A Real-Time Framework for Forecasting Metal Prices
Andrea Bastianin, Luca Rossini, Lorenzo Tonni

TL;DR
This paper presents a real-time forecasting framework for industrial metal prices, integrating macroeconomic and financial data, and evaluates various models' predictive performance over different horizons.
Contribution
It introduces a new real-time dataset and assesses the effectiveness of different forecasting models, highlighting the importance of timely macroeconomic indicators.
Findings
Medium-term forecasts are substantially more accurate than short-term predictions.
Manufacturing activity indicators improve forecast accuracy for aluminum and copper.
Futures and survey forecasts underperform compared to econometric models.
Abstract
This paper develops a real-time forecasting framework for the monthly real prices of four key industrial metals -- aluminum, copper, nickel, and zinc -- whose demand is rising due to their widespread use in manufacturing and low-carbon technologies. To replicate the information set available to forecasters in real time, we construct a new dataset combining daily financial variables with first-release macroeconomic indicators and use nowcasting techniques to address publication lags. Within this real-time environment, we evaluate the predictive accuracy of a broad set of univariate, multivariate, and factor-augmented models, comparing their performance with two industry benchmarks: survey expectations and futures-spot spread models. Results show that although short-run metal price movements remain difficult to predict, medium-term horizons display substantial forecastability. Indicators…
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Taxonomy
TopicsForecasting Techniques and Applications · Market Dynamics and Volatility · Financial Risk and Volatility Modeling
