The authors validate 12 of the most representative sample-balancing methods used for credit-scoring models, finding that a combined SMOTE and Editor Nearest Neighbor method is optimal.
But supervisors cautiously welcome next-gen model risk management
Quantification of model risk with an application to probability of default estimation and stress testing for a large corporate portfolio
This paper discusses the building of obligor-level rather than segment-level hazard rate corporate probability of default models for stress testing.
Machine learning models are seeing increasing demand across the capital markets spectrum. But how can firms improve their chances of gaining internal and regulatory approval for these type of models?
UK regulator’s push to improve model governance could tip non-cleared derivatives market into chaos
General bounds on the area under the receiver operating characteristic curve and other performance measures when only a single sensitivity and specificity point is known
Using a single true positive - true negative pair, the author shows how to calculate the area under a ROC curve.
Many models failed in pandemic, but analysing them in clusters easier than whole-bank view
Predicting financial distress of Chinese listed companies using a novel hybrid model framework with an imbalanced-data perspective
In this paper a novel hybrid model framework is constructed to solve the problem of predicting the financial distress of Chinese listed companies using imbalanced data.
Draft RTS creates validation hurdles and cross-border conflicts, industry warns
In this study different value-at-risk (VaR) models are analyzed under different estimation approaches (filtered historical simulation, extreme value theory and Monte Carlo simulation) and backtested with different techniques.
Covid-19 has caused widespread disruption to banks’ risk models. Some failed in the crisis while others have required significant overlays or frequent recalibration as extreme volatility has given way to ongoing uncertainty. As banks seek more agile…
Deborah Hrvatin discusses integrated risk management, mega-hacks and model risk
Machine learning could help with loan decisions – but only if banks can explain how it works. And that’s not easy
A forum of industry leaders discusses the suitability of Simm for phase five firms, how they can optimise portfolios to minimise margin costs and how the lessons learned from previous phases can help them prepare
OpRisk North America: anchoring idiosyncratic risks to macro scenarios a challenge, say experts
This paper outlines several approaches to benchmarking operational loss projections under stressed scenarios using both accounting metrics and historical loss experience.
Performance measure based on quality of replicating portfolios outperforms ‘P&L explain’, new paper claims
DBS, StanChart and Deutsche build model inventories and draw up standards around use cases
Consortium promises cost savings in outsourcing model validation, but some say pooling doesn’t float
As the business environment becomes more complex – and as regulatory scrutiny increases – it has never been more crucial for financial institutions to ensure their models are robust and fit for purpose.
With the recent announcement of an extended preparation period for those smaller entities needing to post initial margin under the uncleared margin rules, the new timetable could cause a bottleneck for firms busy repapering derivatives contracts linked…
This paper provides practical recommendations for the validation of risk models under the Targeted Review of Internal Models (TRIM).
With the 2022 Fundamental Review of the Trading Book (FRTB) deadline looming, banks are fast coming to grips with the amount of work still to be done to achieve a successful implementation
Missing data is a problem. Expert elicitation taps the knowledge of many, say consultants