| Summary: | The purpose of this paper is to investigate which of the proposed parametric
models for extracting risk-neutral density; among Black-Scholes Merton, mixture
of two log-normals and generalized beta; give the best fit. The model that fits
sample data better is used to describe different characteristics (moments) of the ex
ante probability distribution. The empirical findings indicate that no matter which
parametric model is used, the best fit is always obtained for short maturity horizon,
but when comparing models in short-run, the mixture of two log-normals gives
statistically significant smaller MSE. According to the pair-wise comparison
results, the basic conclusion is that the mixture of two log-normals is superior to
the other parametric models and has proven to be very flexible in capturing
commonly observed characteristics of the underlying financial assets, such as
asymmetries and “fat-tails” in implied probability distribution.
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