WP/16/205. Surprise, Surprise: What Drives the Rand / U.S. Dollar Exchange Rate Volatility? By Nasha Maveé. Roberto Perrelli. Axel Schimmelpfennig - PDF

WP/16/205 Surprise, Surprise: What Drives the Rand / U.S. Dollar Exchange Rate Volatility? By Nasha Maveé Roberto Perrelli Axel Schimmelpfennig 2016 International Monetary Fund WP/16/205 IMF Working Paper

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WP/16/205 Surprise, Surprise: What Drives the Rand / U.S. Dollar Exchange Rate Volatility? By Nasha Maveé Roberto Perrelli Axel Schimmelpfennig 2016 International Monetary Fund WP/16/205 IMF Working Paper African Department and Strategy, Policy, and Review Department Surprise, Surprise: What Drives the Rand / U.S. Dollar Exchange Rate Volatility? Prepared by Nasha Maveé, Roberto Perrelli, and Axel Schimmelpfennig Authorized for distribution by Anne-Marie Gulde-Wolf October 2016 IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF Management. Abstract This paper investigates possible drivers of volatility in the South African rand since the onset of the global financial crisis. We assess the role played by local and international economic surprises, commodity price volatility, global market risk perceptions, and local political uncertainty. As a measure of rand volatility, the study uses a market-based implied volatility indicator for the rand / U.S. dollar exchange rate. Economic surprises the difference between market expectations and data prints are captured by Citi s Economic Surprise Index which is available for South Africa and its main economic partners. The results suggest that rand volatility is mainly driven by commodity price volatility, and global market volatility, as well as domestic political uncertainty. In addition, economic surprises originating in the United States matter, but not those originating from South Africa, Europe, or China. JEL Classification Numbers: F31, F37, G15, G17 Keywords: Rand; volatility; macroeconomic surprises; spillovers; commodities. Author s Address: and 1 I. INTRODUCTION 1 In a purely floating exchange rate system, currency volatility is the nature of the game. In recent years, the South African rand has been on the back foot against major currencies, with investors wary about the country s subdued growth, weak fiscal outlook, rising industrial and social tensions, and external vulnerabilities associated with the current account deficit financed largely through non-fdi inflows. In addition, economic and non-economic surprises, defined as unanticipated news the difference between actual and expected data prints or unanticipated events; have triggered bouts of volatility, with the so-called taper tantrum being just one example of an external economic surprise. Domestic events like the Marikana massacre shocked markets, changing investors perception of the country s credit risk, and triggering episodes of heightened exchange rate volatility. 2 Furthermore, commodity price volatility and global market uncertainty also caused sprouts of rand volatility (Arezki et al, 2012). At the outbreak of the global financial crisis, the rand reached a five-year low of R11.56 against the U.S. dollar on October 22, 2008, as global liquidity dried up. The sudden depreciation was quickly reversed when the Federal Reserve loosened monetary policy. As a result, the rand appreciated for nearly three years until September From that point on, a combination of (early) signs of a recovery in the United Sates, continued problems in the euro zone, concerns over a possible slowdown in China and emerging markets, and the subsequent decline in commodity prices, coupled with an investor focus on South Africa s deteriorating fundamentals, pushed the rand to historical lows against the U.S. dollar and other major currencies (Figure 1). Neither the appreciation nor the depreciation has been smooth, and the rand zigzagged around its multi-year trend, mirroring changes in investors risk aversion and surprises. Therefore, this paper investigates the possible drivers of volatility in the South African rand / U.S. dollar exchange rate (rand volatility) since the onset of the global financial crisis. As a gauge of rand volatility, we use a market-based implied volatility indicator. First, the paper investigates the role that macroeconomic surprises play as determinants of rand volatility, using a surprise index compiled by Citi. Second, we investigate the role that commodity price volatility and global financial market risk perceptions play both reflections of surprises in 1 We are very grateful to Kristjan Kasikov, David Lubin, and Alexander Wolfson from Citi Bank for kindly providing the Economic Surprise Index data and for their useful comments. To Sandile Hlatshwayo and Magnus Saxegaard for allowing us to use the policy uncertainty data, to Leandro Medina, to Shakill Hassan, as well as the IMF South Africa team for their constructive comments. 2 The Marikana massacre occurred during a strike at Lomnin in 2012, which led to the killing of 34 mineworkers, after violent clashes between striking mineworkers and the police. 