Asymmetric heavy-tailed vector auto-regressive processes with application to financial data. Issue 2 (22nd January 2020)

• Record Type: Journal Article
• Authors: Maleki, Mohsen; Wraith, Darren; Mahmoudi, Mohammad R.; Contreras-Reyes, Javier E.
• Is Part Of: Journal of statistical computation and simulation. Volume 90:Issue 2(2020)
• Page Start: 324
• Page End: 340
• Publication Date: 2020-01-22
• Subjects: VAR processes -- scale mixtures of multivariate skew-normal -- EM-type algorithm -- outliers -- asymmetric distributions -- heavy tailed distributions -- financial data -- multivariate time series; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2019.1680675
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: Vector Auto-regressive (VAR) models are commonly used for modelling multivariate time series and the typical distributional form is to assume a multivariate normal. However, the assumption of Gaussian white noise in multivariate time series is often not reasonable in applications where there are extreme and/or skewed observations. In this setting, inference based on using a Gaussian distributional form will provide misleading results. In this paper, we extended the multivariate setting of autoregressive process, by considering the multivariate scale mixture of skew-normal (SMSN) distributions for VAR innovations. The multivariate SMSN family is able to be represented in a hierarchical form which relatively easily facilitates simulation and an EM-type algorithm to estimate the model parameters. The performance of the proposed model is illustrated by using simulated and real datasets.