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Forecasting The Conditional Covariance Matrix Of A Portfolio Under Long-Run Temporal Dependence

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Author Info

  • Antonio Rubia Serrano

    (Universidad de Alicante)

  • Trino-Manuel Ñíguez

    (Universidad de Alicante)

Abstract

Long-range persistence in volatility is widely modelled and forecasted in terms of the so-called fractional integrated models. These models are mostly applied in the univariate framework, since the extension to the multivariate context of assets portfolios, while relevant, is not straightforward. We discuss and apply a procedure which is able to forecast the multivariate volatility of a portfolio including assets with long-memory. The main advantage of this model is that it is feasible enough to be applied on large-scale portfolios, solving the problem of dealing with extremely complex likelihood functions which typically arises in this context. An application of this procedure to a portfolio of five daily exchange rate series shows that the out-of-sample forecasts for the multivariate volatility are improved under several loss-functions when the long-range dependence property of the portfolio assets is explicitly accounted for.

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File URL: http://www.ivie.es/downloads/docs/wpasad/wpasad-2003-34.pdf
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Bibliographic Info

Paper provided by Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie) in its series Working Papers. Serie AD with number 2003-34.

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Length: 30 pages
Date of creation: Oct 2003
Date of revision:
Publication status: Published by Ivie
Handle: RePEc:ivi:wpasad:2003-34

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Related research

Keywords: Exchange Rates; Fractional Integration; Long Memory; MGARCH models; PCA;

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References

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  1. Whitney K. Newey & Kenneth D. West, 1986. "A Simple, Positive Semi-Definite, Heteroskedasticity and AutocorrelationConsistent Covariance Matrix," NBER Technical Working Papers 0055, National Bureau of Economic Research, Inc.
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  3. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
  4. Davidson, James, 2004. "Moment and Memory Properties of Linear Conditional Heteroscedasticity Models, and a New Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 16-29, January.
  5. Robert F. Engle & Victor Ng & Michael Rothschild, 1988. "Asset Pricing with a Factor Arch Covariance Structure: Empirical Estimates for Treasury Bills," NBER Technical Working Papers 0065, National Bureau of Economic Research, Inc.
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  7. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-50, July.
  8. Michel Beine & Sebastien Laurent & Christelle Lecourt, 2002. "Accounting for conditional leptokurtosis and closing days effects in FIGARCH models of daily exchange rates," Applied Financial Economics, Taylor & Francis Journals, vol. 12(8), pages 589-600.
  9. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
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  11. Bollerslev, Tim & Ole Mikkelsen, Hans, 1996. "Modeling and pricing long memory in stock market volatility," Journal of Econometrics, Elsevier, vol. 73(1), pages 151-184, July.
  12. Vilasuso, Jon, 2002. "Forecasting exchange rate volatility," Economics Letters, Elsevier, vol. 76(1), pages 59-64, June.
  13. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
  14. Olivier Ledoit & Michael Wolf, 2001. "Improved estimation of the covariance matrix of stock returns with an application to portofolio selection," Economics Working Papers 586, Department of Economics and Business, Universitat Pompeu Fabra.
  15. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
  16. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
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  18. Brooks, Chris, 2001. "A Double-Threshold GARCH Model for the French Franc/Deutschmark Exchange Rate," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(2), pages 135-43, March.
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Cited by:
  1. Harris, Richard D.F. & Nguyen, Anh, 2013. "Long memory conditional volatility and asset allocation," International Journal of Forecasting, Elsevier, vol. 29(2), pages 258-273.
  2. Christian Conrad, 2007. "Non-negativity Conditions for the Hyperbolic GARCH Model," KOF Working papers 07-162, KOF Swiss Economic Institute, ETH Zurich.
  3. Herwartz, Helmut & Golosnoy, Vasyl, 2007. "Semiparametric Approaches to the Prediction of Conditional Correlation Matrices in Finance," Economics Working Papers 2007,23, Christian-Albrechts-University of Kiel, Department of Economics.

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