Novitzky

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An Empirical Analysis of the Relationship
Between Capital Flows, Commodity Prices and
Exchange Rate Volatility in South Africa
Alex Novitzky
MBA 2010 FT
Supervisor: Sean Gossel
10 December 2010
A Research Report presented to
The Graduate School of Business
University of Cape Town
In partial fulfilment of the requirements for the
Masters of Business Administration Degree
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PLAGIARISM DECLARATION
1. I know that plagiarism is wrong. Plagiarism is to use another’s work and pretend that it is one’s
own.
2. I have used a recognised convention for citation and referencing. Each significant contribution
and quotation from the works of other people has been attributed, cited and referenced.
3. I certify that this submission is all my own work.
4. I have not allowed and will not allow anyone to copy this research report with the intention of
passing it off as his or her own work.
Alex Novitzky
10 December 2010

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ABSTRACT
This research report investigates the extent to which volatility in commodity prices and portfolio
flows affects the volatility of the Rand/U.S. Dollar nominal exchange rate. The study uses time
series data covering the period 1995:Q2 - 2009:Q4 and an ARIMA empirical approach. The
dependent variable, the nominal exchange rate, was regressed against 10 explanatory variables
split into three categories, namely, commodity price movements (gold price), capital flows (net
bond flows; net equity flows) and macroeconomic (GDP growth differentials; relative equity
returns; short-term interest rate differentials; long-term interest rate differentials; money supply
volatility; domestic credit extension; and foreign exchange reserves).
Equity flows were found to have a moderately negative effect on the Rand’s volatility. On the other
hand, bond flows were not found to affect the currency’s stability, which is likely due to cross
border holdings being hedged. Fluctuations in long-term interest rate differentials were found to be
statistically significant, but only translated into slight fluctuations in the Rand. The results also
indicate that commodity prices have a dampening effect on currency volatility, which is in line with
findings in other research. The money supply/GDP ratio was not only found to have a positive
relationship with fluctuations in the Rand, but it also had the strongest influence out of all the
variables tested. Foreign exchange reserves/GDP was also found to have a positive relationship
with currency volatility. However, it is postulated that once the Reserve Banks builds its reserves
beyond a certain level, this relationship will turn negative.
Based on this research, equity flows are the only component of portfolio flows to affect the Rand’s
volatility and hence dismantling capital controls and not imposing transaction taxes should be
considered by fiscal authorities. The literature indicates that rising gold prices induces symptoms of
‘Dutch Disease’ in South Africa’s export sectors. This research also finds that declines in the gold
price leads to currency volatility. In conjunction with declining profit margins of many gold mines,
the implication is that South Africa’s gold mining industry may not be sustainable. Finally, given the
strong effect of the money supply on the Rand’s volatility, as well as success by developing
countries in sterilising currency movements by holding large reserves, this research suggests that
monetary policy can play an important role in smoothing out episodes of high currency volatility.

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ACKNOWLEDGEMENTS
This report is not confidential and may be used freely by the University of Cape Town, Graduate
School of Business.
I wish to thank Sean Gossel for his valuable input and guidance in supervising this research report.
I would like to thank my family for their understanding and continued support throughout this year.
I certify that the report is my own work and all references used are accurately reported.
Signed:
Alex Novitzky
10 December 2010
Keywords: Rand volatility, nominal exchange rate, ARIMA, bonds, equities, commodity prices, productivity
shocks, relative equity returns, interest rate differentials, money supply, domestic credit, foreign exchange
reserves.

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TABLE OF CONTENTS
ABSTRACT..............................................................................................................................................................i
ACKNOWLEDGEMENTS........................................................................................................................................ii
1. INTRODUCTION........................................................................................................................................... 1
1.1. Background to the Study ........................................................................................................................ 1
1.2. Purpose of the Study .............................................................................................................................. 4
1.3. Key Findings ............................................................................................................................................ 5
1.4. Delimitations and Limitations ................................................................................................................ 5
1.4.1 Frequency of Data ............................................................................................................................... 5
1.4.2 Sample Period...................................................................................................................................... 6
1.4.3 Choice of Bilateral Exchange Rate....................................................................................................... 6
1.4.4 Lag Effects............................................................................................................................................ 6
1.4.5 Foreign Direct Investment ................................................................................................................... 7
1.5. Layout of Report ..................................................................................................................................... 7
2. LITERATURE REVIEW................................................................................................................................... 8
2.1. Introduction ............................................................................................................................................ 8
2.2. Key Terms................................................................................................................................................ 8
2.3. Currency Volatility and Commodity Prices ........................................................................................... 9
2.4. Currency Volatility and Portfolio Flows .............................................................................................. 10
2.5. Currency Volatility and Macroeconomic Factors................................................................................ 12
2.6. Conclusion............................................................................................................................................. 13
3. METHODOLOGY AND DATA DEFINITIONS ............................................................................................... 14
3.1. Sample Period....................................................................................................................................... 16
3.2. Dependent Variable: Rand/U.S. Dollar Nominal Exchange Rate ........................................................ 16
3.3. Capital Flow Variables .......................................................................................................................... 16
3.3.1. Net Bond Flows.................................................................................................................................. 16
3.3.2. Net Equity Flows ................................................................................................................................ 17
3.4. Commodity Price Variables .................................................................................................................. 18
3.4.1. Commodity Price Movements............................................................................................................ 18
3.5. Macroeconomic Variables.................................................................................................................... 18
3.5.1. GDP Growth Differentials .................................................................................................................. 18
3.5.2. Relative Equity Returns...................................................................................................................... 19
3.5.3. Short-Term Interest Rate Differentials .............................................................................................. 20
3.5.4. Long-Term Interest Rate Differentials ............................................................................................... 20

