Statistical process control (SPC) suggests tools for the on-line detection of changes in the parameters of the process of interest. For the purpose of the data analysis the observations are divided into two parts, historical and online observations. The historical observations are used to make statements about the distributional properties of the process. Such results are necessary for the calculation of the design of control charts, which are the most important monitoring instruments in SPC. Every newly incoming information is immediately exploited. The new observations are analyzed on-line. It is examined at each time point whether the process parameters identified from the historical observations remain unchanged. The control chart gives a signal if the process parameters have changed in a statistically significant way. A decision maker should carefully analyze possible causes and consequences of any signal.
The rest of the chapter is organized as follows. Section 20.2 provides a brief review of the terminology, instruments, and procedures of SPC. Section 20.3 discusses the application of SPC in asset management for passive as well as for active portfolio investors. In particular, Section 20.3.1 deals with monitoring a fund manager’s performance, while in Section 20.3.2 the monitoring the GMVP weights is discussed. Section 20.4 concludes.
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Bibliography
Andersson, E., Bock, D., and M. Frisén (2002). Statistical surveillance of cyclical processes with application to turns in business cycles, Journal of Forecasting, 24, 465-490.
Ang, A. and Bekaert, G. (2002). International asset allocation with regime shifts, Review of Financial Studies, 15, 1137-1187.
Andreou, E. and Ghysels, E. (2002). Detecting multiple breaks in financial market volatility dynamics, Journal of Applied Econometrics, 17, 579-600.
Andreou, E. and Ghysels, E. (2006). Monitoring disruptions in financial markets, Journal of Econometrics, 135, 77-124.
Best, M. and Grauer, R. (1991). On the sensitivity of meanvariance-efficient portfolios to changes in asset means: some analytical and computational results, Review of Financial Studies, 4, 315-342.
Brook, D. and Evans, D. (1972). An approach to the probability distribution of CUSUM run length, Biometrika, 59, 539-549.
Foster, D. P. and Nelson, D. (1996). Continuous record asymptotics for rolling sample variance estimators, Econometrica, 64, 139-174.
Frisén, M. (2003). Statistical surveillance: optimality and methods, International Statistical Review 71, 403-434.
Frisén, M., ed. (2007). Financial Surveillance, John Wiley & Sons, New York.
Ghosh, B.K. and Sen, P.K., eds.(1991). Handbook of Sequential Analysis, Marcel Dekker, New York.
Golosnoy, V. (2004). Sequential Control of Optimal Portfolio Weights, Ph.D. Thesis, Frank-furt (Oder), Germany.
Golosnoy, V. and Schmid, W. (2007). EWMA control charts for optimal portfolio weights, Sequential Analysis, 26, 195-224.
Juran, J. M. (1997). Early SQC: a historical supplement, Quality Progress 30, 73-81.
Lai, T. L. (1995). Sequential changepoint detection in quality-control and dynamical sys-tems, Journal of the Royal Statistical Society B, 57, 613-658.
Lai, T. L. (2004). Likelihood ratio identities and their applications to sequential analysis, Sequential Analysis 23, 467-497.
Lorden, G. (1971). Procedures for reacting to a change in distribution, Annals of Mathe-matical Statistics, 41, 1897-1908.
Lowry, C.A., Woodall, W.H., Champ, C.W., and Rigdon, S.E. (1992). A multivariate exponentially weighted moving average control chart, Technometrics 34, 46-53.
Lucas, J. M. and Saccucci, M. S. (1990). Exponentially weighted moving average control schemes: Properties and enhancements, Technometrics, 32, 1-12.
Markowitz, H. (1952). Portfolio selection, Journal of Finance, 7, 77-91.
Montgomery, D. C. (2005). Introduction to Statistical Quality Control,5th edn. John Wiley & Sons, New York.
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, 100-114.
Philips, T. K., Yashchin, E. and Stein, D. M. (2003). Using statistical process control to monitor active managers, Journal of Portfolio Management, 30, 86-95.
Roberts, S. W. (1959). Control chart tests based on geometric moving averages, Techno-metrics, 1, 239-250.
Okhrin, Y. and Schmid, W. (2006). Distributional properties of optimal portfolio weights, Journal of Econometrics, 134, 235-256.
Schmid, W. (2007). Eighty years of control charts, Sequential Analysis, 26, 117-122.
Schmid, W. and Tzotchev, D. (2004). Statistical surveillance of the parameters of a onefactor Cox-Ingersoll-Ross model, Sequential Analysis, 23, 379-412.
Srivastava, M. S. and Wu, Y. (1993). Comparison of EWMA, CUSUM and Shiryaev-Roberts procedures for detecting a shift in the mean, The Annals of Statistics, 21, 645-670.
Stoumbos, Z. G., Reynolds Jr, M. R., Ryan, T. P., and Woodall, W. H. (2000). The state of statistical process control as we proceed into the 21st century, Journal of the American Statistical Association, 95, 992-998.
Theodossiou, P. (1993). Predicting shifts in the mean of a multivariate time series pro-cess: an application in predicting business failures, Journal of the American Statistical Association, 88, 441-449.
Yashchin, E., Philips, T. K., and Stein, D. M. (1997). Monitoring active portfolios using sta-tistical process control, in Computational Approaches to Economic Problems, selected papers from the 1st Conference of the Society for Computational Economics, eds. H. Amman et al., Kluwer Academic, Dordrecht, pp. 193-205.
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Golosnoy, V., Schmid, W. (2009). Statistical Process Control in Asset Management. In: Härdle, W.K., Hautsch, N., Overbeck, L. (eds) Applied Quantitative Finance. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69179-2_20
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