Credit risk measurement

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Published Mar 22, 2017
DOI https://doi.org/10.18844/gjhss.v3i4.1562

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Lucia Michalkova Katarina Frajtova Michalikova

Abstract

Focused on globalizaing of economics and still actual financial crisis credit risk becomes one of the most discussing topic in business world. Every investment decision should be accompanied by analysis of the possibility of default. Through the years there were developed many credit risk measures, so research and quantification of them are a subject of interest of many economic publications and studies. So nowadays there are many approaches which can be used by investors to monitor credit risk and it can be calculated through various models and methods. The aim of the article is to present the basic ones as well as the most often used models based on them such like CreditMetrics, CreditRisk or KMV model. There is given a comparison of these models in dimension as risk definition, risk source, recovery rate, types of model etc. Then we also describe pros and cons of them. Eventually we apply the CreditMetrics model for a single bond.    

Keywords: risk; credit risk; model; CreditMetrics

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Vol 3 No 4 (2017): Selected Papers of 5th World Conference on Business, Economics and Management (BEM-2016)
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Articles
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References

Adamko, P., Kliestik, T. & Misankova, M. (2014). Applied comparison of selected credit risk models. In Social Sciences Research (SSR 2014): Proceedings of 2014 2nd international conference: December 10-11, 2014, Hong Kong 155 – 159. Singapore: Singapore Management and Sports Science Institute.
Adamko, P., Spuchlakova, E. & Valaskova, K. (2015). The history and ideas behind VaR. Procedia Economics and finance, 24, 18 – 24.
Duffie, D. & Singleton, K. J. (2003). Credit risk: Pricing, measurement, and management. Princeton, NJ, United States: Princeton University Press.
Grublova, E. (2010). Attitudes to risk and ability to take risky decisions. Ekonomicko-manazerske spectrum, 4 (2), 58-63.
Kliestik, T., Lyakin, A. N. & Valaskova, K. (2014). Stochastic calculus and modelling in economics and finance. In 2nd International Conference on Economics and Social Science (ICESS 2014): July 29-30, 2014, Shenzhen, China, 161-167. Newark: Information Engineering Research Institute.
Kollar, B. & Kliestik, T. (2014). Simulation approach in credit risk models. In 4th International Conference on Applied Social Science: March 20-21, 2014, Singapore, 150-155. Newark: Information Engineering Research Institute.
Kollar, B., Valaskova, K. & Kramarova, K. (2015). Possibility of using credit grades model as an alternative to traditional structural models. In 3rd International Conference on Economics and Social Science, 53 – 5. Newark: Information Engineering Research Institute.
Kollar, B. & Bartosova, V. (2014). Comparison of Credit risk measures as an alternative to VaR, In Social Sciences Research (SSR 2014): Proceedings of 2014 2nd international conference: December 10-11, 2014, Hong Kong, 167 – 171. Singapore: Singapore Management and Sports Science Institute.
Kral, P. & Kliestik, T. (2015). Estimation of the level of risk based on the selected theoretical probability distributions. In 10th International Scientific Conference Financial management of Firms and Financial Institutions, 603-610. Ostrava: VSB – Technical University of Ostrava.
Misankova, M. & Kral, P. (2015). Credit risk optimization by the use of Monte Carlo simulation. In 19th International Conference on Transport Means, Transport Means - Proceedings of the International Conference, 43-46. Kaunas: Kaunas University of Technology.
Saunders, A., Allen, L. & Saunders, L. A. (2002). Credit risk measurement: New approaches to value at risk and other paradigms (2nd ed.). New York, NY: Wiley, John & Sons.
Sivak, R. et al. (2014). Rizika a modely vo financiach a v bankovnictve (3rd ed.). Bratislava: Sprint 2.