Risk Model Recalibration

In a market that's behaving far from normally, it's no longer safe to rely on normal assumptions when calculating your exposure to risk.

To get a truly accurate risk profile, you need to improve on standard 99% Value at Risk (VaR) calculations by adopting enhanced, multi-factor risk models that use non-normal assumptions. But that in itself presents considerable challenges.

Available data and relationships between markets constantly change, so market risk models age - fast. Risk managers and quantitative analysts face an uphill struggle in creating, constantly re-checking and fine-tuning enhanced risk models.

That's where SAS can help. We take the pain out of multi-factor risk modelling, giving you the power to create a more informed analysis of your true exposure to risk - and keep it up to date.

Case Study

Investment Controlling with SAS^®: Identifying, measuring and steering risks.

With SAS Risk Dimensions, we can provide our clients with clear and meaningful risk analyses.

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How SAS Can Help

We give you the solution to build an accurate, constantly evolving picture of every aspect of your market and credit risk:

Improve on VaR: Choose from a wide range of methodologies to enhance your risk modelling, such as sensitivity analysis, profit/loss curve analysis, profit/loss surface analysis, scenario analysis and stress testing, Delta-normal analysis and simulation analysis (including Monte Carlo simulation).
Fine-tune your calculations: You have total control over the mathematical functions that go into your risk models, so you can tailor them to the risk profiles of a wide range of financial instruments quickly and easily.
Consolidate and interrogate: Gather scattered data in inconsistent formats into a central, controlled environment and dig deep into it.
Inter-relate risks for better accuracy: Rather than viewing risks in isolation, use Monte Carlo simulation to meaningfully combine many separately estimated models.