Nickel and Cobalt Price Volatility Forecasting Using a Self-Attention-Based Transformer Model

Abstract

Both Nickel and Cobalt have been extensively used in cutting-edge technologies, such as electric vehicle battery manufacturing, stainless steel, and special alloys production. As governments focus on greener solutions for areas such as transportation and energy generation, both metals are increasingly used for energy storage purposes. However, their price uncertainty makes for an interesting case in the modern economy. This study focuses on the price volatility forecasting of Nickel and Cobalt using ANN (Artificial Neural Network) built on a special class of Transformer models used for multi-step ahead forecasts. Our results suggest that the given model is only slightly better in predictive accuracy compared to traditional sequential deep learning models such as BiLSTM (Bidirectional Long Short-Term Memory) and GRUs (gated recurrent units). Moreover, our findings also show that, like conventional approaches, in-sample behavior does not guarantee out-of-sample behavior. The given study could be utilized by industry participants for an inquiry into new and efficient ways to forecast and identify temporal-based structural patterns in commodity-based time series.

Keywords

• commodity prices
• self attention
• volatility forecasting
• deep learning approaches
• transformer models