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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.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 15188.

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Date of creation: Jul 2009
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Publication status: published as "A Factor Analysis of Bond Risk Premia" (with Serena Ng). Handbook of Empirical Economics and Finance, 2010, e.d. by Aman Uhla and David E. A. Giles, pp. 313-372. Chapman and Hall, Boca Raton, FL.
Handle: RePEc:nbr:nberwo:15188

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  1. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2006. "Forecasting using a large number of predictors: Is Bayesian regression a valid alternative to principal components?," Working Paper Series 0700, European Central Bank.
  2. 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, American Statistical Association, vol. 100, pages 830-840, September.
  3. Piazzesi, Monika & Swanson, Eric T., 2008. "Futures prices as risk-adjusted forecasts of monetary policy," Journal of Monetary Economics, Elsevier, Elsevier, vol. 55(4), pages 677-691, May.
  4. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, Econometric Society, vol. 70(1), pages 191-221, January.
  5. Sharon Kozicki & P.A. Tinsley, 2007. "Term Structure Transmission of Monetary Policy," Working Papers 07-30, Bank of Canada.
  6. Connor, Gregory & Korajczyk, Robert A., 1986. "Performance measurement with the arbitrage pricing theory : A new framework for analysis," Journal of Financial Economics, Elsevier, Elsevier, vol. 15(3), pages 373-394, March.
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Cited by:
  1. Chen, Pu, 2010. "A Grouped Factor Model," MPRA Paper 28083, University Library of Munich, Germany, revised 11 Jan 2011.
  2. 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, Elsevier, vol. 33(3), pages 223-233, August.

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