Modelling the Volatility of Commodities Prices using a Stochastic Volatility Model with Random Level Shifts
We use the approach of Qu and Perron (2013) for the modeling and inference of volatility of a set of commodity prices in the presence of level shifts of unknown timing, magnitude and frequency. The model has two features: (i) it is a stochastic volatility model comprising both a level shift and a short-memory process where the first component is modeled as a compounded binomial process while the second one is an AR(1) process; (ii) the model is estimated using Bayesian techniques in order to obtain posterior distributions of the parameters and the two latent components. We use six commodity series: agriculture, livestock, gold, oil, industrial metals and a general commodity index. All series cover the period from January 1983 until December 2013 in daily frequency. The results show that although the occurrence of a level shift is rare (about once every 1.5 or 1.8 years), this component clearly contributes most to the variation in the volatility. The half-life of a typical shock from the AR(1) component is short, on average 13 days. Furthermore, isolating the level shift component from the overall volatility indicates a stronger relationship between volatility and Peruvian business cycle movements.
Bayesian Inference, Commodity Prices, State-Space Models, Stochastic Volatility, Structural change
C22, C52, G12