Out‐of‐Sample Forecasts and Nonlinear Model Selection with an Example of the Term Structure of Interest Rates

We examined the dynamic linkages among money market interest rates in the so-called “BRICS” countries (Brazil, Russia, India, China, and South Africa) by using weekly data of the overnight, one-, three-, and six- months, as well as of one year, Treasury bills rates covering the period from January 2005 to August 2019. A long-run relationship among interest rates was established by employing the Vector Error Correction modeling (VECM), which revealed the validation of the Expectation Hypothesis Theory (EH) of the term structure of interest rates, taking into account long-run deviations from equilibrium and inherent nonlinearities. We unveiled short-run dynamic adjustments for the term structure of the BRICS, subject to regime switches. We then used Markov Switching Vector Error Correction models (MS-VECM) to forecast them dynamically during an out-of-sample period of May 2016 through August 2019. The MSIH-VECM forecasts were found to be superior to the VECM approaches. The novelty of our paper is mainly due to the exploration of the possibility of parameter instability as a crucial factor, which might explain the rejection of the restricted version of the cointegration space, and on the dynamic out-of-sample forecasts of the term structure over a more recent time span in order to assess further the usefulness of our nonlinear MS-VECM characterization of the term structure, capturing the effects of the global and domestic financial crisis.

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Term Structure, Inflation, and Real Activity

Journal of Financial and Quantitative Analysis ◽

10.1017/s0022109009990184 ◽

2009 ◽

Vol 44 (4) ◽

pp. 987-1011 ◽

Cited By ~ 18

Author(s):

Andrea Berardi

Keyword(s):

Interest Rates ◽

Term Structure ◽

Structural Model ◽

Output Growth ◽

Cross Sectional ◽

Real Activity ◽

Conditional Mean ◽

Inflation Rates ◽

Out Of Sample ◽

Inflation Risk Premia

AbstractThis paper estimates an internally consistent structural model that imposes cross-sectional restrictions on the dynamics of the term structure of interest rates, inflation, and output growth. Distinct from previous term structure settings, this model introduces both time-varying central tendencies and a stochastic conditional mean of output growth. The estimation of the model, which is based on U.S. data over a 1960 to 2005 sample period, provides reliable estimates for the implicit term structures of real interest rates, expected inflation rates, and inflation risk premia, as well as for expectations of macroeconomic variables. The model has better out-of-sample forecasting properties than a number of alternative models, and it contradicts the puzzling evidence that during the “Great Moderation” in inflation subsequent to the mid-1980s, the forecasting ability of structural models deteriorated with respect to atheoretic statistical models.

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Analyzing and forecasting the Chinese term structure of interest rates using functional principal component analysis

China Finance Review International ◽

10.1108/cfri-06-2017-0065 ◽

2018 ◽

Vol 8 (3) ◽

pp. 275-296 ◽

Cited By ~ 4

Author(s):

Pan Feng ◽

Junhui Qian

Keyword(s):

Principal Component Analysis ◽

Interest Rates ◽

Term Structure ◽

Yield Curve ◽

Principal Component ◽

Functional Principal Component Analysis ◽

Content Type ◽

Functional Principal Component ◽

Out Of Sample ◽

Chinese Term

Purpose The purpose of this paper is to analyze and forecast the Chinese term structure of interest rates using functional principal component analysis (FPCA). Design/methodology/approach The authors propose an FPCA-K model using FPCA. The forecasting of the yield curve is based on modeling functional principal component (FPC) scores as standard scalar time series models. The authors evaluate the out-of-sample forecast performance using the root mean square and mean absolute errors. Findings Monthly yield data from January 2002 to December 2016 are used in this paper. The authors find that in the full sample, the first two FPCs account for 98.68 percent of the total variation in the yield curve. The authors then construct an FPCA-K model using the leading principal components. The authors find that the FPCA-K model compares favorably with the functional signal plus noise model, the dynamic Nelson-Siegel models and the random walk model in the out-of-sample forecasting. Practical implications The authors propose a functional approach to analyzing and forecasting the yield curve, which effectively utilizes the smoothness assumption and conveniently addresses the missing-data issue. Originality/value To the best knowledge, the authors are the first to use FPCA in the modeling and forecasting of yield curves.

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Interest Rates under Falling Stars

The American Economic Review ◽

10.1257/aer.20171822 ◽

2020 ◽

Vol 110 (5) ◽

pp. 1316-1354 ◽

Cited By ~ 14

Author(s):

Michael D. Bauer ◽

Glenn D. Rudebusch

Keyword(s):

Interest Rates ◽

Term Structure ◽

Yield Curve ◽

Term Premium ◽

Long Run ◽

Trend Inflation ◽

Bond Returns ◽

Out Of Sample ◽

Finance Theory ◽

Macro Finance

Macro-finance theory implies that trend inflation and the equilibrium real interest rate are fundamental determinants of the yield curve. However, empirical models of the term structure of interest rates generally assume that these fundamentals are constant. We show that accounting for time variation in these underlying long-run trends is crucial for understanding the dynamics of Treasury yields and predicting excess bond returns. We introduce a new arbitrage-free model that captures the key role that long-run trends play in determining interest rates. The model also provides new, more plausible estimates of the term premium and accurate out-of-sample yield forecasts. (JEL E31, E43, E47)

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