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Coherence, Connectedness, Dynamic Linkages Among Oil and China's Sectoral Commodities with Portfolio Implications

CUI Jinxin, ZOU Huiwen   

  1. School of Economics and Management, Fuzhou University, Fuzhou 350108, China
  • Received:2020-05-06 Revised:2020-09-01 Published:2022-06-20
  • Supported by:
    This work was supported by the Science Center Program of National Natural Science Foundation of China under Grant No. 62188101, HIT Wuhu Robot Technology Research Institute, the National Natural Science Foundation of China under Grant No. 62173112, Guangdong Natural Science Foundation under Grant No. 2019A1515011576, and Shenzhen Science and Technology Program under Project No. JCYJ20210324132413034.

CUI Jinxin, ZOU Huiwen. Coherence, Connectedness, Dynamic Linkages Among Oil and China's Sectoral Commodities with Portfolio Implications[J]. Journal of Systems Science and Complexity, 2022, 35(3): 1052-1097.

This paper investigates the time-frequency dependence, return and volatility connectedness, dynamic linkages, and portfolio diversification gains among oil and China's sectoral commodities, namely, Petrochemicals (CIFI), Grains (CRFI), Energy (ENFI), Non-ferrous metals (NFFI), Oil & Fats (OOFI), and Softs (SOFI), utilizing a proposed research framework that contains the wavelet coherence, novel TVP-VAR based connectedness, and the cDCC-, DECO-FIAPARCH (1, d, 1) model. The empirical results demonstrate that global oil market exhibits a relatively higher (lower) coherence with ENFI, NFFI, and OOFI (CRFI) on the long-term time horizon and the oil market leads China's sectoral commodities during most sample periods. The crude oil market transmits significant connectedness to China's sectoral commodities, especially the energy commodity sector (ENFI). The dynamic return and volatility total spillovers tend to intensify and exhibit significant fluctuations during the GFC and the oil price collapse. Further, the time-varying linkages among oil and China's sectoral commodities are positive and fluctuant, mainly at a relatively low level. The dynamic return and volatility connectedness, multi-view linkages, optimal portfolio weights, and hedging ratios display significant time-varying features. The oil-commodity nexus offers diversification benefits and the optimal-weighted portfolio presents the best variance and downside risk reduction performance. Furthermore, risk management effectiveness is market-condition-dependent and heterogeneous across different commodity sectors and sub-samples. This paper can not only help investors and market regulators to capture the complex interconnectedness and risk transmission trajectory among oil and China's sectoral commodities but also benefits for investors and portfolio managers to construct optimal portfolios and hedging strategies.
[1] Balli F, Naeem M A, Shahzad S J H, et al., Spillover network of commodity uncertainties, Energy Economics, 2019, 81:914-927.
[2] Basak S and Pavlova A, A model of financialization of commodities, The Journal of Finance, 2016, 71(4):1511-1556.
[3] Guhathakurta K, Dash S R, and Maitra D, Period specific volatility spillover based connectedness between oil and other commodity prices and their portfolio implications, Energy Economics, 2020, 85:104566.
[4] Kang S H and Yoon S M, Financial crisis and dynamic spillovers among Chinese stock and commodity futures markets, Physica A:Statistical Mechanics and Its Applications, 2019, 531:121776.
[5] Al-Yahyaee K H, Mensi W, Sensoy A, et al., Energy, precious metals, and GCC stock markets:Is there any risk spillover?Pacific-Basin Finance Journal, 2019, 56:45-70.
[6] Mensi W, Hammoudeh S, Rehman M U, et al., Dynamic risk spillovers and portfolio risk management between precious metals and global foreign exchange markets, The North American Journal of Economics and Finance, 2020, 51:101086.
[7] Mensi W, Hammoudeh S, and Kang S H, Precious metals, cereal, oil and stock market linkages and portfolio risk management:Evidence from Saudi Arabia, Economic Modeling, 2015, 51:340-358.
[8] Luo J W and Ji Q, High-frequency volatility connectedness between the US crude oil market and China's agricultural commodity markets, Energy Economics, 2018, 76:424-438.
[9] Tsuji C, New DCC analyses of return transmission, volatility spillovers, and optimal hedging among oil futures and oil equities in oil-producing countries, Applied Energy, 2018, 229:1202-1217.
[10] Wang X X and Wang Y D, Volatility spillovers between crude oil and Chinese sectoral equity markets:Evidence from a frequency dynamics perspective, Energy Economics, 2019, 80:995-1009.
[11] Zhang C G and Tu X H, The effect of global oil price shocks on China's metal markets, Energy Policy, 2016, 90:131-139.
