Volatility forecasting of strategically linked commodity ETFs: gold-silver

References

• Abanomey, W.S. and Mathur, I., International portfolios with commodity futures and currency forward contracts. J. Invest., 2001, 10(3), 61–68.10.3905/joi.2001.319474
   Google Scholar
• Adrangi, B., Chatrath, A. and David, R.C., Price discovery in strategically-linked markets: The case of the gold-silver spread. Appl. Finan. Econ., 2000, 10(3), 227–234.10.1080/096031000331644
   Google Scholar
• Andersen, T. G. and Bollerslev, T., Deutsche mark-dollar volatility: Intraday activity patterns, macroeconomic announcements, and longer run dependencies. J. Financ., 1998, 53(1), 219–265.10.1111/0022-1082.85732
   Web of Science ®Google Scholar
• Andersen, T.G., Bollerslev, T., Diebold, F. and Labys, P., The distribution of realized exchange rate volatility. J. Amer. Stat. Assoc., 2001, 96(453), 42–55.10.1198/016214501750332965
   Web of Science ®Google Scholar
• Andersen, T.G., Bollerslev, T., and Diebold, F.X., Roughing it up: Including jump components in the measurement, modeling, and forecasting of return volatility. Rev. Econ. Stat., 2007, 89(4), 701–720.10.1162/rest.89.4.701
   Web of Science ®Google Scholar
• Andersen, T.G., Dobrev, D. and Schaumburg, E., Jump-robust volatility estimation using nearest neighbor truncation. J. Economet., 2012, 169(1), 75–93.10.1016/j.jeconom.2012.01.011
   Web of Science ®Google Scholar
• Antonakakis, N. and Kizys, R., Dynamic spillovers between commodity and currency markets. Int. Rew. Financ. Anal., 2015, 41(October), 303–319.10.1016/j.irfa.2015.01.016
   Web of Science ®Google Scholar
• Arouri, M.E.H., Hammoudeh, S., Lahiani, A. and Nguyen, D.K., Long memory and structural breaks in modeling the return and volatility dynamics of precious metals. Q Rev. Econ. Financ., 2012, 52(2), 207–218.10.1016/j.qref.2012.04.004
   Google Scholar
• Barndorff-Nielsen, O.E. and Shephard, N., Power and bipower variation with stochastic volatility and jumps. J. Financ. Economet., 2004, 2(1), 1–37.10.1093/jjfinec/nbh001
   Google Scholar
• Barndorff-Nielsen, O.E., Hansen, P.H., Lunde, A. and Shephard, N., Realized kernels in practice: Trades and quotes. Economet. J., 2009, 12(3), C1–C32.10.1111/ectj.2008.12.issue-3
   Web of Science ®Google Scholar
• Barndorff-Nielsen, O., Kinnebrock, S. and Shephard, N. Measuring downside risk–Realised semivariance. In Volatility and Time Series Econometrics: Essays in Honor of Robert F. Engle, edited by T. Bollerslev, J. Russell, and M. Watson, pp. 117–136, 2010 (Oxford University Press: Oxford).
   Google Scholar
• Barndorff-Nielsen, O.E., Hansen, P.H., Lunde, A. and Shephard, N., Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading. J. Economet., 2011, 162(2), 149–169.10.1016/j.jeconom.2010.07.009
   Web of Science ®Google Scholar
• Baruník, J. and Čech, F., On the modelling and forecasting multivariate realized volatility: Generalized heterogeneous autoregressive (GHAR) model. J. Forecasting, 2016. doi:10.1002/for.2423.
   Google Scholar
• Bates, J.M. and Granger, C.W.J., The combination of forecasts. Oper. Res. Quart., 1969, 20(4), 451–468.10.1057/jors.1969.103
   Google Scholar
• Batten, J.A., Ciner, C. and Lucey, B.M., The macroeconomic determinants of volatility in precious metals markets. Resour. Policy, 2010, 35(2), 65–71.10.1016/j.resourpol.2009.12.002
   Web of Science ®Google Scholar
• Batten, J.A., Ciner, C., Lucey, B.M. and Szilagyi, P.G., The structure of gold and silver spread returns. Quant. Finance, 2013, 13(4), 561–570.10.1080/14697688.2012.708777
   Web of Science ®Google Scholar
• Batten, J.A., Ciner, C. and Lucey, B.M., Which precious metals spill over on which, when and why? Some evidence. Appl. Econ. Lett., 2015, 22(6), 466–473.10.1080/13504851.2014.950789
   Web of Science ®Google Scholar
• Bauer, G.H. and Vorkink, K., Forecasting multivariate realized stock market volatility. J. Economet., 2011, 160(1), 93–101.10.1016/j.jeconom.2010.03.021
   Web of Science ®Google Scholar
• Becker, R. and Clements, A.E., Are combination forecasts of S&P 500 volatility statistically superior? Int. J. Forecasting, 2008, 24(1), 122–133.10.1016/j.ijforecast.2007.09.001
   Web of Science ®Google Scholar
• Beine, M., Lahaye, J., Laurent, S., Neely, Ch and Palm, F.C., Central bank intervention and exchange rate volatility, its continuous and jump components. Int. J. Financ. Econ., 2007, 12(2), 201–223.10.1002/(ISSN)1099-1158
   Web of Science ®Google Scholar
• Bernardi, M. and Catania, L. 2014. The model confidence set package for R. arXiv:1410.8504v1.
