记忆驱动的动力学:对经济相互依存的分量费舍尔信息方法
Larissa M Batrancea1, Ömer Akgüller2, Mehmet Ali Balcı2
1Department of Business, Babeş-Bolyai University, 7 Horea Street, 400174 Cluj-Napoca, Romania.
这项研究使用了新的Caputo Fractional Fisher信息框架来分析经济指标. 它揭示了远程记忆效应显著放大了指标之间的信息流,与传统方法不同.
科学领域:
- 量化金融 量化金融
- 信息理论 信息理论
- 计量经济学 计量经济学
背景情况:
- 传统的费舍尔信息指标缺乏捕捉经济数据中长期记忆效应的能力.
- 了解经济指标之间的动态相互作用对于有效的政策和预测至关重要.
研究的目的:
- 为分析经济指标动态引入卡普托分数费舍尔信息框架.
- 为了将拟议的框架与捕获记忆效应的普通费舍尔信息进行比较.
- 调查历史依赖关系对关键经济指标之间的信息流的影响.
主要方法:
- 将分数衍生品集成到费舍尔信息指标中.
- 应用部分信息分解来分析信息流.
- 使用滚动窗口估计的卡普托分数费舍尔信息和普通费舍尔信息进行比较分析.
- 相关性,交叉相关性和转移分析.
主要成果:
- 卡普托分数费舍尔信息捕捉了货币政策,信贷风险,市场波动和通货膨胀的长期记忆效应.
- 深度历史互动在长期记忆条件下显著放大了信息贡献.
- 与分数方法相比,普通的费舍尔信息倾向于低估协同效应.
结论:
- 将记忆效应纳入信息理论模型对于理解财务指标关系至关重要.
- 卡普托分数费舍尔信息框架提供了更全面的分析动态经济相互作用.
- 调查结果对经济预测和政策分析有重大影响.
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