Long Term Effects of Major Macroeconomic Variables on the Brazilian Stock Market: A nonlinear ARDL application

  • WELIGTON GOMES Federal University of Ceara (UFC/Sobral)
Keywords: NARDL;, Stock Market;, Macroeconomic Variables;, Asymmetry


This research used NARDL methodology to investigate relevant macroeconomic variables influence on the Brazilian stock market index. We used monthly data from January/2000 to July/2020 and the six macroeconomic variables investigated are described as follows: net government's debt/GDP (DEBT), exports (EXPORT), consumer confidence (ICC), liquidity ratio (M4_PIB), interest rate (SELIC) besides the stock market index (IBOV). All monthly data were collected from IPEADATA. The main conclusions are that there is long run effect of IBOVESPA due to a decrease of government debt is clear and statistically significant, the long run effect in the liquidity ratio also affects IBOVESPA index. Moreover, the most outstanding result was the long run effect of decrease in the interest rate over the IBOVESPA index. Sustainable reductions in the interest rate would consistently stimulate the stock market index. Research outcomes also indicate that long run asymmetries of government debt, liquidity ratio and interest rate are reliable and statistically significant.


[1]. Bernardelli L. V. and Castro G.H.L. (2020). STOCK MARKET AND MACROECONOMIC VARIABLES: EVIDENCE FOR BRAZIL, Revista Catarinense da Ciência Contábil, ISSN 2237-7662, V.19, 1-15, e2892, 2020, DOI: 10.16930/2237-766220202892 Available at http://revista.crcsc.org.br
[2]. Molefhi, K. (2019). The Impact of Macroeconomic Variables on Capital Market Development in Botswana’s Economy, Botswana Institute for Development Policy Analysis, Working Paper 62, ISBN: 99912-65-69-4, March 2019.
[3]. Gay Jr., R. D. (2016). Effect of Macroeconomic Variables on Stock Market Returns for Four Emerging Economies: Brazil, Russia, India and China; International Business & Economics Research Journal – May/June 2016 Volume 15, Number 3.
[4]. Linck L. and Decourt R. F. (2016). Stock returns, macroeconomic variables and expectations: Evidence from Brazil, Pensamiento y Gestión, N° 40, ISSN 1657-6276, DOI: http://dx.doi.org/10.14482/pege.40.8806.
[5]. Liberty A. N. (2012). AN ECONOMETRIC ANALYSIS OF THE IMPACT OF MACROECONOMIC VARIABLES ON STOCK PRICES IN NIGERIA: A VECTOR AUTOREGRESSIVE (VAR) MODEL APPROACH, International Review of Business and Social Sciences, Vol. 1, No 8 July 2012 [63-77], ISSN: 2226-4124.
[6]. Hsing Y. (2011). IMPACTS OF MACROECONOMIC VARIABLES ON THE STOCK MARKET IN BULGARIA AND POLICY IMPLICATIONS, Journal of Economics and Business, Volume XIV – 2011, No 2 (41-53).
[7]. Maysami R. C., Howe L. C. and Hamzah M. A. (2004). Relationship between Macroeconomic Variables and Stock Market Indices: Cointegration Evidence from Stock Exchange of Singapore’s All-S Sector Indices, Jurnal Pengurusan, 24(2004) 47-77.
[8]. Van, D. T. B., and Bao, H. H. G. (2019). A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis on the Determinants of Vietnam’s Stock Market. In International Econometric Conference of Vietnam (pp. 363-376). Springer, Cham.
[9]. Shin, Y., Yu, B., and Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In Festschrift in Honor of Peter Schmidt (pp. 281-314). Springer, New York, NY.
[10]. Pesaran, M. H., Shin, Y., and Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of applied econometrics, 16(3), 289-326.
[11]. Banerjee, A., Dolado, J. and Mestre, R. (1998), “Error‐correction mechanism tests for cointegration in a single‐equation framework”, Journal of time series analysis, Vol. 19 No.3, pp.267-283.
[12]. Pesaran, M. H., and Shin, Y. (1998). An autoregressive distributed-lag modelling approach to cointegration analysis. Econometric Society Monographs, 31, 371-413.
How to Cite
MELO, M., & GOMES, W. (2021). Long Term Effects of Major Macroeconomic Variables on the Brazilian Stock Market: A nonlinear ARDL application. Archives of Business Research, 9(2), 289-299. https://doi.org/10.14738/abr.92.9771