Abstract
The article is dedicated to the optimization of credit risk through the application of Conditional Value at Risk (CVaR). CVaR is a risk measure, the expected loss exceeding Value-at-Risk and is also known as Mean Excess, Mean Shortfall, or Tail VaR. The link between credit risk and the current financial crisis accentuates the importance of measuring and predicting extreme credit risk. Conditional Value at Risk has become an increasingly popular method for measurement and optimization of extreme market risk. The use of model can regulate all positions in a portfolio of financial instruments in order to minimize CVaR subject to trading and return constraints at the same time. The credit risk distribution is created by Monte Carlo simulations and the optimization problem is solved effectively by linear programming. We apply these CVaR techniques to the optimization of credit risk on portfolio of selected bonds. Keywords: value at risk; conditional value at risk; credit risk; portfolio
Highlights
Risk management is a broad concept involving various perspectives
Results obtained by Larsen et al (2000) are shown in the figure 2 and it is clear that how iteratively Var is decreasing than Conditional Value at Risk (CVaR) is increasing
The article was dedicated to the optimization of credit risk which was provided by the application of Conditional Value at Risk as a more appropriate risk measure in comparison with Value at Risk
Summary
Risk management is a broad concept involving various perspectives. From mathematical perspective it is a procedure for shaping a loss distribution. Credit risk is a risk of loss of principal or loss of a financial reward stemming from a borrower's failure to repay a loan or otherwise meet a contractual obligation. Used tools for assessing and optimizing market risk assume that the portfolio return-loss is normally distributed described by mean and standard deviation. This approach has shown up to be quite useful, but it is inadequate for evaluation of credit risk (Kollar, Valaskova & Kramarova, 2015; Valaskova, Gavlakova & Dengov, 2014)
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