SummaryBull 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.

中文翻译：

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使用多元回报识别和预测牛市和熊市

摘要牛市和熊市的识别通常侧重于通过单变量分析的广泛回报指数。本文提出了一种通过多元回报来识别和预测牛市和熊市的新方法。该模型假设所有资产都由来自分层马尔可夫切换模型的公共离散状态变量引导。分层规范允许特定州的均值和方差的横截面在牛市和熊市中有所不同。我们研究了几个基于经验的现实规范，即使对 100 个资产也允许进行可行的估计。我们的结果表明，与包括单变量马尔可夫切换模型在内的几个基准模型相比，多变量框架提供了有竞争力的牛市和熊市形态识别，并提高了投资组合绩效和密度预测。