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Value-at-Risk (VaR) is one of risk measuring tools widely used at this time. There are several approaches for calculating VaR, such as historical simulation, the variance-covariance, and Monte Carlo simulation. If a portfolio has two kind of variables in there, then we need consider dependence betwe...

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Main Author: MAGDA MUSTILLUCIANA, JULIARIS
Format: Final Project
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/18774
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:18774
spelling id-itb.:187742017-09-27T11:43:12Z#TITLE_ALTERNATIVE# MAGDA MUSTILLUCIANA, JULIARIS Indonesia Final Project INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/18774 Value-at-Risk (VaR) is one of risk measuring tools widely used at this time. There are several approaches for calculating VaR, such as historical simulation, the variance-covariance, and Monte Carlo simulation. If a portfolio has two kind of variables in there, then we need consider dependence between them. The copula theory s a fundamental tool in modelling bivariate distributions. It come within joint distribution of each marginal distributions and the dependence between them, so copula is the right tool to modelling variables which have dependence. One of extend theory of copula is conditional copula, whose components are conditional marginal distributions. It has dynamic structure, so it could used for data whose time variation. This research used conditional copula to calculate VaR of a portfolio composed by Nasdaq and S&P 500 stock indices. text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description Value-at-Risk (VaR) is one of risk measuring tools widely used at this time. There are several approaches for calculating VaR, such as historical simulation, the variance-covariance, and Monte Carlo simulation. If a portfolio has two kind of variables in there, then we need consider dependence between them. The copula theory s a fundamental tool in modelling bivariate distributions. It come within joint distribution of each marginal distributions and the dependence between them, so copula is the right tool to modelling variables which have dependence. One of extend theory of copula is conditional copula, whose components are conditional marginal distributions. It has dynamic structure, so it could used for data whose time variation. This research used conditional copula to calculate VaR of a portfolio composed by Nasdaq and S&P 500 stock indices.
format Final Project
author MAGDA MUSTILLUCIANA, JULIARIS
spellingShingle MAGDA MUSTILLUCIANA, JULIARIS
#TITLE_ALTERNATIVE#
author_facet MAGDA MUSTILLUCIANA, JULIARIS
author_sort MAGDA MUSTILLUCIANA, JULIARIS
title #TITLE_ALTERNATIVE#
title_short #TITLE_ALTERNATIVE#
title_full #TITLE_ALTERNATIVE#
title_fullStr #TITLE_ALTERNATIVE#
title_full_unstemmed #TITLE_ALTERNATIVE#
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url https://digilib.itb.ac.id/gdl/view/18774
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