Connectedness Among Economic Policy Uncertainties: Evidence from the Time and Frequency Domain Perspectives

References

[1] Zhang X Y, Chen L Y. A study on the spillover effect and its formation mechanism of economic policy uncertainty. Statistical Research, 2019, 36(1): 116–128.Search in Google Scholar

[2] Istiak K, Serletis A. Economic policy uncertainty and real output: Evidence from the G7 countries. Applied Economics, 2018, 50(39): 4222–4233.10.1080/00036846.2018.1441520Search in Google Scholar

[3] Junttila J, Vataja J. Economic policy uncertainty effects for forecasting future real economic activity. Economic Systems, 2018, 42(4): 569–583.10.1016/j.ecosys.2018.03.002Search in Google Scholar

[4] Luk P, Cheng M, Ng P, et al. Economic policy uncertainty spillovers in small open economies: The case of Hong Kong. Pacific Economic Review, 2020, 25: 21–46.10.1111/1468-0106.12283Search in Google Scholar

[5] Kang S H, Yoon S M. Dynamic connectedness network in economic policy uncertainties. Applied Economic Letters, 2019, 26(1): 74–78.10.1080/13504851.2018.1438580Search in Google Scholar

[6] Gholipour H F. The effects of economic policy and political uncertainties on economic activities. Research in International Business and Finance, 2019, 48: 210–218.10.1016/j.ribaf.2019.01.004Search in Google Scholar

[7] Mei D, Zeng Q, Cao X, et al. Uncertainty and oil volatility: New evidence. Physica A: Statistical Mechanics and Its Applications, 2019, 525: 155–163.10.1016/j.physa.2019.03.043Search in Google Scholar

[8] Tiwari A K, Jana R K, Roubaud D. The policy uncertainty and market volatility puzzle: Evidence from wavelet analysis. Finance Research Letters, 2018, 110: 1–7.10.1016/j.frl.2018.11.016Search in Google Scholar

[9] Bai L, Zhang X H, Liu Y T, et al. Economic risk contagion among major economies: New evidence from EPU spillover analysis in time and frequency domains. Physica A: Statistical Mechanics and Its Applications, 2019, 535: 122431.10.1016/j.physa.2019.122431Search in Google Scholar

[10] 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: 195–206.10.1016/j.frl.2019.08.004Search in Google Scholar

[11] Antonakakis N, Gabauer D, Gupta R, et al. Dynamic connectedness of uncertainty across developed economies: A time-varying approach. Economics Letters, 2018, 166: 63–75.10.1016/j.econlet.2018.02.011Search in Google Scholar

[12] Gabauer D, Gupta R. On the transmission mechanism of country-specific and international economic uncertainty spillovers: Evidence from a TVP-VAR connectedness decomposition approach. Economics Letters, 2018, 171: 63–71.10.1016/j.econlet.2018.07.007Search in Google Scholar

[13] Xia T S, Yao C X, Geng J B. Dynamic and frequency-domain spillover among economic policy uncertainty, stock and housing markets in China. International Review of Financial Analysis, 2020, 67: 101427.10.1016/j.irfa.2019.101427Search in Google Scholar

[14] Wang X X,Wang Y D. Volatility spillovers between crude oil and Chinese sectoral equity markets: Evidence from a frequency dynamics perspective. Energy Economics, 2019, 80: 995–1009.10.1016/j.eneco.2019.02.019Search in Google Scholar

[15] Barunik J, Krehlik T. Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 2018, 16(2): 271–296.10.1093/jjfinec/nby001Search in Google Scholar

[16] Diebold F X, Yilmaz K. Measuring financial asset return and volatility spillovers with application to global equity markets. The Economic Journal, 2009, 119(534): 158–171.10.1111/j.1468-0297.2008.02208.xSearch in Google Scholar

[17] Diebold F X, Yilmaz K. Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 2012, 28(1): 57–66.10.1016/j.ijforecast.2011.02.006Search in Google Scholar

