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English(EN) Financial Volatility and Risk Forecasting Incorporating a Larger Number of Realized Measures

新的已实现GARCH模型通过非线性降维增强波动性预测能力

作者 PulseAugur 编辑部 · [1 个来源] ·

提出了一种新的已实现GARCH模型扩展，通过综合多个已实现波动率度量的信息来改进波动性预测。该模型利用自编码器进行非线性降维，其表现优于主成分分析和独立成分分析等传统线性方法。在包括COVID-19时期在内的四个主要股票市场进行的实证评估表明，该模型在一步预测滚动波动率方面具有卓越的有效性，并增强了参数估计的灵活性。 AI

影响 这项研究可能通过利用先进的机器学习技术进行数据综合和降维，从而实现更准确的金融风险预测。

排序理由 该条目是一篇提交至arXiv的学术论文，详细介绍了一种新模型和方法论。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

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新的已实现GARCH模型通过非线性降维增强波动性预测能力

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该条目是一篇提交至arXiv的学术论文，详细介绍了一种新模型和方法论。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Qianli Zhao, Chao Wang, Richard Gerlach, Giuseppe Storti, Lingxiang Zhang ·

    金融波动与风险预测：纳入更多已实现度量

    arXiv:2411.17136v2 Announce Type: replace-cross Abstract: Realised volatility has become increasingly prominent in volatility forecasting due to its ability to capture intraday price fluctuations. With a growing variety of realised volatility estimators, each with unique advantag…

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