Analyzing time-varying volatility spillovers between the crude oil markets using a new method

Energy Economics - Tập 87 - Trang 104711 - 2020
Tangyong Liu1, Xu Gong2
1College of mathematics and informatics, Fujian Normal University, Fuzhou 350117, China
2School of Management, China Institute for Studies in Energy Policy, Collaborative Innovation Center for Energy Economics and Energy Policy, Xiamen University, Xiamen 361005, China

Tài liệu tham khảo

Alizadeh, 2002, Range-based estimation of stochastic volatility models, J. Financ., 57, 1047, 10.1111/1540-6261.00454 Andersen, 1998, Answering the critics: yes, ARCH models do provide good volatility forecasts, Int. Econ. Rev., 4, 885, 10.2307/2527343 Andrews, 1993, Tests for parameter instability and structural change with unknown change point, Econometrica: Journal of the Econometric Society, 821, 10.2307/2951764 Antonakakis, 2017 Aromi, 2019, Spillovers between the oil sector and the S&P500: the impact of information flow about crude oil, Energy Econ., 81, 187, 10.1016/j.eneco.2019.03.018 Barunik, 2015, Volatility spillovers across petroleum markets, Energy J., 309 Batten, 2015, Which precious metals spill over on which, when and why? Some evidence, Appl. Econ. Lett., 22, 466, 10.1080/13504851.2014.950789 Baumeister, 2013, Time-varying effects of oil supply shocks on the US economy, Am. Econ. J. Macroecon., 5, 1, 10.1257/mac.5.4.1 Bentzen, 2007, Does OPEC influence crude oil prices? Testing for co-movements and causality between regional crude oil prices, Appl. Econ., 39, 1375, 10.1080/00036840600606344 Bianchi, 2015, Globalization and inflation: evidence from a time-varying VAR, Rev. Econ. Dyn., 18, 406, 10.1016/j.red.2014.07.004 Boldanov, 2016, Time-varying correlation between oil and stock market volatilities: evidence from oil-importing and oil-exporting countries, Int. Rev. Financ. Anal., 48, 209, 10.1016/j.irfa.2016.10.002 Bollerslev, 1986, Generalized autoregressive conditional heteroskedasticity, J. Econ., 31, 307, 10.1016/0304-4076(86)90063-1 Chang, 2010, Analyzing and forecasting volatility spillovers, asymmetries and hedging in major oil markets, Energy Econ., 32, 1445, 10.1016/j.eneco.2010.04.014 Chuang, 2009, Causality in quantiles and dynamic stock return–volume relations, J. Bank. Financ., 33, 1351, 10.1016/j.jbankfin.2009.02.013 Diebold, 2009, Measuring financial asset return and volatility spillovers, with application to global equity markets, Econ. J., 119, 158, 10.1111/j.1468-0297.2008.02208.x Diebold, 2012, Better to give than to receive: predictive directional measurement of volatility spillovers, Int. J. Forecast., 28, 57, 10.1016/j.ijforecast.2011.02.006 Diebold, 2014, On the network topology of variance decompositions: measuring the connectedness of financial firms, J. Econ., 182, 119, 10.1016/j.jeconom.2014.04.012 Ewing, 2000, Co-movements of Alaska North Slope and UK Brent crude oil prices, Appl. Econ. Lett., 7, 553, 10.1080/13504850050033373 Gabauer, 2018, On the transmission mechanism of country-specific and international economic uncertainty spillovers: evidence from a TVP-VAR connectedness decomposition approach, Econ. Lett., 171, 63, 10.1016/j.econlet.2018.07.007 Geweke, 1992, Evaluating the accuracy of sampling-based approaches to the calculations of posterior moments, Bayesian Statistics, 4, 641 Gogolin, 2018, Uncovering long term relationships between oil prices and the economy: a time-varying cointegration analysis, Energy Econ., 76, 584, 10.1016/j.eneco.2018.10.002 Gong, 2017, Forecasting the good and bad uncertainties of crude oil prices using a HAR framework, Energy Econ., 67, 315, 10.1016/j.eneco.2017.08.035 Gong, 2018, The incremental information content of investor fear gauge for volatility forecasting in the crude oil futures market, Energy Econ., 74, 370, 10.1016/j.eneco.2018.06.005 Grigoli, 2019, A crude shock: explaining the short-run impact of the 2014–16 oil price decline across exporters, Energy Econ., 78, 481, 10.1016/j.eneco.2018.11.025 Gülen, 1999, Regionalization in the world crude oil market: further evidence, Energy J., 125 Haigh, 2002, Crack spread hedging: accounting for time-varying volatility spillovers in the energy futures markets, J. Appl. Econ., 17, 269, 10.1002/jae.628 Hendricks, 1992, Hierarchical spline models for conditional quantiles and the demand for electricity, J. Am. Stat. Assoc., 87, 58, 10.1080/01621459.1992.10475175 Hsu, 2007, On the volatility of volatility, Physica A: Statistical Mechanics and its Applications, 380, 366, 10.1016/j.physa.2007.02.041 Jarque, 1980, Efficient tests for normality, homoscedasticity and serial independence of regression residuals, Econ. Lett., 6, 255, 10.1016/0165-1765(80)90024-5 Jebabli, 2014, On the effects of world stock market and oil price shocks on food prices: an empirical