Clustering financial time series: New insights from an extended hidden Markov model

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. Lien vers l'article

DIAS, J.G., RAMOS, S. and VERMUNT, J.K. (2015). Clustering financial time series: New insights from an extended hidden Markov model. European Journal of Operational Research, 243(3), pp. 852-864.

Mots clés : #Data-mining, #Hidden-Markov-model, #Stock-indexes, #Latent-class-model, #Regime, #switching-model