Sequential detection of parameter changes in dynamic conditional correlation models
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Date
2017
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Abstract
A multivariate monitoring procedure is presented to detect changes in the parameter vector of
the dynamic conditional correlation model proposed by Robert Engle in 2002. The benefit of
the proposed procedure is that it can be used to detect changes in both the conditional and
unconditional variance as well as in the correlation structure of the model. The detector is based
on quasi log likelihood scores. More precisely, standardized derivations of quasi log likelihood
contributions of points in the monitoring period are evaluated at parameter estimates calculated
from a historical period. The null hypothesis of a constant parameter vector is rejected if these
standardized terms differ too much from those that were expected under the assumption of a
constant parameter vector. Under appropriate assumptions on moments and the structure of
the parameter space, limit results are derived both under null hypothesis and alternatives. In a
simulation study, size and power properties of the procedure are examined in various scenarios.
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Keywords
dynamic conditional correlation, threshold function, parameter changes, online detection, multivariate sequences