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Common factors in conditional distributions for bivariate time series

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Author Info

  • Granger, Clive W.J.
  • Terasvirta, Timo
  • Patton, Andrew J.

Abstract

A definition for a common factor for bivariate time series is suggested by considering the decomposition of the conditional density into the product of the marginals and the copula,�with the conditioning variable being a common factor if it does not directly enter the copula.� The links of this definition with a common factor being a dominant feature in standard linear representations is shown. An application using a business cycle indicator as the common factor in the relationship between U.S. income and consumption found that both series held the factor� in their marginals but not in the copula.

(This abstract was borrowed from another version of this item.)

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File URL: http://www.sciencedirect.com/science/article/B6VC0-4FMK8MH-6/2/8df7b4317d3573df668ff2484d17c829
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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 132 (2006)
Issue (Month): 1 (May)
Pages: 43-57

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Handle: RePEc:eee:econom:v:132:y:2006:i:1:p:43-57

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Web page: http://www.elsevier.com/locate/jeconom

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References

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  1. Granger, C. W. J., 1987. "Implications of Aggregation with Common Factors," Econometric Theory, Cambridge University Press, vol. 3(02), pages 208-222, April.
  2. Issler, Joao Victor & Vahid, Farshid, 2001. "Common cycles and the importance of transitory shocks to macroeconomic aggregates," Journal of Monetary Economics, Elsevier, vol. 47(3), pages 449-475, June.
  3. Hansen, B.E., 1992. "Autoregressive Conditional Density Estimation," RCER Working Papers 322, University of Rochester - Center for Economic Research (RCER).
  4. Wallis, Kenneth F., 2002. "Chi-squared tests of interval and density forecasts, and the Bank of England's fan charts," Royal Economic Society Annual Conference 2002 181, Royal Economic Society.
  5. White,Halbert, 1994. "Estimation, Inference and Specification Analysis," Cambridge Books, Cambridge University Press, number 9780521252805.
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Citations

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Cited by:
  1. Dominique Guegan & Jing Zhang, 2010. "Change analysis of a dynamic copula for measuring dependence in multivariate financial data," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00368334, HAL.
  2. Fantazzini, Dean, 2008. "Econometric Analysis of Financial Data in Risk Management (continuation). Section III: Managing Operational Risk," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 11(3), pages 87-122.
  3. Kim, Gunky & Silvapulle, Mervyn J. & Silvapulle, Paramsothy, 2007. "Comparison of semiparametric and parametric methods for estimating copulas," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 2836-2850, March.
  4. Leonidas Tsiaras, 2010. "Dynamic Models of Exchange Rate Dependence Using Option Prices and Historical Returns," CREATES Research Papers 2010-35, School of Economics and Management, University of Aarhus.
  5. repec:hal:journl:halshs-00189141 is not listed on IDEAS
  6. repec:hal:journl:halshs-00368336 is not listed on IDEAS
  7. repec:hal:journl:halshs-00368334 is not listed on IDEAS
  8. Fantazzini, Dean, 2008. "Credit Risk Management," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 12(4), pages 84-137.
  9. Campbell, Rachel A.J. & Forbes, Catherine S. & Koedijk, Kees G. & Kofman, Paul, 2008. "Increasing correlations or just fat tails?," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 287-309, March.
  10. Carluccio Bianchi & Alessandro Carta & Dean Fantazzini & Maria Elena De Giuli & Mario A. Maggi, 2009. "A Copula-VAR-X Approach for Industrial Production Modelling and Forecasting," Quaderni di Dipartimento 105, University of Pavia, Department of Economics and Quantitative Methods.
  11. Power, Gabriel J. & Vedenov, Dmitry V., 2008. "The Shape of the Optimal Hedge Ratio: Modeling Joint Spot-Futures Prices using an Empirical Copula-GARCH Model," 2008 Conference, April 21-22, 2008, St. Louis, Missouri 37609, NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
  12. Fantazzini, Dean, 2008. "An Econometric Analysis of Financial Data in Risk Management," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 10(2), pages 91-137.
  13. Chollete, Loran & Ning, Cathy, 2012. "Asymmetric Dependence in the US Economy: Application to Money and the Phillips Curve," UiS Working Papers in Economics and Finance 2012/1, University of Stavanger.
  14. Param Silvapulle & Xibin Zhang, 2006. "Assessing Dependence Changes in the Asian Financial Market Returns Using Plots Based on Nonparametric Measures," Monash Econometrics and Business Statistics Working Papers 9/06, Monash University, Department of Econometrics and Business Statistics.
  15. Cyril Caillault & Dominique Guegan, 2009. "Forecasting VaR and Expected Shortfall using Dynamical Systems: A Risk Management Strategy," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00375765, HAL.
  16. Matthias Fengler & Helmut Herwartz & Christian Werner, 2010. "A dynamic copula approach to recovering the index implied volatility skew," University of St. Gallen Department of Economics working paper series 2010 1132, Department of Economics, University of St. Gallen, revised Nov 2011.
  17. repec:hal:journl:halshs-00375765 is not listed on IDEAS
  18. Wang, Kehluh & Chen, Yi-Hsuan & Huang, Szu-Wei, 2011. "The dynamic dependence between the Chinese market and other international stock markets: A time-varying copula approach," International Review of Economics & Finance, Elsevier, vol. 20(4), pages 654-664, October.
  19. Dominique Guegan & Jing Zhang, 2009. "Pricing bivariate option under GARCH-GH model with dynamic copula: application for Chinese market," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00368336, HAL.

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