Stable Lévy Process
Requires a Wolfram Notebook System
Interact on desktop, mobile and cloud with the free Wolfram Player or other Wolfram Language products.
This Demonstration shows the path of a symmetric stable Lévy process. Such a process has the property of "self-similarity" with Hurst exponent (where is the index of stability) and independent increments (unlike fractional Brownian motion, whose increments are not independent except in the case of Hurst exponent ). For , the stable Lévy process coincides with ordinary Brownian motion. In this Demonstration, you can vary the parameter between 0 and 2 and the vertical range of the plot to zoom in and out.
Contributed by: Andrzej Kozlowski (March 2011)
Open content licensed under CC BY-NC-SA
One of the most remarkable properties of Brownian motion is self-similarity: for all and all , the random variable has the same distribution as . A strictly stable Lévy process can be viewed as a generalization of Brownian motion, and has the property for all , , has the same distribution as , for some index of stability , where . For each index of stability , the distributions are stable distributions . The probability density functions of this distribution are not known in explicit form except in special cases. In this Demonstration we consider the symmetric case, for which . In this case the characteristic function is given by , which shows that for we get the Cauchy distribution and for the normal distribution.
A symmetric -stable process can be represented as a combination of a (compound) Poisson process and a Brownian motion. For small values of we see that the process is dominated by big jumps. For medium values (e.g., , i.e., Cauchy process) we get both small and large jumps. For close to 2 we get Brownian motion with occasional jumps.
-stable processes for have infinite variance, which makes them somewhat inconvenient. Nevertheless, they are important in physics, biology, meteorology, and have been used in option pricing in finance.
G. Samrodnitsky and M. Taqqu, Stable Non-Gaussian Random Processes, New York: Chapman and Hall, 1994.
A. N. Shiryaev, Essentials of Stochastic Finance, River Edge, New Jersey: World Scientific Publishing Co., 1999.