Characterization of the tail behavior of a class of BEKK processes: A stochastic recurrence equation approach
Research output: Contribution to journal › Journal article › Research › peer-review
Documents
- bekk_sre_Rrev_rsp_1Dec2020_vs2
Accepted author manuscript, 250 KB, PDF document
We consider conditions for strict stationarity and ergodicity of a class of multivariate BEKK processes 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 ’s may load into the conditional covariance matrix of . By exploiting that the processes have multivariate stochastic recurrence equation representations, we show the existence of strictly stationary solutions under mild conditions, where only a fractional moment of 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.
Original language | English |
---|---|
Journal | Econometric Theory |
Volume | 38 |
Issue number | 1 |
Pages (from-to) | 1-34 |
ISSN | 0266-4666 |
DOIs | |
Publication status | Published - 2022 |
Number of downloads are based on statistics from Google Scholar and www.ku.dk
No data available
ID: 255114439