Correo Electrónico
DNINFOA - SIA
Bibliotecas
Convocatorias
Identidad U.N.
Publicado
Efeito causal entre o indicador de bolsa de valores Ibovespa e os indicadores Shangai, S&P 500, Merval y Nikkei
Causal effect between the ibovespa stock market and Shangai, , S&P 500, Merval and Nikkei Indicators
Efecto causal entre el indicador de bolsa de valores Ibovespa y los indicadores Shanghai, S&P 500, Merval y Nikkei
DOI:
https://doi.org/10.15446/cuad.econ.v41n87.89520Palabras clave:
Causalidade de Granger, bolsas de valores, fluxo de comércio, séries de tempo, Brasil (pt)Granger's Causality, stock exchanges, trade flow, Time series, Brasil (en)
causalidad de Granger, bolsas de valores, flujo comercial, series temporales, Brasil (es)
Descargas
O estudo analisa a relação de causalidade entre o indicador bursátil brasileiro em relação a outros indicadores de bolsa de valores. Especificamente, o tempo de estudo incorpora a crise mundial causada pela covid-19 e a guerra pelo preço do petróleo.
Utilizaram-se as séries diferenciadas, considerando a existência de raiz unitária; posteriormente, realizou-se a estimação do modelo VAR e a causalidade de Granger. Nos resultados, verifica-se que a causalidade entre o Ibovespa com o S&P500 e o Nikkei é bidirecional. Esses resultados são consistentes ao relacionar o grau de intercâmbio comercial e de origem do investimento estrangeiro no Brasil.
The study analyzes the causal relationship between the Brazilian stock market indicator and other stock exchange indicators. Specifically, the study time incorporates the world crisis caused by the covid-19 and the war over the price of oil. Were used the differentiated series considering the existence of a unit root, the VAR and Granger Causality models were subsequently estimated. The results show that the causality between the Ibovespa with the S&P500 and Nikkei is bidirectional. These results are consistent when relating the degree of commercial exchange and the origin of foreign investment in Brazil.
En este artículo se analiza la relación causal entre el indicador de bolsa de valores de Brasil con relación a otros indicadores. El tiempo de estudio incorpora la crisis mundial causada por la COVID-19 y la guerra por el precio del petróleo.
Se utilizaron series diferenciadas considerando la existencia de una raíz unitaria; luego se estimó la causalidad de Granger a partir de un VAR. Se verifica que la causalidad entre el Ibovespa con el S&P500 y el Nikkei es bidireccional. Estos resultados son consistentes al relacionar el grado de intercambio comercial y el origen de la inversión extranjera en Brasil.
Referencias
Ali, M. S. (2018). Interest rates and investors behaviour: Cointegration and Granger causality. ACRN Journal of Finance and Risk Perspectives, 7(1), 1-15.
Arantes, V. A., Gusmão, I. B., & Costa, M. C. (2019). Análise do relatório de guidance em empresas investigadas pela Polícia Federal: exame sob a perspectiva do gerenciamento de impressões. Revista De Contabilidade E Organizações, 13. DOI: https://doi.org/10.11606/issn.1982-6486.rco.2019.148176
Arevalo, J. L. S., De Souza, G. M., & Meurer, R. M. (2020). The Brazilian stock market indicator: Determinants to measure variation and direction. International Journal of Science and Management Studies, 3(5), 48-59. https://doi.org/10.51386/25815946/ijsms-v3i5p105 DOI: https://doi.org/10.51386/25815946/ijsms-v3i5p105
Arévalo, J. L. S., Lima, J. R. F., de & Araújo, A. F. V. de. (2013). Determinantes de la oferta de exportación de mango: estudio de caso para el Perú. Revista de Economia e Sociologia Rural, 51(supl. 1), s101-s120. https://doi.org/10.1590/S0103-20032013000600006. DOI: https://doi.org/10.1590/S0103-20032013000600006
Balcilar, M., & Bekun, F. V. (2020). Spillover dynamics across price inflation and selected agricultural commodity prices. Journal of Economic Structures, 9(1). https://doi.org/10.1186/s40008-020-0180-0 DOI: https://doi.org/10.1186/s40008-020-0180-0
