  In this article, normal inverse Gaussian (NIG) autoregressive model is
introduced. The parameters of the model are estimated using Expectation
Maximization (EM) algorithm. The efficacy of the EM algorithm is shown using
simulated and real world financial data. It is shown that NIG autoregressive
model fit very well the considered financial data and hence could be useful in
modeling of various real life time-series data.
