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A Factor Analysis of Bond Risk Premia

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  • Sydney C. Ludvigson
  • Serena Ng

Abstract

This paper uses the factor augmented regression framework to analyze the relation between bond excess returns and the macro economy. Using a panel of 131 monthly macroeconomic time series for the sample 1964:1-2007:12, we estimate 8 static factors by the method of asymptotic principal components. We also use Gibb sampling to estimate dynamic factors from the 131 series reorganized into 8 blocks. Regardless of how the factors are estimated, macroeconomic factors are found to have statistically significant predictive power for excess bond returns. We show how a bias correction to the parameter estimates of factor augmented regressions can be obtained. This bias is numerically trivial in our application. The predictive power of real activity for excess bond returns is robust even after accounting for finite sample inference problems. Forecasts of excess bond returns (or bond risk premia) are countercyclical. This implies that investors are compensated for risks associated with recessions.

Suggested Citation

  • Sydney C. Ludvigson & Serena Ng, 2009. "A Factor Analysis of Bond Risk Premia," NBER Working Papers 15188, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:15188
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    References listed on IDEAS

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    1. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
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    6. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
    7. Kozicki, Sharon & Tinsley, P.A., 2008. "Term structure transmission of monetary policy," The North American Journal of Economics and Finance, Elsevier, vol. 19(1), pages 71-92, March.
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    Cited by:

    1. Roberta Fiori & Simonetta Iannotti, 2010. "On the interaction between market and credit risk: a factor-augmented vector autoregressive (FAVAR) approach," Temi di discussione (Economic working papers) 779, Bank of Italy, Economic Research and International Relations Area.
    2. Stefano Giglio & Dacheng Xiu, 2017. "Inference on Risk Premia in the Presence of Omitted Factors," NBER Working Papers 23527, National Bureau of Economic Research, Inc.
    3. Romain Houssa & Lasse Bork & Hans Dewachter, 2008. "Identification of Macroeconomic Factors in Large Panels," Working Papers 1010, University of Namur, Department of Economics.
    4. Han, Xu, 2015. "Tests for overidentifying restrictions in Factor-Augmented VAR models," Journal of Econometrics, Elsevier, vol. 184(2), pages 394-419.
    5. Xyngis, Georgios, 2017. "Business-cycle variation in macroeconomic uncertainty and the cross-section of expected returns: Evidence for scale-dependent risks," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 43-65.
    6. Laura Nowzohour & Livio Stracca, 2020. "More Than A Feeling: Confidence, Uncertainty, And Macroeconomic Fluctuations," Journal of Economic Surveys, Wiley Blackwell, vol. 34(4), pages 691-726, September.
    7. Smeekes, Stephan & Wijler, Etienne, 2018. "Macroeconomic forecasting using penalized regression methods," International Journal of Forecasting, Elsevier, vol. 34(3), pages 408-430.
    8. Hacioglu, Sinem & Tuzcuoglu, Kerem, 2016. "Interpreting the latent dynamic factors by threshold FAVAR model," Bank of England working papers 622, Bank of England.
    9. Jian Yang & Yinggang Zhou & Zijun Wang, 2010. "Conditional Coskewness in Stock and Bond Markets: Time-Series Evidence," Management Science, INFORMS, vol. 56(11), pages 2031-2049, November.
    10. Markus Pelger & Ruoxuan Xiong, 2022. "State-Varying Factor Models of Large Dimensions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1315-1333, June.
    11. Gonçalves, Sílvia & McCracken, Michael W. & Perron, Benoit, 2017. "Tests of equal accuracy for nested models with estimated factors," Journal of Econometrics, Elsevier, vol. 198(2), pages 231-252.
    12. Jonas Krampe & Luca Margaritella, 2021. "Factor Models with Sparse VAR Idiosyncratic Components," Papers 2112.07149, arXiv.org, revised May 2022.
    13. repec:dau:papers:123456789/6800 is not listed on IDEAS
    14. Cui, Qiurong & Xu, Yuqing & Zhang, Zhengjun & Chan, Vincent, 2021. "Max-linear regression models with regularization," Journal of Econometrics, Elsevier, vol. 222(1), pages 579-600.
    15. Helitzer, Deborah & Hollis, Christine & Hernandez, Brisa Urquieta de & Sanders, Margaret & Roybal, Suzanne & Van Deusen, Ian, 2010. "Evaluation for community-based programs: The integration of logic models and factor analysis," Evaluation and Program Planning, Elsevier, vol. 33(3), pages 223-233, August.
    16. Piyachart Phiromswad & Takeshi Yagihashi, 2016. "Empirical identification of factor models," Empirical Economics, Springer, vol. 51(2), pages 621-658, September.
    17. Mehmet Balcilar & Abebe Beyene & Rangan Gupta & Monaheng Seleteng, 2013. "‘Ripple’ Effects in South African House Prices," Urban Studies, Urban Studies Journal Limited, vol. 50(5), pages 876-894, April.
    18. Buckmann, Marcus & Joseph, Andreas, 2022. "An interpretable machine learning workflow with an application to economic forecasting," Bank of England working papers 984, Bank of England.
    19. Chen, Pu, 2010. "A Grouped Factor Model," MPRA Paper 28083, University Library of Munich, Germany, revised 11 Jan 2011.

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    More about this item

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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