ISSN:
1614-0176
Keywords:
Keywords Derivative estimation, EWMA control chart, financial time series, GARCH processes, local polynomial estimators.
Source:
Springer Online Journal Archives 1860-2000
Topics:
Mathematics
Notes:
Summary: This paper studies nonparametric control charts to sequentially monitor dependent stochastic processes in continuous time with arbitrary but smooth drift functions m(t) to detect fast changes of m(t). Such methods are of particular interest when monitoring financial time series in order to detect rapid changes of the process mean. We provide a generalized framework for nonparametric process control where a process is regarded as out-of-control if the derivative of the process mean is too large. For a rich class of control charts based on linear smoothers it is shown how to design appropriate control charts guaranteeing an in-control average run length greater than or equal to a prescribed value. Further, a fundamental property of control charts concerning the average run length in the presence of positive autocorrelation, first established for the EWMA chart applied to a Gaussian process, is extended to the case that it is applied to linear kernel smoothers. In addition, we study control charts based on local linear estimators. The performance of the proposed charts is compared by simulation studies.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/s101820000035
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