Abstract
One way to calculate Value-at-Risk (VaR) is the variation-covariance method. The calculation of VaR covariance assumes stock data is normally distributed. The data needed to calculate VaR by the variance-covariance method is the covariance matrix of Bank Danamon and Bank Mandiri stock data. The main topics discussed in this paper are calculating VaR covariance with a simple cash portfolio approach, factor models and cash flow. For comparison of the use of the three approaches Backtesting, the backtest results indicate that the factor model is the best method.
Highlights
IntroductionRisk can be interpreted as the possibility of undesired or opposite results being desired
Financial activity is very volatile and risky
Normal distribution is often called a Gaussian distribution. This normal distribution depends on three variables, namely the x value, the average distribution notated by μ, and the standard deviation notated by σ
Summary
Risk can be interpreted as the possibility of undesired or opposite results being desired. Investors should be able to face all the risks that might occur to obtain the results as expected and can calculate these risks VaR can be used to compare market risks from all types of activities in a company and provide a single calculation that is understood. VaR can be widely used and has become a standard in risk calculation because it can be applied to all types of risks. By using VaR, investors can calculate investment risk. One method of calculating VaR is variance and covariance. The covariance method has one advantage, i.e. it is very fast and easy to calculate VaR (Redhead, 1997). Puspa Liza Ghazali et al./ Operations Research: International Conference Series Vol 1, No 1, pp. 19-24,2020
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