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Fitting the variance-gamma model to financial data
by
Seneta, Eugene
in
60E07
/ 60E10
/ 60G15
/ 62M05
/ characteristic function
/ Data models
/ dependence
/ Eigenfunctions
/ estimation
/ Estimation methods
/ Estimators
/ Financial data
/ Financial Mathematics
/ increments
/ log returns
/ martingale
/ Martingales
/ Maximum likelihood estimation
/ method of moments
/ Part 3. Financial mathematics
/ Platens
/ Skewed distribution
/ skewness
/ stationarity
/ Student' processes
/ subordinator model
/ T distribution
/ Variance-gamma
2004
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Fitting the variance-gamma model to financial data
by
Seneta, Eugene
in
60E07
/ 60E10
/ 60G15
/ 62M05
/ characteristic function
/ Data models
/ dependence
/ Eigenfunctions
/ estimation
/ Estimation methods
/ Estimators
/ Financial data
/ Financial Mathematics
/ increments
/ log returns
/ martingale
/ Martingales
/ Maximum likelihood estimation
/ method of moments
/ Part 3. Financial mathematics
/ Platens
/ Skewed distribution
/ skewness
/ stationarity
/ Student' processes
/ subordinator model
/ T distribution
/ Variance-gamma
2004
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Fitting the variance-gamma model to financial data
by
Seneta, Eugene
in
60E07
/ 60E10
/ 60G15
/ 62M05
/ characteristic function
/ Data models
/ dependence
/ Eigenfunctions
/ estimation
/ Estimation methods
/ Estimators
/ Financial data
/ Financial Mathematics
/ increments
/ log returns
/ martingale
/ Martingales
/ Maximum likelihood estimation
/ method of moments
/ Part 3. Financial mathematics
/ Platens
/ Skewed distribution
/ skewness
/ stationarity
/ Student' processes
/ subordinator model
/ T distribution
/ Variance-gamma
2004
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Journal Article
Fitting the variance-gamma model to financial data
2004
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Overview
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.
Publisher
Cambridge University Press,Applied Probability Trust
Subject
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