Enhancing stock volatility prediction with the AO-GARCH-MIDAS model
Research has substantiated that the presence of outliers in data usually introduces additional errors and biases, which typically leads to a degradation in the precision of volatility forecasts. However, correcting outliers can mitigate these adverse effects. This study corrects the additive outlier...
| 主要な著者: | , , , , |
|---|---|
| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
Public Library of Science
2024
|
| オンライン･アクセス: | http://psasir.upm.edu.my/id/eprint/113480/1/113480.pdf |