevier, vol. 36(9), pages 2632-2640.
    8. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    9. Yaya, OlaOluwa S & Ogbonna, Ephraim A, 2019. "Do we Experience Day-of-the-week Effects in Returns and Volatility of Cryptocurrency?," MPRA Paper 91429, University Library of Munich, Germany.
    10. Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna, 2023. "Tail risks and forecastability of stock returns of advanced economies: evidence from centuries of data," The European Journal of Finance, Taylor & Francis Journals, vol. 29(4), pages 466-481, March.
    11. Chevapatrakul, Thanaset & Xu, Zhongxiang & Yao, Kai, 2019. "The impact of tail risk on stock market returns: The role of market sentiment," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 289-301.
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    Cited by:

    1. Salisu, Afees A. & Olaniran, Abeeb & Tchankam, Jean Paul, 2022. "Oil tail risk and the tail risk of the US Dollar exchange rates," Energy Economics, Elsevier, vol. 109(C).

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    More about this item

    Keywords

    Conditional Autoregressive Value at Risk; Predictability; Returns; Tail Thickness;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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