Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Zbornik radova Ekonomskog fakulteta u Rijeci :časopis za ekonomsku teoriju/Proceedings of Rijeka Faculty of Economics:Journal of Economics and Business
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
