Prof. Dr. Evgeny Spodarev
Dr. Jürgen Kampf
Time and Place
Thursday, 14-16, Heho 18, room E.60
Tuesday, 14-16, Heho 18, room E.60
2 hours lecture + 1 hours exercises
Basic analysis and linear algebra courses, basic probability course (Elementare WR und Statistik).
Students of master of mathematics, master of business mathematics, master of mathematical biometry, master of finance.
In modern applications, there is a need to model phenomena that can be measured by very high numerical values which occur rarely. In probability theory, one talks about distributions with heavy tails. One class of such distributions are stable laws which (apart from the Gaussian one) do not have a finite variance. They possess a number of striking properties which make them inevitable in modelling of processes in radioelectronics, engineering, radiophysics, astrophysics and cosmology, finance, insurance, etc., to name just a few. This introductory lecture is devoted to basic properties of such distributions.
Main topics are
1) Stability with respect to convolution
2) Characteristic functions and densities
3) Non-Gaussian limit theorem for i.i.d. random summands
4) Representations and tail properties, symmetry and skewness
Prerequisite: 50% of all credits from the exercise sheet.
Exercise sheets can be only found in moodle.
Lecture notes can be downloaded here.
- J. Nolan. Stable Distributions – Models for Heavy Tailed Data. Birkhäuser, Boston, 2013.
- G. Samorodnitsky, M.S. Taqqu. Stable Non-Gaussian Random Processes. Chapman & Hall, New York, 1994.
- K.-I. Sato. Lévy Processes and Infinite Divisibility. Cambridge University Press, Cambridge, 1999 (Chapter 3).
- V. M. Zolotarev. One-Dimensional Stable Distributions. Translations of Mathematical Monographs, vol 65, AMS, Providence RI, 1986.
- S. T. Rachev, S. Mittnik. Stable Paretian Models in Finance. Wiley, New York, 2000.
- V.V. Uchaikin, V. M. Zolotarev. Chance and Stability. Stable Distributions and their Applications. VSP, Utrecht, 1999.
Link to the course reserve of the library.