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A Causality-in-Variance Test and Its Application to Financial Market Prices

UCSC Working Paper 94-298

Posted: 22 Aug 1998  

Lilian K. Ng

York University - Schulich School of Business

Date Written: Not Provided

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 chi-square 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.

JEL Classification: C22, C52, G10

Suggested Citation

Ng, Lilian K., A Causality-in-Variance Test and Its Application to Financial Market Prices (Not Provided ). UCSC Working Paper 94-298. Available at SSRN: https://ssrn.com/abstract=6931

Lilian K. Ng

York University - Schulich School of Business ( email )

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

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