Macroeconomic Shocks on Model Parameters: Stress Testing Mortgage Loan Default Rate

2019 ◽  
Author(s):  
Zheqi Wang ◽  
Jonathan N. Crook ◽  
Galina Andreeva
Keyword(s):  
Stress Testing ◽  
Mortgage Loan ◽  
Model Parameters ◽  
Macroeconomic Shocks ◽  
Loan Default ◽  
Default Rate

scholarly journals In-Situ Measurement of Power Loss for Crystalline Silicon Modules Undergoing Thermal Cycling and Mechanical Loading Stress Testing

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 72
Author(s):  
Sergiu Spataru ◽  
Peter Hacke ◽  
Dezso Sera
Keyword(s):  
Thermal Cycling ◽  
Power Loss ◽  
Stress Testing ◽  
Series Resistance ◽  
Short Circuit ◽  
In Situ Measurement ◽  
Mechanical Degradation ◽  
Model Parameters ◽  
The Impact

An in-situ method is proposed for monitoring and estimating the power degradation of mc-Si photovoltaic (PV) modules undergoing thermo-mechanical degradation tests that primarily manifest through cell cracking, such as mechanical load tests, thermal cycling and humidity freeze tests. The method is based on in-situ measurement of the module’s dark current-voltage (I-V) characteristic curve during the stress test, as well as initial and final module flash testing on a Sun simulator. The method uses superposition of the dark I-V curve with final flash test module short-circuit current to account for shunt and junction recombination losses, as well as series resistance estimation from the in-situ measured dark I-Vs and final flash test measurements. The method is developed based on mc-Si standard modules undergoing several stages of thermo-mechanical stress testing and degradation, for which we investigate the impact of the degradation on the modules light I-V curve parameters, and equivalent solar cell model parameters. Experimental validation of the method on the modules tested shows good agreement between the in-situ estimated power degradation and the flash test measured power loss of the modules, of up to 4.31 % error (RMSE), as the modules experience primarily junction defect recombination and increased series resistance losses. However, the application of the method will be limited for modules experiencing extensive photo-current degradation or delamination, which are not well reflected in the dark I-V characteristic of the PV module.

scholarly journals One-factor model for default rates by loan type

Bankarstvo ◽  
2021 ◽  
Vol 50 (2) ◽  
pp. 88-100
Author(s):  
Miloš Božović
Keyword(s):  
Factor Model ◽  
Risk Measures ◽  
Loan Default ◽  
Macroeconomic Risk ◽  
Default Rates ◽  
Proposed Model ◽  
Default Rate ◽  
Systemic Component ◽  
Very High

This paper investigates the link between default rates by loan types and the systemic credit risk component. This link is described by a linear model that combines systemic and idiosyncratic contributions. The systemic component is a latent factor that depends directly on the aggregate loan default rate, while the idiosyncratic component drives specific variations of default rates across loan types. By transforming observable risk measures, the model can be econometrically represented as a mixed-effects model, where the systemic and idiosyncratic components represent, respectively, the slope and the intercept that are specific for each loan type individually. The proposed model is illustrated on a panel of defaulted loans of the Association of Serbian Banks. The obtained results show the model's very high power in explaining average default rates for all loan types. Thus, the aggregate default rate plays the role of a unique systemic component that mimics the influence of fundamental macroeconomic risk factors easily, without the necessity to model this relationship explicitly.

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.

Sign in / Sign up

Export Citation Format

Share Document