Measuring Risk utilizing Credible Monte Carlo Value at Risk and Credible Monte Carlo Expected Tail Loss - Sulistianingsih, Evy; Rosadi, Dedi; Abdurakhman, Abdurakhman (IAENG International Journal of Applied Mathematics, 2022)
This paper proposes two new methods to measure the risk of individual stocks, which construct a portfolio, namely Credible Monte Carlo Value at Risk (CMC VaR) and Credible Monte Carlo Expected Tail Loss (CMC ETL). The CMC VaR is developed by combining the concept of Credible Value at Risk (Cr VaR) with Monte Carlo VaR (MC VaR). Meanwhile, CMC ETL is constructed by mixing Credible ETL (Cr ETL) and MC ETL. The new method’s performance is empirically verified to evaluate the individual risk of each asset developing three portfolios. The analyzed portfolios are designed by Indonesian five stocks indexed by LQ 45, four stocks traded in New York Stock Exchange (NYSE), two stocks indexed by NASDAQ, and two stocks indexed by London Stock Exchange. We also assess the accuracy of the CMC VaR by Kupiec Backtesting. The empirical results of this paper implied that two novel methods are effective in measuring the risk at 80 percent, 90 percent, and 95 percent confidence levels. The proposed methods can also overcome the drawback of VaR and ETL, which do not contemplate the risk among assets grouped in a portfolio.