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Measuring Risk in Fixed Income Portfolios using Yield Curve Models

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  • João Caldeira
  • Guilherme Moura
  • André Santos

Abstract

We propose a novel approach to measure risk in fixed income portfolios in terms of value-at-risk (VaR). We obtain closed-form expressions for the vector of expected bond returns and for its covariance matrix based on a general class of dynamic factor models, including the dynamic versions of the Nelson-Siegel and Svensson models, to compute the parametric VaR of a portfolio composed of fixed income securities. The proposed approach provides additional modeling flexibility as it can accommodate alternative specifications of the yield curve as well as alternative specifications of the conditional heteroskedasticity in bond returns. An empirical application involving a data set with 15 fixed income securities with different maturities indicate that the proposed approach delivers accurate VaR estimates. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • João Caldeira & Guilherme Moura & André Santos, 2015. "Measuring Risk in Fixed Income Portfolios using Yield Curve Models," Computational Economics, Springer;Society for Computational Economics, vol. 46(1), pages 65-82, June.
  • Handle: RePEc:kap:compec:v:46:y:2015:i:1:p:65-82
    DOI: 10.1007/s10614-014-9438-7
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    Cited by:

    1. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Effects on the Riskless Yield Curve with Regime Switching Nelson†Siegel Models," Working Papers 639, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    2. Schlütter, Sebastian, 2017. "Scenario-based capital requirements for the interest rate risk of insurance companies," ICIR Working Paper Series 28/17, Goethe University Frankfurt, International Center for Insurance Regulation (ICIR).
    3. Tu, Anthony H. & Chen, Cathy Yi-Hsuan, 2018. "A factor-based approach of bond portfolio value-at-risk: The informational roles of macroeconomic and financial stress factors," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 243-268.
    4. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Switching Nelson-Siegel Models," BAFFI CAREFIN Working Papers 19106, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    5. Anthony H. Tu & Cathy Yi-Hsuan Chen, 2016. "What Derives the Bond Portfolio Value-at-Risk: Information Roles of Macroeconomic and Financial Stress Factors," SFB 649 Discussion Papers SFB649DP2016-006, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

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