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On equity risk prediction and tail spillovers

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  • Panos Pouliasis
  • Ioannis Kyriakou
  • Nikos Papapostolou

Abstract

This paper studies the impact of modelling time†varying variances of stock returns in terms of risk measurement and extreme risk spillover. Using a general class of regime†dependent models, we find that volatility can be disaggregated into distinct components: a persistent stable process with low sensitivity to shocks and a high volatility process capturing rather short†lived rare events. Out†of†sample forecasts show that, once regime shifts are accounted for, accuracy is improved compared to the standard generalized autoregressive conditional heteroscedasticity or the historical volatility model. Volatility plays an important role in controlling and monitoring financial risks. Therefore, by means of a risk management application, we illustrate the economic value and the practical implications of risk control ability of the models in terms of value at risk. Finally, tests for predictability in co†movements in the tails of stock index returns suggest that large losses are strongly correlated, supporting asymmetric transmission processes for financial contagion in the left tail of return distributions, whereas contagion in reverse direction (gains) is weak.

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  • Panos Pouliasis & Ioannis Kyriakou & Nikos Papapostolou, 2017. "On equity risk prediction and tail spillovers," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 22(4), pages 379-393, October.
    • Handle: RePEc:wly:ijfiec:v:22:y:2017:i:4:p:379-393
    DOI: 10.1002/ijfe.1594
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    Cited by:

    1. Horpestad, Jone B. & Lyócsa, Štefan & Molnár, Peter & Olsen, Torbjørn B., 2019. "Asymmetric volatility in equity markets around the world," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 540-554.
    2. Yanlai Song & Stanford Shateyi & Jianying He & Xueqing Cui, 2022. "Interactions of Logistic Distribution to Credit Valuation Adjustment: A Study on the Associated Expected Exposure and the Conditional Value at Risk," Mathematics, MDPI, vol. 10(20), pages 1-15, October.
    3. Linh H. Nguyen & Linh X. D. Nguyen & Linzhi Tan, 2021. "Tail risk connectedness between US industries," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3624-3650, July.
    4. Liuguo Shao & Hua Zhang & Senfeng Chang & Ziyang Wang, 2024. "Dynamic connectedness between China's commodity markets and China's sectoral stock markets: A multidimensional analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 903-926, January.
    5. Najaf Iqbal & Elie Bouri & Guangrui Liu & Ashish Kumar, 2024. "Volatility spillovers during normal and high volatility states and their driving factors: A cross‐country and cross‐asset analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 975-995, January.
    6. Zargar, Faisal Nazir & Kumar, Dilip, 2020. "Heterogeneous market hypothesis approach for modeling unbiased extreme value volatility estimator in presence of leverage effect: An individual stock level study with economic significance analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 271-285.
    7. Nguyen, Linh Hoang & Lambe, Brendan John, 2021. "International tail risk connectedness: Network and determinants," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 72(C).
    8. Xu Gong & Boqiang Lin, 2022. "Predicting the volatility of crude oil futures: The roles of leverage effects and structural changes," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 610-640, January.

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