Research Article |
Corresponding author: Dipak Chaudhari ( dipakrchaudhari@rbi.org.in ) Academic editor: Marina Sheresheva
© 2022 Dipak Chaudhari, Pushpa Trivedi.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits to copy and distribute the article for non-commercial purposes, provided that the article is not altered or modified and the original author and source are credited.
Citation:
Chaudhari D, Trivedi P (2022) Efficacy of central bank intervention in the foreign exchange market of the BRICS countries. BRICS Journal of Economics 3(3): 143-172. https://doi.org/10.3897/brics-econ.3.e84676
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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 system
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 (
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 (
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 (
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 (
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.
Exchange Rate History | As per IMF classification | Foreign Exchange Market Size | Foreign Exchange Reserves (USD bn)* | Intervention data availability | |
---|---|---|---|---|---|
Exchange Rate History | As per IMF classification | Foreign Exchange Market Size | Foreign Exchange Reserves (USD bn)* | Intervention data availability | |
Brazil | Soft page with USD (from 1995 to December 1998). From 1999 onwards, inflation targeting for 3.75% (with band +/- 1.5%) | Floating exchange rate | 66 | 356.1 | Daily data |
Russia | From 1995 onwards, pegged exchange rate with crawling band against USD. 2015, inflation targeting regime was adopted with a target of 4% | Free Floating exchange rate | 63 | 586.3 | Monthly |
India | Current account convertibility adopted in 1994. Adoption of flexible inflation targeting in August 2016, with a mandate of 4% (+/-2%) | Floating exchange rate | 110 | 586.7 | Monthly |
China | Pegged with USD No inflation target | Other managed arrangement | 270 | 3528.8 | Do not publish |
South Africa | From 2000 onwards, inflation targeting framework with a range of 3 to 6%. | Floating exchange rate | 62 | 53.3 | Do not publish |
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 (
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
Brazilian Real | Russian Ruble | Indian Rupee | Chinese Yuan | South African Rand | |
Brazilian Real | 1.000 | ||||
Russian Ruble | 0.394 | 1.000 | |||
Indian Rupee | 0.485 | 0.359 | 1.000 | ||
Chinese Yuan | 0.212 | 0.204 | 0.170 | 1.000 | |
South African Rand | 0.405 | 0.336 | 0.485 | 0.209 | 1.000 |
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 (
In the case of the BRICS currencies, Brazil experienced a currency crisis in 1999, with hyperinflation exceeding 900% in 1994 (
Russia experienced a currency crisis in 1997-1998 and recently, in 2014-2015 (
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.
Various theories have been propounded to explain how the exchange rate was determined. However, all these theories can not be considered as a whole due to their specific assumptions and limited scope. Notwithstanding the extensive literature on theories and modelling of the exchange rate, unexpected exchange fluctuations continue to pose concern to governments and policymakers. Possible factors determining the effectiveness of intervention are the size of the market, the duration and the amount of intervention.
Foreign exchange markets are mainly divided into segments – spot and derivatives. A spot market is also called a cash market, where transactions are carried out immediately. Whereas a derivative market is a market for financial instruments such as forwards, futures, swaps and options. Though central bank intervention operations predominate in spot markets, foreign currency derivatives market interventions are more frequent (
According to the latest triennial survey report of 2019 by the BIS (Bank for International Settlements), the overall foreign exchange market turnover per day in the world was USD 6,595 billion. As for the BRICS countries, the Brazilian real turnover was USD 66 billion, the Russian ruble turnover – USD 63 billion, the Indian rupee – USD 110 billion, the Chinese yuan – USD 270 billion, and the turnover of the African rand was USD 62 billion. Together, the BRICS currency share is 8.7% of the total foreign exchange turnover in the world.
