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.
Highlights
In recent years dealing with unobserved heterogeneity has become a predominant topic in many research areas
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
Though the dominant approach followed by both academics and practitioners has been to assume that returns follow a normal distribution (see, e.g., Lundblad (2007) and Fu (2009)), it has been recognized that stock market returns and returns of financial assets contain skewness and excessive kurtosis
Summary
In recent years dealing with unobserved heterogeneity has become a predominant topic in many research areas. For recent surveys on the application of RSMs in empirical finance, we refer to Lange and Rahbek (2009) and Guidolin (2011) These models have broader fields of application, covering manpower systems, where both observable and latent sources of dynamic heterogeneity should be accounted for (Guerry, 2011), and reliability analysis (Zhou et al, 2010). The proposed approach is flexible in the sense that it can deal with the specific features of financial time series data, such as asymmetry, kurtosis, and unobserved heterogeneity, an aspect that tends to be neglected. The paper concludes with a summary of the main findings and a description of possible implications
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
