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Cyclicality and Term Structure of Value-at-Risk in Europe

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  • Bec, Frédérique
  • Gollier, Christian

Abstract

This paper explores empirically the link between stocks returns Value-at-Risk (VaR) and the state of financial markets cycle. The econometric analysis is based on a simple vector autoregression setup. Using quarterly data from 1970Q4 to 2008Q4 for France, Germany and the United-Kingdom, it turns out that the k-year VaR of equities is actually dependent on the cycle phase: the expected losses as measured by the VaR are smaller in recession times than expansion periods, whatever the country and the horizon. These results strongly suggest that the European rules regarding the solvency capital requirements for insurance companies should adapt to the state of the financial market’s cycle.

Suggested Citation

  • Bec, Frédérique & Gollier, Christian, 2009. "Cyclicality and Term Structure of Value-at-Risk in Europe," TSE Working Papers 09-035, Toulouse School of Economics (TSE).
    • Handle: RePEc:tse:wpaper:21942
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    1. Cavaliere, Giuseppe & Rahbek, Anders & Taylor, A.M. Robert, 2010. "Testing for co-integration in vector autoregressions with non-stationary volatility," Journal of Econometrics, Elsevier, vol. 158(1), pages 7-24, September.
    2. Richard Clarida & Jordi Galí & Mark Gertler, 2000. "Monetary Policy Rules and Macroeconomic Stability: Evidence and Some Theory," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 115(1), pages 147-180.
    3. Campbell, John Y, 1991. "A Variance Decomposition for Stock Returns," Economic Journal, Royal Economic Society, vol. 101(405), pages 157-179, March.
    4. Hodrick, Robert J & Prescott, Edward C, 1997. "Postwar U.S. Business Cycles: An Empirical Investigation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(1), pages 1-16, February.
    5. Mise, Emi & Kim, Tae-Hwan & Newbold, Paul, 2005. "On suboptimality of the Hodrick-Prescott filter at time series endpoints," Journal of Macroeconomics, Elsevier, vol. 27(1), pages 53-67, March.
    6. Guillaume Plantin & Haresh Sapra & Hyun Song Shin, 2008. "Marking‐to‐Market: Panacea or Pandora's Box?," Journal of Accounting Research, Wiley Blackwell, vol. 46(2), pages 435-460, May.
    7. Ľuboš Pástor & Robert F. Stambaugh, 2012. "Are Stocks Really Less Volatile in the Long Run?," Journal of Finance, American Finance Association, vol. 67(2), pages 431-478, April.
    8. Rochet, J C., 2008. "Procyclicality of financial systems: is there a need to modify current accounting and regulatory rules?," Financial Stability Review, Banque de France, issue 12, pages 95-99, October.
    9. J. B. Taylor & M. Woodford (ed.), 1999. "Handbook of Macroeconomics," Handbook of Macroeconomics, Elsevier, edition 1, volume 1, number 1.
    10. King, Robert G. & Rebelo, Sergio T., 1993. "Low frequency filtering and real business cycles," Journal of Economic Dynamics and Control, Elsevier, vol. 17(1-2), pages 207-231.
    11. Cogley, Timothy & Nason, James M., 1995. "Effects of the Hodrick-Prescott filter on trend and difference stationary time series Implications for business cycle research," Journal of Economic Dynamics and Control, Elsevier, vol. 19(1-2), pages 253-278.
    12. Nicholas Barberis, 2000. "Investing for the Long Run when Returns Are Predictable," Journal of Finance, American Finance Association, vol. 55(1), pages 225-264, February.
    13. Anders Møller Christensen & Heino Bohn Nielsen, 2009. "Monetary Policy in the Greenspan Era: A Time Series Analysis of Rules vs. Discretion," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(1), pages 69-89, February.
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    Cited by:

    1. Lucyna Gornicka & Sweder van Wijnbergen, 2013. "Financial Frictions and the Credit Transmission Channel: Capital Requirements and Bank Capital," Tinbergen Institute Discussion Papers 13-013/VI/DSF50, Tinbergen Institute.

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