Currency | 2010 | 2013 | 2016 | 2019 | ||||
Amount | Percent | Amount | Percent | Amount | Percent | Amount | Percent | |
USD / Brazilian real | 25 | 0.6 | 48 | 0.9 | 45 | 0.9 | 66 | 1.0 |
USD / Russian ruble | ... | ... | 79 | 1.5 | 53 | 1.1 | 63 | 1.0 |
USD / Indian rupee | 36 | 0.9 | 50 | 0.9 | 56 | 1.1 | 110 | 1.7 |
USD / Chinese yuan | 31 | 0.8 | 113 | 2.1 | 192 | 3.8 | 270 | 4.1 |
USD / African rand | 24 | 0.6 | 51 | 1.0 | 40 | 0.8 | 62 | 0.9 |
All currency pairs | 3,973 | 100.0 | 5,357 | 100.0 | 5,066 | 100.0 | 6,595 | 100.0 |
NDF (non-deliverable forward) is an over-the-counter currency market in the offshore market. It’s a derivative contract providing an avenue for investors to trade in non-convertible currencies. The contract is usually settled in any convertible currency. An NDF market is usually located beyond the borders of domestic currency’s jurisdiction. Being outside the ambit of regulatory jurisdiction, the price discovery depends on the demand and supply forces in the market. Various studies have discovered that there were interlinkages between onshore and offshore markets.
Although global turnover in offshore non-delivery forward (NDF) continues to rise in aggregate, the paths of NDF markets have diverged across currencies: the Chinese yuan shows a sharp drop in turnover, while other emerging market currencies are gaining importance (BIS Triennial Central Bank Survey, 2016). As per the latest report by the Bank of England (January 29, 2019) on the percentage shares of average daily turnover by currency reported at the United Kingdom foreign exchange market, the Indian rupee turnover rose from 0.9% in April 2018 to 1.2% in October 2018, which is equal to the share of the South African rand, Mexican peso and higher than the Brazilian and Russian currencies turnover in the UK market.
Currency | 2013 | 2016 | 2019 | |||
Amount | Percent | Amount | Percent | Amount | Percent | |
Brazilian real | 15.9 | 12.5 | 18.7 | 14.0 | 35.7 | 13.8 |
Russian ruble | 4.1 | 3.2 | 2.9 | 2.2 | 5.5 | 2.1 |
Indian rupee | 17.2 | 13.5 | 16.4 | 12.2 | 50 | 19.3 |
Chinese yuan | 17 | 13.4 | 10.4 | 7.8 | 11.8 | 4.6 |
All currencies | 127.3 | 134 | 258.8 |
Since foreign banks and institutional investors are present in both onshore and offshore NDF markets, they profit from arbitrage opportunities. Such entities buy dollar-rupee forwards in the onshore market and sell forwards in the offshore NDF market. Primarily, major foreign banks (namely HSBC, UBS, JP Morgan, Citibank, Standard Chartered and Deutsche Bank), several international subsidiaries of big Indian corporations and some diamond merchants are the main players in the arbitrage activities between the NDF market and domestic markets. There are two major offshore markets for the Indian rupee: Singapore and London. Probably owing to the difference in trading hours, there is a possibility that the impact of/on these markets on/of the Indian market may vary.
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.
Country | Central Bank | Official stance on intervention |
Brazil | Central Bank of Brazil (BCB) | The BCB may occasionally intervene “to ensure the smooth functioning of the foreign exchange market” |
Russia | Bank of Russia (BoR) | “Currency interventions implemented by the BoR above the determined target amounts are aimed to decrease ruble exchange rate fluctuations that are not caused by the fundamental economic factors” |
India | Reserve Bank of India | “…our forex interventions to maintain the stability of the rupee.” RBI Governor speech on Aug 25, 2021 |
China | Peoples Bank of China (PBOC) | No official statement available on intervention |
South Africa | South African Reserve Bank (SARB) | “The Bank may get involved in the foreign exchange market to smooth out abrupt and severe adjustments of the exchange rate, to facilitate an orderly functioning of the foreign exchange market, as well as for financial stability reasons” |
But, as stated earlier, intervention can pursue different targets: either to change level or to contain volatility, or both (
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.
It is important to understand how exchange rates impact inflation. In this direction,
Regarding monetary policies towards exchange rate in the BRICS countries,
While estimating vulnerability to global crises,
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.
