Dynamic clustering of energy markets: An extended hidden Markov approach
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. Lien vers l'article
RAMOS, S. and DIAS, J. (2014). Dynamic clustering of energy markets: An extended hidden Markov approach. Expert Systems with Applications, 41(17), pp. 7722-7729.
Mots clés : #Hidden-Markov-models-(HMMs), #Clustering, #Time-series, #Energy-markets