Publication: Empirical distributions of stock returns: european securities markets, 1990-95
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1997-04
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Abstract
The assumption that daily stock returns are normally distributed has long been disputed by the
data. In this article we test (and clearly reject) the normality assumption using time series of
daily stock returns for thirteen European securities markets. More importantly, we fit to the
data four alternative specifications, find overall support for the scaled-t distribution (and
partial support for a mixture of two Normal distributions), and quantify the magnitude of the
error that stems from predicting the probability of obtaining returns in specified intervals by
using the Normal distribution. We conclude by arguing that normality may be a plausible
assumption for monthly (but not for daily) stock returns.
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Time series of stock returns, Nonnormality, Forecasting