In the case of Brazil,
A recent study by
The effect of intervention depends on various factors. For example, Humpage (2003) argues that a flexible exchange rate with a higher degree of monetary policy independence provides more power to influence the forex market. A large body of literature suggests an asymmetric impact of sales (negative intervention) and purchase (positive intervention).
The literature shows that intervention impacts the exchange rate through three main channels: 1) monetary policy channel – according to (
Apart from the above three intervention channels, the international coordination channel and the noise trading channel were studied in the literature. A combination of various channels works simultaneously, and the most important channel is referred to as a signal channel.
Although there are various studies of the relationship between central bank intervention and exchange rate volatility, however, in the case of EMEs, there are very few studies on the efficacy of central bank intervention on the forex market due to the lack of transparency of intervention, motive and clear operational guidelines. Adler and Tover (2011) examined foreign exchange intervention practices and their effectiveness using qualitative and quantitative aspects for 15 countries, including India (for which the authors used the change in forex reserve as a proxy for intervention) for a period of 7 years (from 2004 to 2010), using a two-stage Instrumental Variable approach. The results show that interventions moderate the pace of appreciation, but the effects decrease rapidly with the degree of capital account openness, for which Chinn and Ito’s index of capital account openness was used.
Fatum (2003) focused on daily Bundesbank (Germany) and the US official intervention operations, using an event study approach. He found that intervention affected the exchange rate in the short run. The findings were consistent with the literature interpreting intervention as a means to “signal” future policy and the central bank’s views on the fundamental/equilibrium value of the exchange rate.
Neely (2011) examined the effect of coordinated interventions by the G7 countries to prevent volatility in the Japanese yen due to the massive earthquake of March 11, 2011. Due to the high volatility and disorder in the financial markets, the G7 countries decided to jointly intervene in the forex market. Exchange rates reacted strongly and quickly to the interventions, moving 3 to 4% in the desired direction within 30 minutes of the announcement and also exhibited lower volatility in the following days. Thus, he found that coordinated intervention could be a very effective tool in managing volatility in the forex market.
Cicek (2014) examined the effects of Turkey’s central bank’s interventions via auctions on the level and volatility of the Turkish lira/US dollar exchange rate between February 2, 2009 and January 31, 2014 using daily data. The study used the exponential GARCH (1,1) framework and suggested that interventions had no significant effect on the exchange rate level. Regarding volatility, the presence of the Central Bank in the market itself was not statistically significant, however, the size of intervention volume had a minor significant impact on the exchange rate volatility.
At the same time, interventions are more effective in the context of already “overvalued” (appreciated) exchange rates.
In the following table, we present a synoptic view of the criteria for classifying the studies on BRICS intervention.
Effectiveness of intervention | Efficiency of the foreign exchange rate market | Relationship between exchange rate and stock market | Exchange rate pass-through |
Chinese yuan has the least volatility, while South African rand is more volatile. Lower volatility in yuan is due to intervention ( |
BRICS markets are a weak form of market efficiency, indicating a chaotic structure of financial markets (Bhandari & Kamaiah, 2016); BRICS foreign exchange markets give a quick reaction to any foreign news reports. ( |
Stock market returns influence exchange rates movements ( |
Pass-through of the exchange rate is higher when the economy is in a high growth phase ( |
The primary motive behind the study is to analyse the efficacy of intervention in the forex market; thus, daily data is more appropriate. However, due to secrecy in motives (
Actual intervention data related to the BRICS countries are provided with varying frequency. For Brazil, its a daily frequency, for Russia and India its a monthly frequency, while South Africa and China intervention data are not publicly available. In this background, we used a database recently published in an IMF working paper (
We also checked the correlation of the proxy data with the actual available intervention data and found that the correlation was about 0.82 in the case of Brazil and 0.91 for India. The central bank’s general motive for intervention in the forex market is to reduce the volatility component of the exchange rate.
