scholarly journals On association and other forms of positive dependence for Feller processes

2019 ◽  
Vol 56 (2) ◽  
pp. 624-646
Author(s):  
Eddie Tu
Keyword(s):  
State Space ◽  
Lévy Processes ◽  
Levy Processes ◽  
Poisson Processes ◽  
Jump Processes ◽  
Uhlenbeck Process ◽  
Positive Dependence ◽  
Positive Orthant ◽  
Ornstein Uhlenbeck Process ◽  
Lévy Measures

AbstractWe characterize various forms of positive dependence, such as association, positive supermodular association and dependence, and positive orthant dependence, for jump-Feller processes. Such jump processes can be studied through their state-space dependent Lévy measures. It is through these Lévy measures that we will provide our characterization. Finally, we present applications of these results to stochastically monotone Feller processes, including Lévy processes, the Ornstein–Uhlenbeck process, pseudo-Poisson processes, and subordinated Feller processes.

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.

Periodic Ornstein-Uhlenbeck processes driven by Lévy processes

2002 ◽  
Vol 39 (04) ◽  
pp. 748-763 ◽  
Author(s):  
Jan Pedersen
Keyword(s):  
Stationary Distribution ◽  
Random Fields ◽  
Markov Random Fields ◽  
Lévy Processes ◽  
Levy Processes ◽  
Uhlenbeck Process ◽  
Stationary Distributions ◽  
Markov Random ◽  
Ornstein Uhlenbeck Process

In this paper, the class of periodic Ornstein-Uhlenbeck processes is defined. It is shown that periodic Ornstein-Uhlenbeck processes are stationary Markov random fields and the class of stationary distributions is characterized. In particular, any self-decomposable distribution is the stationary distribution of some periodic Ornstein-Uhlenbeck process. As examples, gamma periodic Ornstein-Uhlenbeck processes and Gaussian periodic Ornstein-Uhlenbeck processes are considered.

Periodic Ornstein-Uhlenbeck processes driven by Lévy processes

2002 ◽  
Vol 39 (4) ◽  
pp. 748-763 ◽  
Author(s):  
Jan Pedersen
Keyword(s):  
Stationary Distribution ◽  
Random Fields ◽  
Markov Random Fields ◽  
Lévy Processes ◽  
Levy Processes ◽  
Uhlenbeck Process ◽  
Stationary Distributions ◽  
Markov Random ◽  
Ornstein Uhlenbeck Process

In this paper, the class of periodic Ornstein-Uhlenbeck processes is defined. It is shown that periodic Ornstein-Uhlenbeck processes are stationary Markov random fields and the class of stationary distributions is characterized. In particular, any self-decomposable distribution is the stationary distribution of some periodic Ornstein-Uhlenbeck process. As examples, gamma periodic Ornstein-Uhlenbeck processes and Gaussian periodic Ornstein-Uhlenbeck processes are considered.

Generalized fractional Lévy processes with fractional Brownian motion limit

2015 ◽  
Vol 47 (4) ◽  
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 On transient Markov processes of Ornstein-Uhlenbeck type

1998 ◽  
Vol 149 ◽  
pp. 19-32 ◽  
Author(s):  
Kouji Yamamuro
Keyword(s):  
Markov Processes ◽  
Lévy Processes ◽  
Transition Probabilities ◽  
Levy Processes ◽  
Exit Times ◽  
Linear Drift ◽  
Lévy Measures ◽  
Drift Terms

Abstract.For Hunt processes on Rd, strong and weak transience is defined by finiteness and infiniteness, respectively, of the expected last exit times from closed balls. Under some condition, which is satisfied by Lévy processes and Ornstein-Uhlenbeck type processes, this definition is expressed in terms of the transition probabilities. A criterion is given for strong and weak transience of Ornstein-Uhlenbeck type processes on Rd, using their Lévy measures and coefficient matrices of linear drift terms. An example is discussed.

APPROXIMATING LÉVY PROCESSES WITH A VIEW TO OPTION PRICING

2010 ◽  
Vol 13 (01) ◽  
pp. 63-91 ◽  
Author(s):  
JOHN CROSBY ◽  
NOLWENN LE SAUX ◽  
ALEKSANDAR MIJATOVIĆ
Keyword(s):  
Option Pricing ◽  
Lévy Processes ◽  
Jump Diffusion ◽  
Levy Processes ◽  
Poisson Processes ◽  
Exotic Options ◽  
Option Prices ◽  
Barrier Option ◽  
Systematic Methodology ◽  
The Mean

