Central bank intervention plays a major role in managing exchange rate volatility. In comparison to advanced economies, emerging market economies are generally active in the forex market as excessive volatility of the local currency persists. The BRICS countries (Brazil, Russia, India, China and South Africa) are the major emerging economies influencing the international financial system. The paper empirically investigates the efficacy of central bank intervention in the case of the BRICS countries. It has been observed that intervention generally did not impact the exchange rate level; however, it reduced the volatility of the exchange rate. Furthermore, interventions in spot and derivatives markets are equally effective in containing exchange rate volatility, except in South Africa. It has been identified that sovereign yield spread impacts the exchange rate returns in China and South Africa and impacts the volatility in the returns in Brazil and Russia.

Сentral bank, foreign exchange market, intervention, BRICS, GARCH.

The international monetary system has undergone a drastic change in the past decades, with the collapse of the Bretton Woods system¹ when the era of the fixed exchange rate system became history and flexible exchange rate policies were adopted by most of the central banks. The flexibility of the exchange rate has helped emerging economies (EMEs) to make it more market-driven and reduce intervention by policymakers. The flexible exchange also helped EMEs to (i) attract foreign investment, (ii) overcome their balance of payment crises. However, this also leads to infrequent interest rate wars and central bank intervention in achieving a competitive exchange rate, which has become a usual phenomenon.

Although it is a well-known fact that almost all central banks intervene in the foreign exchange market, there is no clear consensus on the efficacy of intervention. The question arises as to whether the interventional operation really matters? Finding the answer to this question is not easy. The area related to central bank interventions is traditionally considered secret, and the justification for this is that there might be a misuse of information by market participants (Neely, 2006). Particularly, this issue is more pertinent in the case of EMEs where market size is usually larger than the central bank’s ability to intervene in the market (Humpage, 2011).

Due to the secrecy of the area related to the intervention, there are many issues. The main issue in this area is data availability. Barring very few EMEs (Latin American and Eurozone countries), there is a lack of publicly available daily data that would be the best choice to examine the efficacy of intervention. Most EMEs do not publish intervention data or if they do, then monthly, quarterly or yearly with a delay of one or two months. Due to global pressure to disclose exchange rate related activities, many countries publish data on interventions; further, the authenticity of the data is questionable. In the absence of a publicly available and credible dataset, alternative proxies are commonly used in the literature, such as a change in the official foreign exchange reserve as a proxy for intervention. It has been observed that the recent dataset on intervention published in the IMF working paper and compiled by Adler et al. (2021) captured more accurate intervention activities than any other proxies.

Why BRICS? In 2001, Jim O’Neill from Goldman Sachs first coined the acronym “BRIC,” referring to the group of Brazil, Russia, India and China. In 2010, the fifth country, i.e., South Africa, joined the group and BRIC became BRICS. The emergence of new economic power blocks, such as BRICS, has witnessed a new role in international finance. China became a manufacturing hub and the world’s largest foreign exchange reserves holder. The BRICS share in the world’s GDP is around 23%. The five nations comprise 42.58% of the world’s population, 17% of the global trade, and have 13.24% of voting power in the World Bank and 14.91% of the IMF quota (Rao & Padhi, 2020). Further, the global financial crisis (GFC) of 2008-2009 witnessed an overall shift in financial market activities. After the GFC, an increase in capital flows to emerging economies resulted in appreciation pressure on their currencies. During this period, many economies actively intervened in the foreign exchange market.

The BRICS countries have differences in many fields. The five member countries are spread over four continents, with China having the largest population and Russia – the largest land area. The group members have different politics and economics. Nevertheless, there are many commonalities between them, for example, all emerging economies would like to influence the world by internationalizing currency and increasing foreign trade. All five economies formally admit that they intervene in the foreign exchange market to reduce the volatility of the domestic currency. The motives of the intervention are again a debatable area as there is no clear message from central banks. Furthermore, if there is a clear message, there is a difference between de facto and de jure. The issues with the availability of intervention data and the lack of clarity in the motives of intervention by central banks lead to estimation or methodological problems.

In this background, considering the importance from the central bank’s point of view of examining the efficacy of intervention, the paper seeks to study the BRICS countries. Although there are some studies in the literature that discuss the BRICS foreign exchange markets (Kannaiah & Murty, 2017; Aroul & Swanson, 2018; Kannaiah & Murty, 2017), these studies are scant and address other issues besides the efficacy of central bank intervention. However, there are a large number of studies that analyse the effectiveness of intervention for Brazil (Eduardo et al., 2011; Oliveira, 2020; Viola et al., 2019) as Brazil publishes intervention data with daily frequency. In the case of other BRICS members, the studies are limited due to data inadequacy.

