The article discusses a method to measure model risk in financial markets. By calculating worst-case scenarios using different models close to a standard one, regulatory capital can be allocated based on how accurately a market can be predicted. This approach can be applied to various risk measures like Value-at-Risk and Tail Conditional Expectation. The study shows that in stock portfolios and derivative products, the risk of incorrect model specifications is often more significant than the risk of estimation errors.