Monitoring means and covariances of multivariate non linear time series with heavy tails

Robert Garthoff and Wolfgang Schmid

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 21, 10394-10415

Abstract: In this paper, the focus is on sequential analysis of multivariate financial time series with heavy tails. The mean vector and the covariance matrix of multivariate non linear models are simultaneously monitored by modifying conventional control charts to identify structural changes in the data. The considered target process is a constant conditional correlation model (cf. Bollerslev, 1990), an extended constant conditional correlation model (cf. He and Teräsvirta, 2004), a dynamic conditional correlation model (cf. Engle, 2002), or a generalized dynamic conditional correlation model (cf. Capiello et al., 2006). For statistical surveillance we use control charts based on residuals. Further, the procedures are constructed for t-distribution. The detection speed of these charts is compared via Monte Carlo simulation. In the empirical study, the procedure with the best performance is applied to log-returns of the stock market indices FTSE and CAC.

Date: 2017
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DOI: 10.1080/03610926.2015.1085567

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