Explanation

This question tests understanding of skewness in return distributions.

Key Concepts:

1. Skewness measures the asymmetry of a distribution around its mean.
2. Positive skewness (right-skewed): The distribution has a longer tail on the right side, with more extreme positive values. The mean > median > mode.
3. Negative skewness (left-skewed): The distribution has a longer tail on the left side, with more extreme negative values. The mean < median < mode.

Analyzing the question:

• The description states: "distributions concentrated to the right" - This means the bulk of the data is on the right side.
• "with a higher frequency of negative deviation from the mean" - This indicates more negative outliers or extreme negative values.

Interpretation:

• When a distribution is "concentrated to the right," it means most observations are on the higher end (right side) of the distribution.
• The "higher frequency of negative deviation from the mean" means there are more extreme negative values (left tail).
• This combination describes a negatively skewed (left-skewed) distribution: most values are concentrated on the right (higher values), but there are more extreme negative outliers pulling the mean downward.

Why not the other options:

• A. Kurtosis: Measures the "tailedness" or peakedness of a distribution, not its asymmetry.
• B. Positive skewness: Would have a longer right tail with more extreme positive values, not negative deviations.

Real-world context: Stock market returns often exhibit negative skewness because:

1. Markets tend to have gradual upward movements over time
2. But experience sudden, sharp declines (crashes) that create extreme negative outliers
3. This results in more negative deviations from the mean than positive ones

Answer: C. negative skewness