A causality-in-variance test and its application to financial market prices

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

249 Scopus Citations
View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)33-48
Journal / PublicationJournal of Econometrics
Volume72
Issue number1-2
Publication statusPublished - May 1996

Abstract

This paper develops a test for causality in variance. The test is based on the residual cross-correlation function (CCF) and is robust to distributional assumptions. Asymptotic normal and asymptotic χ2 statistics are derived under the null hypothesis of no causality in variance. Monte Carlo results indicate that the proposed CCF test has good empirical size and power properties. Two empirical examples illustrate that the causality test yields useful information on the temporal dynamics and the interaction between two time series.

Research Area(s)

  • Causality, Cross-correlation function, GARCH, Stock price, Volatility spillover

Citation Format(s)

A causality-in-variance test and its application to financial market prices. / Cheung, Yin-Wong; Ng, Lilian K.

In: Journal of Econometrics, Vol. 72, No. 1-2, 05.1996, p. 33-48.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review