The economic importance of rare earth elements volatility forecasts

Juliane Proelss, Denis Schweizer and Volker Seiler
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Juliane Proelss: John Molson School of Business - Concordia University [Montreal]
Denis Schweizer: John Molson School of Business - Concordia University [Montreal]

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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.

Keywords: ARFIMA; Fractional integration; Long-memory; Forecasting; Rare earth elements (search for similar items in EconPapers)
Date: 2019-02
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Citations: View citations in EconPapers (4)

Published in International Review of Financial Analysis, 2019, pp.101316. ⟨10.1016/j.irfa.2019.01.010⟩

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Journal Article: The economic importance of rare earth elements volatility forecasts (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02983233

DOI: 10.1016/j.irfa.2019.01.010

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