The economic importance of rare earth elements volatility forecasts

Juliane Proelss, Denis Schweizer and Volker Seiler

International Review of Financial Analysis, 2020, vol. 71, issue C

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)
JEL-codes: C14 C22 Q02 Q31 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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Working Paper: The economic importance of rare earth elements volatility forecasts (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:71:y:2020:i:c:s1057521918306148

DOI: 10.1016/j.irfa.2019.01.010

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