[18] Diebold F X, Yilmaz K. On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 2014, 182(1): 119–134.10.1016/j.jeconom.2014.04.012Search in Google Scholar

[19] Kang S H, Tiwari A K, Albulescu C T, et al. Exploring the time-frequency connectedness and network among crude oil and agriculture commodities V1. Energy Economics, 2019, 84: 104543.10.1016/j.eneco.2019.104543Search in Google Scholar

[20] Tiwari A K, Nasreen S, Shahbaz M, et al. Time-frequency causality and connectedness between international prices of energy, food, industry, agriculture and metals. Energy Economics, 2020, 85: 104529.10.1016/j.eneco.2019.104529Search in Google Scholar

[21] Nasreen S, Tiwari A K, Eizaguirre J C, et al. Dynamic connectedness between oil prices and stock returns of clean energy and technology companies. Journal of Cleaner Production, 2020, 260: 121015.10.1016/j.jclepro.2020.121015Search in Google Scholar

[22] Wang B C,Wei Y, Xing Y H, et al. Multifractal detrended cross-correlation analysis and frequency dynamics of connectedness for energy futures markets. Physica A: Statistical Mechanics and Its Applications, 2019, 527: 121194.10.1016/j.physa.2019.121194Search in Google Scholar

[23] Ferrer R, Shahzad S J H, Lopez R, et al. Time and frequency dynamics of connectedness between renewable energy stocks and crude oil prices. Energy Economics, 2018, 76: 1–20.10.1016/j.eneco.2018.09.022Search in Google Scholar

[24] Lovcha Y, Laborda A P. Dynamic frequency connectedness between oil and gas volatilities. Economic Modelling, 2020, 84: 181–189.10.1016/j.econmod.2019.04.008Search in Google Scholar

[25] Qarni M O, Gulzar S, Fatima S T, et al. Inter-markets volatility spillover in U.S. Bitcoin and financial markets. Journal of Business Economics and Management, 2019, 20(4): 694–714.10.3846/jbem.2019.8316Search in Google Scholar

[26] Toyoshima Y, Hamori S. Measuring the time-frequency dynamics of return and volatility connectedness in global crude oil markets. Energies, 2018, 11: 2893.10.3390/en11112893Search in Google Scholar

[27] Husain S, Tiwari A K, Sohag K, et al. Connectedness among crude oil prices, stock index and metal prices: An application of network approach in the USA. Resources Policy, 2019, 62: 57–65.10.1016/j.resourpol.2019.03.011Search in Google Scholar

[28] Ji Q, Zhang D Y, Geng J B. Information linkage, dynamic spillovers in prices and volatility between the carbon and energy markets. Journal of Cleaner Production, 2018, 198: 972–978.10.1016/j.jclepro.2018.07.126Search in Google Scholar

[29] Kang S H, Lee JW. The network connectedness of volatility spillovers across global futures markets. Physica A: Statistical Mechanics and Its Applications, 2019, 526: 120756.10.1016/j.physa.2019.03.121Search in Google Scholar

[30] Lundgren A I, Milicevic A, Uddin G S, et al. Connectedness network and dependence structure mechanism in green investments. Energy Economics, 2018, 72: 145–153.10.1016/j.eneco.2018.04.015Search in Google Scholar

[31] Singh V K, Kumar P, Nishant S. Feedback spillover dynamics of crude oil and global assets indicators: A system-wide network perspective. Energy Economics, 2019, 80: 321–335.10.1016/j.eneco.2019.01.005Search in Google Scholar

[32] Ji Q, Xia T S, Liu F, et al. The information spillover between carbon price and power sector returns: Evidence from the major European electricity companies. Journal of Cleaner Production, 2019, 208: 1178–1187.10.1016/j.jclepro.2018.10.167Search in Google Scholar

