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Parisian ruin of self-similar Gaussian risk processes

Krzysztof Dębicki, +2 more
- 01 Sep 2015 - 
- Vol. 52, Iss: 3, pp 688-702
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TLDR
In this paper, the authors derived the exact asymptotics of the probability of Parisian ruin for self-similar Gaussian risk processes and derived the normal approximation of the Parisian time.
Abstract
In this paper we derive the exact asymptotics of the probability of Parisian ruin for self-similar Gaussian risk processes. Additionally, we obtain the normal approximation of the Parisian ruin time and derive an asymptotic relation between the Parisian and the classical ruin times.

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Journal ArticleDOI

Extremes of vector-valued Gaussian processes: exact asymptotics

TL;DR: In this paper, the exact asymptotics of P ( ∃ t ∈ [ 0, T ] ∀ i = 1, …, n X i ( t ) > u ) as u → ∞, for both locally stationary X i and X i ) with a non-constant generalized variance function were derived.
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Extremes of vector-valued Gaussian processes: exact asymptotics

TL;DR: In this paper, the exact asymptotics of the Piterbarg inequality, Borell-TIS inequality, the Slepian lemma and the Pickands-Pitterbarg lemma were derived for mutually independent centered Gaussian processes with continuous sample paths.
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Ruin problem of a two-dimensional fractional Brownian motion risk process

Lanpeng Ji, +1 more
- 29 Jan 2018 - 
TL;DR: In this article, the authors investigated the ruin probability and time of a two-dimensional fractional Brownian motion risk process, where the net loss process of an insurance company is modeled by a fractional brownian motion.
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Uniform tail approximation of homogenous functionals of Gaussian fields

TL;DR: In this paper, the authors derived uniform upper bounds for the probability of double maxima and generalized the Piterbarg-Prisyazhnyuk theorem to more general functions than the supremum.
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Extremal behavior of hitting a cone by correlated Brownian motion with drift

TL;DR: In this paper, the authors derived exact asymptotic expression for P x u { ∃ t ≥ 0 X (t ) − μ t ∈ U }, as u → ∞, where X ( t ) = ( X 1 ( t ), …, X d ( t )) ⊤, t ≥0 is a correlated d-dimensional Brownian motion starting at the point x u = − α u with α ∈ R d, μ ∈R d and U = ∏ i = 1 d [ 0, ∞ ).
References
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Book

Asymptotic Methods in the Theory of Gaussian Processes and Fields

TL;DR: In this article, the double sum method and the method of moments limit theorems for the number of high excursions and for maxima of Gaussian processes and fields are studied.
Book

Sojourns and Extremes of Stochastic Processes

TL;DR: A survey of the normal distribution can be found in this paper, where the authors consider the following classes of Gaussian processes: (1) Stationary Gaussian Processes on a finite interval (2) Processes with stationary independent increments.
Journal ArticleDOI

Brownian excursions and parisian barrier options

TL;DR: In this article, the authors study a new variant of the so-called barrier option: a down-and-out barrier option becomes worthless as soon as a barrier is reached, whereas a down and-out Parisian barrier option is lost by the owner if the underlying asset reaches a prespecified level and remains constantly below this level for a time interval longer than a fixed number.
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