Pepelyshev, Andrey  ORCID: https://orcid.org/0000-0001-5634-5559 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: | 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: | 30 Nov 2024 06:45 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/89506 |
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