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Real-time financial surveillance via quickest change-point detection methods

Pepelyshev, Andrey and Polunchenko, Alexey 2017. Real-time financial surveillance via quickest change-point detection methods. Statistics and Its Interface 10 (1) , pp. 93-106. 10.4310/SII.2017.v10.n1.a9

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Abstract

We consider the problem of efficient financial surveillance aimed at “on-the-go” detection of structural breaks (anomalies) in “live”-monitored financial time series. With the problem approached statistically, viz. as that of multicyclic sequential (quickest) change-point detection, we propose a semi-parametric multi-cyclic change-point detection procedure to promptly spot anomalies as they occur in the time series under surveillance. The proposed procedure is a derivative of the likelihood ratio-based Shiryaev–Roberts (SR) procedure; the latter is a quasi-Bayesian surveillance method known to deliver the fastest (in the multi-cyclic sense) speed of detection, whatever be the false alarm frequency. We offer a case study where we first carry out, step by step, a preliminary statistical analysis of a set of real-world financial data, and then set up and devise (a) the proposed SR-based anomaly-detection procedure and (b) the celebrated Cumulative Sum (CUSUM) chart to detect structural breaks in the data. While both procedures performed well, the proposed SR-derivative, conforming to the intuition, seemed slightly better.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Mathematics
Subjects: Q Science > QA Mathematics
Uncontrolled Keywords: CUSUM chart, financial surveillance, sequential analysis, Shiryaev–Roberts procedure, quickest change-point detection
Publisher: International Press
ISSN: 1938-7989
Date of Acceptance: 8 January 2016
Last Modified: 06 Jun 2017 17:17
URI: http://orca.cf.ac.uk/id/eprint/89506

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