记忆效应,多个时间尺度和局部稳定性在Langevin模型的标普500市场相关性
Tobias Wand1,2, Martin Heßler1,2, Oliver Kamps2
1Institut für Theoretische Physik, Westfälische Wilhelms-Universität Münster, 48149 Münster, Germany.
Entropy (Basel, Switzerland)
|September 28, 2023
概括
市场相关性分析揭示了显著的记忆效应,影响了投资组合选择. 对这种记忆进行核算可以提高预测准确度和降低S&P500等相关资产的风险.
科学领域:
- 量化金融 量化金融
- 统计建模 统计建模
- 市场动态 市场动态
背景情况:
- 最佳的投资组合选择依赖于了解市场相关性.
- 以前的分析往往忽视了市场相关性中的记忆效应.
- 标普500的主要市场模式以其平均相关性来表示.
研究的目的:
- 分析标准普尔500指数的平均市场相关性,考虑记忆效应.
- 调查记忆效应对预测准确性和投资组合选择的影响.
- 提供非马科夫行为和市场数据中隐藏的缓慢时间尺度的证据.
主要方法:
- 将一个通用的朗格温方程 (GLE) 配合到标普500市场相关性数据中.
- 分析GLE的内存内核以量化内存效应.
- 使用贝叶斯弹性估计来评估数据的非马科维性.
主要成果:
- 在标普500市场相关性中发现了显著的记忆效应,可追溯到至少三个交易周.
- 与没有内存的模型相比,GLE中的内存内核显著提高了预测准确性.
- 贝叶斯弹性估计支持非马可维性,并表明隐藏的缓慢时间尺度.
结论:
- 市场相关性记忆效应对于准确的预测和最佳的投资组合选择至关重要.
- 这些发现支持了局部稳定的市场状态的存在,这些状态是由缓慢的时间尺度驱动的.
- 整合记忆效应对于最小化风险和预测未来市场相关性至关重要.
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