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The predictive power of stock market’s expectations volatility: A financial synchronization phenomenon

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  • Nicolás Magner
  • Jaime F Lavin
  • Mauricio Valle
  • Nicolás Hardy

Abstract

We explore the use of implied volatility indices as a tool for estimate changes in the synchronization of stock markets. Specifically, we assess the implied stock market’s volatility indices’ predictive power on synchronizing global equity indices returns. We built the correlation network of 26 stock indices and implemented in-sample and out-of-sample tests to evaluate the predictive power of VIX, VSTOXX, and VXJ implied volatility indices. To measure markets’ synchronization, we use the Minimum Spanning Tree length and the length of the Planar Maximally Filtered Graph. Our results indicate a high predictive power of all the volatility indices, both individually and together, though the VIX predominates over the evaluated options. We find that an increase in the markets’ volatility expectations, captured by the implied volatility indices, is a good Granger predictor of an increase in the synchronization of returns in the following month. Estimating, monitoring, and predicting returns’ synchronization is essential for investment decision-making, especially for diversification strategies and regulating financial systems.

Suggested Citation

  • Nicolás Magner & Jaime F Lavin & Mauricio Valle & Nicolás Hardy, 2021. "The predictive power of stock market’s expectations volatility: A financial synchronization phenomenon," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-21, May.
    • Handle: RePEc:plo:pone00:0250846
    DOI: 10.1371/journal.pone.0250846
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    References listed on IDEAS

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    1. Jeff Fleming & Barbara Ostdiek & Robert E. Whaley, 1995. "Predicting stock market volatility: A new measure," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 15(3), pages 265-302, May.
    2. Nicolás S. Magner & Jaime F. Lavin & Mauricio A. Valle & Nicolás Hardy, 2020. "The Volatility Forecasting Power of Financial Network Analysis," Complexity, Hindawi, vol. 2020, pages 1-17, September.
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    4. Jach, Agnieszka, 2017. "International stock market comovement in time and scale outlined with a thick pen," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 115-129.
    5. Benjamin Miranda Tabak & Thiago Christiano Silva & Ahmet Sensoy, 2018. "Financial Networks," Complexity, Hindawi, vol. 2018, pages 1-2, April.
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    Cited by:

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    2. Mikhail Stolbov & Daniil Parfenov, 2023. "Credit risk linkages in the international banking network, 2000–2019," Risk Management, Palgrave Macmillan, vol. 25(3), pages 1-38, September.
    3. Nicolás Magner & Jaime F. Lavín & Mauricio A. Valle, 2022. "Modeling Synchronization Risk among Sustainable Exchange Trade Funds: A Statistical and Network Analysis Approach," Mathematics, MDPI, vol. 10(19), pages 1-30, October.

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