The present study attempts to assess the efficacy of forex intervention on the example of the BRICS countries. Further, when analysing the efficacy of intervention, the study also compares the differences and similarities of the BRICS countries. The study addresses the question: “Are interventions in the spot market and the derivatives market equally effective?” and examines the main driving forces or techniques involved in the intervention, as well as their intensity and direction of impact. Considering the volatility in the exchange rate variable, we use the GARCH (1,1) methodology to understand the efficacy of central bank intervention and other macroeconomic variables.

The results of the empirical estimates indicate that central bank intervention matters in both spot and derivatives markets as the intervention in both spot and derivatives markets reduces the volatility of the exchange rate returns. However, intervention plays a limited role in influencing the level of the exchange rate.

The rest of the study is divided into five sections. Section 1 provides background information on exchange rate volatility and central bank intervention in the BRICS countries. Section 2 presents currency markets and related policies adopted by the BRICS nations. Section 3 contains a report on the main studies available on the issue of efficiency of forex intervention in the BRICS countries; section 4 explains the data used in the study and the empirical methodology; section 5 empirically estimates the efficacy of central bank intervention, while last section, on policy implications, concludes the study.

Excessive exchange rate volatility adversely impacts the economy. Although excessive volatility results in different outcomes for corporations, from an investor’s point of view, this creates uncertainty about future outcomes (Gulde & Wolf, 1992). Movements in the exchange rate of a particular currency depend on various macroeconomic factors and the exchange rate regime adopted by the country. Mainly there are two extreme regimes: fixed and floating. In between these two extreme regimes, various mixed regimes can be seen. However, the efficiency and suitability of exchange rate regimes have been the subject of research.

In its Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER) the IMF publishes exchange rate practices followed by various members. There are more than 10 exchange regimes, starting from free-floating, mostly adopted by advanced counties, to exchange rate arrangements with no separate legal tender (such as the European Currency Union), and currency board arrangements, such as the Hong Kong Monetary Authority’s fixed exchange rate arrangement.

In the case of the BRICS countries, the IMF categorises Russia as a country with a free-floating exchange regime in which the central bank rarely intervenes in the foreign exchange market. Brazil, India and South Africa are grouped in a floating exchange rate regime under which the market forces largely determine the exchange rate. However, there is no predetermined path in which the central bank can intervene in the exchange rate in the market to prevent undue volatility. However, the IMF has kept it in the residual category (other managed arrangement regime) for China.

There is no clear consensus about which exchange regime is best for a particular country. However, early literature suggests that in smaller countries with open economies, a fixed exchange rate regime is suitable as it eliminates unwanted volatility of the exchange rate and helps the country keep inflation under control. On the other hand, a flexible exchange regime tends to allocate resources efficiently as the market forces determine it. In reality, an optimal exchange rate system is not an option but rather a decision determined by the failure of previous systems to deliver stability and sustainable growth (Dua & Ranjan, 2012).

The performance of the BRICS countries after the exchange rate changes also varies, and there is an interdependence of spillover effect as identified in the correlation matrix of Table 2 below. The sample period is from January 2000 to July 2021 at a non-nominal exchange rate per USD. All currency returns are positively correlated. Returns from the South African rand are positively correlated with the Brazilian real, the Russian ruble and the Indian rupee returns. It can be observed that, except the Chinese yuan, other three currencies indicate interdependency. The Chinese renminbi is the least correlated currency with other BRICS currencies. A possible reason behind this may be the fact that, in comparison to their currencies, the Chinese currency is tightly regulated by the PBOC (Dube, 2019).

Exchange rate volatility has always been a major concern for any central bank. Apart from various other macroeconomic variables, such as money supply, current account balance, external trade, inflation, etc., uneven movements in the exchange rate can play the role of a leading indicator (Rao & Padhi, 2020). Excessive volatility leads to a currency crisis if it cannot be contained on time. It has been observed that large capital flows and heightened volatility in the exchange rate were closely related (Chutasripanich & Yetman, 2015).

In the case of the BRICS currencies, Brazil experienced a currency crisis in 1999, with hyperinflation exceeding 900% in 1994 (Gruben & Kiser, 1999). However, recently, due to continuous monitoring, the Brazilian real has been comparatively stable against the Russian ruble and the Indian rupee.

Russia experienced a currency crisis in 1997-1998 and recently, in 2014-2015 (Rodionov et al., 2015). In the following Figure 1, we can see that the Russian ruble sharply devaluated in 2014-2015. South Africa also experienced episodes of currency crises between 1998 and 2001 (Bhundia & Ricci, 2005). However, the rand could not swing largely as compared to what other currencies usually experience during a currency crisis. India also faced a currency crisis in 1991. China and India also faced currency pressure during the East Asian crisis of 1997 (Peng & Bajona, 2008).

Figure 1. 

Exchange rate per USD. Source: Official websites of each country’s central banks.

The BRICS countries need to be more vigilant considering the past experiences of currency crises. Apart from fiscal prudence, the countries need to ensure financial stability.

Central bank intervention in the foreign exchange market is not a very recent phenomenon, the first kind of intervention policy was used in the US during the Great Depression. Exchange rate regimes are the main determinants of interventions.

China’s exchange rate policy is perhaps the most popular example of intervention. Being an export-oriented economy, China’s central bank always ensured that yuan did not appreciate against the US dollar, as the USA is the main importer of its goods. The Bank of Japan is also a classic case of intervention. As Japan was suffering from chronic depression and other shocks, like a massive earthquake and nuclear disaster in 2011, therefore, to overcome these situations, the Bank of Japan undertook massive intervention activities in collaboration with the US Federal Reserve and the European Central Bank, which is an example of coordinated intervention. For the most part, Japan succeeded in achieving its intervention objectives.

But, as stated earlier, intervention can pursue different targets: either to change level or to contain volatility, or both (Disyatat & Galati, 2005). As emphasised by the Plaza Accord² and the Louvre Accord,³ in the 1980s, intervention by major central banks was mainly directed at the exchange rate level. While after the 1990s, the main objective behind the intervention was to curtail the unwarranted volatility, as declared by various central banks on various occasions.

Literature on the effectiveness of intervention related to BRICS is very limited. However, some studies examine the BRICS foreign exchange market and their comparison, exchange rate pass-through and relationship between exchange rate equity prices.

Maradiaga et al. (2012) evaluated the exchange rate volatility in the case of BRICS currencies. The authors used the vector autoregressive model (VAR) and the Granger Causality test for a coefficient of variation in the real exchange rates. Apart from the exchange rate, other variables used in the paper were GDP, agriculture export, agriculture GDP of monthly frequency for the period from January 1961 to December 2008. The authors observed that exchange rate volatility had a statistically significant impact on agricultural exports in China and Brazil. However, the authors could not find any effect for other countries – India, Russia, and South Africa. Furthermore, the authors advocated that BRICS should have had their own currency for the purposes of trade or the issuance of credits or grants to each other.

It is important to understand how exchange rates impact inflation. In this direction, Balcilar et al. (2021) examined exchange rate pass-through (ERPT) in the BRICS countries. The authors used monthly frequency data from January 1999 to December 2019 on consumer price index, nominal exchange rate, short-term nominal interest rate and output growth. Using non-linear STVAR (smooth transition vector autoregressive model) methodology, the authors observed that the pass-through of the exchange rate was higher when the economy was in a high growth phase, indicating that economic activities impacted ERPT. Another study on exchange rate volatility and the ERPT by Vieiraa and da Silva (2020) found a long-run cointegration relationship between the exchange rate and other macroeconomic variables. The authors used the ARDL bound test on the exchange rate, money supply, inflation, index of industrial production, international reserves, and oil prices from January 2005 to December 2019. Further, the authors claimed that there was a sluggish speed of the exchange rate and inflation towards adjustment in long-run equilibrium.

Das and Roy (2021) investigated the turning points in volatility of the BRICS currencies using the Markov switching autoregressive methodology. Based on daily exchange rate data from April 2006 and March 2018, the authors identified that the Chinese yuan had the least volatility among the BRICS currencies; on the other hand, the authors found the highest volatility in the South African rand. Other variables used in the paper were interest rates, money supply, index of industrial production, foreign exchange reserves and inflation. Furthermore, the authors suggested that lower volatility in the Chinese yuan was the result of China’s more active intervention in the foreign exchange market.

