A causality-in-variance test and its application to financial market prices
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 33-48 |
Journal / Publication | Journal of Econometrics |
Volume | 72 |
Issue number | 1-2 |
Publication status | Published - May 1996 |
Link(s)
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.
In: Journal of Econometrics, Vol. 72, No. 1-2, 05.1996, p. 33-48.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review