Our dependent variable, as well as the residuals using the ordinary least square, shows volatility clustering. Here, “large changes tend to be followed by large changes, of either sign, and small changes tend to be followed by small changes,” meaning there are periods of low volatility and periods when volatility is high. From the simple plot of our dependent variable, i.e., lnrt, it can be observed that the variable has a volatility clustering (Figure
The yield spread on sovereign government bonds against similar US bonds is used as an indicator of country risk in the literature (Chamon et al., 2013;
The entire dataset is publicly available on a monthly basis. The sources and notations used in the estimation are explained in the table 7. The empirical exercise aims to examine how central bank intervention impacts exchange rate volatility. As per the standard literature approach, we used returns as a volatility measure of the exchange rate. The return was calculated using the following formula:
Where, lnrt is the return on the exchange rate; S is the spot exchange rate of the rupee per US dollar. The positive (negative) lnrt shows that local currency depreciates (appreciates) against the US dollar. Intervention variables are in million USD, Sale (negative), Purchase (positive) both in the spot market and the derivatives market. Both markets – spot and derivatives – operate around the clock. However, settlements are done immediately in the spot market, while settlements or product delivery are done on a predetermined future date in the derivatives market. Capturing the efficacy of intervention in the derivatives market is vital as many central banks use foreign exchange swaps
Variable | Notation used | Source |
Return on nominal exchange rate (local currency per USD) | lnrt | IMF exchange rate archives https://www.imf.org |
Intervention in spot market | Spot_intv | ( |
Intervention in derivatives market | Deriv_intv | ( |
Sovereign government bond yield spread between a BRICS country and the US | Yield_spread | IMF’s International Financial Statistics (IFS) dataset https://data.imf.org |
Following the previous literature on determining the exchange rate return, we tried to estimate the following equation for the study:
In the empirical estimation of central bank intervention, a major problem is endogeneity. As intervention impacts exchange rate, exchange rate movements also simultaneously influence central bank behaviour related to intervention (
To check the endogeneity of our data, we estimated the pairwise Granger causality test. The Granger causality test is based on the VAR model, which alternatively places each variable as a dependent variable. Further, the causality test is used to understand the variables’ short-run dynamics. As intervention is a short-run tool used by central banks to reduce volatility, the use of the test is more appropriate.
As our dependent variable, i.e. the change in log of the exchange rate returns, regressed with its own lag, we get a series of residuals that are heteroskedastic (changing variance). Hence, considering the heteroskedastic nature of the data, the most appropriate mode is GARCH type models that treat heteroskedasticity as a variance to be modeled. As per the GARCH (1,1) framework developed by
The above equation is a mean equation, it indicates that the average returns on the exchange rate at time “t” (lnrt) depend on their own lag, intervention in the spot and derivatives market, as well as the intervention differential and yield spread and the error term (εt). Further, εt depends on some lagged information (Ω-1) and εt is assumed normally distributed with zero mean and its variance (ht).
Here, the variance equation can be written as:
The following table presents descriptive statistics of the selected variables. Descriptive statistics of all variables are also given in the table below. Here it can be observed that the exchange return series for China and South Africa are positively skewed, while for Brazil, Russia and India they are negatively skewed.
