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Nonlinear Term Structure Dependence: Copula Functions, Empirics, and Risk Implications

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  • Markus Junker
  • Alexander Szimayer
  • Niklas Wagner

Abstract

This paper documents nonlinear cross-sectional dependence in the term structure of U.S. Treasury yields and points out risk management implications. The analysis is based on a Kalman filter estimation of a two-factor affine model which specifies the yield curve dynamics. We then apply a broad class of copula functions for modeling dependence in factors spanning the yield curve. Our sample of monthly yields in the 1982 to 2001 period provides evidence of upper tail dependence in yield innovations; i.e., large positive interest rate shocks tend to occur under increased dependence. In contrast, the best fitting copula model coincides with zero lower tail dependence. This asymmetry has substantial risk management implications. We give an example in estimating bond portfolio loss quantiles and report the biases which result from an application of the normal dependence model.

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Bibliographic Info

Paper provided by EconWPA in its series Econometrics with number 0401007.

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Length: 50 pages
Date of creation: 30 Jan 2004
Date of revision:
Handle: RePEc:wpa:wuwpem:0401007

Note: Type of Document - pdf; prepared on win00; to print on laserjet; pages: 50
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Web page: http://128.118.178.162

Related research

Keywords: affine term structure models; nonlinear dependence; copula functions; tail dependence; value-at-risk;

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References

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  1. Hans Dewachter & Marco Lyrio & Konstantijn Maes, 2001. "The Effect of Monetary Unification on German Bond Markets," International Economics Working Papers Series ces0205, Katholieke Universiteit Leuven, Centrum voor Economische Studiën, International Economics.
  2. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
  3. Ait-Sahalia, Yacine, 1996. "Testing Continuous-Time Models of the Spot Interest Rate," Review of Financial Studies, Society for Financial Studies, vol. 9(2), pages 385-426.
  4. Y. Malevergne & D. Sornette, 2003. "Testing the Gaussian copula hypothesis for financial assets dependences," Quantitative Finance, Taylor & Francis Journals, vol. 3(4), pages 231-250.
  5. Duan, Jin-Chuan & Simonato, Jean-Guy, 1999. " Estimating and Testing Exponential-Affine Term Structure Models by Kalman Filter," Review of Quantitative Finance and Accounting, Springer, vol. 13(2), pages 111-35, September.
  6. Simon Babbs & K. Nowman, 1998. "Econometric Analysis of a Continuous Time Multi-Factor Generalized Vasicek Term Structure Model: International Evidence," Asia-Pacific Financial Markets, Springer, vol. 5(2), pages 159-183, May.
  7. Chan, K C, et al, 1992. " An Empirical Comparison of Alternative Models of the Short-Term Interest Rate," Journal of Finance, American Finance Association, vol. 47(3), pages 1209-27, July.
  8. Longstaff, Francis A & Schwartz, Eduardo S, 1992. " Interest Rate Volatility and the Term Structure: A Two-Factor General Equilibrium Model," Journal of Finance, American Finance Association, vol. 47(4), pages 1259-82, September.
  9. Tomas Björk & Yuri Kabanov & Wolfgang Runggaldier, 1997. "Bond Market Structure in the Presence of Marked Point Processes," Mathematical Finance, Wiley Blackwell, vol. 7(2), pages 211-239.
  10. Rockinger, M. & Jondeau, E., 2001. "Conditional Dependency of Financial Series: An Application of Copulas," Working papers 82, Banque de France.
  11. Patton, Andrew J, 2001. "Modelling Time-Varying Exchange Rate Dependence Using the Conditional Copula," University of California at San Diego, Economics Working Paper Series qt01q7j1s2, Department of Economics, UC San Diego.
  12. Dong-Hyun Ahn & Robert F. Dittmar, 2002. "Quadratic Term Structure Models: Theory and Evidence," Review of Financial Studies, Society for Financial Studies, vol. 15(1), pages 243-288, March.
  13. Rockinger, M. & Jondeau, E., 2001. "Conditional Dependency of Financial Series: An Application of Copulas," Working papers 82, Banque de France.
  14. Vasicek, Oldrich, 1977. "An equilibrium characterization of the term structure," Journal of Financial Economics, Elsevier, vol. 5(2), pages 177-188, November.
  15. Fama, Eugene F & Bliss, Robert R, 1987. "The Information in Long-Maturity Forward Rates," American Economic Review, American Economic Association, vol. 77(4), pages 680-92, September.
  16. Babbs, Simon H. & Nowman, K. Ben, 1999. "Kalman Filtering of Generalized Vasicek Term Structure Models," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 34(01), pages 115-130, March.
  17. Ang, Andrew & Bekaert, Geert, 2002. "Short rate nonlinearities and regime switches," Journal of Economic Dynamics and Control, Elsevier, vol. 26(7-8), pages 1243-1274, July.
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Citations