Barros, C. M. E., Lopes, L. F., & de Almeida, L. B. (2019). Efeito contágio da Operação Carne Fraca sobre o valor das ações dos principais players do mercado de proteínas do Brasil e do México. Enfoque Reflexão Contábil, 38(1), 105-122. https://doi.org/10.4025/enfoque.v38i1.39966
Beh, W. L., & Yew, W. K. (2020). Macroeconomic factors and stock markets interdependencies: Evidence from United States and China. Journal of Critical Reviews, 7(5), 68-74. DOI: https://doi.org/10.31838/jcr.07.05.11
Blahun, I. S., & Blahun, I. I. (2020). The relationship between world and local stock indices. Montenegrin Journal of Economics, 16(1), 55-67. https://doi.org/10.14254/1800-5845/2020.16-1.4 DOI: https://doi.org/10.14254/1800-5845/2020.16-1.4
Bortoluzzo, M. M., Sakurai, S. N., & Bortoluzzo, A. B. (2013). Allocation of foreign direct investment across Brazilian states. Estudos Econômicos, 43(2), 241-269. https://doi.org/10.1590/S0101-41612013000200002 DOI: https://doi.org/10.1590/S0101-41612013000200002
Bostanci, G., & Yilmaz, K. (2020). How connected is the global sovereign credit risk network? Journal of Banking and Finance, 113. https://doi.org/10.1016/j.jbankfin.2020.105761 DOI: https://doi.org/10.1016/j.jbankfin.2020.105761
Caporale, G. M., Faek, M. A., Spagnolo, F., Spagnolo, F. (2017). International portfolio flows and exchange rate volatility in emerging Asian markets. Journal of International Money and Finance, 76, 1-15. https://doi.org/10.1016/j.jimonfin.2017.03.002 DOI: https://doi.org/10.1016/j.jimonfin.2017.03.002
Camba, A. L., & Camba, A. C. J. (2020). The Effect of COVID-19 Pandemic on the Philippine Stock Exchange, Peso-Dollar Rate and Retail Price of Diesel. The Journal of Asian Finance, Economics and Business, 7(10), 543–553. https://doi.org/10.13106/jafeb.2020.vol7.no10.543 DOI: https://doi.org/10.13106/jafeb.2020.vol7.no10.543
Carrasco, C. A., & Tovar-Garcia, E. D. (2020). Export composition and the eurozone trade balance in manufacturing goods. Romanian Journal of Economic Forecasting, 23(1), 134-150.
Cellmer, R., Belej, M., & Cichulska, A. (2019). Identification of cause-and-effect relationships in the real estate market using the VAR model and the granger test. Real Estate Management and Valuation, 27(4), 85-95. https://doi.org/10.2478/remav-2019-0038 DOI: https://doi.org/10.2478/remav-2019-0038
Chen, A., & Groenewold, N. (2018). The regional effects of macroeconomics shocks in China. China Economic Review, 48(C), 139-154. DOI: https://doi.org/10.1016/j.chieco.2015.12.003
De Araújo, E., Rodrigues, V., Monte-mor, D., & Correia, R. (2018). Corrupção e valor de mercado: os efeitos da Operação Lava Jato sobre o mercado de ações no Brasil. Revista Catarinense da Ciência Contábil, 17(51). https://doi.org/10.16930/2237-7662/rccc.v17n51.2626 DOI: https://doi.org/10.16930/2237-7662/rccc.v17n51.2626
Debata, B., & Mahakud, J. (2018). Interdependence between Monetary Policy and Stock Liquidity: A Panel VAR Approach. Margin: The Journal of Applied Economic Research, 12(4), 387–413. https://doi.org/10.1177/0973801018786270 DOI: https://doi.org/10.1177/0973801018786270
Dickey, D., & Fuller, W. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49, 1057-1072. DOI: https://doi.org/10.2307/1912517
Duran, S., & Ersin, I. (2020). The Effects of Trade Wars Between US and China on the Financial Performances of the Companies. In Dincer, H., Yüksel, S. (eds.), Strategic Priorities in Competitive Environments. Contributions to Management Science. Springer, Cham. https://doi.org/10.1007/978-3-030-45023-6_16 DOI: https://doi.org/10.1007/978-3-030-45023-6_16
Dwumfour, R. A., & Addy, N. A. (2019). Interest rate and exchange rate exposure of portfolio stock returns: Does the financial crisis matter? Journal of African Business, 20(3), 339-357. https://doi.org/10.1080/15228916.2019.1583977 DOI: https://doi.org/10.1080/15228916.2019.1583977
Economatica. (2020). Banco de dados. https://economatica.com
Enders, W. (2009). Applied Econometric Time Series (3a ed.). Wiley.
