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• W2064163886 abstract "In this paper we consider a heavy-tailed stochastic volatility model, $X_t=sigma_tZ_t$, $tinmathbb{Z}$, where the volatility sequence $(sigma_t)$ and the i.i.d. noise sequence $(Z_t)$ are assumed independent, $(sigma_t)$ is regularly varying with index $alpha>0$, and the $Z_t$'s have moments of order larger than $alpha$. In the literature (see Ann. Appl. Probab. 8 (1998) 664-675, J. Appl. Probab. 38A (2001) 93-104, In Handbook of Financial Time Series (2009) 355-364 Springer), it is typically assumed that $(logsigma_t)$ is a Gaussian stationary sequence and the $Z_t$'s are regularly varying with some index $alpha$ (i.e., $(sigma_t)$ has lighter tails than the $Z_t$'s), or that $(Z_t)$ is i.i.d. centered Gaussian. In these cases, we see that the sequence $(X_t)$ does not exhibit extremal clustering. In contrast to this situation, under the conditions of this paper, both situations are possible; $(X_t)$ may or may not have extremal clustering, depending on the clustering behavior of the $sigma$-sequence." @default.
• W2064163886 created "2016-06-24" @default.
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• W2064163886 date "2013-11-01" @default.
• W2064163886 modified "2023-09-30" @default.
• W2064163886 title "Stochastic volatility models with possible extremal clustering" @default.
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• W2064163886 doi "https://doi.org/10.3150/12-bej426" @default.
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