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Cited by:
  1. Hafner, Christian M. & Manner, Hans, 2008. "Dynamic stochastic copula models: Estimation, inference and applications," Research Memorandum 043, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
  2. Marc Gronwald & Janina Ketterer & Stefan Trück, 2011. "The Dependence Structure between Carbon Emission Allowances and Financial Markets - A Copula Analysis," CESifo Working Paper Series 3418, CESifo Group Munich.
  3. Penikas, Henry & Simakova, Varvara, 2009. "Interest Rate Risk Management Based on Copula-GARCH Models," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 13(1), pages 3-36.
  4. Chatrath, Arjun & Christie-David, Rohan A. & Lee, Kiseop & Moore, William T., 2009. "Competitive inventory management in Treasury markets," Journal of Banking & Finance, Elsevier, vol. 33(5), pages 800-809, May.
  5. Koedijk, Kees & Kole, Erik & Verbeek, Marno, 2006. "Selecting Copulas for Risk Management," CEPR Discussion Papers 5652, C.E.P.R. Discussion Papers.
  6. Äijö, Janne, 2008. "Implied volatility term structure linkages between VDAX, VSMI and VSTOXX volatility indices," Global Finance Journal, Elsevier, vol. 18(3), pages 290-302.
  7. Fernandez, Viviana, 2008. "Copula-based measures of dependence structure in assets returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(14), pages 3615-3628.
  8. Wang, Zong-Run & Chen, Xiao-Hong & Jin, Yan-Bo & Zhou, Yan-Ju, 2010. "Estimating risk of foreign exchange portfolio: Using VaR and CVaR based on GARCH–EVT-Copula model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4918-4928.
  9. repec:asi:joabsj:2013:p:59-68 is not listed on IDEAS
  10. Ruijun Bu & Ludovic Giet & Kaddour Hadri & Michel Lubrano, 2009. "Modeling Multivariate Interest Rates using Time-Varying Copulas and Reducible Stochastic Differential Equations," Working Papers halshs-00408014, HAL.
  11. Grundke, Peter & Polle, Simone, 2012. "Crisis and risk dependencies," European Journal of Operational Research, Elsevier, vol. 223(2), pages 518-528.
  12. Chu, Ba, 2011. "Recovering copulas from limited information and an application to asset allocation," Journal of Banking & Finance, Elsevier, vol. 35(7), pages 1824-1842, July.
  13. Viviana Fernández, 2007. "Multi-period hedge ratios for a multi-asset portfolio when accounting for returns comovement," Documentos de Trabajo 242, Centro de Economía Aplicada, Universidad de Chile.
  14. Cem Çakmakli, 2012. "Bayesian Semiparametric Dynamic Nelson-Siegel Model," Working Paper Series 59_12, The Rimini Centre for Economic Analysis, revised Sep 2012.
  15. Manner, Hans, 2007. "Estimation and Model Selection of Copulas with an Application to Exchange Rates," Research Memorandum 056, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
  16. Joe-Ming Lee, 2013. "The Search of Structural Changes in Mutual Fund Industry-Based On the ARMAX-GJR-GARCH Model," Asian Journal of Empirical Research, Asian Economic and Social Society, vol. 3(3), pages 308-316, March.

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