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Forecasting realized (co)variances with a block structure Wishart autoregressive model

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  • Matteo Bonato
  • Massimiliano Caporin
  • Angelo Ranaldo

Abstract

In modelling and forecasting volatility, two main trade-offs emerge: mathematical tractability versus economic interpretation and accuracy versus speed. The authors attempt to reconcile, at least partially, both trade-offs. The former trade-off is crucial for many financial applications, including portfolio and risk management. The speed/accuracy trade-off is becoming more and more relevant in an environment of large portfolios, prolonged periods of high volatility (as in the current financial crisis), and the burgeoning phenomenon of algorithmic trading in which computer-based trading rules are automatically implemented. The increased availability of high-frequency data provides new tools for forecasting variances and covariances between assets. However, there is scant literature on forecasting more than one realised volatility. Following Gourieroux, Jasiak and Sufana (Journal of Econometrics, forthcoming), the authors propose a methodology to model and forecast realised covariances without any restriction on the parameters while maintaining economic interpretability. An empirical application based on variance forecasting and risk evaluation of a portfolio of two US treasury bills and two exchange rates is presented. The authors compare their model with several alternative specifications proposed in the literature. Empirical findings suggest that the model can be efficiently used in large portfolios.

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Bibliographic Info

Paper provided by Swiss National Bank in its series Working Papers with number 2009-03.

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Length: 40 pages
Date of creation: 2009
Date of revision:
Handle: RePEc:snb:snbwpa:2009-03

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Keywords: Wishart process; realized volatility; Granger causality; volatility spillover; Value-at-Risk;

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References

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Citations

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Cited by:
  1. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2012. "Financial Risk Measurement for Financial Risk Management," NBER Working Papers 18084, National Bureau of Economic Research, Inc.
  2. Bonato, Matteo & Caporin, Massimiliano & Ranaldo, Angelo, 2013. "Risk spillovers in international equity portfolios," Journal of Empirical Finance, Elsevier, Elsevier, vol. 24(C), pages 121-137.
  3. Massimiliano Caporin & Michael McAleer, 2010. "Ranking Multivariate GARCH Models by Problem Dimension," CIRJE F-Series, CIRJE, Faculty of Economics, University of Tokyo CIRJE-F-742, CIRJE, Faculty of Economics, University of Tokyo.
  4. Massimiliano Caporin & Michael McAleer, 2009. "Do We Really Need Both BEKK and DCC? A Tale of Two Covariance Models," Documentos de Trabajo del ICAE, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico 0904, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
  5. Nikolaus Hautsch & Lada M. Kyj & Roel C. A. Oomen, 2012. "A blocking and regularization approach to high‐dimensional realized covariance estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 27(4), pages 625-645, 06.
  6. BAUWENS, Luc & STORTI, Giuseppe & VIOLANTE, Francesco, 2012. "Dynamic conditional correlation models for realized covariance matrices," CORE Discussion Papers, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) 2012060, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  7. BAUWENS, Luc & STORTI, Giuseppe, . "Computationally efficient inference procedures for vast dimensional realized covariance models," CORE Discussion Papers RP, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) -2469, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  8. Valeri Voev, 2009. "On the Economic Evaluation of Volatility Forecasts," CREATES Research Papers, School of Economics and Management, University of Aarhus 2009-56, School of Economics and Management, University of Aarhus.
  9. Fengler, Matthias R. & Gisler, Katja I. M., 2014. "A variance spillover analysis without covariances: what do we miss?," Economics Working Paper Series, University of St. Gallen, School of Economics and Political Science 1409, University of St. Gallen, School of Economics and Political Science.

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