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Bond Risk Premia Forecasting: A Simple Approach for Extracting¨Macroeconomic Information from a Panel of Indicators

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  • Francesco Audrino

    ()

  • Fulvio Corsi

    ()

  • Kameliya Filipova

    ()

Abstract

We propose a simple but effective estimation procedure to extract the level and the volatility dynamics of a latent macroeconomic factor from a panel of observable indicators. Our approach is based on a multivariate conditionally heteroskedastic exact factor model that can take into account the heteroskedasticity feature shown by most macroeconomic variables and relies on an iterated Kalman filter procedure. In simulations we show the unbiasedness of the proposed estimator and its superiority to different approaches introduced in the literature. Simulation results are confirmed in applications to real inflation data with the goal of forecasting long-term bond risk premia. Moreover, we find that the extracted level and conditional variance of the latent factor for inflation are strongly related to NBER business cycles.

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Bibliographic Info

Paper provided by Department of Economics, University of St. Gallen in its series University of St. Gallen Department of Economics working paper series 2010 with number 2010-09.

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Length: 24 pages
Date of creation: Mar 2010
Date of revision:
Handle: RePEc:usg:dp2010:2010-09

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Keywords: Macroeconomic variables; Exact factor model; Kalman filter; Heteroskedasticity; Forecasting bond risk premia; Inflation measures; Business cycles;

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  1. Gabriele Fiorentini & Enrique Sentana & Neil Shephard, 2004. "Likelihood-Based Estimation of Latent Generalized ARCH Structures," Econometrica, Econometric Society, vol. 72(5), pages 1481-1517, 09.
  2. Patrick GAGLIARDINI & Christian GOURIEROUX, 2008. "Efficiency in Large Dynamic Panel Models with Common Factor," Swiss Finance Institute Research Paper Series 09-12, Swiss Finance Institute, revised Mar 2009.
  3. John H. Cochrane & Monika Piazzesi, 2002. "Bond Risk Premia," NBER Working Papers 9178, National Bureau of Economic Research, Inc.
  4. Gregory R. Duffee, 2011. "Information in (and not in) the Term Structure," Review of Financial Studies, Society for Financial Studies, vol. 24(9), pages 2895-2934.
  5. 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.
  6. Gabriele Fiorentini & Giorgio Calzolari & Enrique Sentana, 2007. "Indirect estimation of large conditionally heteroskedastic factor models, with an application to the Dow 30 stocks," Working Paper Series 40-07, The Rimini Centre for Economic Analysis, revised Jul 2007.
  7. Francis X. Diebold & Marc Nerlove, 1986. "The dynamics of exchange rate volatility: a multivariate latent factor ARCH model," Special Studies Papers 205, Board of Governors of the Federal Reserve System (U.S.).
  8. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
  9. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
  10. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
  11. Harvey, A. C. & Ruiz, Esther & Sentana, E., 1992. "Unobserved Component Time Series Models with ARCH Disturbances," Open Access publications from Universidad Carlos III de Madrid info:hdl:10016/4702, Universidad Carlos III de Madrid.
  12. Sydney C. Ludvigson & Serena Ng, 2009. "A Factor Analysis of Bond Risk Premia," NBER Working Papers 15188, National Bureau of Economic Research, Inc.
  13. Sentana, Enrique, 2004. "Factor representing portfolios in large asset markets," Journal of Econometrics, Elsevier, vol. 119(2), pages 257-289, April.
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