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The economic importance of rare earth elements volatility forecasts

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.
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https://hal-normandie-univ.archives-ouvertes.fr/hal-02983233
Contributor : Volker Seiler <>
Submitted on : Thursday, October 29, 2020 - 3:05:55 PM
Last modification on : Friday, October 30, 2020 - 3:27:16 AM

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Juliane Proelss, Denis Schweizer, Volker Seiler. The economic importance of rare earth elements volatility forecasts. International Review of Financial Analysis, Elsevier, 2019, pp.101316. ⟨10.1016/j.irfa.2019.01.010⟩. ⟨hal-02983233⟩

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