Financial institutions are required to incorporate a margin of conservatism of type C(MoC C) for the general estimation error into their probability of default estimates.This paper makes three key contributions to the quantification of MoC C. First, theEuropean banking regulation allows financial institutions to choose between over-lapping and nonoverlapping one-year default rates for calculating their calibrationtargets (ie, their long-run average default rates). When overlapping one-year defaultrates are used, it is crucial to account for temporal dependencies to accurately calcu-late MoC C. We provide an MoC C quantification for overlapping one-year defaultrates. Second, the European Central Bank has established MoC C quantification atgrade level, rather than at the calibration segment level, as the standard. However,many financial institutions calculate their long-run average default rates at the cali-bration segment level. Therefore, we approximate the confidence level for MoC Cquantification at grade level to achieve the same sum of risk-weighted exposure amounts as the MoC C at the calibration segment level. Third, we corroborate thatthe common practice of using the maximum likelihood estimator of asset correla-tion on samples with varying probability of default results in a downward bias. Wecompensate for this downward bias using a simulation approach.
This paper analyzes the trade-off between credit risk and financial performance within the Canadian peer-to-peer lending market, focusing on the platforms Lending Loop and goPeer. Using a multistage empirical framework, including nonparametric diagnostics, Cox proportional hazards modeling and logistic regression, we examine how borrower characteristics and platform-assigned credit grades influence loan survival outcomes. Macroeconomic scenario analysis and Monte Carlo simulations are further employed to evaluate portfolio resilience under systemic shocks, including recessionary conditions and interest-rate fluctuations. The results indicate that platform credit grades provide economically meaningful risk segmentation, with higher-grade loans demonstrating relatively stable performance across borrower-level and macroeconomic perturbations, while lower-grade segments exhibit significantly greater sensitivity to borrower fundamentals and adverse economic conditions. These nonlinear responses suggest that borrowers closer to default thresholds amplify both microlevel and macroeconomic risk exposure. Overall, the findings highlight the importance of robust underwriting, adaptive risk management and diversification strategies as fintech lending platforms become increasingly integrated into broader financial and payment ecosystems.
The rapid growth of the Islamic banking industry worldwide presents new challenges and risks that require thorough analysis and effective management. It is therefore important to consider how these banks perform core banking functions, such as liquidity creation, and the subsequent credit risk management, keeping in mind the loan portfolio concentration in various industrial sectors. This study carries out a comparative analysis of the relationship between liquidity creation and credit risk in Islamic banks and conventional banks in Pakistan and Malaysia from 2004 to 2021. Our findings indicate that liquidity creation is positively and significantly linked with credit risk. However, loan concentration moderates this relationship, reversing it to a significant negative association. The study validates these results through fixed effect, random effect, generalized least squares regressions and propensity score matching techniques. Similarly, a comparison of Islamic banks and conventional banks reveals that while creating liquidity, Islamic banks in Pakistan experience greater credit risk in both on-and off-balance-sheet transactions compared with Malaysian Islamic banks and conventional Pakistani and Malaysian banks. This outcome highlights the need for a strategic credit risk management framework to support the robust growth of domestic Islamic banks and to enhance their competitiveness in comparison with conventional banks in Pakistan and Malaysia.
This paper introduces a continuous-time extension to the influential CreditRisk+ model for portfolio credit risk modeling. For capital calculations it introduces a risk measure based on the maximum of the loss process of a portfolio over a specified time interval. An extensive numerical study demonstrates that this extension provides an accurate risk assessment. The new framework has many advantages. First, it enables loss evaluation over a continuous time period rather than at a fixed point in time as in the original CreditRisk+ model. Second, the framework offers great flexibility, for example, for the incorporation of collateral risks, interest income and regime shifts. It also accommodates the calibration of almost any observed dependence between counterparties, including dependencies of the form used in structural credit risk models. Finally, the framework establishes a direct connection to insurance industry ruin theory models, allowing the model to leverage exact results and established algorithms from this field to efficiently and accurately determine the credit loss distribution within the continuous-time framework. This extended model therefore provides a comprehensive and theoretically robust approach to assessing credit risk over time and shows the potential for broader use in risk management.
