Sure, let’s break it down. In most models, a small shift in a key assumption—like a 0.5% change in expected return or a 2% tweak in correlation—can ripple through the optimization algorithm. Because the portfolio is built to maximize a target return while keeping risk under a threshold, even a slight mis‑estimation pushes the allocation toward higher‑volatility assets to hit that return. That increases the overall beta, so the portfolio starts reacting more to market swings than before. If the market is supposedly stable, the model’s “stable” assumption underestimates volatility clustering, so when a shock hits, the portfolio’s leverage amplifies the loss. The data show that a 0.5% mis‑pricing in an index fund can raise the VaR by almost 3%, turning a seemingly safe mix into a high‑risk position. So, a tiny tweak can indeed blow up the portfolio if the model’s sensitivity and the market’s hidden volatility aren’t properly accounted for.

Yeah, one clear case is the mortgage‑backed‑security models that fed into the 2008 crisis. Banks were using historical correlation estimates that assumed the default rates of different mortgage pools were fairly independent. When the housing market started to turn, those defaults became highly correlated – a shock that the models didn’t anticipate. As a result, the portfolio risk was massively under‑estimated and the capital buffers were too low. If you backtrack, you’d find that a single correlation matrix tweak, like raising the off‑diagonal entries from 0.05 to 0.3, would have raised the VaR figures and pushed many banks into a more cautious stance. That’s the kind of leak you can spot by re‑examining the correlation assumptions.