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2014 | OriginalPaper | Buchkapitel

13. Positive Self-similar Markov Processes

verfasst von : Andreas E. Kyprianou

Erschienen in: Fluctuations of Lévy Processes with Applications

Verlag: Springer Berlin Heidelberg

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Abstract

In this chapter, our objective is to explore in detail the general class of so-called positive self-similar Markov processes. Emphasis will be placed on the bijection between this class and the class of Lévy processes which are killed at an independent and exponentially distributed time. This bijection can be expressed through a straightforward space-time transformation and, thereby it, we are able to explore a number of specific examples of positive self-similar Markov processes, which illuminate a variety of explicit and semi-explicit fluctuation identities for Lévy processes. Our first such family of examples will be positive self-similar Markov processes that are obtained when considering path transformations of stable processes and conditioned stable processes. Here, the underlying associated Lévy processes are known as Lamperti-stable processes. Known properties of stable processes, when transferred through the aforementioned space-time transform, will give us explicit fluctuation identities for Lamperti-stable processes; in particular, we will obtain their Wiener–Hopf factorisation. Another family of examples we will consider is continuous-state branching processes and continuous-state branching processes with immigration, which are also self-similar.
Whilst our exposition of general positive self-similar Markov processes will, in the beginning, insist that their initial value lies in (0,∞), we will also look at the more complicated case that the point of issue is the origin. This discussion leads us to the concept of recurrent extensions of positive self-similar Markov processes. With this theory in hand, we will conclude the chapter by looking at elements of fluctuation theory for positive self-similar Markov processes associated with spectrally negative Lévy processes.

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Fußnoten
1
What we call here “positive self-similar Markov processes”, Lamperti (1972) called “semi-stable Markov processes”.
 
2
Recall that X is quasi-left-continuous if it has the following property: For each \(\mathbb{F}\)-stopping time T, if there exists an increasing sequence of \(\mathbb{F}\)-stopping times, {T n n≥1}, satisfying lim n↑∞ T n =T almost surely, then \(\lim_{n\uparrow\infty}X_{T_{n}}= X_{T}\) almost surely on {T<∞}.
 
3
It is important to note that our definition of a positive self-similar Markov process differs slightly from what one normally finds in the literature. Where we have assumed that it is a strong Markov process with paths that are right-continuous with left limits and quasi-left-continuous, a more usual assumption would be that it is a regular Markov process that satisfies the so-called Feller property. The latter assumption implies the former assumption.
 
4
See Lebedev (1972) for further background on Bessel functions.
 
5
Rather obviously, we also rule out the case that −Y is a subordinator.
 
6
Recall again (see the first footnote in Sect. 5.​6) that Euler’s reflection formula for gamma functions says that Γ(1−u)Γ(u)=π/sinπu for \(u\in\mathbb{C}\backslash \mathbb{Z}\).
 
7
Formally the case of Esscher transforms for killed Lévy processes was not discussed in (8.​5). However, it is not difficult to check that one may similarly change measure in this way when the underlying Lévy process has independent exponential killing.
 
8
Let \(\mathbb{D}\) be the space of mappings from [0,∞) to \(\mathbb{R}\) which are right-continuous with left limits. The Skorokhod topology is generated by an appropriate metric on the space \(\mathbb{D}\), which has the property that most events of interest belong to the sigma-algebra generated by its open sets. The details are far too involved to provide a concise overview here. The reader is instead referred to Chap. VI of Jacod and Shiryaev (1987), or indeed Billingsley (1999).
 
9
Our lack of willingness to give a precise description of \(\mathbb{P}^{\uparrow}_{0}\) at the end of Sect. 13.2.1 comes at the price of lack of clarity at this point of our informal discussion. On the other hand, in the case that γ=0, that is to say, there is no upward creeping in ξ, there is no need for us to be clear about the meaning of \(\mathbb{P}^{\uparrow}_{0}\) as then it is not used in this construction.
 
10
As remarked upon earlier in this chapter, although the exponential change of measure has only been defined for processes ξ with no killing, the reader can easily verify that it is equally applicable to spectrally negative Lévy processes with killing.
 
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Metadaten
Titel
Positive Self-similar Markov Processes
verfasst von
Andreas E. Kyprianou
Copyright-Jahr
2014
Verlag
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-642-37632-0_13