In probability theory, a Lévy process, named after the French 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. 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 the Wiener process, often called the Brownian motion process, and the Poisson process. Aside from Brownian motion with drift, all other proper Lévy processes have discontinuous paths.
A stochastic process is said to be a Lévy process if it satisfies the following properties:
- almost surely
- Independence of increments: For any , are independent
- Stationary increments: For any , is equal in distribution to
- Continuity in probability: For any and it holds that
A continuous-time stochastic process assigns a random variable Xt 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 Xs − Xt between its values at different times t < s. To call the increments of a process independent means that increments Xs − Xt and Xu − Xv are 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) independent.
To call the increments stationary means that the probability distribution of any increment Xt − Xs depends only on the length t − s of the time interval; increments on equally long time intervals are identically distributed.
The distribution of a Lévy process has the property of 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 assumptions 2 and 3. Conversely, for each infinitely divisible probability distribution , there is a Lévy process such that the law of is given by .
The distribution of a Lévy process is characterized by its characteristic function, which is given by the Lévy–Khintchine formula (general for all infinitely divisible distributions): If is a Lévy process, then its characteristic function is given by
where , , is the indicator function and is a sigma-finite measure called the Lévy measure of , satisfying the property
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; represents the rate of arrival (intensity) of the Poisson process with jump of size . These three components, and thus the Lévy–Khintchine representation, are fully determined by the Lévy–Khintchine triplet . In particular, the only (nondeterministic) continuous Lévy process is a Brownian motion with drift.
Any Lévy process may be decomposed into the sum of a Wiener process or Brownian motion process, 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 processes.This result is known as the Lévy–Itô decomposition.
Given a Lévy triplet there exist three independent Lévy processes, which lie in the same probability space, , , such that:
- is a Wiener process with drift, corresponding to the absolutely continuous part of a measure and capturing the drift a and diffusion ;
- is a compound Poisson process, corresponding to the pure point part of the singular measure W;
- is a square integrable pure jump 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 is then a Lévy process with triplet .
The process can be written as a sum of two independent processes the first pure jump zero mean martingale of jumps less than In absolute value and the second a compound Poisson process describing the jumps bigger than one in absolute value.
- Independent and identically distributed random variables
- Wiener process
- Poisson process
- Markov process
- Lévy flight
- Gamma process
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