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Application of the Improved Generalized Autoregressive Conditional Heteroskedast Model Based on the Autoregressive Integrated Moving Average Model in Data Analysis

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Document pages: 12 pages

Abstract: This study firstly improved the Generalized Autoregressive Conditional Heteroskedast modelfor the issue that financial product sales data have singular information whenapplying this model, and the improved outlier detection method was used todetect the location of outliers, which were processed by the iterative method.Secondly, in order to describe the peak and fat tail of the financial timeseries, as well as the leverage effect, this work used the skewed-t Asymmetric Power Autoregressive ConditionalHeteroskedasticity model based on the Autoregressive Integrated MovingAverage Model to analyze the sales data. Empirical analysis showed that themodel considering the skewed distribution is effective.

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