Dynamic Price Relationships Among EV Battery Metals: A Cointegration and VECM Approach with Trading Strategy Applications

Abstract

As our planet embraces clean energy as a necessary alternative to fossil fuels, electric vehicles (EVs) are seeing an exponential rise in popularity. To meet the ever-increasing demand, the EV industry requires large and growing amounts of metals such as lithium, cobalt, nickel, manganese, and copper to make up the batteries at the heart of its cars. This study uses cointegration to examine the long-term relationships between the prices of these metals, as well as the changes in those relationships over time. It utilizes Vector Error Correction Models (VECMs) to forecast future price movements, achieving predictions with a statistically significant degree of directional accuracy. The predictions inform a trading strategy around the metals, which yields a positive Sharpe Ratio. These results build on related work in the field of EV battery metals price modeling and provide a foundation for future analysis, both quantitative and qualitative.