文章基本信息

标题：Measuring Risk utilizing Credible Monte Carlo Value at Risk and Credible Monte Carlo Expected Tail Loss
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作者：Evy Sulistianingsih ; Dedi Rosadi ; Abdurakhman 等
期刊名称：Lecture Notes in Engineering and Computer Science
印刷版ISSN：2078-0958
电子版ISSN：2078-0966
出版年度：2022
卷号：52
期号：1
语种：English
出版社：Newswood and International Association of Engineers
摘要：This paper proposes two new methods to measurethe risk of individual stocks, which construct a portfolio, namelyCredible Monte Carlo Value at Risk (CMC VaR) and CredibleMonte Carlo Expected Tail Loss (CMC ETL). The CMC VaR isdeveloped by combining the concept of Credible Value at Risk(Cr VaR) with Monte Carlo VaR (MC VaR). Meanwhile, CMCETL is constructed by mixing Credible ETL (Cr ETL) and MCETL. The new method’s performance is empirically verifiedto evaluate the individual risk of each asset developing threeportfolios. The analyzed portfolios are designed by Indonesianfive stocks indexed by LQ 45, four stocks traded in New YorkStock Exchange (NYSE), two stocks indexed by NASDAQ, andtwo stocks indexed by London Stock Exchange. We also assessthe accuracy of the CMC VaR by Kupiec Backtesting. Theempirical results of this paper implied that two novel methodsare effective in measuring the risk at 80 percent, 90 percent,and 95 percent confidence levels. The proposed methods canalso overcome the drawback of VaR and ETL, which do notcontemplate the risk among assets grouped in a portfolio.
关键词：Conditional-Value-at-Risk; Monte-Carlo;premium; VaR-algorithm.