Login | Register

The economic importance of rare earth elements volatility forecasts


The economic importance of rare earth elements volatility forecasts

Proelss, Juliane ORCID: https://orcid.org/0000-0002-9467-8220, Schweizer, Denis ORCID: https://orcid.org/0000-0001-9115-1178 and Seiler, Volker (2019) The economic importance of rare earth elements volatility forecasts. International Review of Financial Analysis . ISSN 10575219 (In Press)

Text (application/pdf)
Proelss-2019.pdf - Accepted Version
Restricted to Repository staff only until 1 February 2021.
Available under License Spectrum Terms of Access.

Official URL: http://dx.doi.org/10.1016/j.irfa.2019.01.010


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.

Divisions:Concordia University > John Molson School of Business > Finance
Item Type:Article
Authors:Proelss, Juliane and Schweizer, Denis and Seiler, Volker
Journal or Publication:International Review of Financial Analysis
Date:1 February 2019
  • Manulife Professorship
  • XJTLU Research Conference Fund
Digital Object Identifier (DOI):10.1016/j.irfa.2019.01.010
Keywords:ARFIMA; Fractional integration; Long-memory; Forecasting; Rare earth elements
ID Code:984993
Deposited On:12 Feb 2019 23:39
Last Modified:12 Feb 2019 23:39


Akaike, H. (1974): A new look at the statistical model identification, in: IEEE Transactions on Automatic Control, Vol. 19 (6), 716-723.

Alizadeh, S., Brandt, M. W., and Diebold, F. X. (2002): Range-based estimation of stochastic volatility models, in: Journal of Finance, Vol. 57 (3), 1047-1091.

Andersen, T. G., Bollerslev, T., and Diebold, F. X. (2007): Roughing it up: Including jump components in the measurement, modeling, and forecasting of return volatility, in: Review of Economics and Statistics, Vol. 89 (4), 701-720.

Antonakakis, N., Cunado, J., Filis, G., Gabauer, D., and Perez de Gracia, F. (2018): Oil volatility, oil and gas firms and portfolio diversification, in: Energy Economics, Vol. 70, 499-515.

Antonakakis, N., and Kizys, R. (2015): Dynamic spillovers between commodity and currency markets, in: International Review of Financial Analysis, Vol. 41, 303-319.

Apergis, E., and Apergis, N. (2017): The role of rare earth prices in renewable energy consumption: The actual driver for a renewable energy world, in: Energy Economics, Vol. 62, 33-42.

Arouri, M. E. H., Jouini, J., and Nguyen, D. K. (2012): On the impacts of oil price fluctuations on European equity markets: Volatility spillover and hedging effectiveness, in: Energy Economics, Vol. 34 (2), 611-617.

Arsimendi, J. C., Back, J., Prokopczuk, M., Paschke, R., and Rudolf, M. (2016): Seasonal stochastic volatility: Implications for the pricing of commodity options, in: Journal of Banking & Finance, Vol. 66, 53-65.

Auer, B. R. (2016): On time-varying predictability of emerging stock market returns, in: Emerging Markets Review, Vol. 27, 1-13.

Back, J., Prokopczuk, M., and Rudolf, M. (2013): Seasonality and the valuation of commodity options, in: Journal of Banking & Finance, Vol. 37 (2), 273-290.

Bailey Grasso, V. (2013): Rare earth elements in national defense: Background, oversight issues, and options for congress, Congressional Research Service Report R41744, http://www.fas.org/sgp/crs/natsec/R41744.pdf.

Baillie, R. T. (1996): Long memory processes and fractional integration in econometrics, in: Journal of Econometrics, Vol. 73 (1), 5-59.

Barkoulas, J., Labys, W. C., and Onochie, J. (1997): Fractional dynamics in international commodity prices, in: Journal of Futures Markets, Vol. 17 (2), 161-189.

Batten, J. A., Ciner, C., and Lucey, B. M. (2010): The macroeconomic determinants of volatility in precious metals markets, in: Resources Policy, Vol. 35 (2), 65-71.

Baur, D. G. (2014): Gold mining companies and the price of gold, in: Review of Financial Economics, Vol. 23 (4), 174-181. BGR (2014): Volatility monitoring, http://www.deutscherohstoffagentur.de/EN/Themen/Min_rohstoffe/Produkte/produkte_node_en.html?tab=Commo dity+prices.

