Heteroskedasticity Models for Financial Processes Modelling and Forecasting

This study is directed towards modelling financial processes which are widely available on financial markets and forecasting the prices of stock markets. It was approved many times that the financial market is really dynamically changeable, and variety of heteroskedastic models where chosen for studying for this reason. Such models as Autoregressive Moving Average (ARMA), Autoregressive Conditional Heteroskedastic Model (ARCH, and Generalized ARCH model (GARCH)), Exponential Generalized Model of Conditional Heteroscedastic Autoregression (EGARCH), Fractionally Integrated Generalized Model of Conditional Heteroskedastic Autoregression (FIGARCH) were discussed and investigated. An example of real data analysis from stock market illustrates the possibility and reasonability of application the heteroskedasticity models for predicting dynamics of variance.