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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/113480/1/113480.pdf |