Statistical models of KSE100 index using Hybrid Financial Systems

This paper utilizes hybrid financial systems (HFSs) to model Karachi Stock Exchange index data, KSE100. These models are used for short-term forecasting of Karachi Stock Exchange index data, KSE100. These HFSs developed for this purpose are combination of artificial neural networks (ANN) model and ARIMA or ARCH/GARCH models. ARIMA and ARCH/GARCH models were provided as patterns to ANN. We compared ANN with ARIMA and ARCH/GARCH on the basis of forecast mean square of errors (FMSE), ANN gave better forecasting performance and out played ARIMA and ARCH/GARCH models. While comparing the performance of HFSs of ANN_ARIMA and ANN_ARCHGARCH/ with ANN model on the basis of FMSE, it is found that the HFS of ANN_ARCHGARCH/ is superior to ANN and ANN_ARIMA in forecasting