Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

An empirical analysis of heavy-tails behavior of financial data: The case for power laws

Abstract : This article aims at underlying the importance of a correct modelling of the heavy-tail behavior of extreme values of financial data for an accurate risk estimation. Many financial models assume that prices follow normal distributions. This is not true for real market data, as stock (log-)returns show heavy-tails. In order to overcome this, price variations can be modeled using stable distribution, but then, as shown in this study, we observe that it over-estimates the Value-at-Risk. To overcome these empirical inconsistencies for normal or stable distributions, we analyze the tail behavior of price variations and show further evidence that power-law distributions are to be considered in risk models. Indeed, the efficiency of power-law risk models is proved by comprehensive backtesting experiments on the Value-at-Risk conducted on NYSE Euronext Paris stocks over the period 2001-2011.
Document type :
Preprints, Working Papers, ...
Complete list of metadata
Contributor : Antoine Lejay Connect in order to contact the contributor
Submitted on : Wednesday, August 14, 2013 - 10:39:26 AM
Last modification on : Saturday, October 16, 2021 - 11:18:02 AM
Long-term archiving on: : Wednesday, April 5, 2017 - 8:44:16 PM


Files produced by the author(s)


  • HAL Id : hal-00851429, version 1



Nicolas Champagnat, Madalina Deaconu, Antoine Lejay, Nicolas Navet, Souhail Boukherouaa. An empirical analysis of heavy-tails behavior of financial data: The case for power laws. 2013. ⟨hal-00851429⟩



Record views


Files downloads