OLS versus quantile regression in extreme distributions

OLS versus quantile regression in extreme distributions

Abstract Financial data mostly have fat tail and an analyst is much concerned about the tail part. Most of the study in finance extensible uses linear regression but when it comes to tail analysis it becomes ineffective. So, the present study tries to address the same by using Quantile regression in the tail analysis to study the value effect in 10 portfolios formed from BSE 500 stocks based on P/B ratio. The study result clearly indicates that Quantile regression estimates give more comprehensive and vibrant picture of the unpredictable effect of the predictors on the response variables.

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Main Author: Maiti,Moinak
Format: Digital revista
Language:English
Published: Universidad Nacional Autónoma de México, Facultad de Contaduría y Administración 2019
Online Access:http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S0186-10422019000300012
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spelling oai:scielo:S0186-104220190003000122020-03-19OLS versus quantile regression in extreme distributionsMaiti,Moinak C22 G11 G12 Quantile regression decision making factor models Value effect Abstract Financial data mostly have fat tail and an analyst is much concerned about the tail part. Most of the study in finance extensible uses linear regression but when it comes to tail analysis it becomes ineffective. So, the present study tries to address the same by using Quantile regression in the tail analysis to study the value effect in 10 portfolios formed from BSE 500 stocks based on P/B ratio. The study result clearly indicates that Quantile regression estimates give more comprehensive and vibrant picture of the unpredictable effect of the predictors on the response variables.info:eu-repo/semantics/openAccessUniversidad Nacional Autónoma de México, Facultad de Contaduría y AdministraciónContaduría y administración v.64 n.2 20192019-06-01info:eu-repo/semantics/articletext/htmlhttp://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S0186-10422019000300012en10.22201/fca.24488410e.2018.1702
institution SCIELO
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country México
countrycode MX
component Revista
access En linea
databasecode rev-scielo-mx
tag revista
region America del Norte
libraryname SciELO
language English
format Digital
author Maiti,Moinak
spellingShingle Maiti,Moinak
OLS versus quantile regression in extreme distributions
author_facet Maiti,Moinak
author_sort Maiti,Moinak
title OLS versus quantile regression in extreme distributions
title_short OLS versus quantile regression in extreme distributions
title_full OLS versus quantile regression in extreme distributions
title_fullStr OLS versus quantile regression in extreme distributions
title_full_unstemmed OLS versus quantile regression in extreme distributions
title_sort ols versus quantile regression in extreme distributions
description Abstract Financial data mostly have fat tail and an analyst is much concerned about the tail part. Most of the study in finance extensible uses linear regression but when it comes to tail analysis it becomes ineffective. So, the present study tries to address the same by using Quantile regression in the tail analysis to study the value effect in 10 portfolios formed from BSE 500 stocks based on P/B ratio. The study result clearly indicates that Quantile regression estimates give more comprehensive and vibrant picture of the unpredictable effect of the predictors on the response variables.
publisher Universidad Nacional Autónoma de México, Facultad de Contaduría y Administración
publishDate 2019
url http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S0186-10422019000300012
work_keys_str_mv AT maitimoinak olsversusquantileregressioninextremedistributions
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