[12] Cheng L H and Xiong W, Financialization of commodity markets, Annual Review of Financial Economics, 2014, 6(1):419-441.
[13] Henderson B J, Pearson N D, and Wang L, New evidence on the financialization of commodity markets, The Review of Financial Studies, 2014, 28(5):1285-1311.
[14] Adams Z and Glück T, Financialization in commodity markets:A passing trend or the new normal?Journal of Banking&Finance, 2015, 60:93-111.
[15] Avalos F, Do oil prices drive food prices?The tale of a structural break, Journal of International Money and Finance, 2014, 42:253-271.
[16] Ji Q and Fan Y, How does oil price volatility affect non-energy commodity markets?Applied Energy, 2012, 89(1):273-280.
[17] Reboredo J C, Do food and oil prices co-move?Energy Policy, 2012, 49:456-467.
[18] Bildirici M E and Turkmen C, Nonlinear causality between oil and precious metals, Resource Policy, 2015, 46(2):202-211.
[19] Jain A and Ghosh S, Dynamics of global oil prices, exchange rate and precious metal prices in India, Resource Policy, 2013, 38:88-93.
[20] Kang S H, Mclver R, and Yoon S M, Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets, Energy Economics, 2017, 62:19-32.
[21] Shahzad S J H, Rehman M U, and Jammazi R, Spillovers from oil to precious metals:Quantile approaches, Resource Policy, 2019, 61:508-521.
[22] Rehman M U, Shahzad S J H, Uddin G S, et al., Precious metal returns and oil shocks:A time varying connectedness approach, Resource Policy, 2018, 58:77-89.
[23] Mensi W, Tiwari A, Bouri E, et al., The dependence structure across oil, wheat, and corn:A wavelet-based copula approach using implied volatility indexes, Energy Economics, 2017, 66:122-139.
[24] Zhang C G and Qu X Q, The effect of global oil price shocks on China's agricultural commodities, Energy Economics, 2015, 51:354-364.
[25] Hasanov A S, Do H X, and Shaiban M S, Fossil fuel price uncertainty and feedstock edible oil prices:Evidence from MGARCH-M and VIRF analysis, Energy Economics, 2016, 57:16-27.
[26] Ji Q and Guo J F, Market interdependence among commodity prices based on information transmission on the internet, Physica A:Statistical Mechanics and Its Applications, 2015, 426:35-44.
[27] Fung H G, Tse Y, Yau J, et al., A leader of the world commodity futures markets in the making?The case of China's commodity futures, International Review of Financial Analysis, 2013, 27:103-114.
[28] Kang S H and Yoon S M, Dynamic correlation and volatility spillovers across Chinese stock and commodity futures markets, International Journal of Finance&Economics, 2020, 25(2):261-273.
[29] Lin B Q and Xu B, How to effectively stabilize China's commodity price fluctuations?Energy Economics, 2019, 84:104544.
[30] Jiang Y H, Jiang C, Nie H, et al., The time-varying linkages between global oil market and China's sectoral commodities:Evidence from DCC-GJR-GARCH analyses, Energy, 2019, 166:577-586.
[31] Chen P, Global oil prices, macroeconomic fundamentals and China's commodity sector comovements, Energy Policy, 2015, 87:284-294.
[32] Meng J, Nie H, Mo B, et al., Risk spillover effects from global crude oil market to China's sectoral commodities, Energy, 2020, 202:117208.
[33] Yip P S, Brooks R, Do H X, et al., Dynamic volatility spillover effects between oil and agricultural products, International Review of Financial Analysis, 2020, 69:101465.
[34] Dahl R E, Oglend A, and Yahya M, Dynamics of volatility spillover in commodity markets:Linking crude oil to agriculture, Journal of Commodity Markets, 2020, 20:100111.
[35] Diebold E X and Yilmaz K, Measuring financial asset return and volatility spillovers, with application to global equity markets, The Economic Journal, 2009, 119:158-171.
[36] Diebold E X and Yilmaz K, Better to give than to receive:Predictive directional measurement of volatility spillovers, International Journal of Forecasting, 2012, 28:57-66.
[37] Diebold E X and Yilmaz K, On the network topology of variance decompositions:Measuring the connectedness of financial firms, Journal of Econometrics, 2014, 182(1):119-134.
[38] Antonakakis N, Gabauer D, Gupta R, et al., Dynamic connectedness of uncertainty across developed economies:A time-varying approach, Economic Letters, 2018, 166:63-75.