Engle, R. F., & Granger, C. W. J. (1987). Co-Integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251-276. DOI: https://doi.org/10.2307/1913236
Escher, F., & Wilkinson, J. (2019). The political economy of the BrazilChina soy-meat complex. Revista de Economia e Sociologia Rural, 57(4), 656-678. https://doi.org/10.1590/1806-9479.2019.191017 DOI: https://doi.org/10.1590/1806-9479.2019.191017
Farias, H. P., & Sáfadi, T. (2010). Causalidade entre as principais bolsas de valores do mundo. RAM, Revista de Administração Mackenzie (on-line), 11(2), 96-122. https://doi.org/10.1590/S1678-69712010000200005 DOI: https://doi.org/10.1590/S1678-69712010000200005
Feng, L., Zhang, X., & Liu, B. (2020). Multivariate tests of independence and their application in correlation analysis between financial markets. Journal of multivariate analysis, 179. https://doi.org/10.1016/j.jmva.2020.104652 DOI: https://doi.org/10.1016/j.jmva.2020.104652
Fundo Monetário Internacional. (2020). Text: Global Financial Stability Overview: Markets in the Time of COVID-19https://www.imf.org/en/~/link.aspx?_id=7248F3B62F0D4F18ABB71C15A44CDF3E&_z=z#Chapter1
Global Economic Data, Indicators, Charts & Forecasts. (2020). Economic, sector and industrial data. https://www.ceicdata.com/en
Gnangnon, S. K. (2020). Aid for Trade and sectoral employment diversification in recipient-countries. Economic Change and Restructuring, 53(2), 265-295. https://doi.org/10.1111/1467-8454.12200 DOI: https://doi.org/10.1007/s10644-018-9238-5
Gogas, P., Pragidis, I., & Tabak, B. M. (2018). Asymmetric effects of monetary policy in the U.S and Brazil. Journal of Economic Asymmetries, 18. https://doi.org/10.1016/j.jeca.2018.e00108. DOI: https://doi.org/10.1016/j.jeca.2018.e00108
Gouvea, R., Kapelianis, D., & LI, S. (2020). Fostering intra-BRICS trade and investment: The increasing role of China in the Brazilian and South African economies. Thunderbird International Business Review, 62(1), 17-26. https://doi.org/10.1002/tie.22098 DOI: https://doi.org/10.1002/tie.22098
Gujarati, D. N., & Porter, D. C. (2011). Econometria básica (5ª ed.). AMGH.
Gusarova, S. (2019). Role of China in the development of trade and FDI cooperation with BRICS countries. China Economic Review, 57. https://doi.org/10.1016/j.chieco.2019.01.010 DOI: https://doi.org/10.1016/j.chieco.2019.01.010
Hertina, D., Hidayat, M. B. H., & Susanti, N. (2019). Company value: Impact of capital structure, company growth and liquidity. International Journal of Innovation, Creativity and Change, 6(5), 73-84.
Huber, F., & Fischer, M. M. (2018). A Markov switching factor-augmented VAR model for analyzing US business cycles and monetary policy. Oxford Bulleting of Economics and Statistics, 80(3), 575-604. https://doi.org/10.1111/obes.12227 DOI: https://doi.org/10.1111/obes.12227
Iskenderoglu, O., & Akdag, S. (2020). Comparison of the Effect of Vix Fear Index on Stock Exchange Indices of Developed and Developing Countries: The G20 Case. South East European Journal of Economics and Business, 15(1), 105-121. https://doi.org/10.2478/jeb-2020-0009 DOI: https://doi.org/10.2478/jeb-2020-0009
Ito, T., Koibuchi, S., Sato, K., & Shimizu, J. (2016). Exchange rate exposure and risk management: The case of Japanese exporting firms. Journal of the Japanese and International Economies, 41(2), 17-29. https://doi.org/10.1016/j.jjie.2016.05.001 DOI: https://doi.org/10.1016/j.jjie.2016.05.001
Kartika, A., Sunarto, S., Rahman, F. R., & Machmuddah, Z. (2020). Determinants of capital structure and their effect to company’s value: study in LQ 45 companies listed in Indonesia stock exchange. Academic Journal of Interdisciplinary Studies, 9(3), 156-165. https://doi.org/10.36941/ajis-2020-0051 DOI: https://doi.org/10.36941/ajis-2020-0051
Lutkepohl, H., Staszewska-Bystrova, A., & Winker, P. (2020). Constructing joint confidence bands for impulse response functions of VAR models: A review. Econometrics and Statistics, 13, 69-83. https://doi.org/10.1016/j.ecosta.2018.10.002 DOI: https://doi.org/10.1016/j.ecosta.2018.10.002
Markoni, P. L. (2020). Foreign portfolio investments, exchange rates and capital openness: A panel data approach. International Journal of Economics and Business Administration, 8(2), 100-113. http://dx.doi.org/10.35808/ijeba/458 DOI: https://doi.org/10.35808/ijeba/458