Binnemans, K., Jones, P. T., Blanpain, B., Van Gerven, T., Yang, Y., Walton, A., and Buchert, M. (2013): Recycling of rare earths: A critical review, in: Journal of Cleaner Production, Vol. 51, 1-22.

Bloomberg News (2014): Chinese exchange starts rare earths trading trial today, http://www.bloomberg.com/news/2014-03-27/chinese-exchange-starts-rare-earths-tradingtrial-today.html.

Boldanov, R., Degiannakis, S., and Filis, G. (2016): Time-varying correlation between oil and stock market volatilities: Evidence from oil-importing and oil-exporting countries, in: International Review of Financial Analysis, Vol. 48, 209-220.

Bollerslev, T. (1986): Generalized autoregressive conditional heteroscedasticity, in: Journal of Econometrics, Vol. 31 (3), 307-327.

Bollerslev, T., Chou, R. Y., and Kroner, K. F. (1992): ARCH modeling in finance, in: Journal of Econometrics, Vol. 52 (1-2), 5-59.

Bollerslev, T., Patton, A. J., and Quaedvlieg, R. (2016): Exploiting the errors: A simple approach for improved volatility forecasting, in: Journal of Econometrics, Vol. 192 (1), 1-18.

Bouri, E. (2015): Oil volatility shocks and the stock markets of oil-importing MENA economies: A tale from the financial crisis, in: Energy Economics, Vol. 51, 590-598.

Bouri, E., de Boyrie, M. E., and Pavlova, I. (2017): Volatility transmission from commodity markets to sovereign CDS spreads in emerging and frontier countries, in: International Review of Financial Analysis, Vol. 49, 155-165.

Boyer, M. M., and Filion, D. (2007): Common and fundamental factors in stock returns of Canadian oil and gas companies, in: Energy Economics, Vol. 29 (3), 428-453.

Brous, P., Ince, U., and Popova, I. (2010): Volatility forecasting and liquidity: Evidence from individual stocks, in: Journal of Derivatives & Hedge Funds, Vol. 16 (2), 144-159.

Cajueiro, D. O., Gogas, P., and Tabak, B. M. (2009): Does financial market liberalization increase the degree of market efficiency? The case of the Athens stock exchange, in: International Review of Financial Analysis, Vol. 18 (1-2), 50-57.

Cajueiro, D. O., and Tabak, B. M. (2005): Testing for time-varying long-range dependence in volatility for emerging markets, in: Physica A, Vol. 346 (3-4), 577-588.

Caporale, G. M., and Gil-Alana, L. A. (2013): Long memory and fractional integration in high frequency data on the US dollar/British pound spot exchange rate, in: International Review of Financial Analysis, Vol. 29, 1-9.

Charlier, C., and Guillou, S. (2014): Distortion effects of export quota policy: An analysis of the China-Raw Materials dispute, in: China Economic Review, Vol. 31, 320-338.

Chernov, M. (2007): On the role of risk premia in volatility forecasting, in: Journal of Business & Economic Statistics, Vol. 25 (4), 411-426.

Cheung, Y.-W. (1993): Long memory in foreign-exchange rates, in: Journal of Business & Economic Statistics, Vol. 11 (1), 93-101.

Chortareas, G., Jiang, Y., and Nankervis, J. C. (2011): Forecasting exchange rate volatility using high-frequency data: Is the euro different? in: International Journal of Forecasting, Vol. 27 (4), 1089-1107.

Corsi, F. (2009): A simple approximate long-memory model of realized volatility, in: Journal of Financial Econometrics, Vol. 7 (2), 174-196.

Diebold, F. X., and Rudebusch, G. D. (1989): Long memory and persistence in aggregate output, in: Journal of Monetary Economics, Vol. 24 (2), 189-209.

Doran, J. S., and Ronn, E. I. (2008): Computing the market price of volatility risk in the energy commodity markets. Journal of Banking & Finance, Vol. 32 (12), 2541-2552.

Duffee, G. R. (1995): Stock returns and volatility: A firm-level analysis, in: Journal of Financial Economics, Vol. 37 (3), 399-420. Engle, R. (2001): GARCH 101: The use of ARCH/GARCH models in applied econometrics, in: Journal of Economic Perspectives, Vol. 15 (4), 157-168.

