The doctoral thesis treats with the extremal behavior of stochastic processes using the concept of regular variation. In particular, finite and infinite-dimensional regular variation is proved for mixed moving average processes driven by regularly varying Lévy bases. Moreover, the special case of multivariate superpositions of Ornstein-Uhlenbeck processes is considered as well as the related supOU stochastic volatility model, which is analyzed with respect to its tail behavior. Furthermore, limit distributions are derived for random walks with regularly varying and dependent jump sizes.
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The doctoral thesis treats with the extremal behavior of stochastic processes using the concept of regular variation. In particular, finite and infinite-dimensional regular variation is proved for mixed moving average processes driven by regularly varying Lévy bases. Moreover, the special case of multivariate superpositions of Ornstein-Uhlenbeck processes is considered as well as the related supOU stochastic volatility model, which is analyzed with respect to its tail behavior. Furthermore, lim...
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