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Forecasting Government Bond Yields with Large Bayesian VARs

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  • A. Carriero
  • G. Kapetanios
  • M. Marcellino

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 speci.cations. 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 .nd 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) di¤erent loss functions (i.e., "statistical" vs "economic") lead to di¤erent ranking of speci.c models; v) modelling time variation in term premia is important and useful for forecasting.

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Bibliographic Info

Paper provided by European University Institute in its series Economics Working Papers with number ECO2010/17.

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Date of creation: 2010
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Handle: RePEc:eui:euiwps:eco2010/17

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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. Andrea Carriero & Todd Clark & Massimiliano Marcellino, 2011. "Bayesian VARs: specification choices and forecast accuracy," Working Paper 1112, Federal Reserve Bank of Cleveland.
  2. 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.
  3. 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.
  4. Kapetanios, George & Mumtaz, Haroon & Stevens, Ibrahim & Theodoridis, Konstantinos, 2012. "Assessing the economy-wide effects of quantitative easing," Bank of England working papers 443, Bank of England.
  5. 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.
  6. 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.

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