Surveillance of the covariance matrix of multivariate nonlinear time series

Abstract

In this paper, sequential procedures for the surveillance of the covariance matrices of multivariate nonlinear time series are introduced. Two different types of control charts are proposed. The first type is based on the exponential smoothing of each component of a local measure for the covariances. The control statistic is equal to the Mahalanobis distance of this quantity with its in-control mean. In our second approach, the Mahalanobis distance is first determined and after that it is exponentially smoothed. We discuss three examples of local measures.Several properties of the proposed schemes are discussed assuming the target process to be generated by a multivariate GARCH(1, 1) model. The generalization to the family of spherical distributions allows the modelling of frequently observed fat tails in financial data. Some results of an extensive Monte Carlo simulation study are provided in order to judge the performance of the presented control schemes. As a performance measure we use the average run length. An empirical example illustrates the importance of the fast detection of the changes in the covariance structure of the returns of financial assets.

Control charts in financial applications: An overview

“…Śliwa and Schmid 60 were the first who applied control chart procedures for monitoring multivariate nonlinear time series and cross‐covariances in particular. The underlying target process is assumed to be a GARCH(1,1) process.…”

Monitoring the mean of multivariate financial time series

“…We have to distinguish between the monitoring problem and the identification of those components causing a signal (cf. [41]). In the current paper, we focus on the monitoring of nonlinear time series.…”