On Mixture GARCH Models: Long, Short Memory and Application in Finance

In this work, we study the famous model of volatility; called conditional heteroscedastic autoregressive with mixed memory MMGARCH for modeling nonlinear time series. The has two mixing components, one is a GARCH short and other long memory. main objective search finds best between mixtures models made (long memory, memory) Also, existence its stationary solution discussed. Monte Carlo experiments demonstrate discovered theoretical. addition, empirical application (1, 1) to daily index DOW NASDAQ illustrates capabilities; find that mixture APARCH EGARCH superior any tested because it produces smallest errors.