Efficient Asymmetric Causality Tests

ArXiv ID: 2408.03137 “View on arXiv”

Authors: Unknown

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.

Keywords: Asymmetric Causality, Vector Autoregressive (VAR) Models, Multivariate Analysis, Partial Cumulative Sums, Econometric Testing, General Finance

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail
• Why: The paper introduces advanced econometric theory with matrix algebra and multivariate GARCH, indicating high mathematical complexity. It includes a specific empirical application testing ten hypotheses on major financial markets, demonstrating backtest-ready methodology and implementation focus, thus achieving high empirical rigor.

  flowchart TD
    A["Research Goal<br>Efficient Asymmetric Causality Testing"] --> B["Data Input & Model Setup<br>Multivariate VAR with Partial Cumulative Sums"]
    B --> C["Computational Process<br>Estimate System with Feasible GLS for Efficiency"]
    C --> D["Statistical Testing Phase<br>Test Individual Parameters & Differences"]
    D --> E{"Key Findings & Outcomes<br>Ten Hypotheses Tested on Financial Markets"}