SWGARCH : an enhanced GARCH model for time series forecasting
Generalized Autoregressive Conditional Heteroskedasticity (GARCH) is one of most popular models for time series forecasting. The GARCH model uses the long run variance as one of the weights. Historical data is used to calculate the long run variance because it is assumed that the variance of a long...
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| フォーマット: | 学位論文 |
| 言語: | English English |
| 出版事項: |
2017
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| オンライン･アクセス: | https://etd.uum.edu.my/6808/1/s91141_01.pdf https://etd.uum.edu.my/6808/2/s91141_02.pdf https://etd.uum.edu.my/6808/ |
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