Purpose–Stock price prediction is a hot topic and traditional prediction methods are usually
based on statistical and econometric models. However, these models are difficult to deal …

This study presents an integrated system where wavelet transforms and recurrent neural
network (RNN) based on artificial bee colony (abc) algorithm (called ABC-RNN) are …

We document a highly significant, strongly nonlinear dependence of stock and bond returns
on past equity market volatility as measured by the VIX. We propose a new estimator for the …

Stock market is a dynamic and volatile market that is considered as time series data. The
growth of financial data exposed the computational efficiency of the conventional systems …

Supplement to “A specification test for nonlinear nonstationary models”. Further details on
the derivations in the present paper and supporting lemmas and proofs of the main results …

Financial theory and econometric methodology both struggle in formulating models that are
logically sound in reconciling short-run martingale behavior for financial assets with …

A unifying framework for inference is developed in predictive regressions where the
predictor has unknown integration properties and may be stationary or nonstationary. Two …

Because of global lock-downs caused by the unexpected COVID-19, the interactions
between emission trading and related markets have changed significantly compared to the …

For a certain class of martingales, convergence to a mixture of normal distributions is
established under convergence in distribution for the conditional variance. This is less …

This paper considers a nonparametric time series regression model with a nonstationary
regressor. We construct a nonparametric test for whether the regression is of a known …