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3.5.5. Money Supply/GDP............................................................................................................................ 21
3.5.6. Domestic Credit Extension/GDP ........................................................................................................ 22
3.5.7. Foreign Exchange Reserves/GDP....................................................................................................... 22
3.6. Modelling Volatility .............................................................................................................................. 23
3.7. Test for Stationarity.............................................................................................................................. 25
3.8. Goodness of Fit Tests............................................................................................................................ 26
3.8.1. Durbin-Watson Statistic..................................................................................................................... 26
3.8.2. Breusch-Godfrey Serial Correlation LM Test...................................................................................... 27
3.8.3. Histogram and Jarque-Bera Normality Test...................................................................................... 27
3.8.4. Breusch-Godfrey-Pagan Test for Heteroskedasticity......................................................................... 28
4. ECONOMETRIC ANALYSIS ......................................................................................................................... 29
4.1. Test for Stationarity.............................................................................................................................. 29
4.2. ARIMA Model........................................................................................................................................ 31
4.3. Results and Discussion.......................................................................................................................... 31
5. DISCUSSION OF EMPERICAL RESULTS ...................................................................................................... 35
5.1. Significant Variables ............................................................................................................................. 35
5.1.1. Commodity Price Movements............................................................................................................ 35
5.1.2. Net Equity Flows ................................................................................................................................ 36
5.1.3. Foreign Exchange Reserves................................................................................................................ 36
5.1.4. Short-term Interest Rates .................................................................................................................. 37
5.1.5. Long-term Interest Rates................................................................................................................... 37
5.1.6. Money Supply .................................................................................................................................... 38
5.2. Non-significant Variables...................................................................................................................... 38
5.2.1. Net Bond Flows.................................................................................................................................. 38
5.2.2. Domestic Credit Extension................................................................................................................. 39
5.2.3. GDP Growth Differential.................................................................................................................... 40
5.2.4. Relative Equity Returns...................................................................................................................... 40
6. CONCLUSIONS AND RECOMMENDATIONS.............................................................................................. 41
6.1. Conclusions ........................................................................................................................................... 41
6.2. Recommendations................................................................................................................................ 42
6.3. Proposed Future Research.................................................................................................................... 43
7. REFERENCES .............................................................................................................................................. 45

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LIST OF FIGURES
Figure 1: Net Purchase of Equities and Bonds by Non-Residents .........................................................3
Figure 2: Rand/U.S. Dollar Nominal Exchange Rate ............................................................................16
Figure 3: Ratio of Net Bond Flows to Nominal GDP. ...........................................................................17
Figure 4: Ratio of Net Equity Flows to Nominal GDP...........................................................................17
Figure 5: London Gold Price.................................................................................................................18
Figure 6: Productivity Differential Between South Africa and the United States (spread).................19
Figure 7: Relative Equity Returns: JSE ALSI - S&P 500 .........................................................................19
Figure 8: Short-term Interest Rate Spread ..........................................................................................20
Figure 9: Long-term Interest Rate Spread ...........................................................................................21
Figure 10: Ratio of M2 Money Supply to Nominal GDP ......................................................................21
Figure 11: Ratio of Domestic Credit Extension to Nominal GDP .........................................................22
Figure 12: Ratio of Foreign Exchange Reserves to Nominal GDP........................................................23
Figure 13: Actual, residual and fitted graphs.......................................................................................34
LIST OF TABLES
Table 1: Data sources and transformations ....................................................................................................... 15
Table 2: Results from stationarity tests.............................................................................................................. 30
Table 3: Output of regression analysis ............................................................................................................... 33
Table 4: Diagnostics tests ................................................................................................................................... 34

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1. INTRODUCTION
1.1. Background to the Study
Since financial liberalisation in March 1995, when the dual exchange rate was unified and most
international sanctions were officially ended, the Rand has experienced bouts of increased
volatility. Between April and August of 1998, the currency depreciated by 28% against the U.S.
Dollar (Bhundia and Ricci, 2006) and during 2001 by 82% - from R7,60 to the Dollar to 13,84 to the
Dollar (LiPuma and Koelble, 2009). The impact on welfare in South Africa was notable: in a nation-
wide survey of South African business carried out by the World Bank (2005), the volatility of the
exchange rate was found to be the most serious constraint on growth by exporters, and the second
most serious constraint by non-exporting firms1
(World Bank, 2005).
Currency volatility can lead to a significant loss of welfare for a country. Obstfeld and Rogoff (2010)
argue that unexpected appreciations may negatively impact demand for a country’s exports,
exporting firms are forced to reduce output, which leads to lower employment and wages. In
addition, risk adverse agents may choose to divert their resources to other, more predictable
sectors of the economy, which results in underinvestment in certain export sectors (Farrell, 2001).
Furthermore, firms will try to hedge their risk against future volatility by increasing the margins of
their goods, which in turn reduces demand, production and consumption. Medhora (1999) argues
that while forward markets can mitigate some of the risk of exchange rate fluctuations, such
measures only offer partial cover: forward exchange markets are incomplete in the length of cover
offered; the forward exchange rate is a poor predictor of the future spot rate; and traders cannot
always plan the magnitude or timing of all their foreign exchange transactions.
While various empirical studies have offered conflicting results as to whether exchange rate
variability affects trade (Virgil, 2002), South African trade unions and Government perceive the
volatile exchange rate as a constraint on growth (Republic of South Africa, 2006). For this reason
there have been calls for intervention. Over the past few months, Government has considered
following Brazil’s 2009 implementation of a 2% Tobin Tax on short-term capital flows (Isa, 2010),
suggested originally in 1972 by economist and Nobel Laureate James Tobin in order to deter
1
Skills shortage was found to be the most severe constraint on growth for non-exporting firms.