Regarding monetary policies towards exchange rate in the BRICS countries, Mallick and Sousa (2013) stated that contractionary monetary policies in the BRICS countries reduced output and increased appreciation pressure on their domestic currency. Using quarterly data for the period from 1990 Q1 to 2008 Q4, as well as the policy rate and other macroeconomic variables, such as GDP and inflation, the authors observed that commodity price shock played a crucial role in returns in the BRICS currencies.

While estimating vulnerability to global crises, Caporale et al. (2017) observed an asymmetric impact on the BRICS currencies. The authors used newspaper headlines about the exchange rates of the BRICS currencies based on daily data for the period from January 3, 2000 to December 5, 2013. Based on the VAR-GARCH (1,1) model for mean and variance estimation, the authors found that the BRICS foreign exchange markets responded quickly to any foreign news reports.

Rao and Padhi (2020) examined common determining factors for currency crises in the BRICS countries and observed that the Russian ruble was in more stressful conditions than other BRICS currencies. The authors used a panel data approach based on quarterly data related to the BRICS counties for the period from 1996 Q1 to 2015. Q4 The authors evaluated various macroeconomic variables that could impact currency crises and found that the ratio of base money to broad money, growth in broad money, inflation, interest rates trade balance and current account balance provided information on future crises along with respective countries external vulnerability towards the currency crisis.

Jiang (2019) analyses the BRICS exchange rate regimes and provides a comparative analysis of these regimes. The author observes that, apart from China, other BRICS exchange rate systems are more flexible, hence, there is a scope for China to make its exchange rate system more liberalized, which will reflect price discovery by the market forces.

Efficacy of financial markets in the case of the BRICS countries was examined by Bhandari and Kamaiah (2016). The authors applied various non-linear tests to monthly frequency data on NEER (Nominal Effective Exchange Rate) of the BRICS countries from April 1994 to September 2014. The authors observed that the BRICS markets represented a weak form of market efficiency, indicating a chaotic structure of financial markets.

Chkili and Nguyen (2014) evaluated the relationship between volatility in exchange rate and stock market returns using a regime-switching autoregressive methodology for the BRICS countries. The authors discovered that stock market returns influenced exchange rate movements during the whole sample period (from March 1997 to February 2003). A similar study by Raja (2018) on the BRIC (with the exception of South Africa) countries finds that stock market returns and exchange rate returns are correlated. Using correlation estimation on daily data from 2013 to 2018 on the returns of indices of the BRIC countries and returns on the exchange rate, the author finds a short-run and long-run correlation of the variables. However, the paper concludes that there might be other factors impacting the reruns on the exchange rate.

To check the endogeneity issue, we performed a pairwise Granger causality test on the selected variables. The time period of the data is from January 2000 to July 2021. The results of the pairwise Granger causality test are given in Table 10. It shows that it is the intervention that causes the exchange rate returns and not the other way round, except in Russia where the exchange rate returns cause intervention in the derivatives market.

The motive of the empirical exercise is to determine the factors contributing to the volatility in the exchange rate return. Hence, based on the past literature on determining the exchange rate return, we tried to estimate equations 5 and 7. Table 11, based on these equations, provides a summary of the GARCH estimation. In the mean equation, intervention variables have no statistically significant coefficients, except Brazil in derivatives intervention, which indicates that interventions have a limited impact on the exchange rate level. However, in the variance equation, both variables are significant with a negative sign (except Russia for derivatives intervention and South Africa for spot intervention), which indicates that central banks are successful in reducing volatility through intervention.

Further, the yield spread variable showed mixed results. In the case of China and South Africa, the significant coefficient with negative and positive signs indicates that the yield spread appreciates the Chinese yuan, while it depreciates the South African rand. Further in the variance equation, the yield spread impacts volatility in Brazil and Russia. A positive sign for Brazil indicates that the yield spread increases volatility in the returns, while a negative sign for Russia suggests that the yield spread reduces volatility in the returns. We observed that the results were similar to the standard literature (Berganza & Broto, 2012; Broto, 2012).

Table 11.

