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Forecasting a Large Dimensional Covariance Matrix of a Portfolio of Different Asset Classes

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Author Info

  • Lillie Lam

    (Research Department, Hong Kong Monetary Authority)

  • Laurence Fung

    (Research Department, Hong Kong Monetary Authority)

  • Ip-wing Yu

    (Research Department, Hong Kong Monetary Authority)

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    Abstract

    In portfolio and risk management, estimating and forecasting the volatilities and correlations of asset returns plays an important role. Recently, interest in the estimation of the covariance matrix of large dimensional portfolios has increased. Using a portfolio of 63 assets covering stocks, bonds and currencies, this paper aims to examine and compare the predictive power of different popular methods adopted by i) market practitioners (such as the sample covariance, the 250-day moving average, and the exponentially weighted moving average); ii) some sophisticated estimators recently developed in the academic literature (such as the orthogonal GARCH model and the Dynamic Conditional Correlation model); and iii) their combinations. Based on five different criteria, we show that a combined forecast of the 250-day moving average, the exponentially weighted moving average and the orthogonal GARCH model consistently outperforms the other methods in predicting the covariance matrix for both one-quarter and one-year ahead horizons.

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    File URL: http://www.info.gov.hk/hkma/eng/research/working/pdf/HKMAWP09_01_full.pdf
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    Bibliographic Info

    Paper provided by Hong Kong Monetary Authority in its series Working Papers with number 0901.

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    Length: 32 pages
    Date of creation: Jan 2009
    Date of revision:
    Handle: RePEc:hkg:wpaper:0901

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    Related research

    Keywords: Volatility forecasting; Risk management; Portfolio management; Model evaluation;

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    1. Torben G. Andersen & Luca Benzoni, 2008. "Realized volatility," Working Paper Series, Federal Reserve Bank of Chicago WP-08-14, Federal Reserve Bank of Chicago.
    2. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Jin Wu, 2005. "A Framework for Exploring the Macroeconomic Determinants of Systematic Risk," American Economic Review, American Economic Association, American Economic Association, vol. 95(2), pages 398-404, May.
    3. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers, National Bureau of Economic Research, Inc 0169, National Bureau of Economic Research, Inc.
    4. Valeri Voev, 2007. "Dynamic Modeling of Large Dimensional Covariance Matrices," CoFE Discussion Paper, Center of Finance and Econometrics, University of Konstanz 07-01, Center of Finance and Econometrics, University of Konstanz.
    5. Engle, Robert F & Sheppard, Kevin K, 2001. "Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH," University of California at San Diego, Economics Working Paper Series, Department of Economics, UC San Diego qt5s2218dp, Department of Economics, UC San Diego.
    6. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, Econometric Society, vol. 71(2), pages 579-625, March.
    7. Hans Bystrom, 2004. "Orthogonal GARCH and covariance matrix forecasting: The Nordic stock markets during the Asian financial crisis 1997-1998," The European Journal of Finance, Taylor & Francis Journals, Taylor & Francis Journals, vol. 10(1), pages 44-67.
    8. Fleming, Jeff & Kirby, Chris & Ostdiek, Barbara, 2003. "The economic value of volatility timing using "realized" volatility," Journal of Financial Economics, Elsevier, Elsevier, vol. 67(3), pages 473-509, March.
    9. Luc Bauwens & David Veredas & Winfried Pohlmeier, 2005. "High frequency finance," ULB Institutional Repository, ULB -- Universite Libre de Bruxelles 2013/136220, ULB -- Universite Libre de Bruxelles.
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