Tuesday, August 9, 2016

An Application of Data Filtering Extracting Super Cycles in Commodity Prices

Authors and guest post by Daniel L. Jerrett, Ph.D and Abdel M. Zellou, Ph.D.

EViews offers numerous techniques to filter time series including the Hodrick Prescott filter as well as various band-pass filters.

This article will describe an application of one of these filtering techniques, namely the asymmetric Christiano Fitzgerald band pass filter, and its applications to real oil prices in order to extract the various cycle and trend components.

Super Cycles and Christiano Fitzgerald Band Pass Filter

There is a long standing interest in commodity price dynamics, i.e. their trend, cycle and volatility (Cuddington et al. 2007, Cashin and McDermott 2002). Recently, a number of papers have focused on the super cycle hypothesis. A super cycle (SC) is “a prolonged (decades) long trend rise in real commodity prices. Heap (2005) and Cuddington and Jerrett (2008) define a super cycle as a cycle lasting 20 to 70 years (trough to trough) as an economy goes through structural transformation caused by industrialization and urbanization. This structural transformation is accompanied by increased demand for energy and metals commodities as the manufacturing sector expands. Historically, these periods of urbanization and industrialization have occurred in Europe during the Industrial Revolution in the 19th century, in the U.S. at the beginning of the 20th century, in Western Europe again during the reconstruction that followed the Second World War, in South-East Asia in the 1960s and finally in the BRIC1 countries in the 1990s2. The increase in demand for energy and metals commodities during these periods, combined with the delay for the supply to catch up with the demand surge, created sustained periods of high commodity prices according to the super-cycle hypothesis.