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      Credit Defaults under the Factor Model


      Science Impact, Ltd.

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          Financial institutions from around the world provide individuals and businesses with a wide range of products and services that can prove beneficial and enable them to realise their growth ambitions. One of the key services they facilitate revolves around lending, where sums, often vast in size, are deposited into a business's account to help with concerns surrounding such issues as cash flow. Naturally, when a financial institution lends money there is an inherent risk. As such, the institution will perform a range of checks to determine the level of risk. One concern is the corporate credit risk; that is, how susceptible a business is to defaulting on the terms of their borrowing. Should a business end up filing for bankruptcy, for instance, the financial institution stands to lose money, which can often cause significant damage. To improve understanding of the feature of default clustering in credit risk management, a team of researchers has come together to conduct investigations on a range of different projects. Professor Cheng-Der Fuh, who is based at the Graduate Institute of Statistics at National Central University, Taiwan, has done extensive work on a variety of topics related to the financial sector. One of his recent papers is concerned with correlated defaults, in which he, along with Dr Chu-Lan Kao from the National Chiao-Tung University, Taiwan, evaluates the joint default properties for multiple firms. The findings from Fuh's extensive research will have significant bearings on the finance, banking and insurance sectors for a long time to come. However, perhaps what is more important is that the new directions he has taken in his research will encourage others to do the same, paving the way for new breakthroughs that enable more effective credit risk management.

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          Author and article information

          Science Impact, Ltd.
          July 26 2018
          July 26 2018
          : 2018
          : 4
          : 39-41
          © 2018

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          Earth & Environmental sciences, Medicine, Computer science, Agriculture, Engineering


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