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Extreme asymmetric volatility: Stress and aggregate asset prices


  • Sofiane Aboura

    (DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres - CNRS - Centre National de la Recherche Scientifique)

  • Niklas Wagner

    (Passau University - Passau University)


Asymmetric volatility in equity markets has been widely documented in finance (Bekaert and Wu, 2000)). We study asymmetric volatility for daily S&P 500 index returns and VIX index changes, thereby examining the relation between extreme changes in risk-neutral volatility expectations, i.e. market stress, and aggregate asset prices. To this aim, we model market returns, implied VIX market volatility and volatility of volatility, showing that the latter is asymmetric in that past positive volatility shocks drive positive shocks to volatility of volatility. Our main result documents the existence of a significant extreme asymmetric volatility effect as we find contemporaneous volatility-return tail dependence for crashes but not for booms. We then outline aggregate market price implications of extreme asymmetric volatility, indicating that under volatility feedback a one-in-a-hundred trading day innovation to average VIX implied volatility, for example, relates to an expected market drop of more than 4 percent.

Suggested Citation

  • Sofiane Aboura & Niklas Wagner, 2015. "Extreme asymmetric volatility: Stress and aggregate asset prices," Post-Print hal-01275450, HAL.
  • Handle: RePEc:hal:journl:hal-01275450
    DOI: 10.1016/j.intfin.2015.12.004
    Note: View the original document on HAL open archive server:

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    Cited by:

    1. Yiguo Sun & Ximing Wu, 2018. "Leverage and Volatility Feedback Effects and Conditional Dependence Index: A Nonparametric Study," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 11(2), pages 1-1, June.
    2. Alexandra Horobet & Lucian Belascu & Ștefania Cristina Curea & Alma Pentescu, 2019. "Ownership Concentration and Performance Recovery Patterns in the European Union," Sustainability, MDPI, Open Access Journal, vol. 11(4), pages 1-1, February.
    3. Lu Wang & Feng Ma & Guoshan Liu, 2020. "Forecasting stock volatility in the presence of extreme shocks: Short‐term and long‐term effects," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 797-810, August.
    4. Dutta, Anupam, 2018. "Oil and energy sector stock markets: An analysis of implied volatility indexes," Journal of Multinational Financial Management, Elsevier, vol. 44(C), pages 61-68.
    5. Bollerslev, Tim & Xu, Lai & Zhou, Hao, 2015. "Stock return and cash flow predictability: The role of volatility risk," Journal of Econometrics, Elsevier, vol. 187(2), pages 458-471.
    6. Ushir HARRILALL & Yudhvir SEETHARAM, 2015. "Forecasting changes in the South African volatility index: A comparison of methods," EuroEconomica, Danubius University of Galati, issue 2(34), pages 51-70, November.
    7. Silva, Walmir & Kimura, Herbert & Sobreiro, Vinicius Amorim, 2017. "An analysis of the literature on systemic financial risk: A survey," Journal of Financial Stability, Elsevier, vol. 28(C), pages 91-114.


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