ESTIMASI VALUE AT RISK (VaR) UNTUK MODEL EXPONENTIAL GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (EGARCH) DENGAN DISTRIBUSI STUDENT-T

Main Authors: , BONDRA UJI PRATAMA, , Dr. Abdurakhman, S.Si., M.Si.
Format: Thesis NonPeerReviewed
Terbitan: [Yogyakarta] : Universitas Gadjah Mada , 2014
Subjects:
ETD
Online Access: https://repository.ugm.ac.id/133421/
http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=74084
ctrlnum 133421
fullrecord <?xml version="1.0"?> <dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><relation>https://repository.ugm.ac.id/133421/</relation><title>ESTIMASI VALUE AT RISK (VaR) UNTUK MODEL EXPONENTIAL GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (EGARCH) DENGAN DISTRIBUSI STUDENT-T</title><creator>, BONDRA UJI PRATAMA</creator><creator>, Dr. Abdurakhman, S.Si., M.Si.</creator><subject>ETD</subject><description>Quantitative risk measurement can be calculated using Value at Risk (VaR) method. Usually, we use VaR with Student-t distribution to estimate the maximum potential loss of leptokurtic data. This VaR Student-t is constant. In this paper, we employ VaR Student-t with EGARCH Student's-t model to estimate the maximum potential loss of heteroscedasticity and leverage effect data in order to obtain more accurate estimation than VaR Student-t. Backtesting methods used to measure the accuracy of the VaR are the Kupiec test. The Kupiec test stated that VaR Student-t with EGARCH was suitable for estimating the maximum potential loss of the PT WIKA&#xE2;&#xFFFD;&#xFFFD;s stock data in December 3rd 2012 to January 31th 2014. This was shown by the results of the next 20 periods VaR forecasts that was capable for covering some forthcoming losses.</description><publisher>[Yogyakarta] : Universitas Gadjah Mada</publisher><date>2014</date><type>Thesis:Thesis</type><type>PeerReview:NonPeerReviewed</type><identifier> , BONDRA UJI PRATAMA and , Dr. Abdurakhman, S.Si., M.Si. (2014) ESTIMASI VALUE AT RISK (VaR) UNTUK MODEL EXPONENTIAL GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (EGARCH) DENGAN DISTRIBUSI STUDENT-T. UNSPECIFIED thesis, UNSPECIFIED. </identifier><relation>http://etd.ugm.ac.id/index.php?mod=penelitian_detail&amp;sub=PenelitianDetail&amp;act=view&amp;typ=html&amp;buku_id=74084</relation><recordID>133421</recordID></dc>
format Thesis:Thesis
Thesis
PeerReview:NonPeerReviewed
PeerReview
author , BONDRA UJI PRATAMA
, Dr. Abdurakhman, S.Si., M.Si.
title ESTIMASI VALUE AT RISK (VaR) UNTUK MODEL EXPONENTIAL GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (EGARCH) DENGAN DISTRIBUSI STUDENT-T
publisher [Yogyakarta] : Universitas Gadjah Mada
publishDate 2014
topic ETD
url https://repository.ugm.ac.id/133421/
http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=74084
contents Quantitative risk measurement can be calculated using Value at Risk (VaR) method. Usually, we use VaR with Student-t distribution to estimate the maximum potential loss of leptokurtic data. This VaR Student-t is constant. In this paper, we employ VaR Student-t with EGARCH Student's-t model to estimate the maximum potential loss of heteroscedasticity and leverage effect data in order to obtain more accurate estimation than VaR Student-t. Backtesting methods used to measure the accuracy of the VaR are the Kupiec test. The Kupiec test stated that VaR Student-t with EGARCH was suitable for estimating the maximum potential loss of the PT WIKA�s stock data in December 3rd 2012 to January 31th 2014. This was shown by the results of the next 20 periods VaR forecasts that was capable for covering some forthcoming losses.
id IOS2744.133421
institution Universitas Gadjah Mada
institution_id 19
institution_type library:university
library
library Perpustakaan Pusat Universitas Gadjah Mada
library_id 488
collection UGM Repository
repository_id 2744
subject_area Karya Umum
city SLEMAN
province DAERAH ISTIMEWA YOGYAKARTA
repoId IOS2744
first_indexed 2016-09-14T18:34:45Z
last_indexed 2016-09-22T21:48:53Z
recordtype dc
_version_ 1765816671439683584
score 17.538404