Correlation analysis

Correlation analysis can be used to determine the extent to which a relationship exists between two metric variables, how close it is, and in which direction it goes. The correlation coefficient, which expresses the bivariate statistical relationship between two time series, can take on values between +1 and -1, with values near +1 indicating a close positive relationship between the variables, values near 0 indicating no relationship, and values near -1 indicating a close negative relationship.[1]

However, the finding that two time series are correlated does not mean that there is a causal relationship between them. The correlation coefficient alone does not indicate whether one variable was actually influenced by the other or vice versa, or to what extent there is any cause-and-effect relationship between the two variables. Time series can be almost completely correlated with each other without being related in terms of content(spurious correlation[2]). A high correlation is often due to the fact that both time series are influenced by the same factors and therefore tend in the same direction, but have little or no influence on each other. Other time series are hardly correlated with each other, but may nevertheless be related in terms of content, which does not come into play because the time series are influenced by factors whose effects compensate each other.

It would therefore be misleading to conclude a pattern of effects from the expression of a mathematically derived coefficient alone. Such conclusions must always be drawn from the respective context, taking into account plausibility relationships and an overall view of all relevant factors, since the statements based on them can otherwise lead to misinterpretations. In the following, examples from the precious metals sector are given for different correlation constellations.