Bernoulli

  • Bernoulli
  • Volume 19, Number 5A (2013), 1688-1713.

Stochastic volatility models with possible extremal clustering

Thomas Mikosch and Mohsen Rezapour

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Abstract

In this paper we consider a heavy-tailed stochastic volatility model, $X_{t}=\sigma_{t}Z_{t}$, $t\in\mathbb{Z}$, 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 $(\log\sigma_{t})$ 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.

Article information

Source
Bernoulli, Volume 19, Number 5A (2013), 1688-1713.

Dates
First available in Project Euclid: 5 November 2013

Permanent link to this document
https://projecteuclid.org/euclid.bj/1383661199

Digital Object Identifier
doi:10.3150/12-BEJ426

Mathematical Reviews number (MathSciNet)
MR3129030

Zentralblatt MATH identifier
1286.91144

Keywords
EGARCH exponential $\operatorname{AR}(1)$ extremal clustering extremal index $\operatorname{GARCH}$ multivariate regular variation point process stationary sequence stochastic volatility process

Citation

Mikosch, Thomas; Rezapour, Mohsen. Stochastic volatility models with possible extremal clustering. Bernoulli 19 (2013), no. 5A, 1688--1713. doi:10.3150/12-BEJ426. https://projecteuclid.org/euclid.bj/1383661199


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