Forecasting Stock Volatility Using Wavelet-based Exponential Generalized Autoregressive Conditional Heteroscedasticity Methods
نویسندگان
چکیده
In this study, we proposed a new model to improve the accuracy of forecasting stock market volatility pattern. The hypothesized was validated empirically using data set collected from Saudi Arabia Exchange (Tadawul). is daily closed price index August 2011 December 2019 with 2027 observations. combines best maximum overlapping discrete wavelet transform (MODWT) function (Bl14) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model. results show model's ability analyze data, highlight important events that contain most volatile forecast accuracy. were compared number mathematical models, which are non-linear spectral model, integrated moving average (ARIMA) EGARCH performance will be evaluated based on some error functions such as Mean absolute percentage (MAPE), scaled (MASE) Root means squared (RMSE).
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ژورنال
عنوان ژورنال: Intelligent Automation and Soft Computing
سال: 2023
ISSN: ['2326-005X', '1079-8587']
DOI: https://doi.org/10.32604/iasc.2023.024001