Modeling the flow of information between financial time-series by an entropy-based approach

Recent literature has been documented that commodity prices have become more and more correlated with prices of financial assets. Hence, it would be crucial to understand how the amount of information contained in one time series (i.e. commodity prices) reflects on the other one (i.e. financial asset prices). Here, we address these issues by means of an entropy-based approach. In particular, we define two new metrics, namely the Joined Entropy and the Mutual Information, to analyze and model how the information content is (mutually) exchanged between two time series under investigation. The experimental outcomes, applied on volatility indexes, oil and natural gas prices for the period 01/04/1999-01/02/2015, prove the effectiveness of the proposed method in modeling the information flows between the analyzed data.