2 key global markets. Third, we include an algorithm measure of political uncertainty in South Africa as an additional explanatory variable to gauge the effect of political uncertainty in the rand volatility. Figure 1. Historical Trend and Daily Returns of the South African Rand, August Selected Bilateral Rand Exchange Rate Rand / U.S Dollar Rand / Euro Rand / British Pound 4 2 Daily Rand/U.S. Dollar Exchange Rate Returns Source: Bloomberg and Author's calculations The results suggest that rand volatility is mainly driven by global factors expressed by commodity price volatility and the VIX. In addition, macroeconomic surprises originating from the United States also matter for rand volatility. On the domestic front, we find that local political uncertainty is positively associated with rand volatility. Neither domestic macroeconomic surprises nor those originating from other EMs are statistically related to rand volatility, a finding that is broadly in line with existing literature on emerging market currencies (Wong et al., 2014; Mishra et al., 2014). The rest of this paper is organized as follows; section two gives an overview of the literature. Section three describes the data used in the paper. Section four describes our continuous time series empirical approach and discusses the results. Section five provides a sensitivity analysis performed on the continuous time series results. Section six describes our analysis in an event study framework and discusses the results. Section seven concludes. II. LITERATURE REVIEW The existing literature on high frequency exchange rate volatility has generally leaned on the Efficient Market Hypothesis (Fama 1970, 1991) as its basis. At its strongest level, the hypothesis contents that asset prices, at any given time, fully reflect all available and relevant information for their determination. The market usually makes prior assumptions regarding the outcome of a particular scheduled macroeconomic announcement. Thus, the set of available and relevant information should include these assumptions, and they would be 3 reflected in the current exchange rates (e.g. Gürkaynack et al., 2005). Within this framework, exchange rate volatility is mainly caused by the arrival of unanticipated relevant information in the form of a surprise (Galati and Ho, 2001). In fact, as noted by Gupta and Reid (2012), using the surprise component of macroeconomic variables instead of the actual outcome reduces concerns about endogeneity in the study. Researchers have employed different definitions of exchange rate volatility, ranging from simple exchange rate returns, as measured by the daily log change in the exchange rates, to measures of volatility of returns, including squared returns, standard deviation of returns, and volatility implied by GARCH models, inter alia. Several papers relate volatility to unanticipated (or surprise) events, including economic and political developments, in addition to other news that may be relevant for investor perception of a country s risk profile. Engel and Frankel (1984), Almeida et al (1998), Andersen and Bollerslev (1998), Pearce and Solakoglu (2007) find that domestic and external surprises are important drivers of exchange rate volatility. In emerging markets, external surprises, most notably from the United States, are particularly important. Studies focusing on advanced economies find that domestic and external economic surprises matter for exchange rate volatility. Nevertheless, surprises originating in the United States usually have a more significant effect on volatility. Galati and Ho (2001) examined the reaction of the euro / U.S. dollar exchange rate as measured by daily exchange rate returns to news about the macroeconomic situation in the United States and the euro zone during the first two years of European monetary union. They find that macroeconomic surprises have a statistically significant correlation with daily movements of the euro against the dollar, with economic surprise flows from the United States having a greater impact on volatility. Laakkonen (2007) also examined the impact of U.S. and European macroeconomic surprises on the euro / U.S. dollar volatility as measured by absolute value of the 5-minute intraday euro / U.S. dollar exchange rate returns using the Flexible Fourier Form method, and find that while both U.S. and European surprises increased volatility significantly, nevertheless surprise flows from the United States were the most important. Interestingly, Laakkonen (2007) finds that bad surprises had a greater impact on volatility relative to good surprises. Likewise, empirical studies for emerging markets show that exchange rate volatility is driven by economic surprises. Wong et al. (2014), examined the responsiveness of exchange rates as measured by exchange rate returns in the Asian-Pacific market to U.S. and domestic economic surprises. They find that regional macroeconomic shocks are as important as the