Carbon-dioxide emissions trading and hierarchical structure in worldwide finance and commodities markets

TL;DR

This study uses a modified minimal spanning tree approach to analyze financial and commodity markets, revealing stable asset groupings and a predictive time lag in volatility correlations between stock markets, emissions allowances, and oil futures.

Contribution

It introduces a modified MST network based on absolute cross-correlation, capturing anticorrelations and revealing temporal volatility relationships across diverse financial assets.

Findings

Stable connections among similar asset types over time

Identification of a >20-day time lag predicting volatility in emissions and oil futures

Modified MST effectively captures anticorrelations in financial networks

Abstract

In a highly interdependent economic world, the nature of relationships between financial entities is becoming an increasingly important area of study. Recently, many studies have shown the usefulness of minimal spanning trees (MST) in extracting interactions between financial entities. Here, we propose a modified MST network whose metric distance is defined in terms of cross-correlation coefficient absolute values, enabling the connections between anticorrelated entities to manifest properly. We investigate 69 daily time series, comprising three types of financial assets: 28 stock market indicators, 21 currency futures, and 20 commodity futures. We show that though the resulting MST network evolves over time, the financial assets of similar type tend to have connections which are stable over time. In addition, we find a characteristic time lag between the volatility time series of the stock market indicators and those of the EU CO2 emission allowance (EUA) and crude oil futures (WTI). This time lag is given by the peak of the cross-correlation function of the volatility time series EUA (or WTI) with that of the stock market indicators, and is markedly different (>20 days) from 0, showing that the volatility of stock market indicators today can predict the volatility of EU emissions allowances and of crude oil in the near future.