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currency speculation (“The Tobin Tax Links Page,” 2010). Edwards (1999) cautions that the private
sector very often finds ways to evade the controls. Magud and Reinhart (2006) argue that much of
the debate around whether capital controls are effective is due to a lack of consensus on
assessment frameworks and varying definitions of what ‘successful’ capital controls actually mean.
Isa (2010) warns that such a tax could raise the cost of debt to South Africa, as well as possibly
create a negative sentiment amongst investors.
Other currency management option include devaluation or to fix the exchange rate, though market
analysts argue that the amount of reserves required is substantial and thus may lead to domestic
inflation though the required purchase of excess dollars (Keeton, 2009). Indeed, many reserve
banks around the world have taken out ‘self-insurance’ policies by accumulating substantial foreign
exchange so as to periodically sterilise currency volatility (Broto, Diaz-Cassou and Erce-Dominguez,
2007). However it has been argued by Broto et al. (2007: 2) that “...large-scale and protracted
interventions in foreign exchange markets hamper the adjustment of global imbalances, carry
significant sterilization costs and can generate, inter alia, inflationary pressures, unsustainable
increases in credit and asset prices and difficulties for the conduct of monetary policy.” Therefore it
is possible that traditional interventions are not sustainable ways of managing the Rand’s volatility.
Broda and Romalis (2003) find that trade – especially where deep bilateral trade relations exist –
has a dampening effect on the real exchange rate volatility of many countries. Specific to South
Africa, Boshoff (2008) states that while there is a statically significant lagged co-movement between
cycles in production and the Rand, the direction of causality has not been established. Since South
Africa is one of the world’s primary commodity exporters, one question which this report will seek
to answer is whether the volatility of the Rand/U.S. dollar exchange rate is impacted by the
volatility of commodity prices.
A second area of research is identified by the Myburgh Commission (2002), which was formed to
investigate the Rand’s sharp decline during 2001. The Commission reported significant currency
volatility owing to portfolio flows whereby “The sharp fluctuations in portfolio investments of non-
residents contributed materially to greater volatility in the external value of the Rand” (Myburgh
Commission, 2002: 15). A large part of that balance is made up of foreign portfolio investment,
which includes domestic bonds purchased by foreign investors and equity flows (Ahmed, Arezki and

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Funke, 2005). The large swings in purchases of bonds and shares by non-residents post-1995 can be
seen from Figure 1 below:
Figure 1: Net Purchase of Equities and Bonds by Non-Residents
South Africa is thus particularly vulnerable to currency fluctuations due to the composition of its
capital flows. Ahmed, Arezki and Funke (2005) found that between 1994 and 2001, foreign direct
investment (FDI) capital flows amounted to only 30% of total flows into South Africa, compared to
70% in comparative countries. Nowak (2001) explains that in principle, FDI is seen as being less
volatile and less likely to be reversed than short-term capital flows (known as ‘hot money’) and is
thus considered to pose less risk of a capital flow surge or sudden stop.
The year to date of 2010 has seen significant currency volatility as foreign investors took advantage
of interest rate differentials to buy South African bonds. According to South African Reserve Bank
data, from January to July, R72 billion worth of bonds were purchased by overseas investors,
compared to R32 billion over the same period in 2009 (Isa, 2010). As a result of a high level of
portfolio flows coupled with low levels of FDI flows, South Africa’s total capital flows are very
erratic. Consequently, in the December 1999 Quarterly Bulletin, the South African Reserve Bank
warned that inflows “…that enter the economy through the fixed-interest securities market, are
known for their capricious behaviour; they are volatile and their direction of flow is often reversed
abruptly.” (SARB, 1999: 3). Since portfolio flows can be easily reversed, they are not seen as a stable
source of foreign exchange by the Reserve Bank. Thus a second question that this report seeks to
-30,000
-20,000
-10,000
0
10,000
20,000
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
EQUITIES BONDS
RANDS(BILLIONS)
YEAR

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investigate is the extent to which volatility in portfolio flows induces volatility in the Rand/U.S.
dollar exchange rate.
Understanding the volatility of portfolio flows and commodity prices can give monetary and
government authorities a better idea of whether sources of volatility should be managed from the
monetary or financial side of the economy. Thus, a further aim of this study is to determine which
macroeconomic variables exhibit the strongest influence on the Rand/U.S. Dollar exchange rate.
1.2. Purpose of the Study
The purpose of this study is to undertake an empirical analysis to determine the extent to which
volatility of commodity prices and portfolio flows are a cause of volatility in the nominal Rand/U.S.
Dollar exchange rate. The following variables will be included in the analysis: bilateral net bond
flows, bilateral net equity flows, commodity prices, relative equity returns, GDP growth
differentials, short-run interest rate differentials, long-run interest rate differentials, gold price
movements, money supply volatility, domestic credit growth and foreign exchange reserves. By
identifying the most significant determinants of exchange rate volatility, and making
recommendations thereof, this research seeks to contribute to the literature on how best to
dampen the Rand’s volatility.
Hence, the objectives of this research are the following:
i) To determine the degree to which volatility in portfolio flows is associated with
volatility in the nominal Rand/U.S. Dollar exchange rate;
ii) To ascertain whether volatility in commodity prices induces volatility in the exchange
rate;
iii) To investigate whether volatility in key macro-economic variables is associated with
volatility in the Rand.

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1.3. Key Findings
Equity flows were found to have a moderately negative effect on the Rand’s volatility. On the other
hand, bond flows were not found to affect the currency’s stability. The most plausible reason in the
literature appears to be that cross border bond holdings are mostly hedged while this is generally
not the case with equities. Fluctuations in long-term interest rate differentials were found to be
statistically significant, though only translated into slight fluctuations in the Rand. Based on these
findings, equity flows are the only component of portfolio flows to affect the Rand’s volatility and
so dismantling capital controls and not imposing transaction taxes should be considered by fiscal
authorities.
The literature indicates that rising gold prices induces symptoms of ‘Dutch Disease’ in South Africa’s
export sector. This research also finds that declines in the gold price leads to currency volatility. In
conjunction with declining profit margins of many gold mines, as well as limited options to diversify
to upstream production, the implication is that South Africa’s gold mining industry may not be
sustainable.
The money supply/GDP ratio was found to be significant variable, with a positive relationship to
fluctuations in the Rand that was strong than other variables. Foreign exchange reserves/GDP was
found to have a positive relationship with currency volatility. However, it is postulated that once
the Reserve Banks builds its reserves beyond a certain level, the relationship will turn negative in
line with research on other emerging economies. Thus this research suggests that monetary policy
can play an important role in smoothing out episodes of high currency volatility.
1.4. Delimitations and Limitations
1.4.1 Frequency of Data
Brooks, Edison, Kumar and Sløk (2004:514) indicate that monthly data may contain excessive ‘noise’
which may preclude the identification of relationships between variables. In addition, GDP data (of
which the highest frequency is quarterly) is used in volatility analysis in order to estimate growth

