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

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  • Pesaran, Bahram

    ()
    (affiliation not available)

  • Pesaran, M. Hashem

    ()
    (University of Cambridge)

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 Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 2906.

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Length: 37 pages
Date of creation: Jul 2007
Date of revision:
Handle: RePEc:iza:izadps:dp2906

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Keywords: volatilities and correlations; futures market; multivariate t; financial interdependence; VaR diagnostics;

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  1. 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, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-62, November.
  2. Jose A. Lopez, 1998. "Methods for evaluating value-at-risk estimates," Economic Policy Review, Federal Reserve Bank of New York, Federal Reserve Bank of New York, issue Oct, pages 119-124.
  3. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, Cambridge University Press, vol. 11(01), pages 122-150, February.
  4. 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, American Statistical Association, vol. 21(4), pages 532-46, October.
  5. Pesaran, M. Hashem & Timmermann, Allan, 2004. "Real Time Econometrics," IZA Discussion Papers 1108, Institute for the Study of Labor (IZA).
  6. Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Ebens, Heiko, 2001. "The distribution of realized stock return volatility," Journal of Financial Economics, Elsevier, Elsevier, vol. 61(1), pages 43-76, July.
  7. 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.
  8. Engle, Robert F & Manganelli, Simone, 1999. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," University of California at San Diego, Economics Working Paper Series, Department of Economics, UC San Diego qt06m3d6nv, Department of Economics, UC San Diego.
  9. Sentana, E., 1994. "The Likelihood Function of a Conditionally Heteroskdastic Factor Model with Heywood Cases," Papers, Centro de Estudios Monetarios Y Financieros- 9420, Centro de Estudios Monetarios Y Financieros-.
  10. BAUWENS, Luc & LAURENT, Sébastien, . "A new class of multivariate skew densities, with application to generalized autoregressive conditional heteroscedasticity models," CORE Discussion Papers RP, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) -1793, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  11. 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, American Statistical Association, vol. 96, pages 42-55, March.
  12. BAUWENS, Luc & LAURENT, Sébastien & ROMBOUTS, Jeroen, 2003. "Multivariate GARCH models: a survey," CORE Discussion Papers, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) 2003031, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  13. 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.
  14. Berkowitz, Jeremy, 2001. "Testing Density Forecasts, with Applications to Risk Management," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 19(4), pages 465-74, October.
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Citations

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Cited by:
  1. 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.
  2. Pesaran, M. Hashem & Schleicher, Christoph & Zaffaroni, Paolo, 2009. "Model averaging in risk management with an application to futures markets," Journal of Empirical Finance, Elsevier, Elsevier, vol. 16(2), pages 280-305, March.
  3. 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.
  4. 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, Elsevier, vol. 25(C), pages 18-32.
  5. Aielli, Gian Piero & Caporin, Massimiliano, 2014. "Variance clustering improved dynamic conditional correlation MGARCH estimators," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 76(C), pages 556-576.
  6. Rizvi, Syed Aun & Masih, Mansur, 2013. "Do Shariah (Islamic) Indices Provide a Safer Avenue in Crisis? Empirical Evidence from Dow Jones Indices using Multivariate GARCH-DCC," MPRA Paper 57701, University Library of Munich, Germany.

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