Efficient Calibration of a Multi-Objective Artificial Network to Amplify Directional Volatility Spillovers in European Government Bond Markets

G.H. Dash, Jr. and N. Kajiji (USA)


Financial visualization, volatility spillovers, neural networks.


In this paper we extend prior efforts to engineer an efficient mapping of volatility transmission across various western- and central-European government bond markets. The closed-form derivation of the Bayesian-enhanced regularization parameter embodied by the Kajiji-4 RBF ANN is known to produce 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 ĂȘtre for a comparative re-examination of the bond volatility spillover effects obtained from contemporary parametric-based conditional volatility investigations. The research proposes a two step methodology that first engineers an efficient ANN modelling of the latent EGARCH effects in the target financial time-series which is then followed by a focus on comparative modelling efficiency using both linear and nonlinear residual return diagnostics. We report substantial evidence to support the ex-ante expectation for the Kajiji-4 RBF ANN to produce residuals that are devoid of latent economic covariance and conditional volatility effects. Policy inferences generated from the model s weights clearly corroborate reported findings of a weak US spillover effect into established European bond markets while offering new insights into the inverted volatility spillover effects attributed to US support for select developing central-European economies.

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