Causality in Financial Markets

Causality in Financial Markets

Causality in financial markets refers to the relationship between different financial variables and how they influence each other. In time-series analysis, causality establishes whether one time series can predict another, indicating a directional influnce. Causality can be assessed using various statistical methods, such as Granger causality tests, which determine if past values of one variable can help predict future values of another variable. While causality is a loaded term in more general contexts (e.g., using causal DAGs models, similar to Bayesian networks to establish causal relationships or in econometrics, looking at structural equations to identify causal relationships), in financial markets, it is often reduced to the more simple concept of Granger causality.

Granger Causality

Granger causality is a statistical hypothesis test for determining whether one time series can predict another. It is based on the idea that if variable X Granger-causes variable Y, then past values of X should contain information that helps predict future values of Y, beyond the information contained in past values of Y alone.

Understanding causality is crucial for making informed investment decisions, risk management, and developing trading strategies.