与神经网络一起学习极端预期缺陷和条件尾部时刻. 对加密货币数据的应用
Michaël Allouche1, Stéphane Girard2, Emmanuel Gobet3
1Kaiko - Quantitative Data, 2 rue de Choiseul, Paris, 75002, France.
概括
这项研究引入了一种新的神经网络方法,用于估计重尾分布中的极端预期缺陷和尾部时刻. 与现有技术相比,该方法表现出卓越的性能和稳定性,即使在加密货币数据上也是如此.
科学领域:
- 量化金融 量化金融
- 机器学习 机器学习
- 极端价值理论 极端价值理论
背景情况:
- 估计极端条件尾部时刻,如预期缺口,对于金融风险管理至关重要.
- 重尾分布对传统估计方法构成重大挑战.
研究的目的:
- 提出一种基于神经网络的新方法,用于估计极端预期短缺和条件尾部时刻.
- 建立拟议的神经网络估计器的理论收性质.
主要方法:
- 利用极端值理论和高阶尾部条件来分析神经网络近似误差.
- 在神经网络架构中使用特定的激活功能 (eLU和ReLU).
- 将有限样本的性能与偏差降低的极值竞争对手进行比较.
主要成果:
- 拟议的神经网络方法在估计极端尾部时刻方面明显优于现有竞争对手.
- 神经网络方法提供了更简单,更稳定的点选择.
- 当应用到现实世界的加密货币极端损失回报数据时,可以实现出色的准确性.
结论:
- 神经网络提供了一个强大而准确的工具,用于估计在繁忙环境中的极端尾部时刻.
- 开发的方法在性能和参数选择方面提供了实际优势.
- 该方法在合成和真实世界的金融数据上得到了验证,证明了它的稳定性.
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