从高频交互推导多维粗糙波动率
文章 arXiv papers · 作者: Mehdi Tomas et al.
总结
这项研究将金融资产中观察到的粗糙波动率与多个资产价格相互关联的微观动态联系起来。以往许多模型聚焦于单一资产，作者则探讨多变量粗糙波动率模型如何从高频交互中产生。
作者使用霍克斯过程构建微观模型，以表示跨资产交互，并研究这些模型的长期尺度极限。分析考察了微观层面的动量和均值回归如何影响多维价格形成，并再现了高维股票相关矩阵的已知特征。该文属于建模和理论分析，并非交易策略或直接的实证绩效测试；现有摘要没有说明资产、估计流程或数值结果。
核心观点
- 霍克斯过程用于建模多个资产价格之间的高频交互。
- 这些微观模型的长期尺度极限可以产生多维粗糙波动率模型。
- 微观层面的动量和均值回归会影响联合价格的形成。
- 由此得到的分析再现了高维股票相关矩阵的经典特征。
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全文
# From microscopic price dynamics to multidimensional rough volatility models # From microscopic price dynamics to multidimensional rough volatility models Rough volatility is a well-established statistical stylised fact of financial assets. This property has lead to the design and analysis of various new rough stochastic volatility models. However, most of these developments have been carried out in the mono-asset case. In this work, we show that some specific multivariate rough volatility models arise naturally from microstructural properties of the joint dynamics of asset prices. To do so, we use Hawkes processes to build microscopic models that reproduce accurately high frequency cross-asset interactions and investigate their long term scaling limits. We emphasize the relevance of our approach by providing insights on the role of microscopic features such as momentum and mean-reversion on the multidimensional price formation process. We in particular recover classical properties of high-dimensional stock correlation matrices.
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此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。