Tail-dependence evolution for the symmetrized Joe–Clayton copula is proposed to depend on an exponentially weighted moving average (EWMA) of the absolute difference in probability integral transforms. Using these dynamics, time-varying tail dependence between bank and insurance equity prices is assessed in a parametric copula, generalized autoregressive conditional heteroscedastic framework. The results suggest a relatively long lag and support the EWMA lag structure as an effective estimation vehicle. Tail dependence is shown often to tend higher during periods of market stress.
Chen, J. and Gunther, J. (2012), "Copula–GARCH Time-Varying Tail Dependence", Terrell, D. and Millimet, D. (Ed.) 30th Anniversary Edition (Advances in Econometrics, Vol. 30), Emerald Group Publishing Limited, Bingley, pp. 411-425. https://doi.org/10.1108/S0731-9053(2012)0000030018Download as .RIS
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