SIAM Journal on Financial Mathematics, Volume 11, Issue 4, Page 1168-1208, January 2020.
In this paper, we adopt a flexible filtering procedure to extract information from high-frequency data. Specifically, we provide a parsimonious framework to integrate realized measures from high-frequency index and derivative prices. In a simulation study, we document the incremental information offered by realized measures and show that even though high-frequency index prices help identify spot variance and jump price dynamics, it is the addition of high-frequency option prices that enables variance jumps to be identified. A series of empirical studies based on the S&P 500 index and options show that estimation precision improves with the addition of information from intraday option prices.

中文翻译：

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使用日内数据估算跳跃扩散模型：一种基于过滤的方法

SIAM金融数学杂志，第11卷，第4期，第1168-1208页，2020
年1月。在本文中，我们采用了灵活的过滤程序从高频数据中提取信息。具体来说，我们提供了一个简化的框架，以整合高频指数和衍生产品价格中的已实现指标。在模拟研究中，我们记录了已实现的度量提供的增量信息，并表明，即使高频指数价格有助于识别现货差异和跳跃价格动态，也可以通过添加高频期权价格来识别差异跳跃。 。基于标准普尔500指数和期权的一系列实证研究表明，随着日内期权价格信息的增加，估计精度也会提高。