U.S. macroeconomic shocks in affecting exchange rate returns. However, they also find that surprises from the U.S. Federal Reserve policy rate announcements were the most significant event among the 107 macroeconomic announcements examined. Mishra et al., 2014, analyzed the effect of the U.S. Federal Reserve monetary policy announcements in 4 the aftermath of the tapering speech, on the daily bilateral exchange rates returns against the U.S. dollar, government bond yields, and stock prices for 21 emerging markets, using ordinary least squares in an event study framework. They find evidence that emerging market currencies did indeed react negatively to the Fed tapering announcements. Moreover, the study shows that emerging market countries with stronger macroeconomic fundamentals, deeper financial markets, and a tighter macro prudential policy stance in the run-up to the tapering announcements experienced smaller currency depreciations. In the case of South Africa, a number of studies examine the determinants of exchange rate volatility. 3 Fedderke and Flamand (2005) look at the period from June 2001 to June 2004, using ordinary least squares (OLS) in an event study framework. They measure volatility of exchange rate returns expressed as daily rand / U.S. dollar exchange rate returns, and look at domestic and U.S. macroeconomic surprises. To investigate whether there is asymmetry between surprise types, they model the rand volatility as a function of dummy variables for good and bad surprises in each country. They find that macroeconomic surprises from the United States drive changes in rand volatility, and that there is an asymmetry between good and bad surprises, with only good surprises from the United States having a significant impact. Farrell et al (2012) examine the high-frequency response of the rand / U.S. dollar exchange rate returns within ten-minute intervals around (five minutes before, five minutes after) official inflation announcements between January 1997 to August 2010 using OLS. They show that the rand appreciates (depreciates) on impact when inflation is higher (lower) than expected evidence that bad surprises about inflation is a good surprise for the currency under the inflation targeting monetary policy framework. Hassan and Paul (2014), investigate rand movements at half-second intervals during the March 2014 Monetary Policy Committee Statement by the South African Reserve Bank, to illustrate how the rand reacts to information on macro fundamentals at very high frequency. Their results show that the rand responds significantly to changes in expectations of future macro fundamentals (domestic and international) as outlined in the South African Reserve Bank s monetary policy committee statement. 3 While not looking at exchange rate volatility per se, Gupta and Reid (2012) explore the sensitivity of industryspecific stock returns in South Africa to monetary policy and to various unanticipated macroeconomic shocks. 5 Arezki et al (2012) contend that commodity exporting countries, such as South Africa, face large terms of trade fluctuations which render their exchange rate volatile. They investigate the relationship between the volatility of commodity prices particularly the gold price and the volatility of the South African rand both in the short- and long-run for the period They find that gold price volatility as measured by the twelve month rolling window of the standard deviation of the International Monetary Fund (IMF) international gold price index, plays a key role in explaining excessive rand exchange rate volatility as measured by the twelve month rolling window of the standard deviation of real effective exchange rate particularly since the liberalization of capital controls in 1995, pointing to the importance of commodity prices in an economy where commodities represent over 60 percent of exports. Hassan (2015) shows that global market volatility as measured by the VIX drives short-term rand volatility, as periods of high rand volatility follow episodes of high VIX volatility, since the 2007 US sub-prime crisis to Following Campbell et al (1997) seven step event study framework, Mpofu and Peters (2016) investigate the presence of abnormal returns in the daily rand exchange rate (measured as the absolute percentage changes of the daily rand exchange rate against the U.S. dollar, the Euro, and the British pound) following a number of selected monetary policy announcements which did not coincide with other macroeconomic or non-economic announcements or releases and political events (including the Marikana massacre, the release of Nelson Mandela banknotes, and the African National Congress (ANC) South Africa s ruling party elective conferences) using daily data over the period 1 March 2000 to 31 December Their research, finds the presence of significant cumulative abnormal returns for all three exchange rates on the days surrounding the selected monetary policy announcements, the Marikana massacre on 16 August 2012 and the release of Nelson Mandela banknotes on 6 November The ANC elective conferences only have significant cumulative abnormal returns using the rand / U.S. dollar in 2007 and Mpofu (2016) examines real and nominal exchange rate volatility using both bilateral (rand / U.S. dollar) and effective exchange rates from 1986 to His paper relies on a GARCH (1,1) which is augmented with macroeconomic determinants of exchange rate volatility. Mpofu finds output volatility and gold price volatility are 6 positively associated with rand volatility, while that trade openness; coupled with changes in foreign exchange reserves and money supply decrease volatility. 