investigation based on TVP-VAR models with stochastic volatility, Energy Econ., 45, 66, 10.1016/j.eneco.2014.06.008 Ji, 2019, Dynamic connectedness and integration in cryptocurrency markets, Int. Rev. Financ. Anal., 63, 257, 10.1016/j.irfa.2018.12.002 Jin, 2012, Volatility transmission and volatility impulse response functions in crude oil markets, Energy Econ., 34, 2125, 10.1016/j.eneco.2012.03.003 Karali, 2014, Macro determinants of volatility and volatility spillover in energy markets, Energy Econ., 46, 413, 10.1016/j.eneco.2014.06.004 King, 1990, Transmission of volatility between stock markets, Rev. Financ. Stud., 3, 5, 10.1093/rfs/3.1.5 Koop, 2013, Large time-varying parameter VARs, J. Econ., 177, 185, 10.1016/j.jeconom.2013.04.007 Korobilis, 2018, Measuring dynamic connectedness with large Bayesian VAR models, Available at SSRN, 3099725 Liu, 2018, Forecasting the oil futures price volatility: large jumps and small jumps, Energy Econ., 72, 321, 10.1016/j.eneco.2018.04.023 Ljung, 1978, On a measure of lack of fit in time series models, Biometrika, 65, 297, 10.1093/biomet/65.2.297 Lucey, 2014, Gold markets around the world–who spills over what, to whom, when?, Appl. Econ. Lett., 21, 887, 10.1080/13504851.2014.896974 Ma, 2017, Forecasting the realized volatility of the oil futures market: a regime switching approach, Energy Econ., 67, 136, 10.1016/j.eneco.2017.08.004 Ma, 2018, Forecasting realized volatility of oil futures market: a new insight, J. Forecast., 37, 419, 10.1002/for.2511 Magkonis, 2017, Dynamic spillover effects across petroleum spot and futures volatilities, trading volume and open interest, Int. Rev. Financ. Anal., 52, 104, 10.1016/j.irfa.2017.05.005 Mohaddes, 2017, Oil prices and the global economy: is it different this time around?, Energy Econ., 65, 315, 10.1016/j.eneco.2017.05.011 Nakajima, 2011, Bayesian analysis of time-varying parameter vector autoregressive model for the Japanese economy and monetary policy, Journal of the Japanese and International Economies, 25, 225, 10.1016/j.jjie.2011.07.004 Nasir, 2018, Implications of oil prices shocks for the major emerging economies: a comparative analysis of BRICS, Energy Econ., 76, 76, 10.1016/j.eneco.2018.09.023 Parkinson, 1980, The extreme value method for estimating the variance of the rate of return, J. Bus., 61, 10.1086/296071 Prasad, 2018, Time varying volatility indices and their determinants: evidence from developed and emerging stock markets, Int. Rev. Financ. Anal., 60, 115, 10.1016/j.irfa.2018.09.006 Prieto, 2016, Time variation in macro-financial linkages, J. Appl. Econ., 31, 1215, 10.1002/jae.2499 Primiceri, 2005, Time varying structural vector autoregressions and monetary policy, Rev. Econ. Stud., 72, 821, 10.1111/j.1467-937X.2005.00353.x Simo-Kengne, 2015, Time-varying effects of housing and stock returns on US consumption, J. Real Estate Financ. Econ., 50, 339, 10.1007/s11146-014-9470-3 Singh, 2019, Feedback spillover dynamics of crude oil and global assets indicators: a system-wide network perspective, Energy Econ., 80, 321, 10.1016/j.eneco.2019.01.005 Wang, 2018, The dynamic spillover between carbon and energy markets: new evidence, Energy, 149, 24, 10.1016/j.energy.2018.01.145 Wang, 2017, Time-varying parameter realized volatility models, J. Forecast., 36, 566, 10.1002/for.2454 Wei, 2016, An empirical analysis of the relationship between oil prices and the Chinese macro-economy, Energy Econ., 56, 88, 10.1016/j.eneco.2016.02.023 Weiner, 1991, Is the world oil market “one great pool”?, Energy J., 95 Wen, 2016, Forecasting the volatility of crude oil futures using HAR-type models with structural breaks, Energy Econ., 59, 400, 10.1016/j.eneco.2016.07.014 Wen, 2018, Interaction between oil and US dollar exchange rate: nonlinear causality, time-varying influence and structural breaks in volatility, Appl. Econ., 50, 319, 10.1080/00036846.2017.1321838 Wen, 2019, Crude oil price shocks, monetary policy, and China’s economy, International Journal of Finance Economics, 24, 812, 10.1002/ijfe.1692 Wen, 2019, Risk spillovers between oil and stock markets: a VAR for VaR analysis, Energy Econ., 80, 524, 10.1016/j.eneco.2019.02.005 Yang, 2016, Quantitative easing and volatility spillovers across countries and asset classes, Manag. Sci., 63, 333, 10.1287/mnsc.2015.2305 Yarovaya, 2016, Intra-and inter-regional return and volatility spillovers across emerging and developed markets: evidence from stock indices and stock index futures, Int. Rev. Financ. Anal., 43, 96, 10.1016/j.irfa.2015.09.004 Zhou, 2012, Volatility spillovers between the Chinese and world equity markets, Pacific-basin Finance Journal, 20, 247, 10.1016/j.pacfin.2011.08.002 Zhao, 2016, The effects of oil price shocks on output and inflation in China, Energy Econ., 53, 101, 10.1016/j.eneco.2014.11.017