The study analyzes the family of regime switching GARCH neural network models, which allow the generalization of MS type RS-GARCH models to MS-GARCH-NN models by incorparating with neural network architectures with different dynamics and forecasting capabilities both in addition to the family of...

The study proposes and a family of regime switching GARCH neural network models to model volatility. The proposed MS-ARMA-GARCH-NN models allow MS type regime switching in both the conditional mean and conditional variance for time series and further augmented with artificial neural networks to...

We investigate high-frequency volatility models for analyzing intra-day tick by tick stock price changes using Bayesian estimation procedures. Our key interest is the extraction of intra-day volatility patterns from high-frequency integer price changes. We account for the discrete nature of the...