Matuka, A. (2019). Exchange rate pass-through to prices: VAR evidence for Albania. International Journal of Business, Economics and Management, 6(5), 303-315. http://dx.doi.org/10.18488/journal.62.2019.65.303.315 DOI: https://doi.org/10.18488/journal.62.2019.65.303.315
Megaravalli, A. V., & Sampagnaro, G. (2018). Macroeconomic indicators and their impact on stock markets in ASIAN 3: A pooled mean group approach. Cogent Economics and Finance, 6(1). https://doi.org/10.1080/23322039.2018.1432450 DOI: https://doi.org/10.1080/23322039.2018.1432450
Ning W., Jiahui G., Xiang, L., & Tong, F. (2020). A service demand forecasting model for one-way electric car-sharing systems combining long short-term memory networks with Granger causality test. Journal of Cleaner Production, 244. https://doi.org/10.1016/j.jclepro.2019.118812
Padula, A. J. A., & Albuquerque, P. H. M. (2018). Corrupção governamental no mercado de capitais: um estudo acerca da operação lava jato. Revista de Administração de Empresas, 58(4), 405-417. https://doi.org/10.1590/S0034-759020180406 DOI: https://doi.org/10.1590/s0034-759020180406
Park, Y. K., Binh, K. B., & Kim, S. J. (2019). Time varying correlations and causalities between stock and foreign exchange markets: Evidence from China, Japan and Korea. Investment Analysts Journal, 48(4), 278- 297. https://doi.org/10.1080/10293523.2019.1670385 DOI: https://doi.org/10.1080/10293523.2019.1670385
Righi, M. B., Ceretta, P. S., & Coronel, D. A. (2014). Estimation of non -linear dependence between exchange rate and stock market through different time scales: Latin case. Inzinerine Ekonomika-Engineering Economics, 25(3), 302-309. https://doi.org/10.5755/j01.ee.25.3.3710 DOI: https://doi.org/10.5755/j01.ee.25.3.3710
Ruch, F., Balcilar, M., Grupta, R., & Modise, M. P. (2020). Forecasting core inflation: The case of South Africa. Applied Economics, 52(28), 3004-3022. https://doi.org/10.1080/00036846.2019.1701181 DOI: https://doi.org/10.1080/00036846.2019.1701181
Siami-Namini, S., Lyford, C., & Trindade, A. A. (2020). The effects of monetary policy shocks on income inequality across U.S. states. Economic Papers, 39(3), 204-221. https://doi.org/10.1111/1759-3441.12279 DOI: https://doi.org/10.1111/1759-3441.12279
Stats. (2020). National Bureau of Statistics of China. Dados de base estatística. http://www.stats.gov.cn/english/Statisticaldata/AnnualData
Sugianto, S., Oemar, F., Hakim, L., & Endri, E. (2020). Determinants of firm value in the banking sector: Random effects models. International Journal of Innovation, Creativity and Change, 12(8), 208-218.
Tan, D. Y., & Chin, M. Y. (2017). ASEAN China trade Flow: A study on intra-industry trade in manufacturing sector. Advanced Science Letters, 23(4), 2961-2694. DOI: https://doi.org/10.1166/asl.2017.7687
Visalakshmi, S., & Manickavasagam, J. (2018). Causal linkage between stock markets: International evidence. International Journal of Mechanical Engineering and Technology, 9(7), 902-908.
Weigt, T., & Wilfling, B. (2020). An approach to increasing forecast-combination accuracy through VAR error modeling. Journal of Forecasting, 40(4), 686-699. https://doi.org/10.1002/for.2733 DOI: https://doi.org/10.1002/for.2733
Wang, N., Guo, J., Liu, X., & Fang, T. A service demand forecasting model for oneway electric car-sharing systems combining long short-term memory networks with Granger causality test. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2019.118812 DOI: https://doi.org/10.1016/j.jclepro.2019.118812
Zhao, J., & Serieux, J. (2018): Globalization, regionalization and convergence in East Asia. The Journal of International Trade & Economic Development, 28(4), 486-507. https://doi.org/10.1080/09638199.2018.1550801 DOI: https://doi.org/10.1080/09638199.2018.1550801
Cómo citar
APA
ACM
ACS
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Descargar cita
Licencia
Derechos de autor 2022 Cuadernos de EconomíaCuadernos de Economía a través de la División de Bibliotecas de la Universidad Nacional de Colombia promueve y garantiza el acceso abierto de todos sus contenidos. Los artículos publicados por la revista se encuentran disponibles globalmente con acceso abierto y licenciados bajo los términos de Creative Commons Atribución-No_Comercial-Sin_Derivadas 4.0 Internacional (CC BY-NC-ND 4.0), lo que implica lo siguiente:
