Identification and Forecasting of Bull and Bear Markets using Multivariate Returns

Jia Liu, John Maheu and Yong Song

MPRA Paper from University Library of Munich, Germany

Abstract: 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 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 to several benchmark models including univariate Markov switching models.

Keywords: Markov switching; Multivariate analysis; Investment strategies; Market timing (search for similar items in EconPapers)
JEL-codes: C32 C53 C58 G1 (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:119515

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