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Modelling Realized Covariances and Returns

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  • Xin Jin
  • John M Maheu

Abstract

This paper proposes new dynamic component models of realized covariance (RCOV) matrices based on recent work in time-varying Wishart distributions. The specifications are linked to returns for a joint multivariate model of returns and covariance dynamics that is both easy to estimate and forecast. Realized covariance matrices are constructed for 5 stocks using high-frequency intraday prices based on positive semi-definite realized kernel estimates. The models are compared based on a term-structure of density forecasts of returns for multiple forecast horizons. Relative to multivariate GARCH models that use only daily returns, the joint RCOV and return models provide significant improvements in density forecasts from forecast horizons of 1 day to 3 months ahead. Global minimum variance portfolio selection is improved for forecast horizons up to 3 weeks out.

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File URL: http://www.economics.utoronto.ca/public/workingPapers/tecipa-408.pdf
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Bibliographic Info

Paper provided by University of Toronto, Department of Economics in its series Working Papers with number tecipa-408.

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Length: 33 pages
Date of creation: 16 Jul 2010
Date of revision:
Handle: RePEc:tor:tecipa:tecipa-408

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Keywords: eigenvalues; dynamic conditional correlation; predictive likelihoods; MCMC;

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Cited by:
  1. Fang, Yan & Ielpo, Florian & Sévi, Benoît, 2012. "Empirical bias in intraday volatility measures," Finance Research Letters, Elsevier, vol. 9(4), pages 231-237.
  2. Weigand, Roland, 2014. "Matrix Box-Cox Models for Multivariate Realized Volatility," University of Regensburg Working Papers in Business, Economics and Management Information Systems 478, University of Regensburg, Department of Economics.
  3. Minchul Shin & Molin Zhong, 2013. "Does realized volatility help bond yield density prediction?," PIER Working Paper Archive 13-064, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  4. 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.
  5. Fengler, Matthias & Okhrin, Ostap, 2012. "Realized Copula," Economics Working Paper Series 1214, University of St. Gallen, School of Economics and Political Science.
  6. Kevin Sheppard, 2014. "Factor High-Frequency Based Volatility (HEAVY) Models," Economics Series Working Papers 710, University of Oxford, Department of Economics.

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