The advantage of having timely updated forecasts

Every month, price forecasts for over 250 raw materials and semi-finished goods are updated on the PricePedia platform^[1] in order to reflect the latest events and information available. Forecasts cover products from major commodity categories such as Energy, Chemicals, Plastics and Elastomers, Ferrous Metals, and Non-Ferrous Metals, aiming to encompass all those listed in the EU Customs section by the end of 2024.

To better understand the benefits of having continuously updated commodity price forecasts for the procurement office, a description of the components comprising the PricePedia forecasting scenario can be useful.

The Elements of the PricePedia Forecasting Scenario

PricePedia forecasting scenarios are based on dynamic specification models, which combine a theoretical foundation with statistical tools. The former helps understand the long-term dynamics between the forecasted price and its determinants, while the latter account for short-term fluctuations^[2].

At the core of the scenario, alongside the models, exogenous variables are considered, which are determined outside the model. Particularly, different scenarios for exogenous variables are derived by PricePedia, referencing forecasts from specialized institutions such as the World Bank, the European Central Bank, the Energy Information Administration (EIA), etc. Major exogenous variables include oil prices and prices of key commodities traded on financial markets like copper and aluminum. Exchange rates and inflation in major world economies also count as exogenous variables. The global industrial cycle plays a crucial role in shaping future price scenarios, directly produced by PricePedia and forecasted based on the consensus regarding the economic cycle of major world economies.

Once the baseline scenario, determined by exogenous variables, is constructed, forecasting a product's price involves identifying the econometric model, i.e., which variables contribute to explaining its variations and estimating the values of their parameters.

Uncertainty in a scenario stems from two sources. Firstly, the realization of the exogenous variable forecast scenario. Secondly, the quality of the estimated structural model. This generates two types of risks: one related to exogenous variables and the other to the model itself.
In the PricePedia forecasting scenario, efforts to minimize "exogenous" risks primarily involve considering the baseline scenario—the one deemed most probable by various analyzed sources. This means the price forecast for an individual commodity represents the value with the highest probability of occurrence, constrained by the realization of the exogenous variable scenario.

The advantage of timely updates

In a historical phase characterized by numerous elements potentially leading to significant changes in the exogenous variables underlying the scenario, factors include monetary policy choices and their timing, geopolitical conflicts, trade tensions, industrial policies, global value chain reorganization, and economic cycles in different world regions. Even a change in one of these elements can alter the evolution profile of one or more exogenous variables.
Thus, translating changes in exogenous variables—often the result of extensive debates among specialists—into their effects on all commodity prices within the PricePedia forecasting scenario becomes crucial.

Conclusions

In a global market marked by increasing volatility and uncertainty, having a monthly updated commodity price forecasting scenario like that developed by PricePedia can provide not only a reduction in risks associated with price fluctuations but also a competitive advantage derived from making quick, data-driven decisions.