1. The Flaw of Over-Simplified Models
Many risk models fail because they rely on too few factors, especially when dealing with diverse portfolios. Assuming everything ties back to equities ignores the complexity of assets like fixed income or commodities. A robust model must account for multiple risk factors and avoid unstable cross-asset correlations.
2. The Illusion of Stable Correlations
Asset-by-asset correlations are unreliable – much of a stock’s movement is idiosyncratic, driven by news or events a model can’t predict. Diversification helps, but models must recognize the limits of correlation matrices.
3. Risk Tolerance vs. Risk Capacity: A Dangerous Mismatch
Psychometric risk assessments capture a client’s mindset at a single point in time, but emotions change with market swings. Combining risk tolerance with a quantitative risk capacity analysis ensures portfolios align with both goals and financial realities.
4. The Rear-View Mirror Problem
Models overweight recent data, missing critical stress-test lessons from past crises (2008, COVID, etc.). Historical extremes contain valuable insights – ignoring them leaves portfolios vulnerable.
5. AI-Powered Stress Testing: Smarter, Faster, Actionable
Our AI tool automates stress testing, turning vague predictions into concrete insights. It identifies risks across asset classes and historical scenarios, saving time while improving accuracy.
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