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In [[probability theory]], a '''Lévy process''', named after the French mathematician [[Paul Lévy (mathematician)|Paul Lévy]], is a [[stochastic process]]  with independent, stationary increments: it represents the motion of a point whose successive displacements are [[random]] and independent, and statistically identical over different time intervals of the same length.
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A Lévy process may thus be viewed as the continuous-time analog of a [[random walk]].
 
The most well known examples of Lévy processes are  [[Brownian motion]] and the [[Poisson process]].
Aside from Brownian motion with drift, all other Lévy processes, except the deterministic case, have [[discontinuous]] paths.
 
== Mathematical definition ==
A [[stochastic process]] <math>X=\{X_t:t \geq 0\}</math> is said to be a Lévy process if it satisfies the following properties:
# <math>X_0=0 \,</math> [[almost surely]]
# '''Independence of increments:''' For any <math>0 \leq t_1 < t_2<\cdots <t_n <\infty</math>, <math>X_{t_2}-X_{t_1}, X_{t_3}-X_{t_2},\dots,X_{t_n}-X_{t_{n-1}}</math> are [[Independence (probability theory)|independent]]
# '''Stationary increments:''' For any <math>s<t \,</math>, <math>X_t-X_s \,</math> is equal in distribution to <math>X_{t-s}. \,</math>
# '''Continuity in probability:''' For any <math>\epsilon>0</math> and <math>t\ge 0</math> it holds that <math>\lim_{h\rightarrow 0}P(|X_{t+h}-X_t|>\epsilon)=0</math>
 
If <math>X</math> is a Lévy process then one may construct a version of <math>X</math> such that <math>t \mapsto X_t</math> is [[almost surely]] [[càdlàg|right continuous with left limits]].
 
== Properties ==
=== Independent increments ===
A continuous-time stochastic process assigns a [[random variable]] ''X''<sub>''t''</sub> to each point ''t'' ≥ 0 in time. In effect it is a random function of ''t''. The '''increments''' of such a process are the differences ''X''<sub>''s''</sub> − ''X''<sub>''t''</sub> between its values at different times ''t'' < ''s''. To call the increments of a process '''independent''' means that increments ''X''<sub>''s''</sub> − ''X''<sub>''t''</sub> and ''X''<sub>''u''</sub> − ''X''<sub>''v''</sub> are [[Independence (probability theory)|independent]] random variables whenever the two time intervals do not overlap and, more generally, any finite number of increments assigned to pairwise non-overlapping time intervals are mutually (not just [[pairwise independence|pairwise]]) independent.
 
=== Stationary increments ===
To call the increments '''stationary''' means that the [[probability distribution]] of any increment ''X''<sub>''t''</sub> − ''X''<sub>''s''</sub> depends only on the length ''t''&nbsp;−&nbsp;''s'' of the time interval; increments on equally long time intervals are identically distributed.
 
If <math>X</math> is a [[Wiener process]], the probability distribution of ''X''<sub>''t''</sub>&nbsp;−&nbsp;''X''<sub>''s''</sub> is [[normal distribution|normal]] with [[expected value]] 0 and [[variance]] ''t''&nbsp;−&nbsp;''s''.
 
If <math>X</math> is the  [[Poisson process]], the probability distribution of ''X''<sub>''t''</sub>&nbsp;−&nbsp;''X''<sub>''s''</sub> is a [[Poisson distribution]] with expected value λ(''t''&nbsp;−&nbsp;''s''), where λ > 0 is the "intensity" or "rate" of the process.
 
=== Infinite divisibility ===
The distribution of a Lévy process has the property of [[Infinite divisibility (probability)|infinite divisibility]]: given any integer "n", the law of a Lévy process at time t can be represented as the law of n independent random variables, which are precisely the increments of the Lévy process over time intervals of length ''t''/''n,'' which are independent and identically distributed by assumption. Conversely,  for each infinitely divisible probability distribution <math>F</math>, there is a Lévy process <math>X</math> such that the law of <math>X_1</math> is given by <math>F</math>.
 