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rate differentials (Cady and Gonzalez-Garcia, 2007) as well as to scale economic variables. For this
reason, this study makes use of quarterly data.
1.4.2 Sample Period
A common problem in volatility modelling is that of short sample periods (Brooks, Edison and
Kumar, 2004: 523). This is particularly pronounced in the case of studies on the Rand, since the
South African Reserve Bank only began to record data on bond and equity flows as of 1988.
McGough and Tsolacos (1995:15) and Tse (997:160) indicate that at least 50 data points are
required in order to ensure that the ARIMA process is efficient. Only 262
periods were available for
the period 1988:Q2 – 1995:Q1 period, and thus this sample could not be included in the research.
1.4.3 Choice of Bilateral Exchange Rate
South Africa’s commodities and trade exports are frequently denominated in U.S. dollars. Thus, this
research will investigate the volatility of the Rand/U.S. Dollar exchange rate. However, it is possible
that the exchange rate volatility with South Africa’s other major trading partners (such as the U.K.,
Euro-zone and China) will have different dynamics.
1.4.4 Lag Effects
The primary focus of this research is to identify the economic variables that contribute most
significantly to the volatility of the Rand/U.S. Dollar exchange rate, and the construction of an
appropriate model to represent these relationships. Thus, although it is possible that lag effects of
the explanatory variables could provide greater insight3
, it was not possible to include additional
lags due to software limitations (this is considered to be an area for future research, as discussed in
Section 6.3.
2
One period is always ‘lost’ when the data is differenced.
3
See for example Castrèn (2005).

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1.4.5 Foreign Direct Investment
FDI flows are widely considered to be less volatile when compared with portfolio flows, and are less
likely to be reversed (Nowak, 2001)4
. Unfortunately, FDI makes up a smaller portion of capital flows
that enter South Africa (only 30%) and thus this report focuses on portfolio flows, which make up a
greater portion of South Africa’s capital flows and are thus more likely to lead to volatility in the
Rand.
1.5. Layout of Report
The remainder of this research report is laid out as follows. Section 2 reviews the literature. Section
3 details the explanatory variables tested as well as methodology used. Section 4 explains the
econometric analysis undertaken as well as the ARIMA model constructed. The results of the
econometric analysis are outlined in Section 5. Finally section 6 concludes and offers
recommendations.
4
However, literature by Frankel and Rose (1996) has indicated that FDI flows may be as volatile as the traditionally ‘hot’
flows.

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2. LITERATURE REVIEW
2.1. Introduction
This literature review will examine four key areas relating to the research topic: (i) how other
researchers have chosen variables from the monetary, fiscal and real side of the economy to
explain currency volatility; (ii) the choice in measurement methods for exchange rate volatility; and
(iii) methodological reasons for choosing to study a currency nominal or real exchange rate. The
scope of the literature review will also include a review of exchange rate models (real and nominal),
which may offer insight into the choice of explanatory variables for inclusion.
Much of the literature on currency volatility focuses on measuring the effect of fluctuations in
macroeconomic variables on fluctuations in the exchange rate. Significant relationships are
generally found with money supply, interest rates, inflation and foreign exchange reserves. In this
literature review, variables that are used as proxies for commodity prices and portfolio flows (and
which have relationships to currency volatility) are highlighted.
The review is sequenced as follows. Section 2.2 briefly reviews key volatility terms. Section 2.1 and
2.2 consider the extent to which commodity prices and portfolio flows have been studied as
possible causes of exchange rate volatility. Section 2.4 looks at fundamental macroeconomic
variables used in exchange rate models and which may have explanatory power in this study.
Section 2.5 reviews methodological issues and section 2.6 provides a summary conclusion.
2.2. Key Terms
Giannellis and Papadopoulos (2010) define exchange rate volatility as short-run fluctuations of the
exchange rate around its long-run trend. Consequently, currency volatility arises when an exchange
rate is highly misaligned from its equilibrium rate, and will continue to be volatile until returning to
an equilibrium position either through market forces or government intervention (Giannellis and
Papadopoulos, 2010). Since an exchange rate is an endogenous variable, its volatility depends on
the volatility of economic fundamentals in other parts of the economy, such as the monetary side,
the real side and the stock market (Giannellis and Papadopoulos, 2010).

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Medhora (1999) explains that exchange rates link macroeconomic policies and exogenous events to
economic sectors and have an impact on the performance of firms. An exchange rate forms the link
between developments in financial markets to production and employment. They affect the flow of
international trade and capital, and in turn are affected by these flows. However, despite these
definitions, there are numerous approaches to modelling exchange rate volatility (Hansen and
Lunde, 2005) and no generally agreed determinants to currency volatility (Cady and Gonzalez-
Garcia, 2007). Thus much of the literature reviewed discusses the effectiveness of different
volatility measurements, as well as economic variables that may help to explain the source currency
volatility.
2.3. Currency Volatility and Commodity Prices
Numerous studies have been undertaken on the effect of the Rand’s volatility on exports.5
However, Boshoff (2008) explains that the effect of exports on the Rand's volatility has not been
examined sufficiently to conclude the direction of causality. Further, Broda and Romalis (2003) test
the assumption that the effect of trade on currency volatility is non-existent. Using a rolling
standard deviation measure with a five-year window period from 1970 to 1997, the results show
that trade significantly dampens real exchange rate volatility, especially where deep bilateral trade
relations exist. Likewise, Devereux and Lane (2002) report that nominal currency volatility of
developed countries is explained by optimal currency area factors that include trade.
The movement of commodity prices has been identified as a significant variable in the
determination of the Rand’s real exchange rate6
. In their study, MacDonald and Ricci (2003)
represent these price movements with a weighted average ‘basket’ of commodity prices, which
they formulate using the international prices of gold, platinum, coal, iron-ore and nickel. The results
show that commodity price movements have a strong influence on movements of the Rand.
Another area within the literature that examines the interaction between exports and currency
movements relates to productivity shocks. Chowdhury (2004) indicates that productivity shocks can
lead to a country’s currency appreciating over the long run if its productivity growth advantage in
5
For example see Arize et al., 2003 and Raddatz (2008)
6
See for example Akinboade and Makina (2006), Bhundia and Ricci (2006), Frankel (2007), MacDonald and Ricci (2003)
and Mtonga (2006).

