Archived Working Papers:
Research at CINTRAFOR offers a wealth of education and papers.
Back to Blog
PULP (pulp) n. 1. A soft, moist, shapeless mass of matter. 2. A magazine or book containing lurid subject matter and being characteristically printed on rough, unfinished paper.
American Heritage Dictionary, New College Edition (1993)
Many commodity products have similar characteristics. They are relatively homogenous products that can be produced using readily available technology, and are traded internationally on competitive markets. They are based on natural resources, the availability of which is subject to shocks. They are intermediate inputs with few short-term potential substitutes. As a result of these characteristics, commodity prices tend to be volatile.
Market pulp is in many ways a typical commodity. It also has some particular characteristics that contribute further to price volatility. These include high capital intensity, long-lived capital equipment, and speculative inventory management behavior on the part of consumers. Because of this volatility, accurate forecasting of market conditions is a difficult but potentially very useful exercise. Investment decisions are made on the basis of price forecasts, and improving their accuracy can lead to better decision making on the part of producers. This may in turn reduce the volatility of prices.
Considerable energies are devoted to the task of forecasting pulp prices. A number of recognized short-term leading indicators for price movements exist, and these are widely studied by industry participants. However, less attention is paid to forecasts of the more distant future. This is surprising, given that the relevant time horizon for some of the most important decisions made by the industry (namely, investments in new capacity) is at least five years. This may perhaps be explained by a relative dearth of useful long-term leading indicators on which to base forecasts. The purpose of this research is to contribute to our understanding of how the market functions and to our ability to forecast market conditions, in both the short term and the long term.
Cointegration methods provide a means for the modeler to take advantage of the effective treatment of short-term dynamics which time series models provide, while ensuring that long-term forecasts have sensible properties. The concept of cointegration is an intuitively sensible one. A pair of variables is said to be cointegrated if they have a tendency to maintain a fixed ‘equilibrium’ relation to one another over the long term. In a stable cointegrating relationship, the variables will adjust to eliminate any divergence from this equilibrium. Such relationships make a great deal of sense for several variables of relevance to the present case, and forecasts for these variables which show significant and long lasting violations of these relationships are not plausible. Cointegration methods allow us to test for and model the existence of long-term equilibrium relationships between the variables in a system, within the framework of a dynamic time series model. A focus of this research is to assess whether these methods prove to be of practical use for building forecasting models.
One difficulty encountered in this research is that little previous time series research on pulp markets has been published. We are therefore obliged to adopt an incremental approach to model building. First, the time series characteristics of pulp prices themselves are explored. We then develop a series of multivariate time series models. These models will attempt to capture some of the short-term and long-term processes that are thought to determine prices. The success or failure of each model will be judged according to three criteria:
While these models are designed for forecasting purposes, they may also be used to an extent for structural analysis. This research is not designed primarily to test any specific behavioral hypotheses, but where convenient, structural analysis will be used to provide insights into how the market functions.
The structure of this thesis is as follows. A description of the pulp market is provided in Chapter 1. This outlines the short-term and long-term dynamics of the market. It also addresses the issue of how the ‘market’ should be defined in terms of its product and geographical scope. Chapter 2 provides a brief overview of the econometric concepts mentioned above, along with a description of relevant modeling methods. Data availability for the variables found to be of importance is addressed in Chapter 3. The core of the research, which consists of the estimation and testing of several models of pulp prices and other variables, is described in Chapter 4. The forecasting performances of the different models are compared in Chapter 5. Chapter 6 concludes.