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SUMMARY:Non-Extensive Statistical Mechanics and Heavy Tails in Financial L
 og-Returns: Stocks\, FX\, and Cryptocurrencies
DTSTART;VALUE=DATE-TIME:20260414T224600Z
DTEND;VALUE=DATE-TIME:20260414T225300Z
DTSTAMP;VALUE=DATE-TIME:20260413T202317Z
UID:indico-contribution-340@fisindico.uniandes.edu.co
DESCRIPTION:Speakers: Juan Diego Rueda Mantilla (Universidad Nacional de C
 olombia)\nNon-extensive statistical mechanics\, introduced by Tsallis\, pr
 ovides a robust theoretical framework for describing complex systems chara
 cterized by long-range correlations and extreme events. In the context of 
 financial markets\, the q-Gaussian distributions that emerge from this for
 malism constitute a natural generalization of Gaussian approaches\, as the
 y are capable of capturing the heavy tails that characterize empirical log
 -returns\, which standard Gaussian models fail to reproduce. In this work\
 , we present a comprehensive empirical validation of a hybrid model that i
 ntegrates non-extensive statistical mechanics with a microscopic agent-bas
 ed dynamics characterized by herding behavior. The simulated log-returns a
 re compared with high-frequency data from stocks in developed and emerging
  markets\, revealing that the q-Gaussian model reproduces the heavy tails 
 of liquid markets with remarkable accuracy\, whereas the Gaussian model be
 tter captures the statistical structure of assets such as oil and exchange
  rates\, additionally exhibiting a surprising degree of temporal scale inv
 ariance. An additional finding is the identification of an intrinsic limit
  to the validity of the hybrid model\, arising from its own dynamics witho
 ut the need for externally imposed criteria. The analysis is further exten
 ded to high-frequency cryptocurrency and foreign exchange pairs\, where th
 e systematic variation of the parameter q enables the quantification of th
 e degree of non-extensivity inherent to each market. The results indicate 
 that the Tsallis formalism not only successfully captures the heavy tails 
 of the assets considered\, but also that the parameter q serves as a quant
 itative indicator of the degree of statistical complexity inherent in each
  asset class\, raising the question of the potential universality of this 
 approach across financial markets as diverse as digital and traditional on
 es.\n\nhttps://fisindico.uniandes.edu.co/event/23/contributions/340/
LOCATION:Universidad Nacional Edificio 564
URL:https://fisindico.uniandes.edu.co/event/23/contributions/340/
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