The confusion between the correlation and causation is inevitable especially for those who are new in investment and trading. But one must understand the difference between correlation vs causation before opening any investment accounts. The financial analytics bible defines the correlation is a relationship between markets.
For instance, FBMKLCI and DJIA have a positive relationship. But without further statistical test, we cannot say which market is the leader and which market is the laggard. Both markets may share and react on the similar information which is the mediator. So there comes the need of another course of test called causality test. The causation, for example, explains the case when FBMKLCI causes DJIA to move.
The mathematics of assets correlation is simple and straightforward. The correlation test finds the degree of association between the price change of Asset A and Asset B. It is then measured by a statistical tool such as Pearson Correlation coefficient. The causality test on the other hand adopts the similar mathematical formulation but with a little adjustment on the equation parameterization.
Correlation VS Causation
The causality test focuses on finding the correlation of Asset A and Asset B with each other’s history. The most popular causality test used in the Bloomberg terminal is Granger causality test.
The knowledge on assets correlation and causation is crucial for investors as it helps the investors to differentiate and identify market movers. Some of the assets maybe well correlated but not necessarily a price determinant to each other. For instance, the most popular assumption in the agricultural commodity trading is the soybean oil futures traded in US Chicago Board of Trade (CBOT) is the leader for the Malaysian crude palm oil futures (FCPO) in Bursa Malaysia Derivatives (BMD).
A study by Li and Nguyen (2015) provide a crucial piece of evidence where they reveal that the CBOT soybean oil and BMD crude palm oil have a stable long run relationship, but the study discovered that there is bi-directional causality between both futures markets. It shows that the Malaysian crude palm oil price may influence the soybean oil price in the US and vice versa.
Another example, the causality test determines the functionality and reliability of futures market as a hedging avenue for the market players. The futures market is established to be a future price reference for its underlying cash market. The efficient futures market guarantees effective hedging strategy. Therefore, an efficient futures market must have two conditions to be fulfilled.
First, the correlation between the spot and futures must be at perfect positive at all times. Secondly, the futures price must be proven leading the spot price in the causality test. Lacking on any of these prerequisites may render the price risk transfer process from the hedger to speculator to be less efficient. To add further, the causality test helps the global investors in devising their international portfolios.
A good knowledge in cross markets causality will tell whether the bearish mode in the S&P500 tonight maybe spill over to Nikkei 500 in the next morning or not. This is why it is important to have the knowledge about correlation vs causation.
So, the next time you heard an impactful news on a geo-economic event, you can tell that if your portfolio will be impacting or impacted by the global sentiment. Hope you now have a better understanding of correlation vs causation.
About the Author
Dr. Ahmad Danial is a Certified Financial Technician (CFTe) and Senior Lecturer in Finance at Department of Economics and Financial Studies, UiTM Puncak Alam. He has over 10 years’ experience in the financial markets before hopping into the academia. His areas of expertise include financial contagion, trading in stocks and derivatives markets, price discovery, hedging strategy, Econophysics and technical analysis. He can be reached at email@example.com.