The random coefficient autoregressive model with seasonal volatility innovations (RCA-SGARCH)

Abstract

This paper dealt with the autoregressive model when the coefficient is random. The residuals series of the model exhibit two behaviors, kurtosis and volatility. These volatilities are usually seasonal in the real financial data, which always uses GARCH models. So the use of RCA and GARCH models together will provide an appropriate framework to study and analysis of time-varying volatility as well as the presence of seasonal effects in financial series. Applying copper's daily economic close prices when the errors series are distributed, as usual, t_((3)) and t_((7)) distributions are achieved. Therefore, the RCA(1) model, when residuals follow the GARCH(1, 0)x(0, 1)_5 model together, is the appropriate model.