Volatility spillovers: A sparse multivariate GARCH approach with an application to commodity markets

We propose sparse DCC‐GARCH and BEKK‐GARCH models based on L1 ${L}_{1}$ 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.