Precise model performance through continuous monitoring
FRTB Model Monitoring & Re-Calibration
Continuous monitoring and re-calibration of FRTB risk models is essential for sustainable compliance and capital efficiency. We ensure the optimal performance of your models through systematic validation and proactive adjustments.
- ✓Continuous validation of model performance
- ✓Proactive re-calibration in response to market changes
- ✓Optimization of capital requirements through precise models
- ✓Automated backtesting and monitoring processes
Your strategic success starts here
Our clients trust our expertise in digital transformation, compliance, and risk management
30 Minutes • Non-binding • Immediately available
For optimal preparation of your strategy session:
- Your strategic goals and objectives
- Desired business outcomes and ROI
- Steps already taken
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Model Monitoring as the Foundation of FRTB Compliance
ADVISORI in Numbers
11+
Years of Experience
120+
Employees
520+
Projects
We work with you to develop a systematic approach to model monitoring that meets regulatory requirements while maximizing operational efficiency.
Our Approach:
Establishing solid monitoring infrastructures with automated alerts
Implementing statistical tests for early detection of model deviations
Developing data-driven re-calibration strategies
Integrating stress testing into continuous model validation
Building comprehensive documentation and governance processes
"Continuous monitoring and re-calibration of FRTB models is a critical success factor for sustainable compliance. With a systematic approach, banks can not only ensure regulatory certainty but also optimize their capital efficiency through precise risk models."
Melanie Düring
Head of Risk Management
Our Services
We offer you tailored solutions for your digital transformation
Continuous Model Monitoring
Systematic monitoring of the performance of your FRTB risk models through automated processes and statistical validation procedures.
- Daily backtesting routines for Expected Shortfall models
- Implementation of traffic light systems for model performance
- Development of Model Performance Indicators (MPIs)
- Automated alerting systems for model deviations
Strategic Re-Calibration
Proactive and data-driven re-calibration of your risk models to optimize forecast quality and capital efficiency.
- Market regime-specific calibration strategies
- Optimization of lookback periods and decay factors
- Integration of forward-looking elements into model calibration
- Validation and testing of new model parameters
Our Competencies in FRTB Ongoing Compliance
Choose the area that fits your requirements
FRTB Process Optimisation & Training
We optimise your FRTB processes across the entire chain — from data delivery to supervisory reporting — and empower your teams through role-specific training on the Standardised Approach, IMA and Expected Shortfall. You reduce operational risk, accelerate your calculations and anchor FRTB capability firmly in daily operations.
Frequently Asked Questions about FRTB Model Monitoring & Re-Calibration
Why do FRTB models need regular re-calibration?
Market conditions change continuously
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volatility regimes shift, correlations break down in stress phases, and new products alter portfolio composition. Expected Shortfall models are calibrated to historical observation periods, so their parameters inevitably drift away from current market reality over time. In addition, the modellability of risk factors can change as the availability of real price observations evolves, moving factors between modellable and non-modellable status with direct capital impact. If this drift is not addressed, it surfaces as backtesting exceptions and deteriorating P&L attribution results, which in turn trigger higher capital multipliers or the loss of Internal Models Approach eligibility at desk level. Regular, data-driven re-calibration keeps forecast quality high, capital requirements efficient and model approval secure.
How often should FRTB models be reviewed and monitored?
FRTB effectively imposes several overlapping review cycles rather than a single annual check.
🔍 Established monitoring rhythm:
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Daily: backtesting of VaR forecasts against actual and hypothetical P&L at bank-wide and desk level
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Quarterly: evaluation of P&L attribution test metrics for each trading desk in scope of the internal model
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Annually: comprehensive independent model validation covering methodology, data and implementation
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Ad hoc: reviews after significant market stress, methodology changes or the approval of new products
In practice, the decisive factor is not the formal cycle but how quickly threshold breaches become visible. We therefore recommend automated monitoring dashboards with alerting, so deviations are escalated the day they occur rather than discovered at quarter-end.
What role does backtesting play under FRTB?
Backtesting is the central quantitative control for internal model quality under FRTB. Banks compare their one-day Value-at-Risk forecasts at the 97.5% and 99% confidence levels against both actual and hypothetical P&L
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and, unlike under the previous market risk regime, this happens not only bank-wide but at individual trading desk level. Exceptions are counted over a rolling twelve-month window. If a desk exceeds the defined exception thresholds, it loses its eligibility for the Internal Models Approach and must calculate capital under the standardised approach, which is typically significantly more expensive. At aggregate level, accumulating exceptions increase the capital multiplier. A robust backtesting infrastructure with clean P&L data is therefore a direct lever for capital efficiency, not just a regulatory formality.
What happens if a trading desk fails the P&L attribution test?
The P&L attribution test (PLAT) compares the risk-theoretical P&L produced by the risk model with the hypothetical P&L from front office systems, using two statistical metrics
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the Spearman correlation and the Kolmogorov-Smirnov test. Based on the results, each desk is assigned to a green, amber or red zone. Desks in the amber zone remain in the internal model but incur a capital surcharge; desks in the red zone must switch to the standardised approach until their test results recover. In practice, PLAT failures are rarely caused by the model itself
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most stem from data and valuation misalignments between front office and risk systems. A structured root-cause analysis of data lineage, pricing models and risk factor coverage is therefore the first remediation step.
What data do we need for effective FRTB model monitoring?
Effective monitoring stands or falls with data quality and consistency across systems.
🔍 Core data requirements:
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Daily P&L series per desk: actual, hypothetical and risk-theoretical P&L on a consistent valuation basis
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Complete risk factor time series with sufficient history, including stressed periods for Expected Shortfall calibration
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Real price observations to assess risk factor modellability and identify non-modellable risk factors
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Position and sensitivity data reconciled between front office and risk systems
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Model metadata: parameter versions, calibration dates and change history for governance and audit purposes
Beyond availability, lineage matters: supervisors expect banks to demonstrate where monitoring data comes from and how it is quality-assured. We help you build these data pipelines and controls as part of the monitoring infrastructure.
Can FRTB model monitoring and re-calibration be automated?
To a large extent, yes
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and automation is the only realistic way to handle the daily monitoring frequency FRTB demands at reasonable cost. Statistical tests, backtesting engines, threshold-based alerts, modellability assessments and standard reporting can and should run automatically. What remains a human task is the interpretation: root-cause analysis of exceptions, the decision whether an observed deviation warrants re-calibration or a methodology change, and the governance sign-off required for model changes. Our approach combines both layers
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we build automated monitoring pipelines integrated with your existing risk architecture and establish clear escalation and decision processes on top, including the documentation that model risk management and supervisors expect. This reduces manual effort while keeping model ownership and accountability where they belong.
Success Stories
Discover how we support companies in their digital transformation
Digitalization in Steel Trading
Steel trading company from Germany
Digital Transformation in Steel Trading
Case Study
Results
Over 2 billion euros in annual revenue through digital channels
More than half of revenue through online channels as a strategic goal
Improved customer satisfaction through automated processes
AI-Powered Manufacturing Optimization
Industrial group from Germany
Smart Manufacturing Solutions for Maximum Value Creation
Case Study
Results
Significant increase in production performance
Reduction of downtime and production costs
Improved sustainability through more efficient resource utilization
AI Automation in Production
Automation specialist from Germany
Intelligent Networking for Future-Proof Production Systems
Case Study
Results
Improved production speed and flexibility
Reduced manufacturing costs through more efficient resource utilization
Increased customer satisfaction through personalized products
Generative AI in Manufacturing
Technology group from Germany
AI Process Optimization for Improved Production Efficiency
Case Study
Results
Reduction of AI application implementation time to just a few weeks
Improvement in product quality through early defect detection
Increased manufacturing efficiency through reduced downtime
Let's
Work Together!
Is your organization ready for the next step into the digital future? Contact us for a personal consultation.
Your strategic success starts here
Our clients trust our expertise in digital transformation, compliance, and risk management
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