=== Moments ===
In any Lévy process with finite [[moment (mathematics)|moments]], the ''n''th moment <math>\mu_n(t) = E(X_t^n)</math>, is a [[polynomial function]] of ''t''; [[binomial type|these functions satisfy a binomial identity]]:
 
:<math>\mu_n(t+s)=\sum_{k=0}^n {n \choose k} \mu_k(t) \mu_{n-k}(s).</math>
 
== Lévy–Khintchin representation ==
The distribution of a Lévy process is characterized by its [[characteristic function (probability theory)|characteristic function]], which is given by the '''Lévy–Khinchine formula''': If <math>( X_t)_{t\geq 0} </math> is a Lévy process, then its characteristic function is given by
 
:<math>\mathbb{E}\Big[e^{i\theta X_t} \Big] = \exp \Bigg( ait\theta - \frac{1}{2}\sigma^2t\theta^2 + t
\int_{\mathbb{R}\backslash\{0\}} \big( e^{i\theta x}-1 -i\theta x \mathbf{I}_{|x|<1}\big)\,\Pi(dx) \Bigg)
</math>
 
where <math>a \in \mathbb{R}</math>, <math>\sigma\ge 0</math>, <math>\mathbf{I}</math> is the [[indicator function]] and  <math>\Pi</math> is a sigma-finite measure called the Lévy measure of <math>X</math>, which satisfies the property
 
:<math>\int_{\mathbb{R}\backslash\{0\}} \min \{ x^2 , 1 \} \Pi(dx) < \infty. </math>
 
A Lévy process can be seen as having three independent components: a linear drift, a Brownian motion and a superposition of independent (centered) Poisson processes with different jump sizes;  <math>\Pi(dx)</math> represents the rate of arrival (intensity) of the Poisson process with jump of size <math>x</math>.
These three components, and thus the Lévy–Khintchine representation, are fully determined by the Lévy–Khintchine triplet <math>(a,\sigma^2, \Pi)</math>. In particular, the only (nondeterministic) continuous Lévy process is a Brownian motion with drift.
 
== Lévy–Itō decomposition ==
Any Lévy process may be decomposed into the sum of a [[Brownian motion]], a linear drift and a pure jump process which captures all jumps of the original Lévy process. The latter can be thought of as a superposition of centered [[Compound Poisson process|compound Poisson processes]].This result is known as the Lévy–Itō decomposition.
 
Given a Lévy triplet <math>(a,\sigma^2, \Pi)</math> there exists three independent Lévy processes, which lie in the same probability space, <math>X^{(1)}</math>, <math>X^{(2)}</math>, <math>X^{(3)}</math> such that:
* <math>X^{(1)}</math> is a [[Brownian motion]] with drift, corresponding to the absolutely continuous part of a measure and capturing the drift ''a'' and diffusion <math>\sigma^2</math>;
* <math>X^{(2)}</math> is a [[compound Poisson process]], corresponding to the pure point part of the singular measure ''W'';
* <math>X^{(3)}</math> is a [[square integrable]] pure jump [[Martingale (probability theory)|martingale]] that almost surely has a countable number of jumps on a finite interval, corresponding to the singular continuous part of the singular measure ''W''.
 
The process defined by <math>X=X^{(1)}+X^{(2)}+X^{(3)}</math> is then a Lévy process with triplet <math>(a,\sigma^2, \Pi)</math>.
 
The process <math> X^{(3)}</math> can be further decomposed as a sum of two independent processes the first pure jump zero mean martingale of jumps less than <math>1</math> In absolute value and the second a compound Poisson process describing the jumps bigger than one in absolute value.
== See also ==
* [[Independent and identically distributed random variables]]
*[[Brownian motion]]
*[[Poisson process]]
*[[Markov process]]
*[[ Lévy flight]]
 
== References ==
* {{Cite journal | last1 = Applebaum | first1 = David | title = Lévy Processes—From Probability to Finance and Quantum Groups | journal = Notices of the American Mathematical Society | volume = 51 | issue = 11 | pages = 1336–1347 | publisher = American Mathematical Society | location = Providence, RI | date = December 2004 | url = http://www.ams.org/notices/200411/fea-applebaum.pdf |format=PDF| issn = 1088-9477}}
* {{Cite book | last1 = Cont | first1 = Rama |last2 = Tankov | first2 = Peter| title = Financial Modeling with Jump Processes  | publisher = CRC Press |  | year = 2003 | ISBN = 978-1584884132 }}.
* {{Cite book | last1 = Sato | first1 = Ken-Iti | title = Lévy Processes and Infinitely Divisible Distributions | publisher = Cambridge University Press |  | year = 2011 | ISBN = 978-0521553025 }}.
 
{{Stochastic processes}}
 
{{DEFAULTSORT:Levy process}}
[[Category:Stochastic processes]]

Latest revision as of 09:48, 3 December 2014

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