Quantitative Finance Stack Exchange is a question and answer site for finance professionals and academics. Join them; it only takes a minute:

Sign up
Here's how it works:
  1. Anybody can ask a question
  2. Anybody can answer
  3. The best answers are voted up and rise to the top

It seems that some tail-risk centric groups are bent on using Paretian and t-distributions to account for tail risk when fitting log-returns. It has been observed, however, that with and without filtering log-returns with ARMA/GARCH, fitting ecdf's to cdf's still results in better Laplace and logistic distribution fits when compared with stable, normal, or Student's t. Given this, is there a reason why the bulk of the data and its best fitting distribution is not identified first, followed by consideration of tails? If the focus is mostly tails, then the Cauchy is not bad, and a Cauchy with heavy tails could be realized by a stable distribution. Independent of the infinite variance problem, why the constraint for mostly t-distributions?

share|improve this question
Check: quant.stackexchange.com/a/10319/3015 – Quartz Feb 21 '14 at 17:01

Your Answer


By posting your answer, you agree to the privacy policy and terms of service.

Browse other questions tagged or ask your own question.