Forecasting government bond yields with large Bayesian vector autoregressions
Journal of Banking and Finance, 2012, Vol. 36, No. 7, pp. 2026-2047
CARRIERO, Andrea, KAPETANIOS, George, MARCELLINO, Massimiliano, Forecasting government bond yields with large Bayesian vector autoregressions, Journal of Banking and Finance, 2012, Vol. 36, No. 7, pp. 2026-2047 - http://hdl.handle.net/1814/31059
Retrieved from Cadmus, EUI Research Repository
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.
Cadmus permanent link: http://hdl.handle.net/1814/31059
Full-text via DOI: 10.1016/j.jbankfin.2012.03.008
ISSN: 1872-6372; 0378-4266
Initial version: http://hdl.handle.net/1814/13738
Version: Published version of EUI ECO WP 2010/17
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