[39] Antonakakis N, Gabauer D, and Gupta R, Greek economic policy uncertainty:Does it matter for Europe?Evidence from a dynamic connectedness decomposition approach, Physica A:Statistical Mechanics and Its Applications, 2019, 535:122280.
[40] Antonakakis N, Gabauer D, and Gupta R, International monetary policy spillovers:Evidence from a time-varying parameter vector autoregression, International Review of Financial Analysis, 2019, 65:101382.
[41] Gabauer D and Gupta R, On the transmission mechanism of country-specific and international economic uncertainty spillovers:Evidence from a TVP-VAR connectedness decomposition approach, Economic Letters, 2018, 171:63-71.
[42] Gabauer D and Gupta R, Spillovers across macroeconomic, financial and real estate uncertainties:A time-varying approach, Structural Change and Economic Dynamics, 2020, 52:163-173.
[43] Korobilis D and Yilmaz K, Measuring dynamic connectedness with large Bayesian VAR models, Technical Report University of Essex, Essex Business School, 2018.
[44] Jiang Y H, Zhu Z X, Tian G Y, et al., Determinants of within and cross-country economic policy uncertainty spillovers:Evidence from US and China, Finance Research Letters, 2019, 31:195-206.
[45] Antonakakis N, Chatziantoniou I, and Gabauer D, Cryptocurrency market contagion:Market uncertainty, market complexity, and dynamic portfolio, Journal of International Financial Markets, Institutions and Money, 2019, 61:37-51.
[46] Hwang S J and Suh H, Analyzing dynamic connectedness in Korean housing markets, Emerging Markets Finance and Trade, 2021, 57(2):591-609.
[47] Aielli G P, Dynamic conditional correlation:On properties and estimation, Journal of Business&Economic Statistics, 2013, 31:282-299.
[48] Mensi W, Hammoudeh S, AI-Jarrah I M W, et al., Dynamic risk spillovers between gold, oil prices and conventional, sustainability and Islamic equity aggregates and sectors with portfolio implications, Energy Economics, 2017, 67:454-475.
[49] Sensoy A, Nguyen D K, Rostom A, et al., Dynamic integration and network structure of the EMU sovereign bond markets, Annals of Operations Research, 2019, 281:297-314.
[50] Lin L, Zhou Z B, Liu Q, et al., Risk transmission between natural gas market and stock markets:Portfolio and hedging strategy analysis, Finance Research Letters, 2019, 29:245-254.
[51] Mensi W, Hammoudeh S, Al-Jarrah I M W, et al., Risk spillovers and hedging effectiveness between major commodities, and Islamic and conventional GCC banks, Journal of International Financial Markets, Institutions&Money, 2019, 60:68-88.
[52] Singh J, Ahmad W, and Mishra A, Coherence, connectedness and dynamic hedging effectiveness between emerging markets equities and commodity index funds, Resource Policy, 2019, 61:441-460.
[53] Kuskaya S and Bilgili F, The wind energy-greenhouse gas nexus:The wavelet-partial wavelet coherence model approach, Journal of Cleaner Production, 2020, 245:118872.
[54] Raza N, Ali S, Shahzad S J H, et al., Can alternative hedging assets add value to Islamicconventional portfolio mix:Evidence from MGARCH models, Resource Policy, 2019, 61:210-230.
[55] Wang Y D, Wu C F, and Yang L, Oil price shocks and agricultural commodity prices, Energy Economics, 2014, 44:22-35.
[56] Ahmadi M, Behmiri N B, and Manera M, How is volatility in commodity markets linked to oil price shocks?Energy Economics, 2016, 59:11-23.
[57] Silvennoinen A and Thorp S, Crude oil and agricultural futures:An analysis of correlation dynamics, Journal of Futures Market, 2016, 36(6):522-544.
[58] Ji Q, Bouri E, Roubaud D, et al., Risk spillover between energy and agricultural commodity markets:A dependence-switching CoVaR-Copula model, Energy Economics, 2018, 75:14-27.
[59] Shahzad S J H, Hernandez J A, Al-Yahyaee K H, et al., Asymmetric risk spillovers between oil and agricultural commodities, Energy Policy, 2018, 118:182-198.
[60] Tian C X, Zhang B S, and Duan J, Based on Copula-CoVaR model of risk spillover effect of oil markets and other commodity markets, Journal of Intelligent&Fuzzy Systems, 2020, 38(6):7671-7682.
[61] Jiang Y H, Lao J S, Mo B, et al., Dynamic linkages among global oil market, agricultural raw material markets and metal markets:An application of wavelet and copula approaches, Physica A:Statistical Mechanics and Its Applications, 2018, 508:265-279.
