Identification and forecasting of bull and bear markets using multivariate returns
提出一种基于分层马尔可夫转换模型的多元方法，利用多个资产的收益率数据共同识别和预测牛市与熊市，相比单变量模型能提升投资组合表现和密度预测精度。
Summary 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.