Global financial cycle and Brazil’s financial integration

ABSTRACT

Recent studies have discussed the influence of the global financial cycle on capital flows to emerging and developing countries. This paper evaluates the relationship between the greater degree of financial integration, and macroeconomic performance over the last two decades in Brazil. The literature has highlighted the Brazilian experience as being paradigmatic among emerging countries regarding the relationship between financial integration and regulation of capital flows to deal with boom and bust cycles. Methodologically, we employ a vector autoregressive model with error correction that allows us to evaluate the cointegration between the variables. Our main hypothesis is that a greater degree of financial integration is associated with negative developments in variables such as gross domestic product, country risk, interest rates, and exchange rate volatility. In addition, this study presents a further contribution by observing the existence of the interaction between the consequences of financial integration and the global financial cycle. More specifically, we found that: (i) an increase in the degree of financial integration generates deeper effects in downward periods of the global financial cycle; and (ii) a decline in that cycle generates greater impacts when a higher degree of financial integration is present.

KEYWORDS:

• Global financial cycle
• macroeconomic performance
• external financial liberalization
• Brazil

JEL CLASSIFICATION:

• E44
• F36
• F62

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. From the collapse of the Gold Standard up to the emergence of neoliberalism, unrestricted private capital flows have been perceived as a major source of macroeconomic instability (Gallagher 2015; Akyüz 2017, Ocampo 2018). Conventional wisdom was that finance across borders should be regulated. Policymakers counted on controls to be able to pursue domestic goals such as full employment – a view that, as Gallagher noted (2015, pp. 51–52), appears in Keynes’s Tract on Monetary Reform (1929) and Treatise on Money (1930) long before the Mundell-Fleming model formulated the so-called ‘impossible trinity’.

2. Traditionally, the expression ‘capital account liberalization’ is applied to express the deregulation of external financial transactions, which are registered in the financial account of the Balance of Payments. Consequently, we use that expression as a synonym of ‘financial account’ liberalization or deregulation.

3. There are several studies on the negative consequences of financial liberalization, such as Grabel (1995), Arestis and Sawyer (2016), which follow different heterodox traditions, particularly the post-Keynesian one; Prasad, Rajan, and Subramanian (2007), Broner and Ventura (2010) and Ostry et al. (2010) all apply a mainstream perspective.

4. Capital and financial account liberalization amplifies exchange rate volatility, whilst capital controls create an expectation of greater exchange rate stability in the future, reducing exchange rate risk.

5. Studies that rely mainly on de jure measures – like Bekaert, Harvey, and Lundblad (2005), Cardoso and Goldfajn (1998), Edwards (2001), Gallindo, Schiantarelli, and Weiss (2007), Klein (2005) and Quinn (1997) – are not the focus of this paper.

6. There are also papers that support their conclusions in panel econometrics, such as Blanchard et al. (2015), Eichengreen and Leblang (2003), Furceri and Loungani (2015), Mirzae, Kutan, and Igan (2016), among others.

7. The following BPM6 codes of the Central Bank of Brazil were included in the FII: 22,861, 22,862, 22,866, 2867, 22,907, 22,908, 22,925, 22,926, 22,967, 22,968, 22,970 e 22,971.

8. Here a two decades period, January of 1996 to December of 2016, was chosen as a sample. Ideally, more observations would have been better; but we adopted the Real Plan as the initial framework because previous data could make modeling difficult due to the macroeconomic volatility of the inflation period.

9. Further details in Pesaran and Shin (1998), Tsay (2005) and Enders (2010).

10. We present only the tests in which the null hypothesis of absence of Granger-causality is rejected, considering a degree of confidence of .95.

11. As robustness checks, we estimated four variations of our VEC model by (i) using CBOE S&P 100 Volatility Index (VXO) as proxy for the global financial cycle instead of VIX, as suggested by Obstfeld, Ostry, and Qureshi (2017); (ii) including the sum of gross financial flows (FF) as indicator of financial integration instead of the FII; (iii) replacing GDP by gross capital formation (INV) as proxy of the level of activity (monthly capital formation index from IPEA); and (iv) adding to it the inflation rate (INF), measured by the National Consumer Price Index (IPCA). After following the same data treatment and estimation procedures, all models presented well-behaved residuals and existence of cointegration vector, considering a degree of confidence of at least .99. The impulse response functions reinforce our findings and are plotted in the Appendix (see Figures A1, A2, A3 and A4).

12. On the one hand, as the choice of VEC model indicates, we are aware of the potential endogeneity between the variables. On the other hand, considering specifically the relationship between the global financial cycle and the Brazilian economy, empirical studies conducted by Chen, Mancini-Griffoli, and Sahay (2014), Nier, Sedik, and Mondino (2014), Rey (2013) and Ricci and Shi (2016) concluded that the global financial cycle is mainly shaped by advanced countries’ monetary policy, specially the US. In this sense, it is possible to argue that emerging and developing economies like Brazil have a minor influence on the global financial cycle.

13. Details in Paula et al. (2012); Paula (2011), Gallagher (2015), IMF (2016) and Biancarelli, Rosa, and Vergnhanini (2017).

14. See Cardoso and Goldfajn (1998), Arida, Bacha, and Lara-Resende (2005), Paula (2011), Paula et al. (2012), Gallagher (2015) and Chamon and Garcia (2016).

Additional information

Funding

This work was supported by CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico (Bolsa Produtividade em Pesquisa - Processo n. 30312).