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Multi-level factor analysis of bond risk premia

Author

Listed:
  • Kim Dukpa

    (Department of Economics, Korea University, Seongbuk-gu, Seoul, 02841 Korea)

  • Kim Yunjung

    (Department of Economics, Korea University, Seongbuk-gu, Seoul, 02841 Korea)

  • Bak Yuhyeon

    (Department of Economics, Korea University, Seongbuk-gu, Seoul, 02841 Korea)

Abstract

Earlier studies in the finance literature show that macroeconomic fundamentals can predict excess bond returns. We employ a multi-level factor model to estimate global and sectoral factors separately and show that (i) the real factors possess most important predictive power existing in the panel; (ii) the financial factors might have some predictive power but less than the real factors; (iii) the inflation factors have almost no predictive power and (iv) the excess bond returns have a countercyclical component.

Suggested Citation

  • Kim Dukpa & Kim Yunjung & Bak Yuhyeon, 2017. "Multi-level factor analysis of bond risk premia," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(5), pages 1-19, December.
    • Handle: RePEc:bpj:sndecm:v:21:y:2017:i:5:p:19:n:2
    DOI: 10.1515/snde-2015-0080
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    References listed on IDEAS

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    1. Piazzesi, Monika & Swanson, Eric T., 2008. "Futures prices as risk-adjusted forecasts of monetary policy," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 677-691, May.
    2. Scott Joslin & Marcel Priebsch & Kenneth J. Singleton, 2014. "Risk Premiums in Dynamic Term Structure Models with Unspanned Macro Risks," Journal of Finance, American Finance Association, vol. 69(3), pages 1197-1233, June.
    3. Tomohiro Ando & Jushan Bai, 2015. "Asset Pricing with a General Multifactor Structure," Journal of Financial Econometrics, Oxford University Press, vol. 13(3), pages 556-604.
    4. Ang, Andrew & Piazzesi, Monika, 2003. "A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables," Journal of Monetary Economics, Elsevier, vol. 50(4), pages 745-787, May.
    5. Sydney C. Ludvigson & Serena Ng, 2009. "Macro Factors in Bond Risk Premia," The Review of Financial Studies, Society for Financial Studies, vol. 22(12), pages 5027-5067, December.
    6. Bai, Jushan & Ng, Serena, 2006. "Evaluating latent and observed factors in macroeconomics and finance," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 507-537.
    7. Dias Francisco & Pinheiro Maximiano & Rua António, 2013. "Determining the number of global and country-specific factors in the euro area," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(5), pages 573-617, December.
    8. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
    9. Jonathan H. Wright, 2011. "Term Premia and Inflation Uncertainty: Empirical Evidence from an International Panel Dataset," American Economic Review, American Economic Association, vol. 101(4), pages 1514-1534, June.
    10. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
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    Cited by:

    1. Ioannis A. Venetis & Avgoustinos Ladas, 2023. "Co-movement and global factors in sovereign bond yields," Bulletin of Applied Economics, Risk Market Journals, vol. 10(2), pages 17-45.

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    Keywords

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    JEL classification:

    • E0 - Macroeconomics and Monetary Economics - - General
    • E4 - Macroeconomics and Monetary Economics - - Money and Interest Rates
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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