[33] Chen Y F, Li W Q, Qu F. Dynamic asymmetric spillovers and volatility interdependence on China’s stock market. Physica A: Statistical Mechanics and Its Applications, 2019, 523: 825–838.10.1016/j.physa.2019.02.021Search in Google Scholar

[34] Yang K, Wei Y, He J M, et al. Dependence and risk spillovers between mainland China and London stock markets before and after the Stock Connect programs. Physica A: Statistical Mechanics and Its Applications, 2019, 526: 120883.10.1016/j.physa.2019.04.119Search in Google Scholar

[35] Baker S R, Bloom N, Davis S J. Measuring economic policy uncertainty. The Quarterly Journal of Economics, 2016, 131(4): 1593–1636.10.3386/w21633Search in Google Scholar

[36] Huang W L, Lin W Y, Ning S L. The effect of economic policy uncertainty on China’s housing market. The North American Journal of Economics and Finance, 2018, 10: 100850.10.1016/j.najef.2018.09.008Search in Google Scholar

[37] Caldara D, Albero C F, Gilchrist S, et al. The macroeconomic impact of financial and uncertainty shocks. European Economic Review, 2016, 88: 185–207.10.3386/w22058Search in Google Scholar

[38] Moore A. Measuring economic uncertainty and its effects. Economic Record, 2017, 93(303): 550–575.10.1111/1475-4932.12356Search in Google Scholar

[39] Caggiano G, Castelnuovo E, Figueres J M. Economic policy uncertainty and unemployment in the United States: A nonlinear approach. Economics Letters, 2017, 151: 31–34.10.1016/j.econlet.2016.12.002Search in Google Scholar

[40] Mumtaz H, Surico P. Policy uncertainty and aggregate fluctuations. Journal of Applied Econometrics, 2018, 33(3): 319–331.10.1002/jae.2613Search in Google Scholar

[41] Andre C, Bonga L B, Gupta R, et al. Economic policy uncertainty, US real housing returns and their volatility: A nonparametric approach. Journal of Real Estate Research, 2017, 39(4): 493–513.10.1080/10835547.2017.12091484Search in Google Scholar

[42] Christou C, Gupta R, Hassapis C. Does economic policy uncertainty forecast real housing returns in a panel of OECD countries? A Bayesian approach. The Quarterly Review of Economics and Finance, 2017, 65: 50–60.10.1016/j.qref.2017.01.002Search in Google Scholar

[43] Tarassow A. Forecasting US money growth using economic uncertainty measures and regularisation techniques. International Journal of Forecasting, 2019, 35(2): 443–457.10.1016/j.ijforecast.2018.09.012Search in Google Scholar

[44] Liu Z C, Ye Y, Ma F, et al. Can economic policy uncertainty help to forecast the volatility: A multifractal perspective. Physica A: Statistical Mechanics and Its Applications, 2017, 482: 181–188.10.1016/j.physa.2017.04.076Search in Google Scholar

[45] Pastor L, Veronesi P. Uncertainty about government policy and stock prices. The Journal of Finance, 2012, 67(4): 1219–1264.10.3386/w16128Search in Google Scholar

[46] Kang W, Ratti R A. Oil shocks, policy uncertainty and stock market return. Journal of International Financial Markets, Institutions & Money, 2013, 26: 305–318.10.1016/j.intfin.2013.07.001Search in Google Scholar

[47] Liu L, Zhang T. Economic policy uncertainty and stock market volatility. Finance Research Letters, 2015, 15: 99–105.10.1016/j.frl.2015.08.009Search in Google Scholar

[48] Balcilar M, Gupta R, Kim W J, et al. The role of economic policy uncertainties in predicting stock returns and their volatility for Hong Kong, Malaysia and South Korea. International Review of Economics & Finance, 2019, 59: 150–163.10.1016/j.iref.2018.08.016Search in Google Scholar

