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Forecasting government bond yields with large Bayesian vector autoregressions

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  • Carriero, Andrea
  • Kapetanios, George
  • Marcellino, Massimiliano

Abstract

We propose a new approach to forecasting the term structure of interest rates, which allows to efficiently extract the information contained in a large panel of yields. In particular, we use a large Bayesian Vector Autoregression (BVAR) with an optimal amount of shrinkage towards univariate AR models. The optimal shrinkage is chosen by maximizing the Marginal Likelihood of the model. Focusing on the US, we provide an extensive study on the forecasting performance of the proposed model relative to most of the existing alternative specifications. While most of the existing evidence focuses on statistical measures of forecast accuracy, we also consider alternative measures based on trading schemes and portfolio allocation. We extensively check the robustness of our results, using different datasets and Monte Carlo simulations. We find that the proposed BVAR approach produces competitive forecasts, systematically more accurate than random walk forecasts, even though the gains are small.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Banking & Finance.

Volume (Year): 36 (2012)
Issue (Month): 7 ()
Pages: 2026-2047

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Handle: RePEc:eee:jbfina:v:36:y:2012:i:7:p:2026-2047

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Web page: http://www.elsevier.com/locate/jbf

Related research

Keywords: Bayesian methods; Forecasting; Marginal likelihood; Term structure;

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References

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Citations

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Cited by:
  1. Gregory R. Duffee, 2012. "Forecasting interest rates," Economics Working Paper Archive 599, The Johns Hopkins University,Department of Economics.
  2. Gary Koop & Dimitris Korobilis, . "A new index of financial conditions," Working Papers 2013_06, Business School - Economics, University of Glasgow.
  3. Helmut Lütkepohl, 2014. "Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey," SFB 649 Discussion Papers SFB649DP2014-004, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  4. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.

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