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Modelling Volatilities and Conditional Correlations in Futures Markets with a Multivariate t Distribution

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  • Pesaran, B.
  • Pesaran, M.H.

Abstract

This paper considers a multivariate t version of the Gaussian dynamic conditional correlation (DCC) model proposed by Engle (2002), and suggests the use of devolatized returns computed as returns standardized by realized volatilities rather than by GARCH type volatility estimates. The t-DCC estimation procedure is applied to a portfolio of daily returns on currency futures, government bonds and equity index futures. The results strongly reject the normal-DCC model in favour of a t-DCC specification. The t-DCC model also passes a number of VaR diagnostic tests over an evaluation sample. The estimation results suggest a general trend towards a lower level of return volatility, accompanied by a rising trend in conditional cross correlations in most markets; possibly reflecting the advent of euro in 1999 and increased interdependence of financial markets.

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Bibliographic Info

Paper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 0734.

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Length: 29
Date of creation: Jun 2007
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Handle: RePEc:cam:camdae:0734

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Keywords: Volatilities and Correlations; Futures Market; Multivariate t; Financial Interdependence; VaR diagnostics.;

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  1. BAUWENS, Luc & LAURENT, Sébastien & ROMBOUTS, Jeroen VK, . "Multivariate GARCH models: a survey," CORE Discussion Papers RP -1847, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  2. BAUWENS, Luc & LAURENT, Sébastien, . "A new class of multivariate skew densities, with application to generalized autoregressive conditional heteroscedasticity models," CORE Discussion Papers RP -1793, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  3. Pesaran, M.H. & Timmermann, A., 2004. "‘Real Time Econometrics’," Cambridge Working Papers in Economics 0432, Faculty of Economics, University of Cambridge.
  4. M. Hashem Pesaran & Paolo Zaffaroni, 2004. "Model Averaging and Value-at-Risk Based Evaluation of Large Multi Asset Volatility Models for Risk Management," CESifo Working Paper Series 1358, CESifo Group Munich.
  5. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-62, November.
  6. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
  7. Jose A. Lopez, 1998. "Methods for evaluating value-at-risk estimates," Economic Policy Review, Federal Reserve Bank of New York, issue Oct, pages 119-124.
  8. Sentana, E., 1994. "The Likelihood Function of a Conditionally Heteroskdastic Factor Model with Heywood Cases," Papers 9420, Centro de Estudios Monetarios Y Financieros-.
  9. 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.
  10. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(01), pages 122-150, February.
  11. Fiorentini, Gabriele & Sentana, Enrique & Calzolari, Giorgio, 2003. "Maximum Likelihood Estimation and Inference in Multivariate Conditionally Heteroscedastic Dynamic Regression Models with Student t Innovations," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(4), pages 532-46, October.
  12. Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Ebens, Heiko, 2001. "The distribution of realized stock return volatility," Journal of Financial Economics, Elsevier, vol. 61(1), pages 43-76, July.
  13. Berkowitz, Jeremy, 2001. "Testing Density Forecasts, with Applications to Risk Management," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 465-74, October.
  14. Andersen T. G & Bollerslev T. & Diebold F. X & Labys P., 2001. "The Distribution of Realized Exchange Rate Volatility," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 42-55, March.
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Cited by:
  1. Samitas, Aristeidis & Tsakalos, Ioannis, 2013. "How can a small country affect the European economy? The Greek contagion phenomenon," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 25(C), pages 18-32.
  2. Bahram Pesaran & M. Hashem Pesaran, 2010. "Conditional Volatility and Correlations of Weekly Returns and the VaR Analysis of 2008 Stock Market Crash," CESifo Working Paper Series 3023, CESifo Group Munich.
  3. Gian Piero Aielli & Massimiliano Caporin, 2011. "Variance Clustering Improved Dynamic Conditional Correlation MGARCH Estimators," "Marco Fanno" Working Papers 0133, Dipartimento di Scienze Economiche "Marco Fanno".
  4. Pesaran, M.H. & Schleicher, C. & Zaffaroni, P., 2008. "Model Averaging in Risk Management with an Application to Futures Markets," Cambridge Working Papers in Economics 0808, Faculty of Economics, University of Cambridge.
  5. Bas, Maria & Ledezma, Ivan, 2007. "Market Access and the Evolution of within Plant Productivity in Chile," Economics Papers from University Paris Dauphine 123456789/6913, Paris Dauphine University.

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