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Forecasting Government Bond Yields with Large Bayesian VARs

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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. Focusing on the U.S., we provide an extensive study on the forecasting performance of our 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 evaluate the performance of the alternative forecasts when used within trading schemes or as a basis for portfolio allocation. We extensively check the robustness of our results via subsample analysis and via a data based Monte Carlo simulation. We find that: i) our proposed BVAR approach produces forecasts systematically more accurate than the random walk forecasts, though the gains are small; ii) some models beat the BVAR for a few selected maturities and forecast horizons, but they perform much worse than the BVAR in the remaining cases; iii) predictive gains with respect to the random walk have decreased over time; iv) different loss functions (i.e., "statistical" vs "economic") lead to different ranking of specific models; v) modelling time variation in term premia is important and useful for forecasting.

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Bibliographic Info

Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 7796.

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Date of creation: Apr 2010
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Handle: RePEc:cpr:ceprdp:7796

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Keywords: Bayesian methods; Forecasting; Term Structure;

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References

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Citations

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Cited by:
  1. Cristina Fuentes-Albero & Leonardo Melosi, 2011. "Methods for Computing Marginal Data Densities from the Gibbs Output," Departmental Working Papers 201131, Rutgers University, Department of Economics.
  2. Andrea Carriero & Todd Clark & Massimiliano Marcellino, 2011. "Bayesian VARs: specification choices and forecast accuracy," Working Paper 1112, Federal Reserve Bank of Cleveland.
  3. Haroon Mumtaz & Alexandra Solovyeva & Elena Vasilieva, 2012. "Asset prices, credit and the Russian economy," Joint Research Papers 1, Centre for Central Banking Studies, Bank of England.
  4. Caio Almeida & Axel Simonsen & José Vicente, 2012. "Forecasting Bond Yields with Segmented Term Structure Models," Working Papers Series 288, Central Bank of Brazil, Research Department.
  5. George Kapetanios & Haroon Mumtaz & Ibrahim Stevens & Konstantinos Theodoridis, 2012. "Assessing the Economy‐wide Effects of Quantitative Easing," Economic Journal, Royal Economic Society, vol. 122(564), pages F316-F347, November.
  6. Habrov, Vladimir, 2012. "Optimization of portfolio management based on vector autoregression models and multivariate volatility models," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 28(4), pages 35-62.

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