We examine how to approximate a Lévy process by a hyperexponential jump-diffusion (HEJD) process, composed of Brownian motion and of an arbitrary number of sums of compound Poisson processes with double exponentially distributed jumps. This approximation will facilitate the pricing of exotic options since HEJD processes have a degree of tractability that other Lévy processes do not have. The idea behind this approximation has been applied to option pricing by Asmussen et al. (2007) and Jeannin and Pistorius (2008). In this paper we introduce a more systematic methodology for constructing this approximation which allow us to compute the intensity rates, the mean jump sizes and the volatility of the approximating HEJD process (almost) analytically. Our methodology is very easy to implement. We compute vanilla option prices and barrier option prices using the approximating HEJD process and we compare our results to those obtained from other methodologies in the literature. We demonstrate that our methodology gives very accurate option prices and that these prices are more accurate than those obtained from existing methodologies for approximating Lévy processes by HEJD processes.

scholarly journals Maxima of stochastic processes driven by fractional Brownian motion

2005 ◽  
Vol 37 (03) ◽  
pp. 743-764 ◽  
Author(s):  
Boris Buchmann ◽  
Claudia Klüppelberg
Keyword(s):  
Brownian Motion ◽  
Differential Equations ◽  
Stochastic Differential Equations ◽  
State Space ◽  
Fractional Brownian Motion ◽  
Sufficient Conditions ◽  
Domain Of Attraction ◽  
Uhlenbeck Process ◽  
Ornstein Uhlenbeck Process ◽  
Maximum Domain Of Attraction

We study stationary processes given as solutions to stochastic differential equations driven by fractional Brownian motion. This rich class includes the fractional Ornstein-Uhlenbeck process and those processes that can be obtained from it by state space transformations. An explicit formula in terms of Euler's Γ-function describes the asymptotic behaviour of the covariance function of the fractional Ornstein-Uhlenbeck process near zero, which, by an application of Berman's condition, guarantees that this process is in the maximum domain of attraction of the Gumbel distribution. Necessary and sufficient conditions on the state space transforms are stated to classify the maximum domain of attraction of solutions to stochastic differential equations driven by fractional Brownian motion.

scholarly journals Typical long-time behavior of ground state-transformed jump processes

2019 ◽  
Vol 22 (02) ◽  
pp. 1950002 ◽  
Author(s):  
Kamil Kaleta ◽  
József Lőrinczi
Keyword(s):  
Ground State ◽  
Lévy Processes ◽  
Control Function ◽  
Ground States ◽  
Levy Processes ◽  
Jump Processes ◽  
Time Behavior ◽  
Long Time Behavior ◽  
Long Time

We consider a class of Lévy-type processes derived via a Doob transform from Lévy processes conditioned by a control function called potential. These ground state transformed processes (also called [Formula: see text]-processes) have position-dependent and generally unbounded components, with stationary distributions given by the ground states of the Lévy generators perturbed by the potential. We derive precise upper envelopes for the almost sure long-time behavior of these ground state-transformed Lévy processes, characterized through escape rates and integral tests. We also highlight the role of the parameters by specific examples.

scholarly journals On extended stochastic integrals with respect to Lévy processes

2013 ◽  
Vol 5 (2) ◽  
pp. 256-278 ◽  
Author(s):  
N.A. Kachanovsky
Keyword(s):  
Lévy Process ◽  
Lévy Processes ◽  
Stochastic Integral ◽  
Random Measure ◽  
Random Variable ◽  
Levy Processes ◽  
Levy Process ◽  
Jump Processes ◽  
Stochastic Integrals ◽  
Stochastic Derivative

Let $L$ be a Levy process on $[0,+\infty)$. In particular cases, when $L$ is a Wiener or Poisson process, any square integrable random variable can be decomposed in a series of repeated stochastic integrals from nonrandom functions with respect to $L$. This property of $L$, known as the chaotic representation property (CRP), plays a very important role in the stochastic analysis. Unfortunately, for a general Levy process the CRP does not hold. There are different generalizations of the CRP for Levy processes. In particular, under the Ito's approach one decomposes a Levy process $L$ in the sum of a Gaussian process and a stochastic integral with respect to a Poisson random measure, and then uses the CRP for both terms in order to obtain a generalized CRP for $L$. The Nualart-Schoutens's approach consists in decomposition of a square integrable random variable in a series of repeated stochastic integrals from nonrandom functions with respect to so-called orthogonalized centered power jump processes, these processes are constructed with using of a cadlag version of $L$. The Lytvynov's approach is based on orthogonalization of continuous monomials in the space of square integrable random variables. In this paper we construct the extended stochastic integral with respect to a Levy process and the Hida stochastic derivative in terms of the Lytvynov's generalization of the CRP; establish some properties of these operators; and, what is most important, show that the extended stochastic integrals, constructed with use of the above-mentioned generalizations of the CRP, coincide.  

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