Consumer default and optimal consumption decisions

PurposeThe authors examine the optimal consumption decisions of households in a micro-founded framework that introduces endogenous default. They study default in the context of a two-period process, assuming three non-overlapping steps of non-payment: delinquency, non-performing loans and bankruptcy (default).Design/methodology/approachIn their model, the authors extend the analysis of loan default to two periods and include agent heterogeneity by considering also saving households. In the optimization problem, the authors obtain first-order conditions for borrowers who do not repay all of their loans (comparing them to those who fully repay them) and also for savers. In addition, by using nonlinear Generalized Method of Moments (GMM), they obtain consistent estimates of the household preference parameters and present the impulse responses of borrowers' consumption to demand shocks.FindingsThe authors derive an augmented consumption Euler equation for borrowers, which is a function inter alia of an expected default factor. They estimate this equation and find non-negligible differences in preference parameters relative to values reported in the literature. Further, an ordering by size of the household discount factors is provided empirically. Finally, the impulse responses of borrowers' consumption to a demand shock are found to last more for borrowers who do not fully repay their debts.Originality/valueThis work represents a promising line of research by introducing default in one of the basic components of DSGE models, making the latter more appropriate for analyzing monetary and macro-prudential policies.

On the Rise of FinTechs: Credit Scoring Using Digital Footprints

We analyze the information content of a digital footprint—that is, information that users leave online simply by accessing or registering on a Web site—for predicting consumer default. We show that even simple, easily accessible variables from a digital footprint match the information content of credit bureau scores. A digital footprint complements rather than substitutes for credit bureau information and affects access to credit and reduces default rates. We discuss the implications for financial intermediaries’ business models, access to credit for the unbanked, and the behavior of consumers, firms, and regulators in the digital sphere. (JEL G20, G21, G29)

Credit Information and Asset Quality of Commercial Banks in Nakuru Town, Kenya

The study sought to establish the effect of credit information on the asset quality of commercial banks in Nakuru Town, Kenya. The specific objectives of the study are to determine the effect of collateral information on asset quality of commercial banks in Nakuru Town, Kenya, to determine the effect of business ratings information on the asset quality of commercial banks in Nakuru Town, Kenya, to determine the effect of consumer identity verification information on the asset quality of commercial banks in Nakuru Town, Kenya, to determine the effect of customers credit status information on asset quality of commercial banks in Nakuru Town, Kenya and to establish the effect of consumer default information details on asset quality of commercial banks. The literature review focused on bank risk management theory, loanable funds theory, Merton’s default risk model and asymmetric information theory. Primary data was collected using questionnaires in order to get accurate results. The study used regression analysis and the findings revealed that Business Ratings and Collateral Information significantly influences up to 59.4% and 17.6% positive variation on Asset quality respectively. This implies that for every one unit increase in business ratings information asset quality increases by 59.4 % while collateral information increases asset quality increase by 17.6 %. It was also observed that consumer default information significantly influences 36.3% positive variation on asset quality. However, it was noted that Customer’s Credit Status Information significantly influences 32.5% negative variation on Asset quality. This implies that for every one unit increase in Customer’s Credit Status Information, Asset quality decreases by 32.5%. Similarly, Consumer Identity Verification Information influences negatively Asset quality by 9.3%. In this study, Business ratings information is the best predictor of asset quality. It was concluded that Collateral information, business ratings information and consumer default information influences positively asset quality. However, consumer identity verification information and customer’s credit status information influences negatively on asset quality. The study recommends that Collateral information should be controlled in order to promote positive loan performance by commercial banks as well as that business ratings information should be adequately provided in order to enhance quality assets of commercial banks. This is an open-access article published and distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License of United States unless otherwise stated. Access, citation and distribution of this article is allowed with full recognition of the authors and the source.

THE IMPACT OF LEARNING ON BUSINESS CYCLE FLUCTUATIONS IN THE CONSUMER UNSECURED CREDIT MARKET

We document that the credit spread on consumer unsecured debt exhibits a persistent, hump-shaped response to an increase in the charge-off rate. This stylized fact poses a significant challenge for a standard model of consumer default in which lenders have rational expectations and, therefore, the credit spread continuously adjusts to reflect the true default incentives of each borrower. In an effort to explain this feature of the data, we construct a model of consumer default with countercyclical income risk in which lenders learn about default risk over time by observing the history of repayment decisions, as is the case in practice. In addition to matching credit spread dynamics, allowing lenders to learn about default risk substantially improves the model’s ability to generate realistic business cycle fluctuations in the consumer unsecured credit market and match the cross-sectional distribution of unsecured debt and dispersion of interest rates observed in the data.