Yaya, OlaOluwa S. and Ogbonna, Ahamuefula E. and Adesina, Ayobami O. and Alobaloke, Kafayat and Vo, Xuan Vinh (2022): Time-variation between metal commodities and oil, and the impact of oil shocks: GARCH-MIDAS and DCC-MIDAS analyses. Forthcoming in: Resources Policy
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Abstract
Extant literature establishes co-movements among commodity (metal and oil) prices; whereas oil price/shocks aggregate, as a lone predictor, has relative predictability for most financial assets. We assess the predictability of Baumeister and Hamilton's (2019) decomposed oil shocks (economic activity shocks, oil consumption demand shocks, oil inventory demand shocks, and oil supply shocks) for conditional volatilities of prominently traded precious metals (gold, palladium, platinum, and silver) using GARCH-MIDAS-X framework. The asymmetric effect of decomposed oil shocks on precious metals’ volatilities is examined. The DCC-MIDAS framework allows to investigate the conditional correlations and volatility between oil and precious metal prices. Results show that precious metals exhibit hedging potentials against oil demand and supply shocks, with heterogeneity observed in the precious metal-oil shocks nexus. Asymmetry is evident in the responses of metals’ volatility to oil shocks. DCC-MIDAS results reveal significant dynamic correlations between oil prices and precious metals (except for platinum). Our results are robust (sensitive) to precious metals (oil shocks) proxies. The findings are insightful for commodity market stakeholders.
Item Type: | MPRA Paper |
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Original Title: | Time-variation between metal commodities and oil, and the impact of oil shocks: GARCH-MIDAS and DCC-MIDAS analyses |
Language: | English |
Keywords: | GARCH-MIDAS; DCC-MIDAS; Disaggregated oil shocks; Dynamic correlation; Platinum |
Subjects: | C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes |
Item ID: | 114689 |
Depositing User: | Dr OlaOluwa Yaya |
Date Deposited: | 27 Sep 2022 20:30 |
Last Modified: | 27 Sep 2022 20:30 |
References: | Adekoya, O.B. and Oliyide, J. (2022). The hedging effectiveness of industrial metals against different oil shocks: evidence from the four newly developed oil shocks datasets. Resources Policy, 69, December 2020, 101831. Adekoya, O.B., Oliyide, J. A., Yaya, O. S. and Al-Faryan, M. A. S. (2022). Does oil connect differently with prominent assets during war? Evidence from intra-day data during the Russia-Ukraine saga. Resources Policy, Volume 77, 102728. Agyei-Ampomah, S.A.M., Gounopoulos, D., and Mazouz, K. (2014). Does gold offer a better protection against losses in sovereign debt bonds than other metals? Journal of Banking & Finance 40, 507-521. Asgharian, A., Hou, A. J. and Javed, F. (2013). Importance of the Macroeconomic Variables for Variance Prediction: A GARCH-MIDAS Approach. Journal of Forecasting, 612: 1-29. Baumeister, C. and Hamilton, J. D. (2019). Structural interpretation of vector autoregressions with incomplete identification: revisiting the role of oil supply and demand shocks. American Economic Review, 109(5), 1873-1910. Chen, R. and Xu, J. (2019). Forecasting volatility and correlation between oil and gold prices using a novel multivariate GAS model. Energy Economic 87, 379-391. Colacito, R., R.F. Engle, and E. Ghysels (2011). A Component Model for Dynamic Correlations. Journal of Econometrics 164, 45-59. Conrad, C., Loch, K. and Ritter, D. (2014). On the Macroeconomic Determinants of the Long-Term Oil-Stock Correlation. Working Papers 0525, Department of Economics, University of Heidelberg. Diebold, F. X. and Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28 (1), 57-66. Engle, R. F. (2002). Dynamic conditional correlation-a simple class of multivariate GARCH Models. Journal of Business and Economic Statistics, 20: 339-350. Engle, R. F., Ghysels, E. and Sohn, B. (2013). Stock market volatility and macroeconomic fundamentals. Review of Economics and Statistics, 95(3), 776-797. Gil-Alana, L. A., Yaya, O. S. and Awe, O. O. (2017). Time Series Analysis of Co-movements in the Prices of Gold and Oil: Fractional Cointegration Approach. Resources Policy, 53: 117-224. Gil-Alana, L. A., Gupta, R., Olubusoye, O. E. and Yaya, O. S. (2016). Time Series Analysis of Persistence in Crude Oil Price Volatility across Bull and Bear Regimes. Energy, 109: 29-37. Greenwood-Nimmo, M., Nguyen, V.H. and Shin, Y. (2015). Measuring the connectedness of the global economy. In: Melbourne Institute Working Paper. No. 7/15. Hamilton, J. D. (2009). Understanding crude oil prices. Energy Journal, 30(2), 179–206. Husain, S., Tiwari, A.K., Sohag, K. and Shahbaz, M. (2019). Connectedness among crude oil prices, stock index and metal prices: an application of network approach in the USA. Resources Policy 62, 57-65. Khaled, M., Shawkat, H., Ahdi-Noomen, A. and Manel, Y. (2020). Does economic policy uncertainty drive the dynamic connectedness between oil price shocks and gold price? Resources Policy. 