通过AO-GARCH-MIDAS模型提高股票波动性的预测
Ting Liu1, Weichong Choo1, Matemilola Bolaji Tunde1
1School of Business and Economics, Universiti Putra Malaysia, Seri Kembangan, Malaysia.
PloS one
|June 11, 2024
概括
这项研究引入了对一般自回归条件异态度混合数据采样 (GARCH-MIDAS) 模型的异常值校正方法. 新的AO-GARCH-MIDAS模型通过减轻异常值引起的错误来提高波动性预测的准确性.
科学领域:
- * 计量经济学 计量经济学
- * 金融建模 * 金融建模
- * 时间序列分析.
背景情况:
- * 金融数据中的异常值引入错误和偏差,降低了波动性预测的准确性.
- *现有的模型可能无法充分解决异常值对波动性估计的影响.
- *准确的波动性预测对于风险管理和投资策略至关重要.
研究的目的:
- *为GARCH-MIDAS模型引入一种新的异常值校正方法.
- * 为了开发一个额外的异常值纠正的GARCH-MIDAS (AO-GARCH-MIDAS) 模型.
- * 评估拟议的AO-GARCH-MIDAS模型的性能和稳定性.
主要方法:
- *使用权重方法纠正附加异常值,用修正值取代原始异常值.
- *使用修改后的返回序列重新估计模型参数.
- *将新方法应用于通用自回归条件异构二元复杂性混合数据采样 (GARCH-MIDAS) 模型.
主要成果:
- *与标准模型相比,AO-GARCH-MIDAS模型在所有评估标准上始终表现出优越性.
- * 异常值调整显著减少了波动性估计中的扭曲,提高了模型的适应性.
- * GARCH-MIDAS模型比GARCH模型具有更高的预测能力,实现的波动性是关键预测因素.
结论:
- *拟议的AO-GARCH-MIDAS模型有效地减轻了异常效应,从而提高了波动性预测的准确性.
- * 偏差值调整对于强大的金融建模和可靠的波动性预测至关重要.
- * 实现的波动性为GARCH-MIDAS模型提供了比其他低频因子更好的预测信息.
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