A Re-examination Of Volatility Spillovers In
European Government Bond Markets Using
A Multi-objective Artificial Network
Author(s)
G. H. Dash, Jr. & N. Kajiji
Abstract
In this paper we extend prior efforts to engineer an efficient mapping of volatility
transmission across various western- and central-European government bond
markets. Prior research efforts report that the closed-form derivation of the
regularization parameter embodied by the Kajiji-4 RBF ANN results in an
efficient minimization of the ill-effects of multi-collinearity while attaining
maximum smoothness in nonparametric time series analysis. This computational
innovation provides the raison d’être for a comparative re-examination of
volatility spillover effects obtained from the study of parametric-based
conditional volatility investigations. The current research calibrates the Kajiji-4
ANN to produce new evidence on volatility flows. The two step research
method focuses first on the art of ANN engineering of financial time-series. The
method then focuses on the resultant modelling efficiency by introducing an
investigatory ARCH-framework as well as a classification-directed ANN. The
post-modelling efficiency tests certify the ex-ante expectation for the Kajiji-4
RBF ANN to produce residuals that are devoid of latent economic covariance
and conditional volatility effects. Moreover, we find that the estimated Kajiji-4
network parameters yield corroborative evidence that supports the broader
findings in the extant literature on bond volatility-spillover effects. However, the
non-parametric approach also produced results that challenge some
contemporary findings. Most notably, the research findings contradict the view
of a weak US volatility-spillover into EMU countries with a correspondingly
strong spillover effect for non-EMU countries.
Keywords: volatility, spillovers, bond markets, neural networks, radial basis
functions, artificial neural networks.
Keywords
volatility, spillovers, bond markets, neural networks, radial basis
functions, artificial neural networks.
Related Book
Other papers in this volume
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