Efficient Asymmetric Causality Tests

TL;DR

This paper improves asymmetric causality testing by addressing residual dependence and the significance of parameter differences, providing a more accurate and comprehensive analysis of causal relationships in financial markets.

Contribution

It introduces efficient testing methods that account for residual dependence and differences in causal parameters, filling gaps in existing asymmetric causality test literature.

Findings

Enhanced tests for asymmetric causality considering residual dependence

Explicit assessment of differences between causal parameters

Application to major financial markets demonstrating practical utility

Abstract

Asymmetric causality tests are increasingly gaining popularity in different scientific fields. This approach corresponds better to reality since logical reasons behind asymmetric behavior exist and need to be considered in empirical investigations. Hatemi-J (2012) introduced the asymmetric causality tests via partial cumulative sums for positive and negative components of the variables operating within the vector autoregressive (VAR) model. However, since the residuals across the equations in the VAR model are not independent, the ordinary least squares method for estimating the parameters is not efficient. Additionally, asymmetric causality tests mean having different causal parameters (i.e., for positive or negative components), thus, it is crucial to assess not only if these causal parameters are individually statistically significant, but also if their difference is statistically significant. Consequently, tests of difference between estimated causal parameters should explicitly be conducted, which are neglected in the existing literature. The purpose of the current paper is to deal with these issues explicitly. An application is provided, and ten different hypotheses pertinent to the asymmetric causal interaction between two largest financial markets worldwide are efficiently tested within a multivariate setting.