Variable | Mean | Maximum | Minimum | SD | Skewness | Kurtosis | JB | Prob | |
Brazil | rt | -0.00003 | 0.38 | -0.32 | 0.07 | -0.15 | 8.05 | 266.66 | 0.000 |
Spot_Intervn | 800.856 | 15202.01 | -20224.73 | 3535.31 | -0.34 | 9.20 | 408.86 | 0.000 | |
Deriv_Intervn | -220.253 | 34897.29 | -37116.00 | 4983.58 | -0.53 | 27.55 | 6339.07 | 0.000 | |
Yield_Spread | 10.968 | 27.51 | 1.92 | 4.77 | 0.41 | 3.28 | 7.84 | 0.019 | |
Russia | rt | -0.00012 | 0.15 | -0.32 | 0.05 | -1.70 | 12.73 | 1107.54 | 0.000 |
Spot_Intervn | 1861.617 | 35551.73 | -52429.84 | 9567.79 | -1.50 | 11.11 | 784.13 | 0.000 | |
Deriv_Intervn | -29.973 | 9449.06 | -11952.73 | 1682.38 | -0.75 | 24.02 | 4664.98 | 0.000 | |
Yield_Spread | 8.572 | 52.62 | 1.22 | 6.18 | 3.45 | 20.43 | 3660.76 | 0.000 | |
India | rt | -0.00004 | 0.08 | -0.14 | 0.03 | -0.49 | 5.67 | 84.31 | 0.000 |
Spot_Intervn | 1632.208 | 15594.43 | -22199.45 | 4470.23 | 0.02 | 6.83 | 153.94 | 0.000 | |
Deriv_Intervn | 171.393 | 20599.00 | -9449.00 | 2933.32 | 2.06 | 15.79 | 1895.34 | 0.000 | |
Yield_Spread | 4.798 | 10.13 | 0.27 | 2.43 | 0.11 | 2.39 | 4.48 | 0.106 | |
China | rt | -0.00003 | 0.04 | -0.04 | 0.01 | 0.21 | 7.20 | 186.02 | 0.000 |
Spot_Intervn | 7634.590 | 95478.45 | -125944.00 | 28262.97 | -0.74 | 8.06 | 291.41 | 0.000 | |
Deriv_Intervn | No Data | ||||||||
Yield_Spread | 1.171 | 3.14 | -3.49 | 1.82 | -0.96 | 2.96 | 38.04 | 0.000 | |
South Africa | rt | 0.00337 | 0.20 | -0.11 | 0.05 | 0.62 | 3.95 | 25.29 | 0.000 |
Spot_Intervn | 85.438 | 2103.02 | -4777.38 | 549.25 | -2.44 | 27.20 | 6398.76 | 0.000 | |
Deriv_Intervn | 72.877 | 2232.00 | -1257.00 | 457.48 | 1.55 | 8.28 | 394.03 | 0.000 | |
Yield_Spread | 7.265 | 10.73 | 2.19 | 1.89 | -1.21 | 4.00 | 71.40 | 0.000 |
However, all five countries show leptokurtic returns, indicating the presence of volatility. In the case of China, due to the non-availability of data, intervention in the derivatives market was not reported. If we compare the returns of the exchange rates for all BRICS currencies, the renminbi exhibits the lowest volatility (measured by SD-standard deviation), while the Brazilian real, the Russian ruble, the South African rand, and the Indian rupee all exhibit high volatility.
For any empirical estimation that involves time series, it is customary to check the stationarity of data. We checked the unit root test for all variables used in the study and found that all of them are stationary at the 1% significance level, except the yield spread. So, we took the first difference of these variables to transform them into stationary variables. The results of the Augmented Dickey-Fuller test (ADF) for all variables are given in table 9.
Variable | Brazil | Russia | India | China | South Africa |
lnrt | -11.565 | -10.276 | -13.648 | -10.259 | -15.902 |
(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
Spot_Intervn | -4.437 | -7.841 | -9.128 | -4.107 | -15.647 |
(0.000) | (0.000) | (0.000) | (0.003) | (0.000) | |
Deriv_Intervn | -11.711 | -12.744 | -9.301 | na | -12.995 |
(0.000) | (0.000) | (0.000) | na | (0.000) | |
Yield_Spread | -1.443 | -6.866 | -1.400 | -2.149 | -1.937 |
(0.561) | (0.000) | (0.581) | (0.515 | (0.632 | |
Yield_Spread (1st difference) | -16.749 | -13.237 | -10.410 | -11.861 | |
(0.000) | (0.00) | (0.000) | (0.000) |
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
Country | Null Hypothesis | F-Statistic | P-value |
Brazil | Spot intervention does not granger cause exchange rate returns | 2.147* | 0.094 |
Exchange rate returns do not granger cause spot intervention | 0.599 | 0.616 | |
Derivatives intervention does not granger cause exchange rate returns | 7.2885* | 0.007 | |
Exchange rate returns do not granger cause derivatives intervention. | 1.262 | 0.262 | |
Russia | Spot intervnetion does not granger cause exchange rate returns | 6.178* | 0.001 |
Exchange rate returns do not granger cause spot intervention | 1.363 | 0.247 | |
Derivatives intervention does not granger cause exchange rate returns | 0.572 | 0.599 | |
Exchange rate returns do not granger cause derivatives intervention. | 2.901* | 0.056 | |
India | Spot intervention does not granger cause exchange rate returns | 6.708* | 0.015 |
Exchange rate returns do not granger cause spot intervention | 1.522 | 0.220 | |
Derivatives intervention does not granger cause exchange rate returns | 9.336* | 0.002 | |
Exchange rate returns do not granger cause derivatives intervention. | 0.333 | 0.563 | |
China | Spot intervention does not granger cause exchange rate returns | 3.520* | 0.061 |
Exchange rate returns do not granger cause spot intervention | 2.685 | 0.102 | |
South Africa | Spot intervention does not granger cause exchange rate returns | 0.319 | 0.727 |
Exchange rate returns do not granger cause spot intervention | 0.143 | 0.866 | |
Derivatives intervention does not granger cause exchange rate returns | 0.175 | 0.839 | |
Exchange rate returns do not granger cause derivatives intervention. | 0.899 | 0.408 |
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
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 (
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.