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tradable sectors exceeds its productivity growth advantage in non-tradable sectors – the so-called
Balassa-Samuelson effect (Harrod, 1933; Balassa, 1964; Samuelson, 1964). In contrast to this,
however, Bailey and Millard (2001) explain that a productivity gain lead to higher expected profits
and capital flows from overseas investors, and partly explains the general appreciation of the U.S.
Dollar during the 1990’s. Meredith (2001) argues that productivity differentials alone could not
explain the sources of persistent Dollar strengthening. Instead, it is argued that a surge in global
equity values during the mid-1990s resulted in a shock that disproportionately affected the U.S.
economy.
Different proxies for productivity have been suggested in the literature. Égert (2002) indicates that
productivity differences can be proxied by real GDP per capita, or variables connected to education
and demographic factors. Likewise for South Africa, Mtonga (2006) finds that real GDP per capita is
significant when determining the Rand’s real exchange rate. Giannellis and Papadopoulos’ (2010)
also report that industrial production-differentials were partly the cause of exchange rate volatility
for the French Franc and the Italian Lira during the pre-EMU period. On the other hand, Cady and
Gonzalez-Garcia’s (2007) volatility study found that GDP growth - and not the relative GDP growth -
was a highly significant variable in explaining sources of currency volatility.
Therefore the effect of trade flow volatility on the Rand’s volatility has not been explicitly explored
in the literature; however, two proxies for trade have been suggested: commodity prices and GDP
growth differentials.
2.4. Currency Volatility and Portfolio Flows
The effect of portfolio flows on currency movements is studied by Brooks et al. (2004). The authors
examine the depreciation of the Euro and Yen to the U.S. Dollar over the period 1988 to 2000. They
find that productivity gains as a result of innovations in the information and communications
technology sectors and higher expectations of profits from U.S. firms, led to a twelve-fold increase
in net portfolio flows to that country. Evans and Lyons (2002) indicate that investor behaviour is
seen as key in studying exchange rate volatility. The analysis is undertaken using tick-by-tick time-
series data for the Deutsche Mark and Yen over a four month period in 1996. The results show that
up to 60% of the Deutsche Mark’s daily volatility and 40% of the Yen’s daily volatility can be

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explained by inter-dealer order flow. While the order-flow/exchange rate linkage has not yet been
adopted within macroeconomic financial theory (Evans and Lyons, 2002), the finding is nonetheless
relevant in further highlighting the important influence that equity flows have on currency
volatility.
Samson, Ampofo and MacQuene (2003), using standard rolling deviation as a measure of volatility,
could not find with certainty that portfolio or FDI flows were the cause of the Rand’s sharp
depreciation during 2001. The authors indicate that a possible reason for this is that measurement
error due to accounting transactions may obscure underlying relationships between those
variables. For instance unpaid dividends are recorded as capital flows in one quarter, but are then
recorded as capital outflows when they are paid in the next quarter. However, they conclude that
the volatility of private investment flows (private flows that did not include portfolio or FDI flows)
was the cause of currency volatility towards the end of 2001.
Market commentators point to the importance of interest rate differentials in explaining Rand
volatility, since this would lead to overseas investors taking advantage of differentials to buy South
African bonds and shares (Isa, 2010). This is supported by Brooks et al. (2004) who finds that a
significant cause of the depreciation of the Yen to the U.S. Dollar between 1995 and 2000 was due
to short and long-run interest rate differentials. Giannellis and Papadopoulos (2010) use
multivariate GARCH and VAR analysis to examine currency volatility amongst EMU members and
candidate countries. Their study shows that interest rate differentials could explain volatility in the
foreign exchange markets of the Polish Złoty/Euro, Hungarian Forint/Euro and Spanish Peseta/Euro.
However, Hodge (2005) questions the commonly accepted positive relationship between interest
rate differentials, portfolio flows and the Rand. In real terms, he finds that decreases in interest
rates differentials did not always translate into currency depreciation since expectations of higher
returns on the JSE led to the Rand moving in the opposite direction.
The literature also looks at the influence of stock exchanges on currency volatility. Giannellis and
Papadopoulos (2010) report that volatility in the national stock market of Poland generated
volatility in the Polish Złoty. However, a similar relationship was not found in the case of the Czech
Republic or Slovakia and their respective stock exchanges, which the authors explain is due to the
adoption of managed-floating exchange rate regimes where the central bank smoothed out

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currency excessive fluctuations. Brunetti, Scotti, Mariano and Tan (2008), use a Markov switching
GARCH model to investigate currency volatility before and during the Asian crisis. Two significant
factors found to be important predictors of a currency crisis were stock index returns and bank
stock index returns7
. Lee-Lee and Hui-Boon (2007) examine exchange rate volatility in Thailand,
Malaysia, Indonesia and Singapore over the 1990s. Using a VAR analysis, they find that in all four
countries, currency volatility is related to stock market indices8
.
2.5. Currency Volatility and Macroeconomic Factors
Morana (2009) reports bi-directional causality between macroeconomic volatility and exchange
rate volatility but notes that the causality runs more significantly from the former to the latter.
Morana thus concludes that policies to correct macroeconomic volatility may assist to reduce
exchange rate volatility.
Lee-Lee and Hui-Boon (2007) study currency volatility across Thailand, Malaysia, Indonesia and
Singapore. They find that the relative terms of money supplies, trade balances and consumer price
indices are significantly associated with exchange rate volatility. Balg and Metcalf (2010) investigate
bilateral currency volatility for a sample of developed countries using a standard deviation metric.
In contrast to much of the literature, they find that over the longer term, exchange rate volatility is
only associated with money supply differentials.
Cady and Gonzalez-Garcia (2007) test nominal exchange rate volatilities across 48 countries that
include industrial, emerging market and low income countries. Using ordinary least squares (OLS)
regression analysis they find that the most significant explanatory variables are reserve adequacy,
government indebtedness, real GDP growth, improvement of the fiscal balance, inflation and the
volatility of money growth.
Similarly to the study above, Hviding, Nowak and Ricci (2004) study 28 emerging counties between
1986 and 2002 and confirm that a reserve build-up by a central bank leads to a reduction in short-
term real effective exchange rates.
7
The other two factors were real effective exchange rates and money supply relative to reserves.
8
Other factors included the relative terms of money supplies, trade balances and consumer price indices.