Faff, R., and Chan, H. (1998): A multifactor model of gold industry stock returns: Evidence from the Australian equity market, in: Applied Financial Economics, Vol. 8 (1), 21-28.

Fama, E. F. (1963): Mandelbrot and the stable Paretian hypothesis, in: Journal of Business, Vol. 36 (4), 420-429.

Fang, H., Lai, K. S., and Lai, M. (1994): Fractal structure in currency futures price dynamics, in: Journal of Futures Markets, Vol. 14 (2), 169-181.

Fernandez, V. (2010): Commodity futures and market efficiency: A fractional integrated approach, in: Resources Policy, Vol. 35, 276-282.

Fernandez-Perez, A., Fuertes, A.-M., and Miffre, J. (2016): Is idiosyncratic volatility priced in commodity futures markets? In: International Review of Financial Analysis, Vol. 46, 219- 226.

Forsberg, L., and Ghysels, E. (2007): Why do absolute returns predict volatility so well? In: Journal of Financial Econometrics, Vol. 5 (1), 31-67.

Gallant, A. R., Hsu, C.-T., and Tauchen, G. (1999): Using daily range data to calibrate volatility diffusions and extract the forward integrated variance, in: Review of Economics & Statistics, Vol. 81 (4), 617-631.

Garman, M. B., and Klass, M. J. (1980): On the estimation of security price volatilities from historical data, in: Journal of Business, Vol. 53 (1), 67-78.

Geweke, J., and Porter-Hudak, S. (1983): The estimation and application of long memory time series models, in: Journal of Time Series Analysis, Vol. 4 (4), 221-238.

Ghysels, E., Santa-Clara, P., and Valkanov, R. (2006): Predicting volatility: Getting the most out of return data sampled at different frequencies, in: Journal of Econometrics, Vol. 131 (1-2), 59- 95.

Goonan, T. G. (2011): Rare earth elements – end use and recyclability, U.S. Geological Survey Scientific Investigations Report 2011 – 5094, http://pubs.usgs.gov/sir/2011/5094/.

Granger, C. W. J., and Joyeux, R. (1980): An introduction to long-memory time series models and fractional differencing, in: Journal of Time Series Analysis, Vol. 1 (1), 15-29.

Greene, M. T., and Fielitz, B. D. (1977): Long-term dependence in common stock returns, in: Journal of Financial Economics, Vol. 4 (3), 339-349.

Harris, R. D. F., and Nguyen, A. (2013): Long memory conditional volatility and asset allocation, in: International Journal of Forecasting, Vol. 29 (2), 258-273.

Haugom, E., Langeland, H., Molnár, P., and Westgaard, S. (2014): Forecasting volatility of the U.S. oil market, in: Journal of Banking and Finance, Vol. 47, 1-14.

Hayes-Labruto, L., Schillebeeckx, S. J. D., Workman, M., and Shah, N. (2013): Contrasting perspectives on China’s rare earths policies: Reframing the debate through a stakeholder lens, in: Energy Policy, Vol. 63, 55-68.

Hedrick, J. B. (2004): Rare earths in selected U.S. defense applications, 40th Forum on the Geology of Industrial Metals, Bloomington, May 2-7, 2004, http://www.usmagneticmaterials.com/documents/RARE-EARTHS-IN-US-DEFENSE-APPSHendrick.pdf.

Hosking, J. R. M. (1981): Fractional differencing, in: Biometrika, Vol. 68 (1), 165-176.

Hull, M., and McGroarty, F. (2014): Do emerging markets become more efficient as they develop? Long memory persistence in equity indices, in: Emerging Markets Review, Vol. 18, 45-16.

Humphries, M. (2010): Rare earth elements: The global supply chain, Congressional Research Service Report R41347, https://digital.library.unt.edu/ark:/67531/metadc31365/m1/1/high_res_d/R41347_2010Sep30. pdf.

Hurst, C. (2010): China’s rare earth elements industry: What can the West learn? Institute for the Analysis of Global Security (IAGS), http://www.iags.org/rareearth0310hurst.pdf.

Jiang, G. J., and Tian, Y. S. (2005): The model-free implied volatility and its information content, in: Review of Financial Studies, Vol. 18 (4), 1305-1342.

Kumar, R., Sarin, A., and Shastri, K. (1998): The impact of options trading on the market quality of the underlying security: An empirical analysis, in: Journal of Finance, Vol. 53 (2), 717-732.

