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Export Reference (APA) 
Dias, J. G. & Ramos, S. (2014). Dynamic clustering of energy markets: an extended hidden Markov approach. Expert Systems with Applications. 41 (17), 7722-7729
Export Reference (IEEE) 
J. M. Dias and S. C. Ramos,  "Dynamic clustering of energy markets: an extended hidden Markov approach", in Expert Systems with Applications, vol. 41, no. 17, pp. 7722-7729, 2014
Export BibTeX 
@article{dias2014_1765574373676,
	author = "Dias, J. G. and Ramos, S.",
	title = "Dynamic clustering of energy markets: an extended hidden Markov approach",
	journal = "Expert Systems with Applications",
	year = "2014",
	volume = "41",
	number = "17",
	doi = "10.1016/j.eswa.2014.05.030",
	pages = "7722-7729",
	url = "http://www.sciencedirect.com/science/article/pii/S0957417414003108"
}
Export RIS 
TY  - JOUR
TI  - Dynamic clustering of energy markets: an extended hidden Markov approach
T2  - Expert Systems with Applications
VL  - 41
IS  - 17
AU  - Dias, J. G.
AU  - Ramos, S.
PY  - 2014
SP  - 7722-7729
SN  - 0957-4174
DO  - 10.1016/j.eswa.2014.05.030
UR  - http://www.sciencedirect.com/science/article/pii/S0957417414003108
AB  - 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.
ER  -