Calculation of Value At Risk using Historical Simulation, Variance Covariance and Monte Carlo Simulation Methods
DOI:
https://doi.org/10.38035/sijdb.v1i1.7Keywords:
Value At Risk, Historical Simulation, Variance Covariance, Monte Carlo SimulationAbstract
The purpose of this study is to measure the Value at Risk of single assets of companies listed on the Jakarta Islamic Index using the Historical Simulation, Variance Covariance and Monte Carlo Simulation methods. The research method is defined as a scientific way to obtain data with specific purposes and uses. The research design used in this study is a comparative study, this study uses data analysis methods with a quantitative approach. The results of the research are based on validity testing using the Backtesting Kupiec method and Basel traffic light known that all three methods are used in this study produces a valid (accurate) VaR value for used.
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Sugiyono. (2014). "Quantitative, Qualitative, and R&D Educational Research Methods". Bandung: Alphabet
Suhadi. (2012). "Evaluation of Value at Ripple Calculations with Monte Carlo Simulations and Historical Simulations at Three State-Owned Enterprises (BUMN) Banks". Thesis Faculty of Economics, University of Indonesia
Tandelilin, Enduardus (2010). “Portfolio and Investment Theory and Application”. Yogyakarta: Kanisius
Anas, Sudijono. (2009). "Introduction to Educational Statistics". Jakarta: Rajawali Press
Azwar, Saifuddin. (2007). "Research methods". Yogyakarta: Student Libraries
Basel Committee on Banking Supervision. (2011). “Revision to the Basel II Market Risk Framework”. Bus, Basel, Switzerland.
Best, P. (1998). “Implementing Value at Risk”. England: John Wiley & Sons, Inc.
Buchdadi, Agung D. (2008). "Calculation of Optimum Value at Risk Portfolio of Sharia-Based Company Shares Using the Ewma Approach". Indonesian Journal of Accounting and Finance, Vol. 5, No. 2
Ismanto, Hadi. (2016). "Value at Risk Analysis in Forming Optimal Portfolio (Empirical Study on Stocks Joined in LQ 45)". The 3rd University Research Colloquium 2016
Iskana, Febrina Ratna. (2018). "This is what PGN said about declining profits for five consecutive years." Accessed February 2019: http://www.kotan.co.id/news
Jorion, Philippe. (2001). “Value At Risk: The New Benchmark for Managing Financial Risk”. 2nd Ed. Mc. Graw Hill. Californian. North America
Jorion, P. (2007). “Value at Risk the New Benchmark for Managing Finance (3rd Ed.)”. USA: McGraw Hill
Lestdwinanto, Handoyo. (2016). "Comparison of Value at Risk Methods Between Risk Metric Methods, Historical Back Simulation, and Monte Carlo Simulation in the Context of Predicting Investment Risk in Property for the 2008-2014 Period". Journal of Economics, Management and Banking, Vol. 2 No. April 1, 2016
Penza, Pietro and Bansal, Vipul K. (2001). “Measuring Market with Value at Risk”. New York: John Wiley & Sons, Inc.
Purnomo, Herdaru. (2018). "In Five Months of 2018 RI's Investment Risk Soared". Accessed 25 March 2019: http://www.cnbcindonesia.com/market
Please, Ulber. (2009). "Social Research Methods". Bandung: PT. Aditama Refika
Sugiyono. (2014). "Quantitative, Qualitative, and R&D Educational Research Methods". Bandung: Alphabet
Suhadi. (2012). "Evaluation of Value at Ripple Calculations with Monte Carlo Simulations and Historical Simulations at Three State-Owned Enterprises (BUMN) Banks". Thesis Faculty of Economics, University of Indonesia
Tandelilin, Enduardus (2010). “Portfolio and Investment Theory and Application”. Yogyakarta: Kanisius
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Published
2023-07-01
How to Cite
Mahaputra, M. R. ., Yandi, A. ., & Maharani, A. . (2023). Calculation of Value At Risk using Historical Simulation, Variance Covariance and Monte Carlo Simulation Methods. Siber International Journal of Digital Business (SIJDB), 1(1), 1–8. https://doi.org/10.38035/sijdb.v1i1.7
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