   Google Scholar
• Birkelund, O.H., Haugom, E., Molnár, P. and Opdal, M., A comparison of implied and realized volatility in the Nordic power forward market. Energ. Econ., 2015, 48(March), 288–294.10.1016/j.eneco.2014.12.021
   Web of Science ®Google Scholar
• Bollerslev, T., Patton, A.J. and Quaedvlieg, R., Exploiting the errors: A simple approach for improved volatility forecasting. J. Economet., 2016, 192(1), 1–18.10.1016/j.jeconom.2015.10.007
   Web of Science ®Google Scholar
• Borovkova, S. and Mahakena, D., News, volatility and jumps: The case of natural gas futures. Quant. Finance, 2015, 15(7), 1217–1242.10.1080/14697688.2014.986513
   Web of Science ®Google Scholar
• Caporin, M., Ranaldo, A. and Velo, G.G., Precious metals under the microscope: A high-frequency analysis. Quant. Finance, 2015, 15(5), 743–759.10.1080/14697688.2014.947313
   Web of Science ®Google Scholar
• Cheng, X. and Hansen, B.E., Forecasting with factor-augmented regression: A frequentist model averaging approach. J. Economet., 2015, 186(2), 280–293.10.1016/j.jeconom.2015.02.010
   Web of Science ®Google Scholar
• Chiriac, R. and Voev, V., Modelling and forecasting multivariate realized volatility. J. Appl. Economet., 2011, 26(6), 922–947.10.1002/jae.v26.6
   Web of Science ®Google Scholar
• Chkili, W., Hammoudeh, S. and Nguyen, D.K., Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory. Energ Econ., 2014, 41(January), 1–18.10.1016/j.eneco.2013.10.011
   Web of Science ®Google Scholar
• Christie-David, R., Chaudhry, M. and Koch, T.W., Do macroeconomics news releases affect gold and silver prices? J. Econ. Bus., 2000, 52(5), 405–421.10.1016/S0148-6195(00)00029-1
   Google Scholar
• Christoffersen, P., Jacobs, K. and Mimouni, K., Volatility dynamics for the S&P500: Evidence from realized volatility, daily returns, and option prices. Rev. Finan. Stud., 2010, 23(8), 3141–3189.10.1093/rfs/hhq032
   Web of Science ®Google Scholar
• Clemen, R.T., Combining forecasts: A review and annotated bibliography. Int. J. Forecasting, 1989, 5, 559–583.10.1016/0169-2070(89)90012-5
   Web of Science ®Google Scholar
• Corsi, F., A simple approximate long-memory model of realized volatility. J. Fin. Economet., 2009, 7(2), 174–196.
   Web of Science ®Google Scholar
• Diebold, F.X. and Mariano, R.S., Comparing predictive accuracy. J. Bus. Econ. Stat., 1995, 20(1), 134–144.
   Web of Science ®Google Scholar
• Dixit, A. K. and Pindyck, R. S. Investment under uncertainty, 1994 (Princeton University Press: Princeton, NJ).
   Google Scholar
• Elder, J., Miao, H. and Ramchander, S., Impact of macroeconomic news on metal futures. J. Bank Financ., 2012, 36(1), 51–65.10.1016/j.jbankfin.2011.06.007
   Web of Science ®Google Scholar
• Erb, C. and Harvey, C., The strategic and tactical value of commodity futures. Financ. Anal. J., 2006, 62(2), 69–97.10.2469/faj.v62.n2.4084
   Web of Science ®Google Scholar
• Fleming, J., Kirby, C. and Ostdiek, B., The economic value of volatility timing using “realized” volatility. J. Financ. Econ., 2003, 67(3), 473–509.10.1016/S0304-405X(02)00259-3
   Web of Science ®Google Scholar
• Fuertes, A.-M., Miffre, J. and Rallis, G., Tactical allocation in commodity futures markets: Combining momentum and term structure signals. J. Bank Financ., 2010, 34(10), 2530–2548.10.1016/j.jbankfin.2010.04.009
   Web of Science ®Google Scholar
• Georgiev, G., Benefits of commodity investment. J. Altern. Invest., 2001, 4(1), 40–48.10.3905/jai.2001.318997
   Google Scholar
• GFMS, Gold fields minerals gold survey 2013, 2014.
