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Financial volatility and economic activity

  • Fabio Fornari
  • Antonio Mele

Does capital markets uncertainty affect the business cycle? We find that financial volatility predicts 30% of post-war economic activity in the United States, and that during the Great Moderation, aggregate stock market volatility explains, alone, up to 55% of real growth. In out- of-sample tests, we find that stock volatility helps predict turning points over and above traditional financial variables such as credit or term spreads, and other leading indicators. Combining stock volatility and the term spread leads to a proxy for (i) aggregate risk, (ii) risk-premiums and (iii) monetary policy, which is found to track, and anticipate, the business cycle. At the heart of our analysis is a notion of volatility based on a slowly changing measure of return variability. This volatility is designed to capture long-run uncertainty in capital markets, and is particularly successful at explaining trends in the economic activity at horizons of six months and one year.

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File URL: http://eprints.lse.ac.uk/29309/
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Paper provided by London School of Economics and Political Science, LSE Library in its series LSE Research Online Documents on Economics with number 29309.

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Length: 60 pages
Date of creation: 2009
Date of revision:
Handle: RePEc:ehl:lserod:29309
Contact details of provider: Postal: LSE Library Portugal Street London, WC2A 2HD, U.K.
Phone: +44 (020) 7405 7686
Web page: http://www.lse.ac.uk/
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  1. Nicholas BARBERIS & Ming HUANG & Tano SANTOS, 2000. "Prospect Theory and Asset Prices," FAME Research Paper Series rp16, International Center for Financial Asset Management and Engineering.
  2. Tobias Adrian & Joshua Rosenberg, 2008. "Stock Returns and Volatility: Pricing the Short-Run and Long-Run Components of Market Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2997-3030, December.
  3. Malkiel, Burton & Campbell, John & Lettau, Martin & Xu, Yexiao, 2001. "Have Individual Stocks Become More Volatile? An Empirical Exploration of Idiosyncratic Risk," Scholarly Articles 3128707, Harvard University Department of Economics.
  4. Robert C. Merton, 1980. "On Estimating the Expected Return on the Market: An Exploratory Investigation," NBER Working Papers 0444, National Bureau of Economic Research, Inc.
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  6. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  7. Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
  8. Arturo Estrella & Gikas A. Hardouvelis, 1989. "The term structure as a predictor of real economic activity," Research Paper 8907, Federal Reserve Bank of New York.
  9. Olivier Blanchard & John Simon, 2001. "The Long and Large Decline in U.S. Output Volatility," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 32(1), pages 135-174.
  10. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409 National Bureau of Economic Research, Inc.
  11. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
  12. Mele, Antonio, 2007. "Asymmetric stock market volatility and the cyclical behavior of expected returns," Journal of Financial Economics, Elsevier, vol. 86(2), pages 446-478, November.
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