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The Signal of Volatility

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  • Till Strohsal
  • Enzo Weber

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

  • Till Strohsal & Enzo Weber, 2012. "The Signal of Volatility," SFB 649 Discussion Papers SFB649DP2012-043, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    • Handle: RePEc:hum:wpaper:sfb649dp2012-043
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    References listed on IDEAS

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    Cited by:

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    2. 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.
    3. 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.
    4. Helmut Lütkepohl & Anton Velinov, 2016. "Structural Vector Autoregressions: Checking Identifying Long-Run Restrictions Via Heteroskedasticity," Journal of Economic Surveys, Wiley Blackwell, vol. 30(2), pages 377-392, April.
    5. 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.
    6. 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.
    7. 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.
    8. 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).
    9. 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.
    10. Helmut Lütkepohl & Aleksei Netšunajev, 2015. "Structural Vector Autoregressions with Heteroskedasticy," SFB 649 Discussion Papers SFB649DP2015-015, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

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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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