Asymmetric linear double autoregression

Songhua Tan and Qianqian Zhu

Journal of Time Series Analysis, 2022, vol. 43, issue 3, 371-388

Abstract: This article proposes the asymmetric linear double autoregression, which jointly models the conditional mean and conditional heteroscedasticity characterized by asymmetric effects. A sufficient condition is established for the existence of a strictly stationary solution. With a quasi‐maximum likelihood estimation (QMLE) procedure introduced, a Bayesian information criterion (BIC) and its modified version are proposed for model selection. To detect asymmetric effects in the volatility, the Wald, Lagrange multiplier and quasi‐likelihood ratio test statistics are put forward, and their limiting distributions are established under both null and local alternative hypotheses. Moreover, a mixed portmanteau test is constructed to check the adequacy of the fitted model. All asymptotic properties of inference tools including QMLE, BICs, asymmetric tests and the mixed portmanteau test, are established without any moment condition on the data process, which makes the new model and its inference tools applicable for heavy‐tailed data. Simulation studies indicate that the proposed methods perform well in finite samples, and an empirical application to S&P500 index illustrates the usefulness of the new model.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1111/jtsa.12618

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jtsera:v:43:y:2022:i:3:p:371-388

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0143-9782

Access Statistics for this article

Journal of Time Series Analysis is currently edited by M.B. Priestley

More articles in Journal of Time Series Analysis from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().