scholarly journals Functional laws for trimmed Lévy processes

2017 ◽  
Vol 54 (3) ◽  
pp. 873-889 ◽  
Author(s):  
Boris Buchmann ◽  
Yuguang F. Ipsen ◽  
Ross Maller
Keyword(s):  
Lévy Processes ◽  
Limit Theorems ◽  
Stable Process ◽  
Sample Path ◽  
Continuous Mapping ◽  
Levy Processes ◽  
Ruin Time ◽  
Limit Process ◽  
Functional Convergence ◽  
Time Problem

AbstractTwo different ways of trimming the sample path of a stochastic process in 𝔻[0, 1]: global ('trim as you go') trimming and record time ('lookback') trimming are analysed to find conditions for the corresponding operators to be continuous with respect to the (strong)J1-topology. A key condition is that there should be no ties among the largest ordered jumps of the limit process. As an application of the theory, via the continuous mapping theorem, we prove limit theorems for trimmed Lévy processes, using the functional convergence of the underlying process to a stable process. The results are applied to a reinsurance ruin time problem.

Generalized fractional Lévy processes with fractional Brownian motion limit

2015 ◽  
Vol 47 (04) ◽  
pp. 1108-1131 ◽  
Author(s):  
Claudia Klüppelberg ◽  
Muneya Matsui
Keyword(s):  
Brownian Motion ◽  
Fractional Brownian Motion ◽  
Lévy Processes ◽  
Sample Path ◽  
Levy Processes ◽  
Order Structure ◽  
Uhlenbeck Process ◽  
Sample Paths ◽  
Volatility Models ◽  
Ornstein Uhlenbeck Process

Fractional Lévy processes generalize fractional Brownian motion in a natural way. We go a step further and extend the usual fractional Riemann-Liouville kernel to a regularly varying function. We call the resulting stochastic processes generalized fractional Lévy processes (GFLPs) and show that they may have short or long memory increments and that their sample paths may have jumps or not. Moreover, we define stochastic integrals with respect to a GFLP and investigate their second-order structure and sample path properties. A specific example is the Ornstein-Uhlenbeck process driven by a time-scaled GFLP. We prove a functional central limit theorem for such scaled processes with a fractional Ornstein-Uhlenbeck process as a limit process. This approximation applies to a wide class of stochastic volatility models, which include models where possibly neither the data nor the latent volatility process are semimartingales.

scholarly journals Sample path large deviations for Lévy processes and random walks with Weibull increments

2020 ◽  
Vol 30 (6) ◽  
pp. 2695-2739
Author(s):  
Mihail Bazhba ◽  
Jose Blanchet ◽  
Chang-Han Rhee ◽  
Bert Zwart
Keyword(s):  
Large Deviations ◽  
Random Walks ◽  
Lévy Processes ◽  
Sample Path ◽  
Levy Processes ◽  
Sample Path Large Deviations

Spectrally negative Lévy processes with applications in risk theory

2001 ◽  
Vol 33 (1) ◽  
pp. 281-291 ◽  
Author(s):  
Hailiang Yang ◽  
Lianzeng Zhang
Keyword(s):  
Lévy Processes ◽  
Compound Poisson Process ◽  
Risk Process ◽  
Levy Processes ◽  
Point Of View ◽  
Ruin Time ◽  
Classical Risk Process ◽  
Risk Processes ◽  
First Time ◽  
Spectrally Negative Lévy Processes

In this paper, results on spectrally negative Lévy processes are used to study the ruin probability under some risk processes. These processes include the compound Poisson process and the gamma process, both perturbed by diffusion. In addition, the first time the risk process hits a given level is also studied. In the case of classical risk process, the joint distribution of the ruin time and the first recovery time is obtained. Some results in this paper have appeared before (e.g., Dufresne and Gerber (1991), Gerber (1990), dos Reis (1993)). We revisit them from the Lévy process theory's point of view and in a unified and simple way.

scholarly journals Application of Lévy processes in modelling (geodetic) time series with mixed spectra

