A Markov Chain Study on Mortgage Loan Default Stages
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 on bank’s required reserve for capital adequacy against possible default loss. If the probability of shifting credit for default loans can be understood, calculated, controlled, or even predicted, reserve cost for the banking institutions can be alleviated to achieve higher economic efficiency. Due to the practical need to study and forecast bad credit, this research tries to explore probability distribution of past-due loans and to estimate average survival time before transferring into next non-performing loan stages. This information may be useful for bank managers to understand how to deal with the problems of classification and average delinquency related with mortgage loans for the purpose of better managing and granting loans. Bank asset may be better protected by restricting the period of years in mortgage financing especially when loans become dangerously delinquent and collaterals fail to offer adequate protection. Banking institutions may even use life insurance to match the period of mortgage loans against potential default in the case of borrower accidents. Prediction of mortgage probability among credit stages may facilitate loan granting decision because of better quality in credit evaluation, which may, in
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 on bank’s required reserve for capital adequacy against possible default loss. If the probability of shifting credit for default loans can be understood, calculated, controlled, or even predicted, reserve cost for the banking institutions can be alleviated to achieve higher economic efficiency. Due to the practical need to study and forecast bad credit, this research tries to explore probability distribution of past-due loans and to estimate average survival time before transferring into next non-performing loan stages. This information may be useful for bank managers to understand how to deal with the problems of classification and average delinquency related with mortgage loans for the purpose of better managing and granting loans. Bank asset may be better protected by restricting the period of years in mortgage financing especially when loans become dangerously delinquent and collaterals fail to offer adequate protection. Banking institutions may even use life insurance to match the period of mortgage loans against potential default in the case of borrower accidents. Prediction of mortgage probability among credit stages may facilitate loan granting decision because of better quality in credit evaluation, which may, in
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