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Statistical surveillance of the mean vector and the covariance matrix of nonlinear time series

Content Provider	Springer Nature Link
Author	Garthoff, Robert Okhrin, Iryna Schmid, Wolfgang
Copyright Year	2013
Abstract	The purpose of this paper is to jointly monitor the mean vector and the covariance matrix of multivariate nonlinear times series. The underlying target process is assumed to be a constant conditional correlation process Bollerslev (Rev Econ Stat 72:498–505, 1990) or a dynamic conditional correlation model Engle (J Bus Econ Stat 20:339–350, 2002). We introduce several EWMA and CUSUM control charts. These control schemes are based on univariate EWMA statistics, multivariate EWMA recursions, and different types of cumulative sums. The recursions are applied to local measures for means and covariances, e.g. the present observations and the conditional covariances. Further, they are applied to means and covariances of residuals. The control statistics are obtained by computing the Mahalanobis distance between the EWMA or CUSUM statistics and their expectations if no change occurs. Via Monte Carlo simulation the performance of the proposed charts is compared. Our empirical study illustrates an application of these control procedures to bivariate logarithmic returns of the European indices FTSE100 and DAX. In order to assess the performance of the introduced schemes we apply the average run length and the maximum conditional expected delay.
Starting Page	225
Ending Page	255
Page Count	31
File Format	PDF
ISSN	18638171
Journal	AStA Advances in Statistical Analysis
Volume Number	98
Issue Number	3
e-ISSN	1863818X
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-09-29
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Statistical process control Multivariate CUSUM charts Multivariate EWMA charts Conditional correlation model Statistics Statistics for Business/Economics/Mathematical Finance/Insurance Probability Theory and Stochastic Processes Econometrics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistics and Probability Social Sciences Economics and Econometrics Analysis Modeling and Simulation

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