[62] Pal D and Mitra S K, Time-frequency dynamics of return spillover from crude oil to agricultural commodities, Applied Economics, 2020, 52(49):5426-5445.
[63] Zhang C G and Chen X Q, The impact of global price shocks on China's bulk commodity markets and fundamental industries, Energy Policy, 2014, 66:32-41.
[64] Zhang C G, Liu F, and Yu D L, Dynamic jumps in global oil price and its impacts on China's bulk commodities, Energy Economics, 2018, 70:297-306.
[65] Jin X J and Zhu F F, Global oil shocks and China's commodity markets:The role of OVX, Emerging Markets Finance and Trade, 2021, 57(3):914-929.
[66] Ahmed A D and Huo R, Volatility transmissions across international oil market, commodity futures and stock markets:Empirical evidence from China, Energy Economics, 2021, 93:104741.
[67] Zhu H M, Duan R, Peng C, et al., The heterogeneous dependence between global crude oil and Chinese commodity futures markets:Evidence from quantile regression, Applied Economics, 2019, 51(28):3031-3048.
[68] Li Z H and Su Y Y, Dynamic spillovers between international crude oil market and China's sectoral commodities:Evidence from time-frequency perspective of stochastic volatility, Frontiers in Energy Research, 2020, 8(45):1-15.
[69] Hudgins L, Friehe C A, and Mayer M E, Wavelet transforms and atmospheric turbulence, Physical Review Letters, 1993, 71:82-94.
[70] Torrence C and Compo G P, A practical guide to wavelet analysis, Bulletin of the American Meteorological Society, 1998, 79:61-78.
[71] Aguiar-Conraria L and Soares M J, The continuous wavelet transform:Moving beyond uni-and bivariate analysis, Journal of Economic Surveys, 2014, 28:344-375.
[72] Koop G and Korobilis D, A new index of financial conditions, European Economic Review, 2014, 71:101-116.
[73] Koop G, Pesaran M, and Potter S M, Impulse response analysis in nonlinear multivariate models, Journal of Econometrics, 1996, 74(1):119-147.
[74] Pesaran H and Shin Y, Generalized impulse response analysis in linear multivariate models, Economic Letters, 1998, 58(1):17-29.
[75] Engle R F, Dynamic conditional correlation:A simple class of multivariate generalized autoregressive conditional heteroskedasticity models, Journal of Business&Economic Statistics, 2004, 20:339-350.
[76] Fiorentini G, Sentana E, and Calzolari G, Maximum likelihood estimation and inference in multivariate conditionally heteroscedastic dynamic regression models with Student-t innovations, Journal of Business&Economic Statistics, 2003, 21:532-546.
[77] Tse Y, The conditional heteroscedasticity of the yen-dollar exchange rate, Journal of Applied Economics, 1998, 13:49-55.
[78] Engle R F and Kelly B, Dynamic equicorrelation, Journal of Business&Economic Statistics, 2012, 30:212-228.
[79] Dickey D and Fuller W, Distribution of the estimators for autoregressive time series with a unit root, Journal of the American Statistical Association, 1979, 74:427-431.
[80] Phillips P C B and Perron P, Testing for a unit root in time series regression, Biometrika, 1998, 75:335-346.
[81] Kwiatkowski D, Phillips P C B, Schmidt P, et al., Testing the null hypothesis of stationarity against the alternative of a unit root:How sure are we that economic time series have a unit root?Journal of Econometrics, 1992, 54:159-178.
[82] Stock J, Elliott G, and Rothenberg T, Efficient tests for an autoregressive unit root, Econometrica, 1996, 64(4):813-836.
[83] Engle R F, Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 1982, 50:987-1007.
[84] Yoon S M, Mamun M A, Uddin G S, et al., Network connectedness and net spillover between financial and commodity markets, The North American Journal of Economics and Finance, 2019, 48:801-818.
[85] Cheung Y W, Fatum R, and Yamamoto Y, The exchange rate effects of macro news after the Global Financial Crisis, Journal of International Money and Finance, 2019, 95:424-443.
[86] Mensi W, Al-Yahyaee K H, Kang S H, Time-varying volatility spillovers between stock and precious metal markets with portfolio implications, Resources Policy, 2017, 53:88-102.
[87] Hammoudeh S, Nguyen D K, Reboredo J C, et al., Dependence of stock and commodity futures markets in China:Implications for portfolio investment, Emerging Markets Review, 2014, 21:183-200.
[88] Korner K F and Ng V K, Modeling asymmetric comovements of asset returns, The Review of Financial Studies, 1998, 11:817-844.
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