The economic importance of rare earth elements volatility forecasts

Juliane Proelss, Denis Schweizer*, Volker Seiler

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)

Abstract

We compare the suitability of short-memory models (ARMA), long-memory models (ARFIMA), 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) baseline 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 baseline model also convey material forward-looking information for companies in the REEs 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.

Original languageEnglish
Article number101316
JournalInternational Review of Financial Analysis
Volume71
DOIs
Publication statusPublished - Oct 2020

Keywords

  • ARFIMA
  • Forecasting
  • Fractional integration
  • Long-memory
  • Rare earth elements

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