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This paper seems to outline what you are looking for. You want to be careful about mean/variance/kurtosis to make sure you are working in the correct measure.


Since the variance gamma process can actually be expressed as the difference of two gamma processes, the parameters are quite easy to estimate. Taking the mean (rate) and variance (rate) of the positive values and negatives will give you the variables necessary to estimate the total variance gamma process parameters. They are described in a more recent ...


The parameters θ, ν and r need to be estimated from the sample with some technique, but unfortunately there is no easy way to do that for a VG process. There is, for example, "maximum likelihood estimation" that gives you the parameters that are "most likely" to have generated your sample, assuming your sample comes from a VG process. But MLE involves ...


If you say stock prices are following GBM then you can say $dS_t = \mu S_tdt + \sigma S_t dW_t$ solving which it brings where $\sigma$ is volatility and $r$ is risk free rate . **EDITED For a Variance Gamma process theta is the deterministic drift in subordinated Brownian motion and sigma standard deviation in subordinated Brownian motion. I ...

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