Model validation
Covid chaos spurs on search for model risk aggregation
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.
EU’s IM model validation rules may put Simm in jeopardy
Draft RTS creates validation hurdles and cross-border conflicts, industry warns
Evaluation of backtesting techniques on risk models with different horizons
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.
Rethinking the model lifecycle: from quick fixes to long-term gain
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…
Stronger together: CLS’s chief risk officer on risk culture
Deborah Hrvatin discusses integrated risk management, mega-hacks and model risk
Show your workings: lenders push to demystify AI models
Machine learning could help with loan decisions – but only if banks can explain how it works. And that’s not easy
Sharpening the tools – Preparation for UMR phase five
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
Banks fold climate, pandemic and cyber risks into CCAR
OpRisk North America: anchoring idiosyncratic risks to macro scenarios a challenge, say experts
Benchmarking operational risk stress testing models
This paper outlines several approaches to benchmarking operational loss projections under stressed scenarios using both accounting metrics and historical loss experience.
A tale of two (or three, or four) models
Performance measure based on quality of replicating portfolios outperforms ‘P&L explain’, new paper claims
Singapore banks tighten ML governance amid regulatory scrutiny
DBS, StanChart and Deutsche build model inventories and draw up standards around use cases
Outsourced model validation: is it viable?
Consortium promises cost savings in outsourcing model validation, but some say pooling doesn’t float
How AI can automate model validation testing and continuous monitoring
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.
Initial margin – A regulatory bottleneck
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…
Validation of index and benchmark assignment: adequacy of capturing tail risk
This paper provides practical recommendations for the validation of risk models under the Targeted Review of Internal Models (TRIM).
The Fundamentals of market risk rules
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
When the data’s not there, expert-led models could help
Missing data is a problem. Expert elicitation taps the knowledge of many, say consultants
Competitive differentiation – Reaping the benefits of XVA centralisation
A forum of industry leaders discusses the latest developments in XVA and the strategic, operational and technological challenges of derivatives valuation in today’s environment, including the key considerations for banks looking to move to a standardised…
Stress-testing: still worth the stress?
There may be more efficient ways to assess if banks are misjudging their risks
Deploying agile analytics in the fight against fraud
Financial firms are under pressure to tackle the widespread problem of financial fraud. As the speed, scale and sophistication of fraudulent activity grows, a panel of financial crime experts reveal how firms can develop an agile analytics capability to…
Risk Technology Awards 2019: Making machines more helpful
Machine learning can be too efficient; now, vendors are looking for ways to make it more accurate. Clive Davidson looks at the stories behind this year’s Risk Technology Awards
Model risk management transformation
Financial institutions have been maturing their approaches to MRM and – as models become more complex and pervasive, and regulatory expectations continue to increase – leading financial institutions seek faster and further movement. Ashutosh Nawani, head…