Incorporating lifecycle and environment in loan-level forecasts and stress tests

Breeden, Joseph L.

doi:10.1016/j.ejor.2016.06.008

Cited by 11 publications

(14 citation statements)

References 8 publications

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“…Other authors like Breeden (2016) and Breeden and Thomas (2016) consider the APC model and its implementation in stress tests on retail lending and as a mechanism to forecast the loan performance at a point-in-time for the loan's lifetime. Particularly, they consider an APC model where the Period Component is in function of other macroeconomic variables to avoid a specification error.…”

Section: Literature Reviewmentioning

confidence: 99%

What can credit vintages tell us about non-performing loans?

Gamba-Santamaría¹,

Melo‐Velandia²,

Orozco-Vanegas³

2021

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Using Colombian credit vintage data, we decompose the non-performing loans into one component that captures the evolution of the payment capacity of borrowers, and other component that captures changes in the credit risk taken by the financial system at the time of loan disbursement. We use intrinsic estimators and penalized regression techniques to overcome the perfect multicollinearity problem that the model entails. We find that these two type of components have evolved differently over time, and that good economic conditions and loose financial conditions improve the payment capacity of borrowers to meet their obligations, and in turn, they tend to coincide with the financial system engaging in riskier loans. Finally, we advocate the use of this methodology as a policy tool that is easy to apply by financial and economic authorities that dispose of a constant flow of credit vintage information. Through it, they will be able to identify the origin of the credit risk materialization and curb the risk taken by the financial system.

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Section: Literature Reviewmentioning

confidence: 99%

What can credit vintages tell us about non-performing loans?

Gamba-Santamaría¹,

Melo‐Velandia²,

Orozco-Vanegas³

2021

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“…However, a linear model may lead to some restrictions in practice when absence of risk newtrality recommends a nonlinear model. The use of FICO scores and more sophisticated models (Sengupta and Bhardwaj, 2015;Breeden, 2016) may help overcome this limitation. In this sense, a further advantage of our approach is that equation ( 6) can be replaced by a non-linear risk-based interest rate model without compromising the rest of our approach.…”

Section: Scoring Firms By the Mp Rulementioning

confidence: 99%

A multicriteria approach to manage credit risk under strict uncertainty

Pla‐Santamaria

Bravo

Mullor

et al. 2020

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Assessing the ability of applicants to repay their loans is generally recognized as a critical task in credit risk management. Credit managers rely on financial and market information, usually in the form of ratios, to estimate the quality of credit applicants. However, there's no guarantee that a given set of ratios contains the information needed for credit classification. Decision rules under strict uncertainty aim to mitigate this drawback. In this paper, we propose the use of a moderate pessimism decision rule combined with dimensionality reduction techniques and compromise programming. Moderate pessimism ensures that neither extreme optimistic nor pessimistic decisions are taken. Dimensionality reduction from a set of ratios facilitates the extraction of the relevant information. Compromise programming allows to find a balance between quality of debt and risk concentration. Our model produces two critical outputs: a quality assessment and the optimum allocation of funds. To illustrate our multicriteria approach, we include a case study on 29 firms listed in the Spanish stock market. Our results show that dimensionality reduction contributes to avoid redundancy and that quality-diversification optimization is able to produce budget allocations with a reduced number of firms.

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“…Stress testing is an important operational research tool to assess bank risk levels and to provide a basis to assist decision making by financial institutions and regulators (Breeden, 2016;Ju, Jeon, & Sohn, 2015;Schechtman & Gaglianone, 2012). Stress tests are designed to measure how sensitive risk exposures are to external or internal shocks to a financial system, an individual financial institution, a portfolio or an account (Misina, Tessier, & Dey, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…Many papers (Kanas & Molyneux, 2018;Schechtman & Gaglianone, 2012;Vazquez, Tabak, & Souto, 2012) perform macro stress tests with aggregate data at the system level or with data of groups of financial institutions. Some (Bangia, Diebold, Kronimus, Schagen, & Schuermann, 2002;Bellotti & Crook, 2013, 2014Breeden, 2016;Ju et al, 2015;Pesaran, Schuermann, Treutler, & Weiner, 2006) implement micro stress testing methods with granular data at an individual account, portfolio or institution level. Studies find that increases in output growth tend to reduce credit risk (Bikker & Hu, 2002;Laeven & Majnoni, 2003;Sorge & Virolainen, 2006), whereas rises in interest rate or unemployment tend to increase credit risk (Bellotti & Crook, 2013;Bikker & Hu, 2002;Pesaran et al, 2006).…”

Section: Introductionmentioning

confidence: 99%