Feasible Estimation Of The Long Term Interest
Rate Dynamics By Nonlinear Techniques
Author(s)
S. Fink & J. Walde
Abstract
Due to the importance of the risk-free capital market interest rate, nearly all large
economic and financial institutions deal with the analysis of its future
development. Although sometimes advanced econometric methods (VAR, ECM)
are used instead of or alongside the standard OLS regression approach, almost all
of the work in this field deals with the basic assumption of (multi-variate,
multi-equation) linear relationships between the variables. In our paper we try to
find out whether nonlinearity can really be neglected. We apply artificial neural
networks as a nonlinear modelling tool. Using monthly data from 1960–2005, we
forecast the interest rate by means of multi-layer perceptrons (MLP). As a
benchmark method we use vector autoregression models dealing with the
identical dataset. The obtained results give evidence of the underlying
nonlinearity of the problem. The MLP outperform the classical tools with regard
to different error measures and especially in capturing the turning points of the
interest curve.
Keywords: error correction model, multi-layer perceptrons, interest rate,
nonlinear modelling.
1 Introduction
Due to the importance of the risk-free capital market rate, nearly all large
economic and financial institutions deal with the analysis of its determinants and
future development. In most cases the German 10-year Government Bond, the
so-called \“Bund”, is used as a benchmark for the European capital market. As
the futures markets are highly liquid, the most widely used forecasting technique
Keywords
error correction model, multi-layer perceptrons, interest rate,
nonlinear modelling.
Related Book
Other papers in this volume
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