Labys, W. C. (2006): Modeling and forecasting primary commodity prices, Routledge.

Lau, M. C. K., Vigne, S. A., Wang, S., and Yarovaya, L. (2017): Return spillovers between white precious metal ETFs: The role of oil, gold, and global equity, in: International Review of Financial Analysis, Vol. 52, 316-332.

Ledolter, J., and Abraham, B. (1981): Parsimony and its importance in time series forecasting, in: Technometrics, Vol. 23 (4), 411-414.

Li, D., Nishimura, Y., and Men, M. (2016): Why the long-term auto-correlation has not been eliminated by arbitragers: Evidence from NYMEX, in: Energy Economics, Vol. 59, 167-178.

Ljung, G. M., and Box, G. E. P. (1978): On a measure of lack of fit in time series models, in: Biometrika, Vol. 65 (2), 297-303.

Lucey, B. M., Vigne, S. A., Ballester, L., Barbopoulos, L., Brzeszczynski, J., Garachno, O., Dimic, N., Fernandez, V., Gogolin, F., González-Urteaga, A., Goodell, J. W., Helbing, P., Ichev, R., Kearney, F., Laing, E., Larkin, C. J., Lindblad, A., Lončarski, I., Ly, K. C., Marinč, M., McGee, R. J., McGroarty, F., Neville, C., O’Hagan-Luff, M., Piljak, V., Sevic, A., Sheng, X., Stafylas, D., Urquhart, A., Versteeg, R., Vu, A. N., Wolfe, S., Yarovaya, L., and Zaghini, A. (2018): Future directions in international financial integration – A crowdsourced perspective, in: International Review of Financial Analysis, Vol. 55, 35-49.

Malik, F., and Ewing, B. T. (2009): Volatility transmission between oil prices and equity sector returns, in: International Review of Financial Analysis, Vol. 18 (3), 95-100.

Malik, F., and Hammoudeh, S. (2007): Shock and volatility transmission in the oil, US and Gulf equity markets, in: International Review of Economics & Finance, Vol. 16 (3), 357-368.

Mancheri, N. A., Sprecher, B., Bailey, G., Ge, J., and Tukker, A. (2019): Effect of Chinese policies on rare earth supply chain resilience, in: Resources, Conservation & Recycling, Vol. 142, 101-112.

Mandelbrot, B. B. (1967): Some noises with spectrum, a bridge between direct current and white noise, in: IEEE Transactions on Information Theory, Vol. 13 (2), 289-298.

Mandelbrot, B. B. (1971): When can price be arbitraged efficiently? A limit to the validity of the random walk and martingale models, in: Review of Economics & Statistics, Vol. 53 (3), 225- 236.

Mandelbrot, B. B. (1972): Statistical methodology for nonperiodic cycles: From the covariance to R/S analysis, in: Annals of Economic and Social Measurement, Vol. 1 (3), 259-290.

Mandelbrot, B. (1977): Fractals: Form, chance, and dimension, Freeman: San Francisco. McCarty, D., and Casey, S. (2015): Bankruptcy as rare-earth prices drop, Bloomberg, http://www.bloomberg.com/news/articles/2015-06-25/molycorp-files-for-bankruptcyproposes-debt-restructuring-plan.

McLeod, A. I. (1993): Parsimony, model adequacy and periodic correlation in time series forecasting, in: International Statistical Review, Vol. 61 (3), 387-393.

Mensi, W., Beljid, M., Boubaker, A., and Managi, S. (2013): Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold, in: Economic Modelling, Vol. 32 (1), 15-22.

Molnár, P. (2012): Properties of range-based volatility estimators, in: International Review of Financial Analysis, Vol. 23, 20-29.

Monahan, K. (2012). China’s rare-earth policies erode technology profits. Bloomberg Government Study.

Morrison, W. M., and Tang, R. (2012): China’s rare earth industry and export regime: Economic and trade implications for the United States, Congressional Research Service Report R42510, http://www.fas.org/sgp/crs/row/R42510.pdf.

Müller, M., Schweizer, D., and Seiler, V. (2016): Wealth effects of rare earth prices and China’s rare earth elements policy, in: Journal of Business Ethics, Vol. 138 (4), 627-648.

