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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA)
Dias, J. G., Vermunt, J. K.  & Ramos, S. (2015). Clustering financial time series: new insights from an extended hidden Markov model. European Journal of Operational Research. 243 (3), 852-864
Exportar Referência (IEEE)
J. M. Dias et al.,  "Clustering financial time series: new insights from an extended hidden Markov model", in European Journal of Operational Research, vol. 243, no. 3, pp. 852-864, 2015
Exportar BibTeX
@article{dias2015_1714830321826,
	author = "Dias, J. G. and Vermunt, J. K.  and Ramos, S.",
	title = "Clustering financial time series: new insights from an extended hidden Markov model",
	journal = "European Journal of Operational Research",
	year = "2015",
	volume = "243",
	number = "3",
	doi = "10.1016/j.ejor.2014.12.041",
	pages = "852-864",
	url = "http://www.sciencedirect.com/science/article/pii/S0377221714010595#"
}
Exportar RIS
TY  - JOUR
TI  - Clustering financial time series: new insights from an extended hidden Markov model
T2  - European Journal of Operational Research
VL  - 243
IS  - 3
AU  - Dias, J. G.
AU  - Vermunt, J. K. 
AU  - Ramos, S.
PY  - 2015
SP  - 852-864
SN  - 0377-2217
DO  - 10.1016/j.ejor.2014.12.041
UR  - http://www.sciencedirect.com/science/article/pii/S0377221714010595#
AB  - In recent years, large amounts of financial data have become available for analysis. We propose exploring returns from 21 European stock markets by model-based clustering of regime switching models. These econometric models identify clusters of time series with similar dynamic patterns and moreover allow relaxing assumptions of existing approaches, such as the assumption of conditional Gaussian returns. The proposed model handles simultaneously the heterogeneity across stock markets and over time, i.e., time-constant and time-varying discrete latent variables capture unobserved heterogeneity between and within stock markets, respectively. The results show a clear distinction between two groups of stock markets, each one characterized by different regime switching dynamics that correspond to different expected return-risk patterns. We identify three regimes: the so-called bull and bear regimes, as well as a stable regime with returns close to 0, which turns out to be the most frequently occurring regime. This is consistent with stylized facts in financial econometrics.
ER  -