Explanation

In a QQ plot (Quantile-Quantile plot), we compare the quantiles of an empirical distribution against the quantiles of a theoretical distribution (typically normal distribution). The pattern of the points provides insights about the shape of the empirical distribution:

• Leptokurtosis (Excess kurtosis > 0): When the empirical distribution has heavier tails than the normal distribution, the QQ plot typically shows an S-shaped curve where the points deviate from the straight line at both ends. The tails of the empirical distribution have more extreme values than expected under normality.

• Positive skew: Would show points deviating upward at the right tail and downward at the left tail.

• Parametric distribution: Cannot be determined from a QQ plot alone.

• Linear transformation to normality: If the distribution were approximately normal after linear transformation, the QQ plot would show points closely following a straight line.

Since the question asks for the "most likely true statement" and option C describes leptokurtosis, which is a common pattern observed in financial return data where extreme events occur more frequently than predicted by normal distribution.