   Google Scholar
• Golosnoy, V. and Okhrin, Y., Using information quality for volatility model combinations. Quant. Finance, 2015, 15(6), 1055–1073.10.1080/14697688.2012.739728
   Web of Science ®Google Scholar
• Gorton, G. and Rouwenhorst, K.G., Facts and fantasies about commodity futures. Financ. Anal. J., 2006, 62(2), 47–68.10.2469/faj.v62.n2.4083
   Web of Science ®Google Scholar
• Hammoudeh, S. and Yuan, Y., Metal volatility in presence of oil and interest rate shocks. Energ. Econ., 2008, 30(2), 606–620.10.1016/j.eneco.2007.09.004
   Web of Science ®Google Scholar
• Hammoudeh, S., Malik, F. and McAleer, M., Risk management of precious metals. Q Rev. Econ. Financ., 2011, 51(4), 435–441.10.1016/j.qref.2011.07.002
   Google Scholar
• Hammoudeh, S., Santos, P.A. and Al-Hassan, A., Downside risk management and VaR-based optimal portfolios for precious metals, oil and stocks. North Amer. J. Econ. Financ., 2013, 25(August), 318–334.10.1016/j.najef.2012.06.012
   Web of Science ®Google Scholar
• Hansen, P.R. and Lunde, A., Realized variance and market microstructure noise. J. Bus. Econ. Stat., 2006, 24(2), 127–161.10.1198/073500106000000071
   Web of Science ®Google Scholar
• Hansen, P.R., Lunde, A. and Nason, J.M., The model confidence set. Econometrica, 2011, 79(2), 453–497.
   Web of Science ®Google Scholar
• Haugom, E., Langeland, H., Molnár, P. and Westgaard, S., Forecasting volatility of the U.S. oil market. J. Bank Financ., 2014, 47, 1–14.10.1016/j.jbankfin.2014.05.026
   Web of Science ®Google Scholar
• Hillier, D., Draper, P. and Faff, R., Do precious metals shine? An investment perspective. Financ Anal. J., 2006, 62(2), 98–106.
   Web of Science ®Google Scholar
• Ivanov, S.I., The influence of ETFs on the price discovery of gold, silver and oil. J. Econ. Financ., 2013, 37(3), 453–462.10.1007/s12197-011-9205-8
   Google Scholar
• Kim, H.H. and Swanson, N.R. Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence, J. Economet., 2014, 178(part 2, January), 353–367.
   Web of Science ®Google Scholar
• Lucey, B.M., Larkin, C. and O’Connor, F.A., London or New York: Where and when does the gold price originate? Appl. Econ. Lett., 2013, 20, 813–817.10.1080/13504851.2012.748175
   Web of Science ®Google Scholar
• Lucey, B.M., Larkin, C. and O’Connor, F., Gold markets around the world – Who spills over what, to whom, when? Appl. Econ. Lett., 2014, 21, 887–892.10.1080/13504851.2014.896974
   Web of Science ®Google Scholar
• Miffre, J. and Rallis, G., Momentum strategies in commodity futures markets. J. Bank Financ., 2007, 31(6), 1863–1886.10.1016/j.jbankfin.2006.12.005
   Web of Science ®Google Scholar
• Murray, S., LOCO London liquidity survey. Alchemist, 2011, 63, 9–10.
   Google Scholar
• Nijman, T.E. and Swinkels, L.A.P., Strategic and tactical allocation to commodities for retirement savings schemes. CentER Discussion Paper No. 2003-20. Available at SSRN: http://ssrn.com/abstract=425320 or doi:10.2139/ssrn.425320 (26 February 2003).
   Google Scholar
• O’Connor, F.A., Lucey, B.M., Batten, J.A. and Baur, D.G., The financial economics of gold – A survey. Int. Rev. Finan. Anal., 2015, 41(October), 186–205.10.1016/j.irfa.2015.07.005
   Web of Science ®Google Scholar
• Patton, A.J., Volatility forecast comparison using imperfect volatility proxies. J. Economet., 2011, 160(1), 246–256.10.1016/j.jeconom.2010.03.034
   Web of Science ®Google Scholar
• Patton, A.J. and Sheppard, K., Good volatility, bad volatility: Signed jumps and the persistence of volatility. Rev. Econ. Stat., 2015, 97(3), 683–697.
   Web of Science ®Google Scholar
• Reboredo, J.C. and Ugolini, A., Downside/upside price spillovers between precious metals: A vine copula approach. North Amer. J. Econ. Financ., 2015, 34(November), 84–102.10.1016/j.najef.2015.08.001
   Web of Science ®Google Scholar
• Sévi, B., Forecasting the volatility of crude oil futures using intraday data. Eur. J Oper. Res., 2014, 235(3), 643–659.10.1016/j.ejor.2014.01.019
   Web of Science ®Google Scholar
• Smales, L.A., News sentiment in the gold futures market. J. Bank Financ., 2014, 49(December), 275–286.10.1016/j.jbankfin.2014.09.006
   Web of Science ®Google Scholar
• Timmermann, A. Forecast combinations. In Handbook of Economic Forecasting, edited by G. Elliott, C. Granger, and A. Timmermann, vol. 1, pp. 136–196, 2006 (North Holland: Amsterdam).
   Google Scholar
• Žikeš, F. and Baruník, J., Semi-parametric conditional quantile models for financial returns and realized volatility. J. Financ. Economet., 2016, 14(1), 185–226.
   Web of Science ®Google Scholar