Predicting the Yield Curve Using Forecast Combinations

34 Pages Posted: 18 Aug 2013

See all articles by João Caldeira

João Caldeira

Universidade Federal do Rio Grande do Sul (UFRGS)

Guilherme V. Moura

Universidade Federal de Santa Catarina (UFSC) - Department of Economics

Andre A. P. Santos

Universidade Federal de Santa Catarina (UFSC) - Department of Economics; Universidad Carlos III de Madrid - Department of Statistics and Econometrics

Date Written: August 11, 2013

Abstract

We examine the statistical accuracy and economic value of modelling and forecasting the term structure of interest rates using forecast combinations. We adopt five alternative methods to combine point forecasts from several univariate and multivariate autoregressive specifications, as well as from factor models for the yield curve such as the dynamic versions of the Nelson-Siegel and Svensson specifications. Moreover, we conduct a detailed performance evaluation based not only on statistical measures of forecast accuracy, but also an economic criteria like Sharpe ratios of optimal mean-variance fixed income portfolios constructed based upon forecasts from individual models and their alternative combinations. Our empirical application based on a large panel of Brazilian interest rate future contracts with different maturities shows that combined forecasts consistently outperform individual models in several instances, specially when economic criteria are taken into account.

Keywords: yield curve, dynamic factor models, forecast combinations, economic value of forecasts, Kalman

Suggested Citation

Caldeira, João and Moura, Guilherme Valle and A. P. Santos, Andre, Predicting the Yield Curve Using Forecast Combinations (August 11, 2013). Available at SSRN: https://ssrn.com/abstract=2311733 or http://dx.doi.org/10.2139/ssrn.2311733

João Caldeira

Universidade Federal do Rio Grande do Sul (UFRGS) ( email )

Av. Carlos Gomes 1111
Porto Alegre, Rio Grande do Sul 90480-004
Brazil

Guilherme Valle Moura

Universidade Federal de Santa Catarina (UFSC) - Department of Economics ( email )

PO Box 476
Florianopolis, SC 88010-970
Brazil

Andre A. P. Santos (Contact Author)

Universidade Federal de Santa Catarina (UFSC) - Department of Economics ( email )

PO Box 476
Florianopolis, SC 88010-970
Brazil

HOME PAGE: http://sites.google.com/site/andreportela

Universidad Carlos III de Madrid - Department of Statistics and Econometrics ( email )

c/ Madrid 126
Getafe (Madrid), 28903
Spain

HOME PAGE: http://sites.google.com/site/andreportela

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