TY - JOUR
T1 - The economic importance of rare earth elements volatility forecasts
AU - Proelss, Juliane
AU - Schweizer, Denis
AU - Seiler, Volker
N1 - Funding Information:
We are grateful to Brian M. Lucey (Editor), Samuel A. Vigne (Associate Editor), and two anonymous referees for their many helpful comments. We thank Christopher F. Baum, Dirk Baur, Nicole Branger, Harald Elsner, Qingliang Fan, Yuanhua Feng, Sarah Forstinger, Tony Klein, Christian Peitz, Joerg Picard, Ivilina Popova, Marcel Prokopczuk, Malte Rieth, and Thomas Walther, as well as participants of the seminar at the John Molson School of Business (Montreal, Canada), the Commodity Markets Conference 2016 (Hannover, Germany), the Infiniti Conference on International Finance 2018 (Poznán, Poland), the 2018 OR Conference (Brussels, Belgium), the International Conference on Global Economy & Governance 2018 (Shanghai, China), and the Lixin 90th Anniversary Celebration Corporate Finance Workshop (Shanghai, China) for helpful comments and suggestions. Denis Schweizer gratefully acknowledges the financial support provided through the Manulife Professorship, and Volker Seiler gratefully acknowledges financial support provided by the XJTLU Research Conference Fund. Part of this research was conducted while Volker Seiler was Visiting Research Professor at New York University's Leonard N. Stern School of Business. Volker Seiler thanks Anthony Saunders and the NYU Stern School of Business for their hospitality.
Funding Information:
We are grateful to Brian M. Lucey (Editor), Samuel A. Vigne (Associate Editor), and two anonymous referees for their many helpful comments. We thank Christopher F. Baum, Dirk Baur, Nicole Branger, Harald Elsner, Qingliang Fan, Yuanhua Feng, Sarah Forstinger, Tony Klein, Christian Peitz, Joerg Picard, Ivilina Popova, Marcel Prokopczuk, Malte Rieth, and Thomas Walther, as well as participants of the seminar at the John Molson School of Business (Montreal, Canada), the Commodity Markets Conference 2016 (Hannover, Germany), the Infiniti Conference on International Finance 2018 (Pozn?n, Poland), the 2018 OR Conference (Brussels, Belgium), the International Conference on Global Economy & Governance 2018 (Shanghai, China), and the Lixin 90th Anniversary Celebration Corporate Finance Workshop (Shanghai, China) for helpful comments and suggestions. Denis Schweizer gratefully acknowledges the financial support provided through the Manulife Professorship, and Volker Seiler gratefully acknowledges financial support provided by the XJTLU Research Conference Fund. Part of this research was conducted while Volker Seiler was Visiting Research Professor at New York University's Leonard N. Stern School of Business. Volker Seiler thanks Anthony Saunders and the NYU Stern School of Business for their hospitality.
Publisher Copyright:
© 2019 Elsevier Inc.
PY - 2020/10
Y1 - 2020/10
N2 - 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.
AB - 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.
KW - ARFIMA
KW - Forecasting
KW - Fractional integration
KW - Long-memory
KW - Rare earth elements
UR - http://www.scopus.com/inward/record.url?scp=85061808222&partnerID=8YFLogxK
U2 - 10.1016/j.irfa.2019.01.010
DO - 10.1016/j.irfa.2019.01.010
M3 - Article
AN - SCOPUS:85061808222
SN - 1057-5219
VL - 71
JO - International Review of Financial Analysis
JF - International Review of Financial Analysis
M1 - 101316
ER -