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Evaluating Interest Rate Covariance Models Within a Value-at-Risk Framework

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  • Miguel A. Ferreira
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    Abstract

    A key component of managing international interest rate portfolios is forecasts of the covariances between national interest rates and accompanying exchange rates. How should portfolio managers choose among the large number of covariance forecasting models available? We find that covariance matrix forecasts generated by models incorporating interest-rate level volatility effects perform best with respect to statistical loss functions. However, within a value-at-risk (VaR) framework, the relative performance of the covariance matrix forecasts depends greatly on the VaR distributional assumption, and forecasts based just on weighted averages of past observations perform best. In addition, portfolio variance forecasts that ignore the covariance matrix generate the lowest regulatory capital charge, a key economic decision variable for commercial banks. Our results provide empirical support for the commonly used VaR models based on simple covariance matrix forecasts and distributional assumptions. Copyright 2005, Oxford University Press.

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    Bibliographic Info

    Article provided by Society for Financial Econometrics in its journal Journal of Financial Econometrics.

    Volume (Year): 3 (2005)
    Issue (Month): 1 ()
    Pages: 126-168

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    Handle: RePEc:oup:jfinec:v:3:y:2005:i:1:p:126-168

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    Cited by:
    1. Caporin, M. & McAleer, M.J., 2012. "Robust Ranking of Multivariate GARCH Models by Problem Dimension," Econometric Institute Research Papers EI2012-13, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Massimiliano Caporin & Michael McAleer, 2011. "Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation," Documentos del Instituto Complutense de Análisis Económico 2011-20, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales.
    3. Massimiliano Caporin & Michael McAleer, 2010. "Ranking Multivariate GARCH Models by Problem Dimension," CIRJE F-Series CIRJE-F-742, CIRJE, Faculty of Economics, University of Tokyo.
    4. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "Are realized volatility models good candidates for alternative Value at Risk prediction strategies?," MPRA Paper 30364, University Library of Munich, Germany.
    5. Coudert, Virginie & Gex, Mathieu, 2010. "Contagion inside the credit default swaps market: The case of the GM and Ford crisis in 2005," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 20(2), pages 109-134, April.
    6. Goeij, P. C. de & Marquering, W., 2009. "Stock and bond market interactions with level and asymmetry dynamics: An out-of-sample application," Open Access publications from Tilburg University urn:nbn:nl:ui:12-3557318, Tilburg University.
    7. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "The role of high frequency intra-daily data, daily range and implied volatility in multi-period Value-at-Risk forecasting," MPRA Paper 35252, University Library of Munich, Germany.
    8. de Goeij, Peter & Marquering, Wessel, 2009. "Stock and bond market interactions with level and asymmetry dynamics: An out-of-sample application," Journal of Empirical Finance, Elsevier, vol. 16(2), pages 318-329, March.
    9. Kalbaska, A. & Gątkowski, M., 2012. "Eurozone sovereign contagion: Evidence from the CDS market (2005–2010)," Journal of Economic Behavior & Organization, Elsevier, vol. 83(3), pages 657-673.
    10. Mitra, Sovan & Date, Paresh & Mamon, Rogemar & Wang, I-Chieh, 2013. "Pricing and risk management of interest rate swaps," European Journal of Operational Research, Elsevier, vol. 228(1), pages 102-111.

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