Modelling the volatility of commodities prices using a stochastic volatility model with random level shifts

• Autores: Alvaro, Dennis, Guillén, Ángel, Rodríguez, Gabriel
• Formato: documento de trabajo
• Fecha de Publicación: 2016
• Institución: Pontificia Universidad Católica del Perú
• Repositorio: PUCP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.pucp.edu.pe:20.500.14657/189159
• Enlace del recurso: https://repositorio.pucp.edu.pe/index/handle/123456789/189159
• Nivel de acceso: acceso abierto
• Materia: Volatilidad Estocástica; Modelos en Forma Espacio-Estado; Inferencia Bayesiana; Cambio Estructural; Precios de Commodities; Stochastic Volatility; State-Space Models; Bayesian Inference; Structural Change; Commodity Prices; https://purl.org/pe-repo/ocde/ford#5.02.01

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 .rst 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.