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-poi... View more
University, Cardiff, Wales, UK), and the Editor-in-Chief, Prof. Heping Zhang (Yale University, New Haven, Connecticut, USA), for the time and effort they invested to produce this special issue of the Journal. The authors are also personally thankful to Prof. Zhigljavsky for the invitation to contribute this work to the special issue. The constructive feedback provided by the two anonymous referees is greatly appreciated as well. The effort of A.S. Polunchenko was supported, in part, by the Simons Foundation (www.simonsfoundation.org) via a Collaboration Grant in Mathematics (Award # 304574). A.S. Polunchenko is also equally indebted to the Office of  Lai, T. L. (1998). Information bounds and quick detection of parameter changes in stochastic systems. IEEE Transactions on the Dean of the Harpur College of Arts and Sciences at the Information Theory 44 2917-2929.
State University of New York at Binghamton for the support  Lai, T. L. and Xing, H. (2015). Active Risk Management: Finanprovided through the Dean's Research Semester Award for cial Models and Statistical Methods. Chapman and Hall/CRC FiJunior Faculty granted for the Fall semester of 2014. nancial Mathematics Series. Chapman & Hall/CRC Press, Boca Raton, FL. The work of A. Pepelyshev was partly supported by  Lorden, G. (1971). Procedures for reacting to a change in distrithe St. Petersburg State University, Russia, under project bution. Annals of Mathematical Statistics 42 1897-1908.
# 6.38.435.2015.  McDonald, D. (1990). A CUSUM Procedure Based on Sequential Ranks. Journal of Naval Research 37 627-646.  Moskvina, V. and Zhigljavsky, A. (2003). An Algorithm Based Received January 2015 on Singular Spectrum Analysis for Change-Point Detection. Communications in Statistics-Simulation and Computation 32 319- 352. REFERENCES  Moustakides, G. V. (1986). Optimal stopping times for detecting changes in distributions. Annals of Statistics 14 1379-1387.
 Page, E. S. (1954). Continuous Inspection Schemes. Biometrika 41 100-115.
 Pollak, M. (1985). Optimal detection of a change in distribution. Annals of Statistics 13 206-227.
 Pollak, M. (1987). Average run lengths of an optimal method of detecting a change in distribution. Annals of Statistics 15 749- 779.
 Pollak, M. (2010). A Robust Changepoint Detection Method. Sequential Analysis 29 146-161.
 Pollak, M. and Krieger, A. M. (2013). Shewhart Revisited. Sequential Analysis 32 230-242.
 Pollak, M. and Tartakovsky, A. G. (2008). Exact Optimality of the Shiryaev-Roberts Procedure for Detecting Changes in Distributions. In Proceedings of the 2008 International Symposium on Information Theory and Its Applications 1-6.
 Pollak, M. and Tartakovsky, A. G. (2009). Optimality Properties of the Shiryaev-Roberts procedure. Statistica Sinica 19 1729- 1739.