Sequential monitoring for conditional quantiles of general conditional heteroscedastic time series models

IF 1.3 4区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Applied Stochastic Models in Business and Industry Pub Date : 2024-05-13 DOI:10.1002/asmb.2865
Sangyeol Lee, Chang Kyeom Kim
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Abstract

In this study, we introduce an online monitoring procedure designed to sequentially detect change points in the conditional quantiles of location-scale time series models. This statistical process control issue holds great significance in risk management, particularly in measuring the value-at-risk or expected shortfall of financial assets. Our approach employs suitable detectors, including cumulative sum statistics. We then define a stopping rule and determine control limits based on asymptotic theorems to signal an anomaly. To further evaluate the proposed methods, we conduct a comprehensive empirical study analyzing various aspects of our monitoring procedures when applied to location-scale time series models. Additionally, we perform a real data analysis using the daily returns of the Korea Composite Stock Price Index (KOSPI) and EuroStoxx 50 indices to affirm the adequacy of the proposed monitoring procedures in real-world applications.

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对一般条件异方差时间序列模型的条件定量进行序列监测
在本研究中,我们介绍了一种在线监测程序,旨在依次检测位置尺度时间序列模型条件量级的变化点。这一统计过程控制问题在风险管理中具有重要意义,尤其是在衡量金融资产的风险价值或预期缺口时。我们的方法采用了合适的检测器,包括累积和统计量。然后,我们根据渐近定理定义停止规则并确定控制限值,以发出异常信号。为了进一步评估所提出的方法,我们进行了一项全面的实证研究,分析了我们的监控程序在应用于位置尺度时间序列模型时的各个方面。此外,我们还利用韩国综合股价指数(KOSPI)和 EuroStoxx 50 指数的日收益率进行了实际数据分析,以肯定所提出的监控程序在实际应用中的充分性。
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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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