CHARACTERIZATION OF THE TAIL BEHAVIOR OF A CLASS OF BEKK PROCESSES: A STOCHASTIC RECURRENCE EQUATION APPROACH

【Author】 Matsui, Muneya; Pedersen, Rasmus Sondergaard

【Source】ECONOMETRIC THEORY

【影响因子】1.968

【Abstract】We consider conditions for strict stationarity and ergodicity of a class of multivariate BEKK processes (X-t : t = 1,2,...) and study the tail behavior of the associated stationary distributions. Specifically, we consider a class of BEKK-ARCH processes where the innovations are assumed to be Gaussian and a finite number of lagged X-t's may load into the conditional covariance matrix of Xt. By exploiting that the processes have multivariate stochastic recurrence equation representations, we show the existence of strictly stationary solutions (u)nder mild conditions, where only a fractional moment of X-t may be finite. Moreover, we show that each component of the BEKK processes is regularly varying with some tail index. In general, the tail index differs along the components, which contrasts with most of the existing literature on the tail behavior of multivariate GARCH processes. Lastly, in an empirical illustration of our theoretical results, we quantify the model-implied tail index of the daily returns on two cryptocurrencies.

【Keywords】

【发表时间】2022 FEB

【收录时间】2022-02-14

【文献类型】期刊

【主题类别】

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