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Pitfalls in tests for changes in correlations

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  • Brian H. Boyer
  • Michael S. Gibson
  • Mico Loretan

Abstract

Correlations are crucial for pricing and hedging derivatives whose payoff depends on more than one asset. Typically, correlations computed separately for ordinary and stressful market conditions differ considerably, a pattern widely termed "correlation breakdown." As a result, risk managers worry that their hedges will be useless when they are most needed, namely during "stressful" market situations. ; We show that such worries may not be justified since "correlation breakdowns" can easily be generated by data whose distribution is stationary and, in particular, whose correlation coefficient is constant. We make this point analytically, by way of several numerical examples, and via an empirical illustration. ; But, risk managers should not necessarily relax. Although "correlation breakdown" can be an artifact of poor data analysis, other evidence suggests that correlations do in fact change over time, though not in a way that is correlated with "stressful" market conditions.

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File URL: http://www.federalreserve.gov/pubs/ifdp/1997/597/default.htm
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File URL: http://www.federalreserve.gov/pubs/ifdp/1997/597/ifdp597.pdf
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Bibliographic Info

Paper provided by Board of Governors of the Federal Reserve System (U.S.) in its series International Finance Discussion Papers with number 597.

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Date of creation: 1997
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Handle: RePEc:fip:fedgif:597

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Related research

Keywords: Bank management ; Risk ; Derivative securities;

References

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  1. G. Andrew Karoly & Rene Stulz, . "Why do Markets Move Together? An Investigation of U.S.-Japan Stock Return Comovements," Research in Financial Economics 9603, Ohio State University.
  2. Jose M. Campa & P. H. Kevin Chang, 1997. "The Forecasting Ability of Correlations Implied in Foreign Exchange Options," NBER Working Papers 5974, National Bureau of Economic Research, Inc.
  3. Andrews, Donald W.K., 1988. "Laws of Large Numbers for Dependent Non-Identically Distributed Random Variables," Econometric Theory, Cambridge University Press, vol. 4(03), pages 458-467, December.
  4. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
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