engleski

Nonlinear Extension of Asymmetric GARCH Model within Neural Network Framework

The importance of volatility for all market participants has led to the development and application of various econometric models. The most popular models in modelling volatility are GARCH type models because they can account excess kurtosis and asymmetric effects of financial time series. Since standard GARCH(1, 1) model usually indicate high persistence in the conditional variance, the empirical researches turned to GJR-GARCH model and reveal its superiority in fitting the asymmetric heteroscedasticity in the data. In order to capture both asymmetry and nonlinearity in data, the goal of this paper is to develop a parsimonious NN model as an extension to GJR- GARCH model and to determine if GJR-GARCH-NN outperforms the GJR-GARCH model.

conditional volatility ; GARCH model ; GJR model ; Neural Networks ; emerging markets

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