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GARCH model estimates

Variable		Brazil	Russia	India	China	South Africa
Variable		Brazil	Russia	India	China	South Africa
Dependent Variable: Return on Exchange Rate: lnrt
Mean equation:
β0	-0.0012		0.006	0.002	-0.0000005	0.14
	(0.58)		(0.58)	(0.92)	(0.98)	(0.66)
β1 (lnrt-1)	-0.577*		-0.382*	0.42*	-0.35*	-0.55*
	(0.00)		(0.00)	(0.00)	(0.00)	(0.00)
β2 (Spot_intvt)	0.00000008		0.000000006	-0.00000003	0.000000000007	0.0005
	(0.11)		(0.87)	(0.71)	(0.99)	(0.55)
β3 (Deriv_intvt)	0.00000011*		0.00000029	0.000000018		0.0005
	(0.00)		(0.87)	(0.71)		(0.47)
β5 (Yield_spreadt)	-0.006		-0.00068	0.0018	-0.0017*	2.47*
	(0.27)		(0.37)	(0.77)	((0.04)	(0.00)
Variance equation:
α0	0.0004*		0.0019*	0.0002*	0.000004*	9.8
	(0.00)		(0.00)	(0.00)	(0.00)	(0.10)
${\alpha }_1\left({\epsilon }_{t-1}^2\right)$	0.149*		0.15*	0.15*	0.15*	0.28*
	(0.00)		(0.09)	(0.08)	(0.00)	(0.04)
α2(ht – 1)	0.599*		0.60*	0.59*	0.60*	0.42*
	(0.00)		(0.00)	(0.00)	(0.00)	(0.08)
α3 (Spot_intt)	-0.0000000019*		-0.0000006*	-0.00000003*	-0.000000000008*	0.006*
	(0.00)		(0.00)	(0.00)	(0.00)	(0.00)
α4 (Deriv_intvt)	-0.0000000022*		0.000000025*	-0.000000001*		-0.0019
	(0.02)		(0.00)	(0.01)		(0.57)
α6 (Yield_spreadt)	0.000077*		-0.000106*	0.000008	0.0000002	4.29
	(0.00)		(0.00)	(0.57)	(0.04)	(0.60)
Diagnostics
R2	0.35		0.14	0.17	0.13	0.28
DW	2.36		2.11	2.21	2.22	2.20
Log Likelihood	363.02		430.82	549.13	908.95	-774.02
Q(20)	43.47 (0.00)		57.88 (0.00)	87.80 (0.00)	29.36 (0.08)	34.77 (0.02)
Q(20)2	17.26 (0.69)		47.08 (0.00)	48.20 (0.00)	0.74 (1.00)	15.66 (0.73)
ARCH-LM	0.28(0.59)		2.09 (0.14)	0.081 (0.77)	0.009 (0.92)	0.193 (0.66)

Note: DW – Durbin Watson Statistic; LM – Lagrange Multiplier. Source: Compiled by the authors.

Regarding the residual diagnostics, the DW statistics for all five currencies are close to 2, implying no autocorrelation of residuals, while adjusted R-squares range from 0.13% to 0.35% indicating the variation in the returns is explained by 13 to 35% in the model. Further ARCH LM test rejects.

It is a recognised fact that most central banks intervene in the foreign exchange market to anchor exchange rates or tame volatility as per the country’s macroecnomic situation and the monetary policy stance. However, there is no consensus in the literature on the effectiveness of the intervention in the exchange rate. In our empirical analysis, we find that central bank intervention matters, whether in the spot market or the derivatives market. Intervention can reduce the volatility in the exchange rate returns. However, intervention is not impacting the exchange rate level, which indicates that intervention can only be used to reduce undue volatility and not to change the exchange rate level. Central banks may use other policy tools to change the exchange rate level, such as the interest rate differential or the yield spread. Although intervention helps in achieving the desired aim of reducing undue exchange rate volatility, intervention is not an effective tool for managing the exchange rate level. The results confirm that the BRICS central banks generally do not impact the exchange rate level; however, they reduce the exchange rate volatility. Furthermore, intervention in the spot and derivatives markets is equally effective in containing exchange rate volatility. It is found that the yield spread also impacts the exchange rate volatility in Brazil and Russia.

These results are important for central banks when assessing the efficacy of forex interventions. However, the analysis still lacks other relevant elements, namely generalization of the model to include other characteristics of forex interventions, such as persistence, or further control variables in the level equation, i. e. the degree of exchange rate misalignment.

Foreign exchange market intervention requires constant assessment of market conditions, such as global and domestic liquidity conditions, government securities market conditions and forward market projections. Raj et al. (2018) observed that many EMEs had successfully managed the “impossible trinity”22 by using country-specific mix of sterilised intervention, exchange rate flexibility and capital flow management. Therefore, to ensure effective intervention in the desired direction, not only intervention is required, but a combination of various market analysis measures, such as forex swaps (sell-buy or buy-sell), intervention in onshore and offshore (NDF) markets and integration of financial markets.