The Economic Importance of Rare Earth Elements Volatility Forecasts

Abstract

We compare the suitability of short-memory (ARMA models), long-memory (ARFIMA models), and a GARCH model to describe the volatility of rare earth elements (REEs). We find strong support for the existence of long-memory effects. A simple long-memory ARFIMA(0,𝑑,0) base model shows generally superior accuracy both in- and out-of-sample, and is robust for various subsamples and estimation windows. Volatility forecasts produced by the base model also convey material forward-looking information for companies in the REE industry. Thus, an active trading strategy based on REE volatility forecasts for these companies significantly outperforms a passive buy-and-hold strategy on both, an absolute and a risk-adjusted return basis.

Keywords: ARFIMA, Fractional Integration, Long Memory, Forecasting, Rare Earth Elements

JEL Classification: C14, C22, Q02, Q31