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In South Africa, Knedlik (2006) developed a model in order to predict future crises of the Rand. The
model was effective in predicting the 1996 and 1998 crisis, but not the 2001 crisis9
. The model
made use of eight significant variables to predict a crisis 24 months in advance: international
liquidity, the gold price, imports, bank deposits, foreign debt, credit to GDP, the budget deficit and
the interest rate. The ratio of domestic credit extension10
to GDP ratio is of particular interest, given
the degree of credit growth prior to the 2008 financial crisis. In 1995, the ratio stood at 0,45 and
increased to 5,63 by the end of 2009. The private sector dominates the demand for this credit: 90
to 100% of domestic credit extended by the Reserve Bank went to the private sector in the past ten
years (Brink and Kock, 2009: 18). In June 2010, this amount totalled R2 trillion, of which about 50%
goes to households (Banking Association of South Africa, 2010). It is therefore postulated that
consumer spending on imported goods will lead to fluctuations in the Rand.
2.6. Conclusion
The literature described previously indicates that the effect of volatility in commodity prices and
portfolio flows on the Rand has not been extensively investigated (Boshoff, 2008 and Samson et al.,
2003). In addition, the effect of macroeconomic variables has not been sufficiently examined. It is
important to note that there are no commonly accepted explanatory variables in models of
exchange rates. This research will look to contribute to existing literature by determining whether
portfolio flows, commodity prices or macroeconomic factors are most significant in explaining the
volatility of the Rand/U.S. Dollar exchange rate. For this reason, various methodologies highlighted
in the literature will be examined more closely in Section 3, so as to determine the most
appropriate approach for modelling the data.
9
While Knedlik’s model did indicate a high degree of currency risk around 2001, the change in SARB policy – to no
longer intervene when the Rand faced speculative pressure - lowered the measurement of risk to the currency.
10
Total domestic credit extension is defined by Brink and Kock (2009:16) as the sum of claims on the private sector
(which includes asset backed loans and credit card advances) and net claims on the government sector (claims on the
government sector minus government deposits)

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3. METHODOLOGY AND DATA DEFINITIONS
The methodology applied in this research follows the study by Cady and Gonzalez-Garcia (2007)11
.
The econometric modelling was undertaken as follows. The first step involved constructing a basic
estimable model that consisted of the explanatory variables highlighted in the literature as being
significant determinants of nominal exchange rate volatility. Thereafter the data was tested for
stationarity using the Augmented Dickey-Fuller (1979) and Phillips and Perron (1988) unit root
tests. Non-stationary data was then differenced so as to remove possible movements in variances
and means. An ARIMA model was then constructed which included those variables found to be
significant in the regression analysis as well as an autoregressive (AR) term. All analysis was
undertaken using EViews 6.
The data used in this research consists of one dependent variable, the Rand/U.S. Dollar nominal
exchange rate, and 10 explanatory variables split into three categories, namely:
i) Capital flows: net bond flows; net equity flows
ii) Commodity price movements: gold price
iii) Macroeconomic: GDP growth differentials, relative equity returns; short-term
interest rate differentials; long-term interest rate differentials; money supply
volatility: domestic credit; and foreign exchange reserves
An explanation for each variable follows as well as an overview in Table 1 of the transformations
performed on the data before they were inputted into the model.
11
In addition, de la Cruz (2008) provides a detailed methodology on the steps taken to obtain and analyse volatility
variables.

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Table 1: Data sources and transformations
Mnemonic Variable Source and code Transformation of raw data12
ZAR_USD_LOGDIFF Rand/U.S. Dollar
Nominal Exchange
Rate
SARB: KBP5339 Monthly exchange rate values were averaged to quarters, and then converted to a logarithmic scale in
order to smooth out excess fluctuations. Stationarity tests indicated that the level data was not
stationery, and hence the first difference was computed.
GOLDP_LOGDIFF Commodity Price
Movements
SARB: KBP5357 Monthly U.S. Dollar gold prices as quoted on the London Stock Exchange were averaged to quarterly
figures. To remove excess fluctuations, the data was converted to a logarithmic scale. Stationarity tests
indicated that the level data was not stationery, and thus the first difference was computed.
BONDOGDP_RTO Net Bond Flows/GDP SARB: KBP2051 Net monthly bond flows were summed to quarters and then divided by quarterly nominal GDP. Data
was found to be I(0) stationery.
EQUITIESOGDP_RTO Net Equity
Flows/GDP
SARB: KBP2050 Net monthly equity flows were aggregated into quarters and then divided by quarterly nominal GDP.
Data was found to be I(0) stationery.
GDPGROWTH_SPR Real GDP Growth
Differential
IFS: 19999BPXZF...
IFS: 11199BPXZF...
The spread between the two growth rates was computed (absolute value of SA – US). The data was
found to be I(0) stationery. Since this data already indicates the GDP volatility, it was not differenced.
RER_SPR_LOG Relative Equity
Returns
Inet-Bridge: J203
Datastream:
S&PCOMP(PI)
Quarterly averages were computed for the JSE ALSI and S&P 500 monthly indices. Quarterly returns
were computed as well as the spread (JSE – S&P 500). This was converted to a logarithmic scale to
measure the change in the spread.
STIR_SPR_D Short-term Interest
Rate Differentials
SARB: KBP1405;
USFR13
: H15/H15/
RIFSGFSM03_N.M
The spread in short term interest rates was computed as SA – US. To measure the change in the spread,
the data was differenced.
LTIR_SPR_D Long-term Interest
Rate Differentials
SARB: KBP2003
USFR:
RIFLGFCY10_N.B
The spread in long term interest rates was computed as SA – US. To measure the change in the spread,
the data was differenced.
M2OGDP_RTO_LOGDIFF Money Supply
(M2)/GDP
SARB: KBP1373M
SARB: KBP6633D
Monthly data was summed to quarterly values, which was scaled to nominal GDP. The data was not
stationery at first difference or in logarithmic form, so the logarithmic difference was used.
DCEOGDP_RTO_D Domestic Credit
Extension/GDP
SARB: KBP1368M
SARB: KBP6633D
Monthly data was summed to quarterly values, and then divided by nominal GDP. Data was differenced
to measure volatility of the series, and this was also found to be stationery.
FORRESOGDP_RTO_D Foreign Exchange
Reserves/GDP
SARB: KBP1021M
SARB: KBP6633D
Monthly data was augmented to quarterly values, and scaled to nominal GDP. Data was differenced to
measure volatility of the series, and this was also found to be stationery.
12
Since data from SARB on bond and equity flows was only available as of February 1998, quarterly data observations were formulated from 1988:Q2 onwards.
13
Denotes United States Federal Reserve. Refer to http://www.federalreserve.gov/

