Covariance is simple in theory but often prone to errors when applied incorrectly. Common mistakes include:
1. Using Price Instead of Returns
Covariance must be calculated using returns, not raw prices. Using prices gives misleading results.
2. Incorrect Time Period Selection
Different timeframes (daily, monthly) produce different covariance values. Consistency is essential.
3. Not Standardising Data Before Comparison
If return scales differ significantly, covariance may appear large or small without meaning.
4. Misinterpreting Magnitude
Covariance values are unbounded. A large value does not necessarily mean a strong relationship.
5. Ignoring Sample Size Adjustments
Population vs. sample covariance differs. For sample data, always divide by (n – 1).
6. Confusing Covariance with Correlation
Correlation is scaled between -1 and +1. Covariance is not—an important distinction.
Avoiding these mistakes ensures more accurate portfolio risk analysis.