Lévy process

In probability theory, a Lévy process, named after the French mathematician Paul Lévy, is any continuous-time stochastic process that starts at 0, admits càdlàg modification and has "stationary independent increments" — this phrase will be explained below. The most well-known examples are the Wiener process and the Poisson process.

## Contents

* 1 Properties
* 1.1 Independent increments
* 1.2 Stationary increments
* 1.3 Divisibility
* 1.4 Moments
* 2 Lévy-Khinchin representation
* 3 External links

## Properties

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### Independent increments

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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.

### Stationary increments

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To call the increments stationary means that the probability distribution of any increment Xs − Xt depends only on the length s − t of the time interval; increments with equally long time intervals are identically distributed.

In the Wiener process, the probability distribution of Xs − Xt is normal with expected value 0 and variance s − t.

In the Poisson process, the probability distribution of Xs − Xt is a Poisson distribution with expected value λ(s − t), where λ > 0 is the "intensity" or "rate" of the process.

### Divisibility

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The probability distributions of the increments of any Lévy process are infinitely divisible. There is a Lévy process for each infinitely divisible probability distribution.

### Moments

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In any Lévy process with finite moments, the nth moment <math>\mu_n(t) = E(X_t^n)</math> is a polynomial function of t; 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-Khinchin representation

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It is possible to characterise all Lévy processes by looking at their characteristic function. This leads to the Lévy-Khinchin representation. If <math> X_t </math> is a Lévy process, then its characteristic function satisfies the following relation:

<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)\,W(dx) \Bigg) </math>

where <math>a \in \mathbb{R}</math>, <math>\sigma\ge 0</math> and <math>\mathbf{I}</math> is the indicator function. The Lévy measure <math>W</math> must be such that

<math>\int_{\mathbb{R}\backslash\\{0\\}} \min \\{ x^2 , 1 \\} W(dx) < \infty. </math>

A Lévy process can be seen as comprising of three components: a drift, a diffusion component and a jump component. These three components, and thus the Lévy-Khinchin representation of the process, are fully determined by the Lévy-Khinchin triplet <math>(a,\sigma^2, W)</math>. So one can see that a purely continuous Lévy process is a Brownian motion with drift.

## External links

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* Applebaum, David (December 2004), "Lévy Processes--From Probability to Finance and Quantum Groups" (PDF), Notices of the American Mathematical Society, Providence, RI: American Mathematical Society, 51 (11): 1336–1347, ISSN 1088-9477CS1 maint: Date and year (link)

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