The Markowitz mean-variance model is known to suffer from estimation error due to financial returns not meeting the assumptions of a normal distribution, providing portfolio weights that underperform out-of-sample.
Does this mean that if asset returns that have a:
- Mean of 0,
- standard deviation of 1,
- skewness of 0 and
- excess kurtosis of 0
are fed into the model, this allows the model to perform best and fully (or partially) recover perfect out-of-sample performance/accuracy (given that the out-of-sample returns are also normally distributed)?