Model risk
Not random, and not a forest: black-box ML turns white
Bayesian analysis can replace forest with a single, powerful tree, writes UBS’s Giuseppe Nuti
Models need longer datasets to handle economic cycles – research
Decades, not years, of credit losses required for accurate risk modelling, argues expert
A new approach to the quantification of model risk for practitioners
This paper's aim is twofold: to introduce a mathematical framework that is sufficiently general and sound to cover the main areas of model risk, and to illustrate how a practitioner can identify the relevant abstract concepts and put them to work.
Converging on sound model risk management practices
Although most banks are progressing rapidly towards a certain standard in MRM practices, the rate of progress is uneven and so are the ambition levels. Management Solutions provides a summarised overview of the state of MRM evolution and how banks are…
Common validation techniques for risk proxies found wanting
Research finds two out of three methods for checking index prices as proxies don’t properly gauge tail risk
Quantification of model risk in stress testing and scenario analysis
In this paper, the author's aim is to empirically analyze the numerical quantification of model risk, yielding exact buffers in currency amounts (for a given model uncertainty).
At US G-Sibs, 11 VAR breaches in 2018
The final quarter of 2018 saw a record number of VAR breaches at the biggest US banks
Honesty is key to machine learning’s future – Roberts
Oxford-Man Institute director on why tomorrow’s models will gracefully admit defeat
Model risk chiefs warn on machine learning bias
ML model outputs open to “potential bias sitting in your datasets”, says RBS model risk head
Teach history to avoid mistakes of yesterday’s quants
Quant grads should be taught follies of LTCM, Gaussian copula and London Whale, writes UBS’s Gordon Lee
EU banks punished over lowball credit risk estimates
Two of 17 firms facing follow-up inspections will be hit by capital add-ons
Valuation model risk on the rise at EU banks
Over two-thirds of fair value assets priced using banks' models
Pooled resources offer way to keep credit models afloat
Supervisors drive banks to seek more corporate default data and cost-effective model improvements
HSBC hires new head of model validation
Bank appoints Credit Suisse veteran to key role
BNPP, Credit Suisse, State Street incur VAR breaches
BNP Paribas capital multiplier increases on seventh breach in nine months
The disputed terrain of model risk scoring
There is no concord on how banks should police their model risk. But two Fed economists have an idea
Fed’s Brainard wary of black box AI models in consumer credit
Speech raises explainability issue; says existing model risk guidelines are “a good place to start” in regulating AI
National supervisors put pressure on global risk models
Varied supervisory and external audit demands stretch cross-border risk management
Compliance preparations amid uncertain rules
A forum of industry leaders discusses how banks will define individual trading desks under FRTB, whether BCBS 239 compliance projects can help banks meet FRTB risk data challenges, which model validation obstacles banks still face and other key topics
Finally, a professional group for model-risk managers
As models of all stripes crowd into finance, the people who screen them form an association
Procyclicality of capital and portfolio segmentation in the advanced internal ratings-based framework: an application to mortgage portfolios
This paper investigates the procyclicality of capital in the advanced internal ratings based (A-IRB) Basel approach for retail portfolios, and identifies the fundamental assumptions required for stable A-IRB risk weights over the economic cycle.
Bermudan swaption model risk analysis: a local volatility approach
This paper seeks to contribute a simple and (almost) model-free way of assessing the economic value of the Bermudan exercise right derived from a “minimal” local volatility enhanced interest rate model.
Optimal allocation of model risk appetite and validation threshold in the Solvency II framework
In this paper, the authors derive an analytical solution for sub-SCR VTs starting with a model risk appetite (MRA) that defines acceptable errors for an insurer’s total SCR.