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Macroeconomic Determinants of Stock Market Volatility and Volatility Risk-Premiums

Author

Listed:
  • Valentina Corradi

    (University of Warwick)

  • Walter Distaso

    (Imperial College Business School)

  • Antonio Mele

    (Swiss Finance Institute, University of Lugano, and Centre for Economic Policy Research (CEPR))

Abstract

How does stock market volatility relate to the business cycle? We develop, and estimate, a no-arbitrage model to study the cyclical properties of stock volatility and the risk-premiums the market requires to bear the risk of uctuations in this volatility. The level of stock market volatility cannot be explained by the mere existence of the business cycle. Rather, it relates to the presence of some unobserved factor. At the same time, our model predicts that such an unobservable factor cannot explain the ups and downs stock volatility experiences over time - the "volatility of volatility." Instead, the volatility of stock volatility relates to the business cycle. Finally, volatility risk-premiums are strongly countercyclical, even more so than stock volatility, and are partially responsible for the large swings in the VIX index occurred during the 2007-2009 subprime crisis, which our model does capture in out-of-sample experiments.

Suggested Citation

  • Valentina Corradi & Walter Distaso & Antonio Mele, 2012. "Macroeconomic Determinants of Stock Market Volatility and Volatility Risk-Premiums," Swiss Finance Institute Research Paper Series 12-18, Swiss Finance Institute.
  • Handle: RePEc:chf:rpseri:rp1218
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    File URL: http://ssrn.com/abstract=2005021
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    Cited by:

    1. Fabio Fornari & Antonio Mele, 2013. "Financial Volatility and Economic Activity," Journal of Financial Management, Markets and Institutions, Società editrice il Mulino, issue 2, pages 155-198, December.
    2. Vedolin, Andrea, 2012. "Uncertainty and leveraged Lucas Trees: the cross section of equilibrium volatility risk premia," LSE Research Online Documents on Economics 43091, London School of Economics and Political Science, LSE Library.
    3. Yves Dominicy & Harry-Paul Vander Elst, 2015. "Macro-Driven VaR Forecasts: From Very High to Very Low Frequency Data," Working Papers ECARES ECARES 2015-41, ULB -- Universite Libre de Bruxelles.
    4. Anghelache, Gabriela Victoria & Kralik, Lorand Istvan & Acatrinei, Marius & Pete, Stefan, 2014. "Influence of the EU Accession Process and the Global Crisis on the CEE Stock Markets: A Multivariate Correlation Analysis," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 35-52, June.
    5. repec:ags:apstra:265587 is not listed on IDEAS

    More about this item

    Keywords

    Aggregate stock market volatility; volatility risk-premiums; volatility of volatility; business cycle; no-arbitrage restrictions; simulation-based inference;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • 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
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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