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

详细信息    查看全文

• 作者：Dennis Alvaro ; Ángel Guillén ; Gabriel Rodríguez
• 关键词：Stochastic volatility ; State ; space models ; Bayesian inference ; Random level shifts ; Commodity prices long memory
• 刊名：Review of World Economics
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：153
• 期：1
• 页码：71-103
• 全文大小：
• 刊物主题：International Economics; European Integration; Macroeconomics/Monetary Economics//Financial Economics; Economic Policy;
• 出版者：Springer Berlin Heidelberg
• ISSN：1610-2886
• 卷排序：153

文摘

We use the approach of Qu and Perron (Econom J 16(3):309–339, 2013) for the modeling and inference of volatility of a set of commodity prices in the presence of random level shifts of unknown timing, magnitude and frequency. Our approach contributes to the study of commodities in several aspects. First, we test for the presence of a genuine long-memory process in the volatility of commodities. Second, we determine that the random level shifts are certainly the main source of variation in the commodity price volatility. Finally, we estimate the volatility and its components as latent variables, thereby making it possible to evaluate their level of correlation with macroeconomic variables in small open economies such as Latin-American countries where the dependence on commodity price volatility is high. 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. Furthermore, isolating the level shift component from the overall volatility indicates a strong relationship of this component with a set of business cycle indicators of several Latin American countries.