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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. 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.
    2. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    3. 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.
    4. Geweke, John & Zhou, Guofu, 1996. "Measuring the Pricing Error of the Arbitrage Pricing Theory," Review of Financial Studies, Society for Financial Studies, vol. 9(2), pages 557-587.
    5. 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.
    6. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2006. "Forecasting Using a Large Number of Predictors: Is Bayesian Regression a Valid Alternative to Principal Components?," CEPR Discussion Papers 5829, C.E.P.R. Discussion Papers.
    7. Connor, Gregory & Korajczyk, Robert A., 1986. "Performance measurement with the arbitrage pricing theory : A new framework for analysis," Journal of Financial Economics, Elsevier, vol. 15(3), pages 373-394, March.
    8. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2008. "Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components?," Journal of Econometrics, Elsevier, vol. 146(2), pages 318-328, October.
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    Cited by:

    1. repec:eee:econom:v:198:y:2017:i:2:p:231-252 is not listed on IDEAS
    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. 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.
    4. Han, Xu, 2015. "Tests for overidentifying restrictions in Factor-Augmented VAR models," Journal of Econometrics, Elsevier, vol. 184(2), pages 394-419.
    5. repec:eee:empfin:v:44:y:2017:i:c:p:43-65 is not listed on IDEAS
    6. Smeekes, Stephan & Wijler, Etiënne, 2016. "Macroeconomic Forecasting Using Penalized Regression Methods," Research Memorandum 039, Maastricht University, Graduate School of Business and Economics (GSBE).
    7. Hacioglu, Sinem & Tuzcuoglu, Kerem, 2016. "Interpreting the latent dynamic factors by threshold FAVAR model," Bank of England working papers 622, Bank of England.
    8. 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.
    9. 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.
    10. Chen, Pu, 2010. "A Grouped Factor Model," MPRA Paper 28083, University Library of Munich, Germany, revised 11 Jan 2011.

    More about this item

    JEL classification:

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

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