Nieto, A., Guelly, K., and Kleit, A. (2013): Addressing criticality for rare earth elements in petroleum refining: The key supply factors approach, in: Resources Policy, Vol. 38 (4), 496-503.

Opdyke, J. D. (2008): Comparing Sharpe ratios: So where are the p-values? in: Journal of Asset Management, Vol. 8 (5), 308-336.

Phillips, P. C. B. (1999): Discrete Fourier transforms of fractional processes, Cowles Foundation Discussion Paper, Yale University.

Phillips, P. C. B. (2007): Unit root log periodogram regression, in: Journal of Econometrics, Vol. 138 (1), 104-124.

Pong, S., Shackleton, M. B., Taylor, S. J., and Xu, X. (2004): Forecasting currency volatility: A comparison of implied volatilities and AR(FI)MA models, in: Journal of Banking & Finance, Vol. 28 (10), 2541-2563.

Proelss, J., and Schweizer, D. (2008): Efficient frontier of commodity portfolios, in: Fabozzi, F., Füss, R., and Kaiser, D. G. (Eds.): The Handbook of Commodity Investing, John Wiley and Sons, New Jersey, 454-478.

Proelss, J., Schweizer, D., and Seiler, V. (2018): Do announcements of WTO dispute resolution cases matter? Evidence from the rare earth elements market, in: Energy Economics, Vol. 73, 1-23.

Reuters (2015): Rare earths miner Molycorp files for Chapter 11 bankruptcy, http://www.reuters.com/article/2015/06/25/us-molycorp-bankruptcyidUSKBN0P50QS20150625.

Sadorsky, P. (2012): Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies, in: Energy Economics, Vol. 34 (1), 248-255.

Schwarz, G. E. (1978): Estimating the dimension of a model, in: Annals of Statistics, Vol. 6 (2), 461- 464.

Shen, H. (2014): China to add rare earths to the Shanghai Futures Exchange, http://investorintel.com/rare-earth-intel/china-plans-add-rare-earths-shanghai-futuresexchange/.

Shih, J.-S., Linn, J., Brennan, J. D., Macauley, M. K., and Preonas, L. (2012): The supply chain and industrial organization of rare earth materials. Implications of the U.S. wind energy sector, RFF Report, http://www.rff.org/files/sharepoint/WorkImages/Download/RFF-RptShih%20etal%20RareEarthsUSWind.pdf.

Silvennoinen, A., and Thorp, S. (2013): Financialization, crisis and commodity correlation dynamics, in: Journal of International Financial Markets, Institutions & Money, Vol. 24 (1), 42-65.

Sowell, F. (1992): Maximum likelihood estimation of stationary univariate fractionally integrated time series models, in: Journal of Econometrics, Vol. 53 (1-3), 165-188.

Tabak, B. M., and Cajueiro, D. O. (2007): Are the crude oil markets becoming weakly efficient over time? A test for time-varying long-range dependence in prices and volatility, in: Energy Economics, Vol. 29 (1), 28-36.

TMR (Technology Metals Research) (2015): TMR advanced rare-earth projects index, http://www.techmetalsresearch.com/metrics-indices/tmr-advanced-rare-earth-projects-index/.

Tufano, P. (1998): The determinants of stock price exposure: Financial engineering and the gold mining industry, in: Journal of Finance, Vol. 53 (3), 1015-1052.

Van Gosen, B. S., Verplanck, P. L., Long, K. R., Gambogi, J., and Seal, R. R. II (2014): The rareearth elements – Vital to modern technologies and lifestyles, U.S. Geological Survey Fact Sheet 2014 – 3078, http://pubs.usgs.gov/fs/2014/3078/.

Xingguo, L., Shihua, Q., and Ye, Z. (2016): The information content of implied volatility and jumps in forecasting volatility: Evidence from the Shanghai gold futures market, in: Finance Research Letters, Vol. 19, 105-111

Yip, P. S., Brooks, R., and Do, H. X. (2017): Dynamic spillover between commodities and commodity currencies during United States Q.E., in: Energy Economics, Vol. 66, 399-410.

Zhang, K., Kleit, A. N., and Nieto, A. (2017): An economics strategy for criticality – Application to rare earth element Yttrium in new lighting technology and its sustainable availability, in: Renewable and Sustainable Energy Reviews, Vol. 77, 899-915.
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Back to top Back to top