Lévy alpha-stable distribution and modelling of stock prices.

Question

Since Mandelbrot, Fama and others have performed seminal work on the topic, it has been suspected that stock price fluctuations can be more appropriately modeled using Lévy alpha-stable distrbutions other than the normal distribution law. Yet, the subject is somewhat controversial, there is a lot of literature in defense of the normal law and criticizing distributions without bounded variation. Moreover, precisely because of the the unbounded variation, the whole standard framework of quantitative analysis can not be simply copy/pasted to deal with these more "exotic" distributions.

Yet, I think there should be something to say about how to value risk of fluctuations. After all, the approaches using the variance are just shortcuts, what one really has in mind is the probability of a fluctuation of a certain size. So I was wondering if there is any literature investigating that in particular.

In other words: what is the current status of financial theories based on Lévy alpha-stable distributions? What are good review papers of the field?

Answer 1

I recently read "Modeling financial data with stable distributions" (Nolan 2005) which gives a survey of this area and might be of interest (I believe it was contained in "Handbook of Heavy Tailed Distributions in Finance"). Another more recent reference is "Alpha-Stable Paradigm in Financial Markets" (2008).

I'm not aware of anything covering "risk of fluctuations" and this is still certainly not at the center of the field (i.e. most theory still includes some version of Gaussian or mixture of Gaussians). Would also be interested in other references.

Answer 2

There are several application of Lévy alpha-stable distributions to finance, especially in insurance and reinsurance. I believe that Embrechts-Kluppelberg-Mikosh's "Modelling Extremal Events for Insurance and Finance" is still an excellent reference. However, in the modeling of stock prices, this line of research is essentially inactive. The reason is that there is conclusive evidence that stock prices have finite second moments (for a survey, see Taylor's book or Cont's nice survey. This essentially rules out all stable distributions except the gaussian one. Stochastic volatility models using mixture of normals for the unconditional distributions and/or diffusion/jump processes are far more popular.

Answer 3

I am still a beginner to this topic, and have been working through Cont and Tankov's textbook Financial Modelling With Jump Processes (2003), which is a fairly elementary treatment of the subject. I think a revised second edition is to come out later this year.

One interesting area of applications that has become more prominent with a recent wave of papers are those that use Bayesian methodology to evaluate stochastic volatility, for example see: Jacquier, Polson & Rossi and Szerszen among others.

Answer 4

I just ran across an interesting presentation comparing the effectiveness of Normal, Cauchy, and Student's-t distributions in modeling the S&P. It concludes that the normal distribution underestimates extreme movements, the Cauchy overestimates them (although a comment on the presentation points out that Mandelbrot used different parameters than the author did), and concludes that the student's-t is a fairly good fit.

Answer 5

Fat tailed distributions have extreme values that follow a Frechet distribution. Try calculating VAR using method outlined in Tsay's Analysis of Financial Time Series.

If you are trying to determine a good distribution for stock price in a non academic setting use hyperbolic-secant. It is a wrong answer but it is much easier to fit then a Levy and it gives better answers then normal. Hyperbolic-Secant is bell shaped so many time series techniques "plug" right in.