Reducing Estimation Risk Using a Bayesian Approach: Application to Stress Testing Mortgage Loan Default

2019 ◽  
Author(s):  
Zheqi Wang ◽  
Jonathan N. Crook ◽  
Galina Andreeva
Keyword(s):  
Bayesian Approach ◽  
Stress Testing ◽  
Mortgage Loan ◽  
Estimation Risk ◽  
Loan Default

scholarly journals Malaysian residential mortgage loan default: a micro-level analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Azira Abdul Adzis ◽  
Hock Eam Lim ◽  
Siew Goh Yeok ◽  
Asish Saha
Keyword(s):  
Financial Institutions ◽  
Mortgage Loan ◽  
Mortgage Loans ◽  
Loan Default ◽  
Content Type ◽  
Future Studies ◽  
Residential Mortgage ◽  
The Government ◽  
The Individual ◽  
Lending Institutions

PurposeThis study investigates factors contributing to residential mortgage loans default by utilizing a unique dataset of borrowers' default data from one of the pioneer lending institutions in Malaysia that provides home financing to the public. Studies on mortgage loan default have been extensively examined, but limited studies utilize the individual borrower's data, as financial institutions generally hesitant to reveal their customers' data due to confidentiality issue.Design/methodology/approachThis study uses logistic regression model to analyze 47,158 housing loan borrowers' data for the year 2016.FindingsThe findings suggest that male borrowers, Malay and other type of ethnicity, guarantor availability, loan original balance, loan tenure, loan interest rate and loan-to-value (LTV) ratio are the significant factors that influence mortgage loans default in Malaysia.Research limitations/implicationsFuture studies may expand the sample by employing data from other types of financial institutions that would give greater insights as findings might vary due to differences in objectives, functions and regulations. In addition, the findings are subjected to the censoring bias where future studies could perform the survival analysis to control for censoring bias and re-validating the findings of the present study.Practical implicationsThe findings provide valuable insights for lending institutions and the government to formulate housing loan policy in Malaysia.Originality/valueTo the best of the authors' knowledge, this is the first study in the context of emerging economies that uses financial institution's internal data to investigate factors of mortgage loan default.

Stress testing and model validation: application of the Bayesian approach to a credit risk portfolio

2015 ◽  
Vol 9 (3) ◽  
pp. 41-70 ◽  
Author(s):  
Michael Jacobs Jr. ◽  
Ahmet K. Karagozoglu ◽  
Frank J. Sensenbrenner
Keyword(s):  
Credit Risk ◽  
Model Validation ◽  
Bayesian Approach ◽  
Stress Testing ◽  
The Bayesian Approach

scholarly journals An assessment of mortgage loan default propensity in Ghana

2019 ◽  
Author(s):  
De-Graft Owusu-Manu ◽  
Richard Ohene Asiedu ◽  
David John Edwards ◽  
Kenneth Donkor-Hyiaman ◽  
Pius Akanbang Abuntori ◽  
...  
Keyword(s):  
Mortgage Loan ◽  
Loan Default

scholarly journals Stress Testing and Calibration of Macroprudential Policy Tools

2020 ◽  
Vol 20 (165) ◽  
Author(s):  
Lucyna Gornicka ◽  
Laura Valderrama
Keyword(s):  
Default Risk ◽  
Stress Testing ◽  
Structural Model ◽  
Mortgage Lending ◽  
Macroprudential Policy ◽  
Loan Default ◽  
Structural Shocks ◽  
Policy Interventions ◽  
History Of ◽  
Tail Events

We present a semi-structural model of default risk, which is a function of loan and borrower characteristics, economic conditions, and the regulatory environment. We use this model to simulate bank credit losses for stress-testing purposes and to calibrate borrower-based macroprudential tools. The proposed approach is very flexible and is particularly useful when there is limited history of crisis episodes, when crises bring unanticipated shocks where past tail events offer little guidance and when structural shocks or changes in financial regulations have altered the loan default process. We apply the model to quantify mortgage lending risk in two distinct mortgage markets. For each application, we show a range of modeling adjustments that can be made to capture country-specific institutional features. The model uses bank portfolio data broken down by risk bucket and vintage, which enables us to take explicit account of the loan life cycle and to incorporate the housing and economic cycles. This feature facilitates a timely assessment of banks’ loss-absorbing capacity and the buildup of systemic risk conditional on policy. It also enables counterfactual analysis and the evaluation of macroprudential policy interventions.

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