On Adaptive Modeling of Nonlinear Episodic Regions in KSE-100 Index Returns

This paper employs the Hinich portmanteau bicorrelation test with the windowed testing method to identify nonlinear behavior in the rate of returns series for Karachi Stock Exchange indices. The stock returns series can be described to be comprising of few brief phases of highly significant nonlinearity, followed by long phases in which the returns follow a pure noise process. It has been identified that major political and economic events have contributed to the short bursts of nonlinear behavior in the returns series. Finally, these periods of nonlinear behavior are used to predict the behavior of the rest of the regions using a feedforward neural network and dynamic neural network with Bayesian Regularization Learning. The dynamic neural network outperforms the traditional feedforward networks because Bayesian regularization learning method is used to reduce the training epochs. The time-series generating process is found to closely resemble a white noise process with weak dependence on value at lag one.