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Fitting the Variance-Gamma Model to Financial Data

Eugene Seneta
Journal of Applied Probability
Vol. 41, Stochastic Methods and Their Applications (2004), pp. 177-187
Stable URL: http://www.jstor.org/stable/3215976
Page Count: 11
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Fitting the Variance-Gamma Model to Financial Data
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Abstract

This paper has as its main theme the fitting in practice of the variance-gamma distribution, which allows for skewness, by moment methods. This fitting procedure allows for possible dependence of increments in log returns, while retaining their stationarity. It is intended as a step in a partial synthesis of some ideas of Madan, Carr and Chang (1998) and of Heyde (1999). Standard estimation and hypothesis-testing theory depends on a large sample of observations which are independently as well as identically distributed and consequently may give inappropriate conclusions in the presence of dependence.

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