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The asymmetric relationship between returns and implied volatility: Evidence from global stock markets

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  • Bekiros, Stelios
  • Jlassi, Mouna
  • Naoui, Kamel
  • Uddin, Gazi Salah

Abstract

We investigate the asymmetric relationship between returns and implied volatility for 20 developed and emerging international markets. In particular we examine how the sign and size of return innovations affect the expectations of daily changes in volatility. Our empirical findings indicate that the conditional contemporaneous return-volatility relationship varies not only based on the sign of the expected returns but also upon their magnitude, according to recent results from the behavioral finance literature. We find evidence of an asymmetric and reverse return-volatility relationship in many advanced, Asian, Latin-American, European and South African markets. We show that the US market displays the highest reaction to price falls, Asian markets present the lowest sensitivity to volatility expectations, while the Euro area is characterized by a homogeneous response both in terms of direction and impact. These results may be safely attributed to cultural and societal characteristics. An extensive quantile regression analysis demonstrates that the detected asymmetric pattern varies particularly across the extreme distribution tails i.e., in the highest/lowest quantile ranges. Indeed, the classical feedback and leverage hypotheses appear not plausible, whilst behavioral theories emerge as the new paradigm in real-world applications.

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  • Bekiros, Stelios & Jlassi, Mouna & Naoui, Kamel & Uddin, Gazi Salah, 2017. "The asymmetric relationship between returns and implied volatility: Evidence from global stock markets," Journal of Financial Stability, Elsevier, vol. 30(C), pages 156-174.
  • Handle: RePEc:eee:finsta:v:30:y:2017:i:c:p:156-174
    DOI: 10.1016/j.jfs.2017.05.006
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    2. Jupeng Li & Xiaoli Yu & Xingguo Luo, 2019. "Volatility index and the return–volatility relation: Intraday evidence from Chinese options market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(11), pages 1348-1359, November.
    3. Xiao, Jihong & Hu, Chunyan & Ouyang, Guangda & Wen, Fenghua, 2019. "Impacts of oil implied volatility shocks on stock implied volatility in China: Empirical evidence from a quantile regression approach," Energy Economics, Elsevier, vol. 80(C), pages 297-309.
    4. Bekiros, Stelios & Jlassi, Mouna & Naoui, Kamel & Uddin, Gazi Salah, 2018. "Risk perception in financial markets: On the flip side," International Review of Financial Analysis, Elsevier, vol. 57(C), pages 184-206.
    5. Sanjay Sehgal & Sakshi Saini & Florent Deisting, 2019. "Examining Dynamic Interdependencies Among Major Global Financial Markets," Multinational Finance Journal, Multinational Finance Journal, vol. 23(1-2), pages 103-139, March - J.
    6. Newaz, Mohammad Khaleq & Park, Jin Suk, 2019. "The impact of trade intensity and Market characteristics on asymmetric volatility, spillovers and asymmetric spillovers: Evidence from the response of international stock markets to US shocks," The Quarterly Review of Economics and Finance, Elsevier, vol. 71(C), pages 79-94.
    7. Giovanni Campisi & Silvia Muzzioli, 2020. "Fundamentalists heterogeneity and the role of the sentiment indicator," Department of Economics 0167, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".

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    More about this item

    Keywords

    Implied volatility; Quantile regression; Behavioral bias; Predictability;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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