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Comparison of Variance Covariance and Historical Simulation Methods to Calculate Value At Risk on Banking Stock Portfolio

Jurnal Matematika, Statistika dan Komputasi · Vol. 19 No. 1 (2022) · pp. 241–250

DOI
10.20956/j.v19i1.21436
ISSN
2614-8811, 1858-1382
Published
2022-09-07
Publisher
Department of Mathematics, Hasanuddin University
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

In investing, all investors must be faced with risk that must be borne. Therefore, to determine the best strategy in investing, every investor must calculate the risk. One statistical approach that can be used to measure the risk is Value at Risk (VaR). VaR is defined as a tolerable loss with a certain level of confidence. The purpose of this research is to estimate VaR using Variance Covariance and Historical Simulation methods on banking stock portfolio consisting of three stocks for the period 11 September 2020-30 September 2021. Both methods will then be evaluated using backtesting to determine the accuracy of VaR and to obtain the best method. From the research results, if the holding period is 1 day, then the VaR calculation for banking stock portfolio using both methods can be used to estimate the risk at 99% and 95% confidence levels, except for the VaR value using the Variance Covariance method for banking stock portfolio at 95% confidence level. The results show that Variance Covariance method is the best method for 99% confidence level. As for the 95% confidence level, Historical Simulation method is the best method.

Keywords

Collaboration network of the authors

40 authors · 132 co-authorship links · showing the 40 most connected

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  • Padjadjaran University
  • Universitas Halu Oleo
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Anisa KalondengAffiliation unknown4
Sri Astuti ThamrinAffiliation unknown3
Sitti SahrimanAffiliation unknown2
Andi Isna YunitaAffiliation unknown2
Andi M Alfin BasoAffiliation unknown2
M. Zaky Hisyam GozhiAffiliation unknown2
Vika Yugi KurniawanAffiliation unknown2
Nurtiti SunusiAffiliation unknown2
Ade Safira SeptianyAffiliation unknown2
Raupong RaupongAffiliation unknown2
Surya GumilarAffiliation unknown2
Budi AyuningsihPadjadjaran University1
Iman HernamanPadjadjaran University1
Tidi DhalikaPadjadjaran University1
Destu Putra RosmawanAffiliation unknown1
Dedi RahmatPadjadjaran University1
Ansharullah AnsharullahUniversitas Halu Oleo1
Abdu Rahman BacoAffiliation unknown1
Alfiana AntohAffiliation unknown1
Yundy HafizriandaAffiliation unknown1
Anna IslamiyatiAffiliation unknown1
Hajratul Ashwad KAffiliation unknown1
Afifah Mutiah NurdinAffiliation unknown1
Muh. Indirwan ArfanAffiliation unknown1
Muhammad FadilAffiliation unknown1
A.Muthiah Nur AngrianyAffiliation unknown1
Rini JamalAffiliation unknown1
Andi FebriyantiAffiliation unknown1
Rahmiati RahimAffiliation unknown1
Nirwan IlyasAffiliation unknown1
Haksar HaksarAffiliation unknown1
Fauziah NurhidayahAffiliation unknown1
Nurhaliza RaisAffiliation unknown1
Maryana MaharaniAffiliation unknown1
Rahmat HermawanAffiliation unknown1
Fadlan AminAffiliation unknown1
Ahmad Ryan Al AqshaAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/jmsk/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Comparison of Variance Covariance and Historical Simulation Methods to Calculate Value At Risk on Banking Stock Portfolio</dc:title>
	<dc:creator xml:lang="en">Irsan, Maria Yus Trinity</dc:creator>
	<dc:creator xml:lang="en">Priscilla, Evelyn </dc:creator>
	<dc:creator xml:lang="en">Siswanto, Siswanto</dc:creator>
	<dc:subject xml:lang="en">Value at Risk</dc:subject>
	<dc:subject xml:lang="en">Variance Covariance</dc:subject>
	<dc:subject xml:lang="en">Historical Simulation</dc:subject>
	<dc:subject xml:lang="en">Backtesting</dc:subject>
	<dc:subject xml:lang="en">Banking Stock Portfolio</dc:subject>
	<dc:description xml:lang="en">In investing, all investors must be faced with risk that must be borne. Therefore, to determine the best strategy in investing, every investor must calculate the risk. One statistical approach that can be used to measure the risk is Value at Risk (VaR). VaR is defined as a tolerable loss with a certain level of confidence. The purpose of this research is to estimate VaR using Variance Covariance and Historical Simulation methods on banking stock portfolio consisting of three stocks for the period 11 September 2020-30 September 2021. Both methods will then be evaluated using backtesting to determine the accuracy of VaR and to obtain the best method. From the research results, if the holding period is 1 day, then the VaR calculation for banking stock portfolio using both methods can be used to estimate the risk at 99% and 95% confidence levels, except for the VaR value using the Variance Covariance method for banking stock portfolio at 95% confidence level. The results show that Variance Covariance method is the best method for 99% confidence level. As for the 95% confidence level, Historical Simulation method is the best method.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2022-09-07</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/jmsk/article/view/21436</dc:identifier>
	<dc:identifier>10.20956/j.v19i1.21436</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 19 No. 1 (2022): SEPTEMBER, 2022; 241-250</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/21436/8607</dc:relation>
	<dc:relation>10.20956/j.v19i1.21436.g8607</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2022 Author and publisher</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
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