4 The literature mainly relies on the event study methodology to investigate the drivers of volatility. In this framework, the series usually consists of only the instance an economic data point is released and a surprise may be observed, and a window surrounding the data release; the time unit could be seconds, minutes, hours or days. As such, the event study approach models the relationship between economic surprises and volatility conditional on a surprise having taken place. Alternatively, in this paper we use a continuous-time framework to model the relationship between exchange rate volatility and other covariates such as global volatility or commodity price volatility. Almost by design, this should reduce the impact of economic surprises which now have a lower frequency of occurring in the sample. However, using the economic surprise index compiled by Citi a 3-month moving average of economic surprises complements this approach by allowing for a cumulative or lasting effect of economic surprises. For comparability with the literature which uses a daily surprise matter, to analyze whether domestic inflation surprises matter, and to assess sensitivity to the econometric approach, we also use an event study framework. In addition, this paper adds to the literature in 4 ways: firstly, we focus on the period since the onset of the global financial crisis, a time during which South Africa s fundamentals have deteriorated relative to peers. Secondly, we use a market-based rand / U.S. dollar implied volatility measure to assess to impact of different sources of volatility. Thirdly, we use the economic surprise index compiled by Citi, available for a number of advanced and emerging markets, which gives us a broadly consistent surprise metric for domestic and external economic surprises. This allows us to consider external news surprises from countries other than the United States that have important linkages with South Africa, namely China and the euro zone. Fourthly, we combine the effect of macroeconomic surprises, commodity price volatility, global financial market volatility and local political uncertainty as determinants of the rand / U.S. dollar exchange rate volatility. 4 However these results output volatility only increases exchange rate volatility only hold when using bilateral exchange rate, but not when using the real effective exchange rate. 7 III. DATA A. Volatility Metrics Our analysis is carried out based on daily data from August 24, 2009 to August 24, The use of daily data avoids loss of information between discrete events of macroeconomic news releases, and allows for surprises to slowly find their way into exchange rate volatility (Gupta and Reid, 2012). While there is no consensus over the most appropriate measure of exchange rate volatility, the literature leans towards the use of implied measures of volatility. Clark et al (2004) argue that if the focus is on countries with well developed financial markets such as South Africa, then one should take into account forward markets or currency option market to obtain a measure of implied exchange rate volatility. In this paper we use the Johannesburg Stock Exchange (JSE) measure of implied rand / U.S. dollar exchange rate the South African Volatility Index for the rand / U.S. dollar exchange rate (SAVID). The SAVID is a forecast of the 90 day implied volatility of the rand against the U.S. dollar derived from actual options traded data. 5 A high value for the SAVID corresponds to a more volatile market and therefore more risk of currency value change, while a low value is indicative of a less volatile market and therefore less risk. For example, when daily currency market returns are sufficiently volatile, the SAVID will tend to spike upward, reflecting a higher level of expected risk. Figure 2. Selected Measures of Volatility, August SAVID, RHS 3-month moving standard deviation GARCH (1,1) GARCH (2,1) *All series are set equal to 100 at the beginning Source: JSE, Bloomberg, Author's own calculations 5 Investment houses are polled regarding whether they are prepared to price Rand / U.S. dollar currency options in the market and their views are then averaged out. These investment houses price options higher when they expect a high risk of a change in prices as they require a greater premium from traders to insure against su
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