霍克斯波动率在指数结构中过滤高频价格过程观察中的应用

IF 1.3 4区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Kyungsub Lee
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引用次数: 0

摘要

霍克斯模型适用于描述自我激励和相互激励的随机事件。此外,霍克斯过程中的指数衰减使我们能够计算模型的力矩特性。然而,由于模型和公式的复杂性,很少有研究对霍克斯波动率进行检验。在本研究中,我们推导了一个方差公式,该公式直接适用于未标记和标记Hawkes模型的一般设置。在标记模型中,考虑了线性影响函数和标记与底层过程之间可能存在的依赖关系。Hawkes波动率应用于中间价格过程,以0.1-s的间隔过滤,以显示可靠的结果。此外,日内估计有望在实时风险管理中得到广泛应用。我们还注意到随着时间的推移,日内霍克斯波动率的预测能力越来越强,并研究了期货和股票波动率之间的关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Application of Hawkes Volatility in the Observation of Filtered High-Frequency Price Process in Tick Structures

Application of Hawkes Volatility in the Observation of Filtered High-Frequency Price Process in Tick Structures

The Hawkes model is suitable for describing self and mutually exciting random events. In addition, the exponential decay in the Hawkes process allows us to calculate the moment properties of the model. However, owing to the complexity of the model and formula, few studies have examined the Hawkes volatility. In this study, we derive a variance formula that is directly applicable under the general settings of both unmarked and marked Hawkes models for tick-level price dynamics. In the marked model, the linear impact function and possible dependency between the marks and underlying processes are considered. The Hawkes volatility is applied to the mid-price process filtered at 0.1-s intervals to show reliable results. Furthermore, intraday estimation is expected to widely utilized in real-time risk management. We also note the increasing predictive power of the intraday Hawkes volatility over time and examine the relationship between futures and stock volatilities.

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来源期刊
CiteScore
2.70
自引率
0.00%
发文量
67
审稿时长
>12 weeks
期刊介绍: ASMBI - Applied Stochastic Models in Business and Industry (formerly Applied Stochastic Models and Data Analysis) was first published in 1985, publishing contributions in the interface between stochastic modelling, data analysis and their applications in business, finance, insurance, management and production. In 2007 ASMBI became the official journal of the International Society for Business and Industrial Statistics (www.isbis.org). The main objective is to publish papers, both technical and practical, presenting new results which solve real-life problems or have great potential in doing so. Mathematical rigour, innovative stochastic modelling and sound applications are the key ingredients of papers to be published, after a very selective review process. The journal is very open to new ideas, like Data Science and Big Data stemming from problems in business and industry or uncertainty quantification in engineering, as well as more traditional ones, like reliability, quality control, design of experiments, managerial processes, supply chains and inventories, insurance, econometrics, financial modelling (provided the papers are related to real problems). The journal is interested also in papers addressing the effects of business and industrial decisions on the environment, healthcare, social life. State-of-the art computational methods are very welcome as well, when combined with sound applications and innovative models.
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