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The signal of volatility

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  • Strohsal, Till
  • Weber, Enzo

Abstract

The present study addresses the economic interpretation of stock market volatility. We argue that its character is inherently ambivalent, being considered as an indicator of either information flow or uncertainty.We discriminate between these views by measuring the fraction of price changes that feeds into other markets depending on the prevailing level of volatility. This exploits the revealed reaction of investors to gauge the degree of information and uncertainty ascribed to volatility. We estimate simultaneous timevarying coefficient models, using data of US and further stock markets. We find the signal of volatility to depend crucially on the combination of its sender and receiver.

Suggested Citation

  • Strohsal, Till & Weber, Enzo, 2012. "The signal of volatility," SFB 649 Discussion Papers 2012-043, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    • Handle: RePEc:zbw:sfb649:sfb649dp2012-043
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    References listed on IDEAS

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    Cited by:

    1. Gustavo Peralta, 2016. "The Nature of Volatility Spillovers across the International Capital Markets," CNMV Working Papers CNMV Working Papers no. 6, CNMV- Spanish Securities Markets Commission - Research and Statistics Department.
    2. Lütkepohl, Helmut & Velinov, Anton, 2016. "Structural Vector Autoregressions : Checking Identifying Long-Run Restrictions via Heteroskedasticity," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 30, pages 377-392.
    3. repec:hum:wpaper:sfb649dp2015-015 is not listed on IDEAS
    4. repec:diw:diwwpp:dp1464 is not listed on IDEAS
    5. Mehmet Balcilar & Rangan Gupta & Duc Khuong Nguyen & Mark E. Wohar, 2018. "Causal effects of the United States and Japan on Pacific-Rim stock markets: nonparametric quantile causality approach," Applied Economics, Taylor & Francis Journals, vol. 50(53), pages 5712-5727, November.
    6. Maderitsch, R., 2015. "Information transmission between stock markets in Hong Kong, Europe and the US: New evidence on time- and state-dependence," Pacific-Basin Finance Journal, Elsevier, vol. 35(PA), pages 13-36.
    7. Bajaj, Vimmy & Kumar, Pawan & Singh, Vipul Kumar, 2022. "Linkage dynamics of sovereign credit risk and financial markets: A bibliometric analysis," Research in International Business and Finance, Elsevier, vol. 59(C).
    8. Jung, R.C. & Maderitsch, R., 2014. "Structural breaks in volatility spillovers between international financial markets: Contagion or mere interdependence?," Journal of Banking & Finance, Elsevier, vol. 47(C), pages 331-342.
    9. Hou, Yang (Greg) & Li, Steven, 2020. "Volatility and skewness spillover between stock index and stock index futures markets during a crash period: New evidence from China," International Review of Economics & Finance, Elsevier, vol. 66(C), pages 166-188.
    10. Helmut Lütkepohl & Aleksei Netsunajev, 2015. "Structural Vector Autoregressions with Heteroskedasticity: A Comparison of Different Volatility Models," Discussion Papers of DIW Berlin 1464, DIW Berlin, German Institute for Economic Research.
    11. repec:hum:wpaper:sfb649dp2014-009 is not listed on IDEAS
    12. Hao, Xinlei & Ma, Yong & Pan, Dongtao, 2024. "Geopolitical risk and the predictability of spillovers between exchange, commodity and stock markets," Journal of Multinational Financial Management, Elsevier, vol. 73(C).
    13. Dilip Kumar, 2019. "Structural Breaks in Volatility Transmission from Developed Markets to Major Asian Emerging Markets," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 18(2), pages 172-209, August.

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    More about this item

    Keywords

    Information; Uncertainty; Spillover; Simultaneous Equations; Identification;
    All these keywords.

    JEL classification:

    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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