Dynamic clustering of energy markets: An extended hidden Markov approach

•We analyze cycles in energy prices using an extension of the hidden Markov framework to panel data.•U.S. energy markets are heterogeneous with two distinct clusters.•Electricity markets are modeled by five states.•Oil and gas markets are in just two states 96.6% of the time.•We find little synchronization of states.

This paper studies the synchronization of energy markets using an extended hidden Markov model that captures between- and within-heterogeneity in time series by defining clusters and hidden states, respectively. The model is applied to U.S. data in the period from 1999 to 2012. While oil and natural gas returns are well portrayed by two volatility states, electricity markets need three additional states: two transitory and one to capture a period of abnormally high volatility. Although some states are common to both clusters, results favor the segmentation of energy markets as they are not in the same state at the same time.