PEMODELAN VOLATILITAS UNTUK PENGHITUNGAN VALUE AT RISK (VaR) MENGGUNAKAN FEED FORWARD NEURAL NETWORK DAN ALGORITMA GENETIKA - Yasin, Hasbi; Suparti, Suparti (MEDIA STATISTIKA, 2014)
High fluctuations in stock returns is one problem that is considered by the investors. Therefore we need a model that is able to predict accurately the volatility of stock returns. One model that can be used is a model Generalized Autoregressive Conditional Heteroskedasticity (GARCH). This model can serve as a model input in the model Feed Forward Neural Network (FFNN) with Genetic Algorithms as a training algorithm, known as GA-Neuro-GARCH. This modeling is one of the alternatives in modeling the volatility of stock returns. This method is able to show a good performance in modeling the volatility of stock returns. The purpose of this study was to determine the stock return volatility models using a model GA-Neuro-GARCH on stock price data of PT. Indofood Sukses Makmur Tbk. The result shows that the determination of the input variables based on the ARIMA (1,0,1) -GARCH (1,1), so that the model used FFNN consists of 2 units of neurons in the input layer, 5 units of neurons in the hidden layer neuron layer and 1 unit in the output layer. then using a genetic algorithm with crossover probability value of 0.4, was obtained that the Mean Absolute Percentage Error (MAPE) of 0,0039%.Â Keywords: FFNN, Genetic Algorithm, GARCH, Volatility