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The generalized autoregressive conditional heteroskedasticity (GARCH) model has become the most popular choice in the analysis of time series datas. In this paper, an autoregressive moving average (ARMA)-GARCH model was built, and it also provided parameter estimation, diagnostic checking procedures to model, and predict Dow and S&P 500 indices data from 1988 to 2008, which extracted from yahoo...
An autoregressive integrated moving average (ARIMA) model was one of the most popular linear models in financial time series forecasting in the past. In this context, a time series analysis of the NASDAQ composite indices is provided study its movement in 1998-2008. This paper proposed a general expression of seasonal ARIMA models with periodicity and provide parameter estimation, diagnostic checking...
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