A Markov Chain Study on Mortgage Loan Default Stages

7482 Words Oct 31st, 2012 30 Pages
A Markov Chain Study on Mortgage Loan Default Stages
Ying-Shing Lin, PhD
Associate Professor,
Dept. of Accounting Information Systems.
National Kaohsiung First University of Science and Technology e-mail:yslin@nkfust.edu.tw (NKFUST)
Sheng-Jung Li, PhD
Assistant Professor,
Dept. of Finance
Shu-Te University e-mail:botato@stu.edu.tw Shenn-Wen Lin
PhD Candidate
National Kaohsiung First University of Science and Technology e-mail:059180@landbank.com.tw September, 2012
Abstract
Shifting probability of credit status of past due or non-performing loans across stage has always been the center of attention not only for banking institutions but also for academicians. Mortgage loans’ changing credit status has a major influence
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A significant portion of those overdue bad loans will cause the rapid rise in the Non-Performing Loans (NPL) ratio for financial institutions, resulting in a serious erosion of profit, and causing a chain reaction of bankruptcy and escalated financial crisis. The century-old Lehman Brothers declared bankruptcy in 2008 which caused a domino effect, not only hitting the U.S. economy, but also triggering a global financial tsunami. Such disaster may be explained by the fact that banks recently owns excessive amount of poor credits which may be the result of highly competitive banking environment and reckless credit imprudence, even reaching an alarming level in bank 's NPL ratios. In order to correct this problem, the banking industry must make prudent and cautious decisions in the beginning of loan auditing process and also recognize the dynamic fact that credit status is not static. It shifts greatly throughout the life span of the loan credit. The change is more dramatic especially in the case of past due or non-performing loans. Only when banking institutions are fully aware of the dynamic nature of credit status can the banking institutions avoid making the same mistake again.
This paper focuses on the study of the shifting probability of credit status of past due or non-performing loans. In this study, the samples are selected based on the number of different credit

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