Reliable estimates of volatility and correlation are fundamental in economics and finance for understanding the impact of macroeconomics events on the market and guiding future investments and policies. Dependence across financial returns is likely to be subject to sudden structural changes, especially in correspondence with major global events, such as the COVID-19 pandemic. In this work, we are interested in capturing abrupt changes over time in the dependence across US industry stock portfolios, over a time horizon that covers the COVID-19 pandemic. The selected stocks give a comprehensive picture of the US stock market. To this end, we develop a Bayesian multivariate stochastic volatility model based on a time-varying sequence of graphs capturing the evolution of the dependence structure. The model builds on the Gaussian graphical models and the random change points literature. In particular, we treat the number, the position of change points, and the graphs as object of posterior inference, allowing for sparsity in graph recovery and change point detection. The high dimension of the parameter space poses complex computational challenges. However, the model admits a hidden Markov model formulation. This leads to the development of an efficient computational strategy, based on a combination of sequential Monte-Carlo and Markov chain Monte-Carlo techniques. Model and computational development are widely applicable, beyond the scope of the application of interest in this work.
翻译:对波动和相关性的可靠估计是了解宏观经济事件对市场的影响以及指导未来投资和政策的经济和金融的基础。金融回报之间的依赖性可能受到突然结构变化的影响,特别是在与COVID-19大流行等重大全球事件相对应的情况下。在这项工作中,我们有兴趣在覆盖COVID-19大流行的时空范围内捕捉美国工业股票组合依赖性的长期突变变化。选定的股票提供了美国股票市场的全面图象。为此,我们开发了一个巴耶西亚多变异性随机波动模型,其基础是记录依赖性结构演变的图表时间变化序列。该模型建立在高斯图形模型和随机变化点文献的基础上。特别是,我们把数字、变化点的位置和图表作为后方推推力的客体,允许在图形恢复和变化点检测中产生恐慌。参数空间的高维度构成复杂的计算挑战。然而,模型承认了一个隐蔽的Markov模型模型,反映了依赖性结构的演变。该模型建立在高位图形图形的图形模型和随机变化点点文献文献中。我们把数字、变化点位置和图表作为后推算法的模型应用范围,这是基于现代计算模型的组合,这是在现代-卡索价计算方法的模型应用中。