Normal Inverse Gaussian Autoregressive Model Using EM Algorithm

TL;DR

This paper introduces a Normal Inverse Gaussian autoregressive model estimated via EM algorithm, demonstrating its effectiveness in modeling financial time-series data with promising applications.

Contribution

The paper presents a novel NIG autoregressive model and applies EM algorithm for parameter estimation, showing its suitability for financial data modeling.

Findings

NIG autoregressive model fits financial data well

EM algorithm effectively estimates model parameters

Model has potential for various real-world time-series applications

Abstract

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.