Prices of commodities have shown large fluctuations. A high volatility of one commodity today may impact the volatility of another commodity tomorrow. As such, agricultural and energy commodities are closely dependent due to the expansion of the biofuel industry. We study volatility spillovers among a large number of energy, agriculture and biofuel commodities using the vector auto regressive (VAR) model. To account for the possible fat-tailed distribution of the model errors, we propose the t-lasso method for obtaining a large VAR. The t-lasso is shown to have excellent properties, and a forecast analysis shows that the t-lasso attains better forecast accuracy than standard estimators. Our empirical analysis shows the existence of volatility spillovers between energy and biofuel, and between energy and agricultural commodities.
- c32 - "Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models"
- q02 - Global Commodity Crises
- c58 - Financial Econometrics
- Multivariate t-distribution
- Vector AutoRegressive model
- Volatility spillover
- Vector autoregressive model
- SYSTEMIC RISK
- OIL PRICES
- REALIZED VOLATILITY
Barbaglia, L., Croux, C., & Wilms, I. (2020). Volatility Spillovers in Commodity Markets: A large t-vector autoregressive approach. Energy Economics, 85, . https://doi.org/10.1016/j.eneco.2019.104555