The Normal Inverse Gaussian (NIG) process is a type of Lévy process used in probability theory and financial modeling. It is a stochastic process that belongs to the class of generalized hyperbolic distributions and is particularly useful for modeling heavy-tailed and asymmetric distributions.
I calculated the characteristic function of the process for density inversion via Fourier methods and compared the simulated histogram to the theoretical density of the Normal Inverse Gaussian distribution.
Additionally, I compared Merton Jump-Diffusion process, Variance Gamma process and Normal Inverse Gaussian process.
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The Python code is uploaded to https://github.com/AIMLModeling/NormalInverseGaussian