2021 ◽  
Vol 28 (1) ◽  
pp. 121-134
Author(s):  
Jean-Philippe Montillet ◽  
Xiaoxing He ◽  
Kegen Yu ◽  
Changliang Xiong
Keyword(s):  
Time Series ◽  
Lévy Processes ◽  
Stable Process ◽  
Functional Model ◽  
Noise Spectrum ◽  
Levy Processes ◽  
Infinite Variance ◽  
Residual Time ◽  
First Case ◽  
Heavy Tailed

Abstract. Recently, various models have been developed, including the fractional Brownian motion (fBm), to analyse the stochastic properties of geodetic time series together with the estimated geophysical signals. The noise spectrum of these time series is generally modelled as a mixed spectrum, with a sum of white and coloured noise. Here, we are interested in modelling the residual time series after deterministically subtracting geophysical signals from the observations. This residual time series is then assumed to be a sum of three stochastic processes, including the family of Lévy processes. The introduction of a third stochastic term models the remaining residual signals and other correlated processes. Via simulations and real time series, we identify three classes of Lévy processes, namely Gaussian, fractional and stable. In the first case, residuals are predominantly constituted of short-memory processes. The fractional Lévy process can be an alternative model to the fBm in the presence of long-term correlations and self-similarity properties. The stable process is here restrained to the special case of infinite variance, which can be only satisfied in the case of heavy-tailed distributions in the application to geodetic time series. Therefore, the model implies potential anxiety in the functional model selection, where missing geophysical information can generate such residual time series.

scholarly journals Exit Problems for Spectrally Negative Lévy Processes Reflected at Either the Supremum or the Infimum

2007 ◽  
Vol 44 (4) ◽  
pp. 1012-1030 ◽  
Author(s):  
Xiaowen Zhou
Keyword(s):  
Lévy Processes ◽  
Stable Process ◽  
Levy Processes ◽  
Exit Problem ◽  
Brownian Motion With Drift ◽  
Spectrally Negative Lévy Process ◽  
The Times ◽  
Exit Problems ◽  
Supremum Process ◽  
Spectrally Negative Lévy Processes

For a spectrally negative Lévy process X on the real line, let S denote its supremum process and let I denote its infimum process. For a > 0, let τ(a) and κ(a) denote the times when the reflected processes Ŷ := S − X and Y := X − I first exit level a, respectively; let τ−(a) and κ−(a) denote the times when X first reaches Sτ(a) and Iκ(a), respectively. The main results of this paper concern the distributions of (τ(a), Sτ(a), τ−(a), Ŷτ(a)) and of (κ(a), Iκ(a), κ−(a)). They generalize some recent results on spectrally negative Lévy processes. Our approach relies on results concerning the solution to the two-sided exit problem for X. Such an approach is also adapted to study the excursions for the reflected processes. More explicit expressions are obtained when X is either a Brownian motion with drift or a completely asymmetric stable process.

scholarly journals Application of Levy Processes in Modelling (Geodetic) Time Series With Mixed Spectra

2019 ◽  
Author(s):  
Jean-Philippe Montillet ◽  
Xiaoxing He ◽  
Kegen Yu
Keyword(s):  
Time Series ◽  
Lévy Processes ◽  
Stable Process ◽  
Functional Model ◽  
Noise Spectrum ◽  
Levy Processes ◽  
Residual Time ◽  
Heavy Tailed Distributions ◽  
First Case ◽  
Heavy Tailed

Abstract. Recently, various models have been developed, including the fractional Brownian motion (fBm), to analyse the stochastic properties of geodetic time series, together with the extraction of geophysical signals. The noise spectrum of these time series is generally modeled as a mixed spectrum, with a sum of white and coloured noise. Here, we are interested in modelling the residual time series, after deterministically subtracting geophysical signals from the observations. This residual time series is then assumed to be a sum of three random variables (r.v.), with the last r.v. belonging to the family of Levy processes. This stochastic term models the remaining residual signals and other correlated processes. Via simulations and real time series, we identify three classes of Levy processes: Gaussian, fractional and stable. In the first case, residuals are predominantly constituted of short-memory processes. Fractional Levy process can be an alternative model to the fBm in the presence of long-term correlations and self-similarity property. Stable process is characterized by a large variance, which can be satisfied in the case of heavy-tailed distributions. The application to geodetic time series implies potential anxiety in the functional model selection where missing geophysical information can generate such residual time series.

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