[49] Yu H H, Fang L B, Sun W C. Forecasting performance of global economic policy uncertainty for volatility of Chinese stock market. Physica A: Statistical Mechanics and Its Applications, 2018, 505: 931–940.10.1016/j.physa.2018.03.083Search in Google Scholar

[50] Chiang T C. Economic policy uncertainty, risk and stock returns: Evidence from G7 stock markets. Finance Research Letters, 2019, 29: 41–49.10.1016/j.frl.2019.03.018Search in Google Scholar

[51] Jiang Y H, He L L, Meng J, et al. Nonlinear impact of economic policy uncertainty shocks on credit scale: Evidence from China. Physica A: Statistical Mechanics and Its Applications, 2019, 521: 626–634.10.1016/j.physa.2019.01.100Search in Google Scholar

[52] Michael D B, John V D, Christoffer K. Economic policy uncertainty and the credit channel: Aggregate and bank level U.S. evidence over several decades. Journal of Financial Stability, 2016, 26: 90–106.10.1016/j.jfs.2016.07.002Search in Google Scholar

[53] Guo P, Zhu H M, YouWH. Asymmetric dependence between economic policy uncertainty and stock market returns in G7 and BRIC: A quantile regression approach. Finance Research Letters, 2018, 25: 251–258.10.1016/j.frl.2017.11.001Search in Google Scholar

[54] Hu Z J, Kutan A M, Sun P W. Is U.S. economic policy uncertainty priced in China’s A-shares market? Evidence from market, industry, and individual stocks. International Review of Financial Analysis, 2018, 57: 207–220.10.1016/j.irfa.2018.03.015Search in Google Scholar

[55] Fang L B, Yu H H, Li L. The effect of economic policy uncertainty on the long-term correlation between U.S. stock and bond markets. Economic Modelling, 2017, 66: 139–145.10.1016/j.econmod.2017.06.007Search in Google Scholar

[56] Liow K H, Liao W C, Huang Y T. Dynamics of international spillovers and interaction: Evidence from financial market stress and economic policy uncertainty. Economic Modelling, 2018, 68: 96–116.10.1016/j.econmod.2017.06.012Search in Google Scholar

[57] Wang Z X, Yao P Y. Dynamic spillover effects of economic policy uncertainty of China. Chinese Journal of Management Science, 2019, 27(5): 78–85.Search in Google Scholar

[58] Antonakakis N, Gabauer D, Gupta R, et al. Dynamic connectedness of uncertainty across developed economies: A time-varying approach. Economic Letters, 2018, 166: 63–75.10.1016/j.econlet.2018.02.011Search in Google Scholar

[59] Antonakakis N, Gabauer D, 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: 122280.10.1016/j.physa.2019.122280Search in Google Scholar

[60] Dew-Becker I, Giglio S. Asset pricing in the frequency domain: Theory and empirics. The Review of Financial Studies, 2016, 29(8): 2029–2068.10.3386/w19416Search in Google Scholar

[61] Stiassny A. A spectral decomposition for structural VAR models. Empirical Economics, 1996, 21(4): 535–555.10.1007/BF01180700Search in Google Scholar

[62] Qiao H, Zheng F T, Jiang H D, et al. The greenhouse effect of the agriculture-economic growth-renewable energy nexus: Evidence from G20 countries. Science of the Total Environment, 2019, 671: 722–731.10.1016/j.scitotenv.2019.03.336Search in Google Scholar PubMed

[63] Martikainen J P. Response to a comparative analysis of electricity generation costs from renewable, fossil fuel and nuclear sources in G20 countries for the period 2015–2030. Journal of Cleaner Production, 2019, 208: 142–143.10.1016/j.jclepro.2018.10.049Search in Google Scholar

[64] Fang J C, Lau C K M, Lu Z, et al. Natural disasters, climate change, and their impact on inclusive wealth in G20 countries. Environmental Science and Pollution Research, 2019, 26(2): 1455–1463.10.1007/s11356-018-3634-2Search in Google Scholar PubMed