Quantifying Stock Return Distributions in Financial Markets

Quantifying Stock Return Distributions in Financial Markets

Being able to quantify the probability of large price changes in stock markets is of crucial importance in understanding financial crises that affect the lives of people worldwide. Large changes in stock market prices can arise abruptly, within a matter of minutes, or develop across much longer time...

Full description

Bibliographic Details
Main Authors: Botta, Federico, Moat, Helen Susannah, Stanley, H. Eugene, Preis, Tobias
Format: Online
Language:English
Published: Public Library of Science 2015
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4556674/
id pubmed-4556674
recordtype oai_dc
spelling pubmed-45566742015-09-10 Quantifying Stock Return Distributions in Financial Markets Botta, Federico Moat, Helen Susannah Stanley, H. Eugene Preis, Tobias Research Article Being able to quantify the probability of large price changes in stock markets is of crucial importance in understanding financial crises that affect the lives of people worldwide. Large changes in stock market prices can arise abruptly, within a matter of minutes, or develop across much longer time scales. Here, we analyze a dataset comprising the stocks forming the Dow Jones Industrial Average at a second by second resolution in the period from January 2008 to July 2010 in order to quantify the distribution of changes in market prices at a range of time scales. We find that the tails of the distributions of logarithmic price changes, or returns, exhibit power law decays for time scales ranging from 300 seconds to 3600 seconds. For larger time scales, we find that the distributions tails exhibit exponential decay. Our findings may inform the development of models of market behavior across varying time scales. Public Library of Science 2015-09-01 /pmc/articles/PMC4556674/ /pubmed/26327593 http://dx.doi.org/10.1371/journal.pone.0135600 Text en © 2015 Botta et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Botta, Federico
Moat, Helen Susannah
Stanley, H. Eugene
Preis, Tobias
spellingShingle Botta, Federico
Moat, Helen Susannah
Stanley, H. Eugene
Preis, Tobias
Quantifying Stock Return Distributions in Financial Markets
author_facet Botta, Federico
Moat, Helen Susannah
Stanley, H. Eugene
Preis, Tobias
author_sort Botta, Federico
title Quantifying Stock Return Distributions in Financial Markets
title_short Quantifying Stock Return Distributions in Financial Markets
title_full Quantifying Stock Return Distributions in Financial Markets
title_fullStr Quantifying Stock Return Distributions in Financial Markets
title_full_unstemmed Quantifying Stock Return Distributions in Financial Markets
title_sort quantifying stock return distributions in financial markets
description Being able to quantify the probability of large price changes in stock markets is of crucial importance in understanding financial crises that affect the lives of people worldwide. Large changes in stock market prices can arise abruptly, within a matter of minutes, or develop across much longer time scales. Here, we analyze a dataset comprising the stocks forming the Dow Jones Industrial Average at a second by second resolution in the period from January 2008 to July 2010 in order to quantify the distribution of changes in market prices at a range of time scales. We find that the tails of the distributions of logarithmic price changes, or returns, exhibit power law decays for time scales ranging from 300 seconds to 3600 seconds. For larger time scales, we find that the distributions tails exhibit exponential decay. Our findings may inform the development of models of market behavior across varying time scales.
publisher Public Library of Science
publishDate 2015
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4556674/
_version_ 1613471624709799936

Similar Items