Volatility spillovers: A sparse multivariate GARCH approach with an application to commodity markets
提出基于正则化的稀疏DCC-GARCH和BEKK-GARCH模型，研究2000-2018年彭博商品指数24种成分的日收益率波动与相关性溢出，发现金属和能源对农产品存在相关性溢出。
Abstract We propose sparse DCC‐GARCH and BEKK‐GARCH models based on regularization. We use the models to study daily return volatility and correlation spillovers for the 24 constituents of the Bloomberg commodity index in the period 2000–2018. The sparse models outperform the diagonal models out‐of‐sample in terms of model fit and other criteria. We also test whether the higher visibility of metals and energy markets compared with agricultural commodities affects the speed of information processing. We find correlation spillovers from metals and energy to agricultural commodities even though the latter tend to settle somewhat later.