Abstract
Non-traded financial assets like bonds and loans have credit risk quantification methods and models which are still under debate for their effectiveness. Quantifying credit risk in portfolio context is all the more challenging as it incorporates covariance and weights. This article quantifies credit risk prevalent in non-traded financial assets by applying Transition Probabilities and Mahalanobis distance in the portfolio context. Bonds are rated by rating agencies and when the credit rating drops the bonds lose value as there is higher risk for the investors. The Investors and lenders have to assess the collective loss expected by them if rating migrates in portfolio context. This portfolio credit risk is affected by two parameters covariance and proportion of funds invested (weight) in a particular bond. We chose a real bond portfolio invested by a Malaysian mutual fund company and demonstrate the complex computations through soft computing by a MATLAB algorithm compiled by us. We computed the credit risk by classical weighted average method and also by Mahalanobis Distance method. The results show the classical method underestimate the credit risk leading to suboptimal hedging.
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MATLAB Programme for Mahalanobis Distance and risk calculation
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Ramasamy, R., Kumar, B.C., Saldi, S.B.M. (2018). Soft Computing of Credit Risk of Bond Portfolios. In: Pant, M., Ray, K., Sharma, T., Rawat, S., Bandyopadhyay, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 583. Springer, Singapore. https://doi.org/10.1007/978-981-10-5687-1_73
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