Identification and forecasting of bull and bear markets using multivariate returns
Bibliographic Data
| ID | 21653267 |
|---|---|
| Authors | Jia Liu (0000-0002-9209-1215, Sobey School of Business Saint Mary's University Halifax Nova Scotia Canada), John M Maheu (DeGroote School of Business McMaster University Hamilton Ontario Canada, corresponding author), Yong Song (0000-0002-1067-7598, Faculty of Business and Economics University of Melbourne Parkville Victoria Australia) |
| Year | 2024 |
| Volume | 39 |
| Issue | 5 |
| Pages | 723-745 |
| Publication date | 2024-08-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Applied Econometrics (JOURNAL) |
| Journal identifiers | ISSN: 1099-1255 • E-ISSN: 0883-7252 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/jae.3048 |
| OpenAlex | W4393948979 |
| Language | EN |
| References cited | 31 |
Bull and bear market identification generally focuses on a broad index of returns through a univariate analysis. This paper proposes a new approach to identify and forecast bull and bear markets through multivariate returns. The model assumes that all assets are directed by a common discrete state variable from a hierarchical Markov switching model. The hierarchical specification allows the cross‐section of state‐specific means and variances to differ over bull and bear markets. We investigate several empirically realistic specifications that permit feasible estimation even with 100 assets. Our results show that the multivariate framework provides competitive bull and bear regime identification and improves portfolio performance and density prediction compared with several benchmark models including univariate Markov switching models
Biology · Econometrics · Economics · Financial economics · Multivariate statistics · Statistics · Financial Risk and Volatility Modeling · Forecasting Techniques and Applications · Market Dynamics and Volatility · Mathematics
| Highly cited | No |
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