Model risk is risks that come from using a model, where the predictions are not following reality. The danger we bear is even higher if it involves significant amounts of money, such as in investment decisions and budgeting.
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Model risks arise for several reasons, including incorrect assumptions, inaccurate data, unavailability of data, or model specification errors.
A model does not cover all scenarios or variables. Because, in fact, a model is only a simplification of reality. Therefore, a model certainly has an error, and we cannot eliminate it. Our task is to minimize the error so that the model depicts reality quite accurately. For this reason, we need to choose representative samples and variables.