Asymmetric autoregressive models: statistical aspects and a financial application under COVID-19 pandemic.

ABSTRACT: In the present study, we provide a motivating example with a financial application under COVID-19 pandemic to investigate autoregressive (AR) modeling and its diagnostics based on asymmetric distributions. The objectives of this work are: (i) to formulate asymmetric AR models and their estimation and diagnostics; (ii) to assess the performance of the parameters estimators and of the local influence technique for these models; and (iii) to provide a tool to show how data following an asymmetric distribution under an AR structure should be analyzed. We take the advantages of the stochastic representation of the skew-normal distribution to estimate the parameters of the corresponding AR model efficiently with the expectation-maximization algorithm. Diagnostic analytics are conducted by using

SUBMITTER: Liu Y 

PROVIDER: S-EPMC9041637 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature