Explanation

Model error refers to flaws in the fundamental assumptions or structure of a financial model that can lead to inaccurate predictions or risk assessments.

Let's analyze each option:

• A. Assuming a non-normal distribution of returns: This is actually a more realistic assumption than assuming normality, as financial returns often exhibit fat tails, skewness, and other non-normal characteristics. This represents model improvement rather than error.

• B. Assuming perfectly liquid markets: This is a classic model error. In reality, markets are not perfectly liquid - there are transaction costs, bid-ask spreads, and liquidity constraints. Assuming perfect liquidity can significantly underestimate trading costs and execution risk, especially during stress periods.

• C. Assuming variable distribution of asset price: This is actually a more sophisticated approach that acknowledges changing market conditions, rather than a model error.

• D. Assuming imperfect capital markets: This is a more realistic assumption that accounts for market frictions, making it less of a model error.

Therefore, option B is the best example of a model error because assuming perfectly liquid markets ignores real-world market frictions and can lead to substantial underestimation of trading costs and liquidity risk.