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What does financial volatility tell us about macroeconomic fluctuations?

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  • Chauvet, Marcelle
  • Senyuz, Zeynep
  • Yoldas, Emre

Abstract

We provide an extensive analysis of the predictive ability of financial volatility for economic activity. We consider monthly measures of realized and implied volatility from the stock and bond markets. In a dynamic factor framework, we extract the common long-run component of volatility that is likely to be linked to economic fundamentals. Based on powerful in-sample predictive ability tests, we find that the stock volatility measures and the common factor significantly improve macroeconomic forecasts of conventional financial indicators, especially over short horizons. A real-time out of sample assessment yields similar conclusions under the assumption of noisy revisions in macroeconomic data. In a nonlinear extension of the dynamic factor model, we identify two distinct volatility regimes, and show that the high-volatility regime provides early signals of the Great Recession, which was associated with severe financial distress and credit disintermediation.

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  • Chauvet, Marcelle & Senyuz, Zeynep & Yoldas, Emre, 2015. "What does financial volatility tell us about macroeconomic fluctuations?," Journal of Economic Dynamics and Control, Elsevier, vol. 52(C), pages 340-360.
  • Handle: RePEc:eee:dyncon:v:52:y:2015:i:c:p:340-360
    DOI: 10.1016/j.jedc.2015.01.002
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    Cited by:

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    2. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2016. "Testing for Granger causality in large mixed-frequency VARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 418-432.
    3. Charlotte Christiansen & Maik Schmeling & Andreas Schrimpf, 2012. "A comprehensive look at financial volatility prediction by economic variables," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 956-977, September.
    4. Ergemen, Yunus Emre & Velasco, Carlos, 2017. "Estimation of fractionally integrated panels with fixed effects and cross-section dependence," Journal of Econometrics, Elsevier, vol. 196(2), pages 248-258.
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    6. Ferrara, Laurent & Marsilli, Clément & Ortega, Juan-Pablo, 2014. "Forecasting growth during the Great Recession: is financial volatility the missing ingredient?," Economic Modelling, Elsevier, vol. 36(C), pages 44-50.
    7. Borjigin, Sumuya & Yang, Yating & Yang, Xiaoguang & Sun, Leilei, 2018. "Econometric testing on linear and nonlinear dynamic relation between stock prices and macroeconomy in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 493(C), pages 107-115.
    8. John Cotter & Mark Hallam & Kamil Yilmaz, 2017. "Mixed-Frequency Macro-Financial Spillovers," Koç University-TUSIAD Economic Research Forum Working Papers 1704, Koc University-TUSIAD Economic Research Forum.
    9. Amélie Charles & Olivier Darné & Fabien Tripier, 2018. "Uncertainty and the Macroeconomy: Evidence from an uncertainty composite indicator," Post-Print hal-01757042, HAL.
    10. Amélie Charles & Olivier Darné & Fabien Tripier, 2017. "Uncertainty and the Macroeconomy," Post-Print hal-01549625, HAL.
    11. Vu, Nam T., 2015. "Stock market volatility and international business cycle dynamics: Evidence from OECD economies," Journal of International Money and Finance, Elsevier, vol. 50(C), pages 1-15.
    12. Oguzhan Ozcelebi & Nurtac Yildirim, 2017. "Interrelations Between External and Internal Macroeconomic Factors: Empirical Evidence on Some OECD Countries," South-Eastern Europe Journal of Economics, Association of Economic Universities of South and Eastern Europe and the Black Sea Region, vol. 15(2), pages 147-174.
    13. Amélie Charles & Olivier Darné & Fabien Tripier, 2018. "Uncertainty and the macroeconomy: evidence from an uncertainty composite indicator," Applied Economics, Taylor & Francis Journals, vol. 50(10), pages 1093-1107, February.
    14. C. Marsilli, 2014. "Variable Selection in Predictive MIDAS Models," Working papers 520, Banque de France.
    15. Yin, Libo & Feng, Jiabao & Liu, Li & Wang, Yudong, 2019. "It's not that important: The negligible effect of oil market uncertainty," International Review of Economics & Finance, Elsevier, vol. 60(C), pages 62-84.
    16. Wang, Yudong & Wei, Yu & Wu, Chongfeng & Yin, Libo, 2018. "Oil and the short-term predictability of stock return volatility," Journal of Empirical Finance, Elsevier, vol. 47(C), pages 90-104.

    More about this item

    Keywords

    Financial volatility; Real-time data; Predictive ability tests; Dynamic factor model; Markov switching;

    JEL classification:

    • 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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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