The economic importance of rare earth elements volatility forecasts |
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| Affiliation: | 1. Concordia University, John Molson School of Business Building, 1450 Guy, Montreal, Quebec H3H 0A1, Canada;2. Concordia University, John Molson School of Business Building, 1450 Rue Guy, Montreal, Quebec H3G 1M8, Canada;3. Xi''an Jiaotong-Liverpool University, International Business School Suzhou (IBSS), No 111 Ren''ai Road, Suzhou Dushu Lake Higher Education Town, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China;1. Concordia University, John Molson School of Business Building, 1450 Guy, Montreal, Quebec H3H 0A1, Canada;2. Xi''an Jiaotong-Liverpool University, International Business School Suzhou (IBSS), No 111 Ren''ai Road, Suzhou Dushu Lake Higher Education Town, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, PR China |
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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. |
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