Article ID Journal Published Year Pages File Type
417896 Computational Statistics & Data Analysis 2008 16 Pages PDF
Abstract

Mixture Periodically Correlated Autoregressive Conditionally Heteroskedastic (MPARCH) model, which extends the ARCH model, is proposed. The primary motivation behind this extension is to make the model consistent with high kurtosis, outliers and extreme events, and at the same time, able to capture the periodicity feature exhibited by the autocovariance structure. The second and the fourth moment periodically stationary conditions and their closed-forms are derived. Maximum likelihood estimation is obtained via the iterative Expectation Maximization algorithm and the performance of this algorithm is shown via a simulation studies and the MPARCH models are fitted to a real data set.

Related Topics
Physical Sciences and Engineering Computer Science Computational Theory and Mathematics
Authors
, ,