69: 0301-4207 Kilian, B. L. (2008). Exogenous oil supply shocks: how big are they and how much Do They matter for the U.S. Economy? Review of Economic Statistics, 90(2): 216–240. Kilian, B. L. (2009). Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil market. American Economic Review, 99(3), 1053–1069. Kilian, L. and Park, C. (2009). The impact of oil price shocks on the US stock market. International Economic Review, 50(4), 1267–1287. Lucey, B. M. and Li, S. (2015). What precious metals act as safe havens, and when? Some US evidence. Appl. Econ. Lett. 22 (1), 35–45. Lucey, B. M. and Li, S. (2015). What precious metals act as safe havens, and when? Some US evidence. Appl. Econ. Lett. 22 (1), 35–45. Qadan, M. (2019). Risk appetite and the prices of precious metals. Resources Policy, 62, 136–153. Ready, R.C. 2018. Oil Prices and Stock Market. Review of Finance 22 (1), 155 – 76. Reboredo, J.C. (2013). Is gold a hedge or safe haven against oil price movements? Resources Policy 38, 130–137. Salisu, A. A. and Adediran, I. A. (2020). Gold as a hedge against oil shocks: Evidence from new datasets for oil shocks. Resources Policy 66, 101606. Salisu, A. A. and Gupta, R. (2021). Oil shocks and stock market volatility of the BRICS: A GARCH-MIDAS approach. Global Finance Journal, 48, May 2021, 100546 https://doi.org/10.1016/j.gfj.2020.100546 Salisu, A. A., Akanni, L. and Raheem, I. (2020b). The COVID-19 global fear index and the predictability of commodity price returns. Journal of Behavioral and Experimental Finance, 27, 100383. Salisu, A. A., Ogbonna, A. E., & Adewuyi, A. (2020a). Google trends and the predictability of precious metals. Resources Policy, 65, 101542. doi:10.1016/j.resourpol.2019.101542 Salisu, A. A., Vo, X. V., & Lawal, A. (2021). Hedging oil price risk with gold during COVID-19 pandemic. Resources Policy, 70, 101897. doi:10.1016/j.resourpol.2020.101897 Sari, R., Hammoudeh, S. and Soytas, U. (2010). Dynamics of oil price, precious metal prices, and exchange rate. Energy Economics, 32: 351-362. Shafiullah, M., Chaudhry S.M., Shahbaz, M, Reboredo, J.C. (2021). Quantile causality and dependence between crude oil and precious metal prices. International Journal of Finance and Economics, 26(4): 6264-6280. Singhal, S., Shoudhary, S. and Biswal, P.C. (2019). Return and volatility linkages among international crude oil price, gold price, exchange rate and stock markets: evidence from Mexico. Resources Policy 60, 255–261. Tiwari, A.K., Aye, C., G Gupta, R. and Gkillas, K. (2020). Gold-oil dependence dynamics and the role of geopolitical risks: evidence from a Markov-switching time-varying copula model. Energy Economic 88, 104748. Uddin, G.S., Rahman, M.L., Shahzad, S.J.H. and Rehman, M.U. (2018). Supply and demand driven oil price changes and their non-linear impact on precious metal returns: a Markov regime switching approach. Energy Economics 73, 108–121. Yaya O. S., Lukman A. F. and Vo, X. V. (2022a). Persistence and volatility spillovers of Bitcoin price to gold and silver prices. Resources Policy. Resources Policy 79, 103011. Yaya, O. S., Ogbonna, A. E. and Vo, X. V. (2022b). Oil shocks and volatility of green investments: GARCH-MIDAS analyses. Resources Policy, 78, 102789. Yaya, O. S., Tumala, M. M. and Udomboso, C. G. (2016). Volatility persistence and Returns spillovers between Oil and Gold Prices: Analysis before and after the global financial crisis. Resources Policy, 49: 273-281. Yaya, O. S., Vo, X. V. and Olayinka, H. A. (2021). Gold and Silver prices, their stocks and market fear gauges: Testing fractional cointegration using a robust approach. Resources Policy, 72, August 2021, 102045. Zhang, Y.J. and Wei, Y.M. (2010). The crude oil market and the gold market: evidence for cointegration, causality and price discovery. Res. Pol. 35 (3), 168–177. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/114689 |