Reference: | 1. Abduraimova, K. (2022). Contagion and tail risk in complex financial networks. Journal of Banking & Finance, 143, 106560. 2. Brandes, U., Borgatti, S. P., & Freeman, L. C. (2016). Maintaining the duality of closeness and betweenness centrality. Social networks, 44, 153-159. 3. Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer networks and ISDN systems, 30(1-7), 107-117. 4. Ciaschini, M., Pretaroli, R., Severini, F., & Socci, C. (2012). Regional double dividend from environmental tax reform: An application for the Italian economy. Research in Economics, 66(3), 273-283. 5. De Benedictis, L., & Tajoli, L. (2010). Comparing sectoral international trade networks. Aussenwirtschaft. 6. De Benedictis, L., & Tajoli, L. (2011). The world trade network. The World Economy, 34(8), 1417-1454. 7. Demirer, M., Diebold, F. X., Liu, L., & Yilmaz, K. (2018). Estimating global bank network connectedness. Journal of Applied Econometrics, 33(1), 1-15. 8. Diebold, F. X., & Yılmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of econometrics, 182(1), 119-134. 9.Ferrier, G. D., Reyes, J., & Zhu, Z. (2016). Technology diffusion on the international trade network. Journal of Public Economic Theory, 18(2), 291-312. 10. Forbes, K. J., & Rigobon, R. (2002). No contagion, only interdependence: measuring stock market comovements. The journal of Finance, 57(5), 2223-2261. 11. Freeman, L. C. (2002). Centrality in social networks: Conceptual clarification. Social network: critical concepts in sociology. Londres: Routledge, 1, 238-263. 12. He, J., & Deem, M. W. (2010). Structure and response in the world trade network. Physical review letters, 105(19), 198701. 13. Ilori, A. E., Paez-Farrell, J., & Thoenissen, C. (2022). Fiscal policy shocks and international spillovers. European Economic Review, 141, 103969. 14. Kali, R., & Reyes, J. (2010). Financial contagion on the international trade network. Economic Inquiry, 48(4), 1072-1101. 15. Liang, S., Feng, Y., & Xu, M. (2015). Structure of the global virtual carbon network: revealing important sectors and communities for emission reduction. Journal of Industrial Ecology, 19(2), 307-320. 16. Liu, B. Y., Fan, Y., Ji, Q., & Hussain, N. (2022). High-dimensional CoVaR network connectedness for measuring conditional financial contagion and risk spillovers from oil markets to the G20 stock system. Energy Economics, 105, 105749. 17. Ligthart JE (1999) g-fiscalmacro-imf.pdf. IMF Work Pap WP/98/75:1--35 18. Miller, S., & Vela, M. (2013). Are environmentally related taxes effective?Inter-American Dev Bank IDB-WP-467. 19. Nobi, A., Lee, T. H., & Lee, J. W. (2020). Structure of trade flow networks for world commodities. Physica A: Statistical Mechanics and its Applications, 556, 124761. 20. Patton, A. J. (2006). Modelling asymmetric exchange rate dependence. International economic review, 47(2), 527-556. 21. Pons, P., & Latapy, M. (2005). Computing communities in large networks using random walks. In Computer and Information Sciences-ISCIS 2005: 20th International Symposium, Istanbul, Turkey, October 26-28, 2005. Proceedings 20 (pp. 284-293). Springer Berlin Heidelberg. 22. Shahzad, U. (2020). Environmental taxes, energy consumption, and environmental protection: Theoretical survey with policy implications. Environmental Science and Pollution Research, 27(20), 24848-24862. 23. Tong, C., Chen, J., & Buckle, M. J. (2018). A network visualization approach and global stock market integration. International Journal of Finance & Economics, 23(3), 296-314. 24. Wen, F., Yang, X., & Zhou, W. X. (2019). Tail dependence networks of global stock markets. International Journal of Finance & Economics, 24(1), 558-567. 25. Wu, B., Zhu, P., Yin, H., & Wen, F. (2023). The risk spillover of high carbon enterprises in China: Evidence from the stock market. Energy Economics, 126, 106939. 26. Xia, L., Li, Y., & Ma, X. (2023). Identification of key carbon emitters from the erspective of network analysis. Ecological Indicators, 150, 110284. 27. Yanquen, E., Livan, G., Montanez-Enriquez, R., & Martinez-Jaramillo, S. (2022). Measuring systemic risk for bank credit networks: A multilayer approach. Latin American Journal of Central Banking, 3(2), 100049. 28. Zhang, X., Yang, X., Li, J., & Hao, J. (2023). Contemporaneous and noncontemporaneous idiosyncratic risk spillovers in commodity futures markets: A novel network topology approach. Journal of Futures Markets, 43(6), 705-733. 29. Mahanti, A., Carlsson, N., Mahanti, A., Arlitt, M., & Williamson, C. (2013). A tale of the tails: Power-laws in internet measurements. IEEE Network, 27(1), 59-64. |