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Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers

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  • Francis X. Diebold

    (University of Pennsylvania and NBER)

  • Kamil Yilmaz

    ()
    (Koc University)

Abstract

Using a generalized vector autoregressive framework in which forecast-error variance decompositions are invariant to variable ordering, we propose measures of both total and directional volatility spillovers. We use our methods to characterize daily volatility spillovers across U.S. stock, bond, foreign exchange and commodities markets, from January 1999 through September 2009. We show that despite significant volatility fluctuations in all four markets during the sample, cross-market volatility spillovers were quite limited until the global financial crisis that began in 2007. As the crisis intensified so too did the volatility spillovers, with particularly important spillovers from the bond market to other markets taking place after the collapse of Lehman Brothers in September 2008.

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Bibliographic Info

Paper provided by Koc University-TUSIAD Economic Research Forum in its series Koç University-TUSIAD Economic Research Forum Working Papers with number 1001.

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Length: 28 pages
Date of creation: Jan 2010
Date of revision: Mar 2010
Handle: RePEc:koc:wpaper:1001

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Keywords: Asset Market; Asset Return; Stock Market; Market Linkage; Financial Crisis; Contagion; Vector Autoregression; Variance Decomposition;

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  1. Francis X. Diebold & Kamil Yılmaz, 2007. "Measuring Financial Asset Return and Volatility Spillovers, With Application to Global Equity Markets," Koç University-TUSIAD Economic Research Forum Working Papers 0705, Koc University-TUSIAD Economic Research Forum.
  2. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Practical Volatility and Correlation Modeling for Financial Market Risk Management," NBER Working Papers 11069, National Bureau of Economic Research, Inc.
  3. Mervyn King & Enrique Sentana & Sushil Wadhwani, 1990. "Volatiltiy and Links Between National Stock Markets," NBER Working Papers 3357, National Bureau of Economic Research, Inc.
  4. Pesaran, M. H. & Shin, Y., 1997. "Generalised Impulse Response Analysis in Linear Multivariate Models," Cambridge Working Papers in Economics 9710, Faculty of Economics, University of Cambridge.
  5. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
  6. Sassan Alizadeh & Michael W. Brandt & Francis X. Diebold, 2002. "Range-Based Estimation of Stochastic Volatility Models," Journal of Finance, American Finance Association, vol. 57(3), pages 1047-1091, 06.
  7. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
  8. Kristin J. Forbes & Roberto Rigobon, 2002. "No Contagion, Only Interdependence: Measuring Stock Market Comovements," Journal of Finance, American Finance Association, vol. 57(5), pages 2223-2261, October.
  9. Carmen M. Reinhart & Kenneth S. Rogoff, 2008. "Is the 2007 U.S. Sub-Prime Financial Crisis So Different? An International Historical Comparison," NBER Working Papers 13761, National Bureau of Economic Research, Inc.
  10. Robert F. Engle & Takatoshi Ito & Wen-Ling Lin, 1988. "Meteor Showers or Heat Waves? Heteroskedastic Intra-Daily Volatility in the Foreign Exchange Market," NBER Working Papers 2609, National Bureau of Economic Research, Inc.
  11. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
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