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3.1. Sample Period
Financial liberalisation in South Africa took place in March 1995 (Roux, 2005). Consequently, the
sample period used in this research is 1995:Q2 to 2009:Q4.
3.2. Dependent Variable: Rand/U.S. Dollar Nominal Exchange Rate
Medhora (1999) argues that the choice of whether to study real or nominal volatility depends on
which of the two has varied the most in the period under review. In addition, it is recommended
that if exchange rates are changing faster than prices, then nominal exchange rates should be used.
Since both of these conditions are applicable in the case of South Africa, this research considers
nominal exchange rate volatility. Rand/U.S. dollar exchange rate data was obtained from the South
African Reserve Bank (code: KBP5339).
Figure 2: Rand/U.S. Dollar Nominal Exchange Rate
3.3. Capital Flow Variables
3.3.1. Net Bond Flows
The Myburgh Commission (2002) found that relative bond flows have an important impact on the
volatility of the Rand, and thus in this research a positive relationship can be expected between the
volatility of the two variables. Data on net bond flows by non-residents was obtained from SARB
(code: KBP2051).
-.08
-.04
.00
.04
.08
.12
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
ZAR_USD_LOGDIFF
ZAR_USD_LOGDIFF
YEAR

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Figure 3: Ratio of Net Bond Flows to Nominal GDP.
3.3.2. Net Equity Flows
Net equity flows were also highlighted in the Myburgh Commission (2002) as having an effect on
Rand volatility. Data on net equity flows by non-residents was obtained from SARB (code:
KBP2050). A positive relationship with the dependent variable is expected.
Figure 4: Ratio of Net Equity Flows to Nominal GDP.
-.08
-.06
-.04
-.02
.00
.02
.04
.06
.08
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
BONDOGDP_RTO
BONDOGDP_RTO
-.08
-.06
-.04
-.02
.00
.02
.04
.06
.08
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
BONDOGDP_RTO
BONDOGDP_RTO
YEAR
YEAR

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3.4. Commodity Price Variables
3.4.1. Commodity Price Movements
MacDonald and Ricci (2003) used a weighted average ‘basket’ of the international price of gold,
platinum, coal, iron ore and nickel. However, since gold dominates South Africa’s mineral exports,
the gold price alone will be used as a proxy for commodity price movements. Gold price data as
quoted on the London Stock Exchange was obtained from SARB (code: KBP5357). Broda and
Romalis (2003) determined that trade significantly dampens real exchange rate volatility – i.e. a
negative relationship exists between the two variables. Based on these studies, an increase in trade
flow volatility can be expected to lead to a decrease in nominal exchange rate volatility.
Figure 5: London Gold Price
3.5. Macroeconomic Variables
3.5.1. GDP Growth Differentials
Following Brooks et al. (2004:12), productivity will be modelled by comparing relative GDP growth
rates. Data for South Africa and the United States was obtained from IFS (codes: 19999BPXZF... and
11199BPXZF...). Brooks et al. (2004) report that an increase in U.S. productivity led to an
appreciation in the Euro/U.S. Dollar exchange rate. Thus it is expected that in this research,
volatility of productivity differentials will lead to volatility in the nominal exchange rate (i.e. a
positive relationship).
-.04
-.02
.00
.02
.04
.06
.08
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
GOLDP_LOGDIFF
GOLDP_LOGDIFF
YEAR

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Figure 6: Productivity Differential Between South Africa and the United States (spread).
3.5.2. Relative Equity Returns
Brooks et al. (2004) compared return differentials between the S&P 500 and the Eurostoxx and
found a negative14
relationship between positive equity returns and currency depreciation. Thus in
this research, it is expected that volatility in relative equity returns will have a negative effect on
currency volatility. The data used to construct the relative equity return series was obtained from
Inet-Bridge (code: J203) and Datastream (code: S&PCOMP(PI).
Figure 7: Relative Equity Returns: JSE ALSI - S&P 500
14
The study suggests that investors repatriate their funds from the U.S. back to the Euro area once they realise large
excess returns on U.S. assets, which indicates that they believe markets to be mean reverting.
0
1
2
3
4
5
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
GDPGROWTH_SPR
GDPGROWTH_SPR
-2.5
-2.0
-1.5
-1.0
-0.5
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
RER_SPR_LOG
RER_SPR_LOG
YEAR
YEAR

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3.5.3. Short-Term Interest Rate Differentials
Giannellis and Papadopoulos (2010) find that movements in short-run interest rate differentials
can impact the volatility of the exchange rate. 91-day Treasury bill data was obtained from SARB
(code: KBP1405) while 3-month Treasury bill data was obtained from the U.S. Federal Reserve
(code: H15/H15/RIFSGFSM03_N.M).
Figure 8: Short-term Interest Rate Spread
3.5.4. Long-Term Interest Rate Differentials
The long-run interest rate differential was found to be a significant variable in the formulation of
the Rand’s real exchange rate in MacDonald and Ricci (2003), and is one of the fundamental
variables in many exchange rate models. Thus volatility in this variable is expected to lead to
nominal exchange rate volatility.
The data used to construct the long-term interest rate differential consisted of 10 year bond yields
from SARB (code: KBP2003) and the U.S. Federal Reserve (code: RIFLGFCY10_N.B).
-4
-2
0
2
4
6
8
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
STIR_SPR_D
STIR_SPR_D
YEAR

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Figure 9: Long-term Interest Rate Spread
3.5.5. Money Supply/GDP
Money supply growth was found to effect currency volatility in numerous papers (Brunetti et al.,
2008; Cady and Gonzalez-Garcia, 2007; Balg and Metcalf, 2010) and a positive relationship is
expected in this study. The ratio of M2 to nominal GDP was selected as the most appropriate proxy.
M2 data (code: KBP1373M) and nominal GDP data (code: KBP6633D) were obtained from SARB.
Figure 10: Ratio of M2 Money Supply to Nominal GDP
-2
-1
0
1
2
3
4
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
LTIR_SPR_D
LTIR_SPR_D
-.02
-.01
.00
.01
.02
.03
.04
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
M2OGDP_RTO_LOGDIFF
M2OGDP_RTO_LOGDIFF
YEAR
YEAR

