Clustering financial time series

New insights from an extended hidden Markov model

J.G. Dias, J.K. Vermunt, S. Ramos

Research output: Contribution to journalArticleScientificpeer-review

Abstract

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.
Keywords: Data mining, Hidden Markov model, Stock indexes, Latent class model, Regime-switching model
Original languageEnglish
Pages (from-to)852 - 864
JournalEuropean Journal of Operational Research
Volume243
Issue number3
DOIs
Publication statusPublished - 2015

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Financial Time Series
Hidden Markov models
Stock Market
Markov Model
Time series
Clustering
Regime-switching Model
Econometrics
Unobserved Heterogeneity
Stylized Facts
Model-based Clustering
Latent Class Model
Regime Switching
Stock Index
Financial Data
Discrete Variables
Latent Variables
Time Constant
Time-varying
Data Mining

Cite this

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abstract = "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.Keywords: Data mining, Hidden Markov model, Stock indexes, Latent class model, Regime-switching model",
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Clustering financial time series : New insights from an extended hidden Markov model. / Dias, J.G.; Vermunt, J.K.; Ramos, S.

In: European Journal of Operational Research, Vol. 243, No. 3, 2015, p. 852 - 864.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

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T2 - New insights from an extended hidden Markov model

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AU - Vermunt, J.K.

AU - Ramos, S.

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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.Keywords: Data mining, Hidden Markov model, Stock indexes, Latent class model, Regime-switching model

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