Study | Country | Methodology and variables | Key findings |
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Study | Country | Methodology and variables | Key findings |
(Kamaiah, 2016) | BRICS | Monthly data from April 1994 to Sept 2014; variance tests | The authors found the presence of non-linearity in the five BRICS currencies. The findings also confirmed the presence of the underlying chaotic structure of the markets |
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BRICS | Weekly data from March 1997 to Feb 2013 on stock prices and USD exchange rates linkages. | The US dollar movements impact the BRICS currencies. However, the impact of exchange rates on stock market returns is not significant. Stock markets influence exchange rates in all business cycles of economic activities |
( |
Theoretical; India | Micro-market structure industrial organisation theory | Intervention operations are effective in devaluing the currency. However, this leads to a build-up of excess reserves |
( |
BRICS | Daily data form January 3, 2000 to May 12, 2013 are used to understand how negative news impact the exchange rate in the BRICS currencies. VAR-GARCH (1,1) | The authors examine the effects of newspaper headlines on the exchange rates. The paper uses the US dollar and the euro in the BRICs currencies. The findings reconfirm the role of the BRICS currencies in the international market. Furthermore, the foreign exchange markets have become more responsive to foreign news |
( |
Theoretical | DSGE simulation approach attempts to understand how multiple policy tools potentially improve monetary policy | Central bank intervention and capital flow management tools may improve policy efficiency, especially in inflation-targeting economies |
( |
BRICS | Daily data for the period from May 10, 2007 to May 16, 2017. The authors use VOX as a measure for oil market volatility. Cross-quantilogram model proposed | The authors examine the direction and volatility predictability from oil price to the stock return of the BRICS countries. In overall, oil price volatility has directional predictability for the stock returns in the BRICS countries |
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BRIC (4 countries) | Data on oil prices and exchange rate related to the BRICS from 1994 to 2007 | Movements in oil prices accurately predict the direction of change in the exchange rates in the case of Brazil and Russia. However, for China, oil prices failed to display any directional predictive power |
( |
BRICS | Daily data from 2013 to 2018 | Returns from the BRICS stock market indices and exchange rates returns are correlated |
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BRICS | Daily data from January 2008 to December 30, 2011 on returns on exchange rate using DCC-GARCH model | It was observed that, except the Chinese yuan, other 4 currencies indicate interdependency. The Chinese renminbi is the least correlated currency with other BRICS currencies |
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73 countries | Monthly data from 2002 to 2013 on the exchange rate, net foreign assets position | This paper provides the conceptual basis of the intervention cost. The paper finds that annual costs of intervention are 0.2 to 0.7% of GDP per year in countries with limited intervention. At the same time, the cost reaches 0.3 to 1.2% of GDP per year in heavy-intervening economies |
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Summary of the studies | Review of the studies | The paper identified that central bank interventions in the foreign exchange markets moved the exchange rate level in the desired direction. However, interventions increased volatility in the short run, but in the long run, interventions reduced volatility. Intervention operations can be more successful if they are coordinated by central banks |