估计金融动态的稳定固定点和Langevin潜力.
Tobias Wand1, Timo Wiedemann2, Jan Harren2
1Institut für Theoretische Physik, Universität Münster, Wilhelm-Klemm-Str. 9, 48149 Münster, Germany and Center for Nonlinear Science, Universität Münster, Corrensstr. 2, 48149 Münster, Germany.
这项研究通过多项式漂移增强了几何布朗运动 (GBM),找到一个二次模型 (q=2) 最好描述财务数据. 这种概括揭示了稳定的价格潜力,解决了标准GBM模型的局限性.
科学领域:
- 量化金融 量化金融
- 随机模型建模 随机模型建模
- 计量经济学 计量经济学
背景情况:
- 几何布朗运动 (GBM) 是量化金融的一个基本模型.
- 标准的GBM随机微分方程 (SDEs) 缺乏稳定,非零价格的能力.
- 现有的模型很难捕捉到金融市场中观察到的长期稳定性.
研究的目的:
- 通过引入顺序q的多项式漂移来概括GBM.
- 通过模型选择,确定金融数据的多项式漂移的最佳顺序.
- 通过潜在的功能分析,调查金融市场中价格水平是否稳定.
主要方法:
- 将GBM泛化为具有多项式漂移的SDE.
- 应用模型选择标准来确定最佳漂移顺序 (q).
- 使用马尔科夫链蒙特卡洛 (MCMC) 合集来分析潜在函数.
主要成果:
- 模型选择经常确定二次偏移 (q=2) 是描述财务数据的最佳方法.
- 对潜在功能的分析显示出一个明显而显著的潜在井.
- 潜力井表明存在一个稳定的,非零价格平衡.
结论:
- 一个带有二次多项式漂移的通用化GBM为金融资产定价提供了一个更强大的模型.
- 鉴定出的潜在井提供了稳定的价格动态的有力证据,克服了标准GBM的一个关键限制.
- 这种增强的模型对风险管理和财务预测有影响.
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