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An empirical analysis of heavy-tails behavior of financial data: The case for power laws
Year Of Publication: 2013
Month Of Publication: August
Resource Link: Click here to open
Pages: 18
Download Count: 0
View Count: 1146
Comment Num: 0
Language: English
Source: working paper
Who Can Read: Free
Date: 8-30-2013
Publisher: Administrator
Summary
Résumé : 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.
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Author(s)
Navet, Nicolas Sign in to follow this author
Boukherouaa, Souhail Sign in to follow this author
Champagnat, Nicolas Sign in to follow this author
Deaconu, Madalina Sign in to follow this author
Lejay, Antoine Sign in to follow this author
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