Volatility Modelling Using Hybrid Autoregressive Conditional Heteroskedasticity (ARCH) - Support Vector Regression (SVR)

Abstract

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 Autoregressive Conditional Heteroskedasticity (ARCH). This model can serve as a model input in the Support Vector Regression (SVR) model, known as Hybrid ARCH-SVR. 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 Hybrid ARCH-SVR model on stock price data of PT. Indofood Sukses Makmur Tbk. The result shows that the determination of the input variables based on the ARIMA (3,0,3)-ARCH (5), so that the SVR model consists of 5 lags as input vector. Using a this model was obtained that the Mean Absolute Percentage Error (MAPE) of 1,98% and R2 = 99.99%. Keywords: ARCH; ARIMA; SVR; Volatility