Back when I began my trading career, risk management was almost non-existent. We didn't have many tools, and traders marked their positions. It was simpler, but there were many blind spots. As the markets got more advanced, we started using tools like basis point value (BPV) and simple yield curve shifts to understand risk better. By the 1990s, value at risk (VaR) became popular—it was a single figure that showed how much you could lose. It worked well in normal times, but it didn’t hold up when markets became highly volatile.
Because of this weakness, we now use a variety of risk measures to get a more complete picture and cross-checking these different measures is a practical way to ensure our information is reliable.
It's like verifying a news story—you want to see if multiple trustworthy sources confirm it before accepting it as accurate.
In a recent speech, Huw Pill, Chief Economist at the Bank of England, talked about why cross-checking forecasts is important for making better decisions. He explained that relying on just one forecast could lead to blind spots, especially in a complex and unpredictable economy. Pill emphasised using different models to double-check assumptions and build a stronger foundation for decision-making.
This isn’t just about monetary policy—it’s also a lesson for risk management in finance. In the past, relying on just one tool like VaR led to mistakes because it was too narrow and didn’t provide the full picture, especially when markets became unpredictable. Cross-checking different risk measures, like Pill does with forecasts, helps make sure we’re not putting too much faith in just one model or number.
Pill’s approach of using multiple models is about turning guesswork into a solid analysis. But there is a downside: just because a model is complex doesn't mean it's always reliable. If a model is a "black box"—meaning we don’t fully understand how it works—we need to question how reliable it truly is.
To be fair, Pill identifies this risk; all that complexity might not hold up because the connections in the models aren't fully understood. Common sense and an understanding that the economy is a complex system remind us of the limits of any risk model.
For senior executives, the message is simple: do not rely on just one perspective. Cross-reference your metrics. Use different models, stress-test different scenarios, and make sure you’re getting independent confirmation.
Also, remember that complexity in models can be a risk itself—many people do not want to admit they do not fully understand a model, which can lead to weak assumptions becoming the basis for important decisions.
Whether you’re looking at interest rate risk, credit exposures, or market volatility, using multiple trustworthy measures provides greater confidence that what you’re seeing is accurate. Just like Pill uses cross-checks to validate forecasts, you should be cross-referencing to validate your risk measures—as long as you understand the models you are using.
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