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3.5.6. Domestic Credit Extension/GDP
Domestic credit growth was found to be a significant crisis predictor in Knedlik (2006). Extension in
credit would induce greater spending by consumers, including in imported goods. Hence changes in
the ratio of domestic credit to nominal GDP will be compared to the Rand’s volatility, where a
positive relationship is expected. Data on domestic credit extension (code: KBP1368M) and nominal
GDP data (code: KBP6633D) were both obtained from SARB.
Figure 11: Ratio of Domestic Credit Extension to Nominal GDP
3.5.7. Foreign Exchange Reserves/GDP
Increasing levels of foreign exchange reserves would be better able to finance a deteriorating
current account balance and thus lower exchange rate volatility (Cady and Gonzalez-Garcia, 2007).
Following Ricci (2006) a suitable proxy would be the ratio of foreign exchange reserves to GDP. The
numerator relates to the assets held by the Reserve Bank and commercial banks, as well as the
Reserve Bank’s open position in the forward market (SARB codes KBP1021M and KBP6633D).
-.02
-.01
.00
.01
.02
.03
.04
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
DCEOGDP_RTO_LOGDIFF
DCEOGDP_RTO_LOGDIFF
YEAR

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Figure 12: Ratio of Foreign Exchange Reserves to Nominal GDP
3.6. Modelling Volatility
Virgil (2002) indicates that there is no single theoretically correct way to measure exchange rate
volatility. According to Virgil (2002) and Schnabel (2007) the most common metrics include: (i)
cycles around a constant level as measured by the standard deviation of percent changes in the
exchange rate; (ii) squared residual from an ARIMA process; (iii) Gini mean difference co-efficient;
(iv) Vector Autoregression; and (v) the family of ARCH models. Most studies make use of the sample
standard deviation method to measure currency volatility. However, this method has two
important drawbacks (Bah and Amusa, 2003). Firstly, it assumes incorrectly that an exchange rate
follows the normal distribution and secondly, it does not take into account the predictable and
unpredictable components of the exchange rate process.
An alternate approach is the autoregressive (AR) integrated (I) moving-average (MA) methodology
developed by Box and Jenkins which includes autoregressive and moving average parameters.
ARIMA is a form of regression analysis that examines the differences between values in the series
instead of using the actual data values (Batchelor, 2004). A time series which needs to be
differenced to be made stationary is said to be an "integrated" version of a stationary series. Lags of
the differenced series appearing in the forecasting equation are referred to as "auto-regressive"
terms, while lags of the forecast errors are "moving average" terms (Nau, 2008). Since this research
-.15
-.10
-.05
.00
.05
.10
.15
.20
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
FORRESOGDP_RTO_D
FORRESOGDP_RTO_D
YEAR

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looks at modelling past volatility, it is worthwhile to examine the autoregressive term in more
detail. Most time series consist of elements that are serially dependent. That is, a coefficient (or a
set of coefficients) can be estimated that describes consecutive elements of the series from
specific, time-lagged (previous) elements (StatSoft, 2010).
If the empirical model includes lags of the dependant variable then it is deemed an autoregressive
model AR(p) and can be defined by the following equation (Brooks, 2008):
t t t p t p tY Y Y Y u1 1 2 2 ... (1)
In the case of a moving average model, it is the error term not the dependant variable that is
included as an autoregressive term and thus the MA(q) model can be defined as:
t t t t q t qY u u u u1 1 2 2 ... (2)
Hence a model that includes both AR(p) and MA(q) terms is catagorised as an ARMA model and is
represented as:
t t t p t p t t q t q tY Y Y Y u u u u1 1 2 2 1 1 2 2... ... (3)
Nau (2008) explains that a non-seasonal ARIMA model is classified as an ARIMA(p,d,q) model,
where the letters are defined as the number of autoregressive terms (p), the number of non-
seasonal differences (d) and the number of lagged forecast errors in the prediction equation (q)
Hence an ARIMA model is an ARMA model including differenced variables I(d) in the equation.
The Box-Jenkins Methodology consists of a three step process:
i) Identification

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The input series for an ARIMA model must be stationary whereby the mean, variance, and
autocorrelation are constant through time. A series will often need to be differenced until it
is stationary, which involves a logarithmic transforming of the data to stabilise the variance.
This involves subtracting each datum in a series from its predecessor (StatSoft, 2010).
ii) Estimation and diagnostic checking
The parameters of the model are estimated so that the sum of squared residuals is minimised
(SAS User’s Guide, 1999). Significance levels for each variable, is checked, and ‘goodness of fit’
statistics are evaluated. These would include the Durbin-Watson statistic and the Schwarz
criterion. Residuals are examined and ‘dummy’ variables are included.
iii) Forecasting
Future values of the time series can be forecasted using an ARIMA model (SAS User’s Guide,
1999), however this step will be omitted from the research since it falls outside the scope.
If the errors of the random walk model are autocorrelated, then an autoregressive term is
introduced by regressing the dependent variable on itself lagged by p periods (Nau, 2008).
3.7. Test for Stationarity
Variables whose means or variances do not vary over a period of time are known as stationary or
unit root variables (Glynn, Perera and Verma, 2007:65). Prior to undertaking estimation using time
series data, it is imperative that the stationary properties of the data are established so as to avoid
spurious regression problems. Thus this research used two unit root tests to determine the
presence and form of non-stationarity: the Augmented Dickey-Fuller (ADF) (1979) test and the
Phillips and Perron (PP) (1988) test.
The ADF test makes use of the following prediction equation (Khozan, 2010:72):
(4)

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where is a constant, is the coefficient on a time trend and is the lag order of the
autoregressive process. The test determines whether the null hypothesis, that is non-stationery,
can be rejected using the test statistic:
(5)
Although the ADF test measures whether a time series is affected by temporary or permanent
shocks, it fails to account for an existing break, which lowers its ability to reject a false unit root null
hypothesis (Glynn et al., 2007). Therefore Khozan (2010:73) suggests the use of the Phillips-Perron
tests when diagnostics reveal significant values for normality, autocorrelation or heterogeneity.
In the prediction equation below, the assumption of as being identically distributed residuals, is
relaxed:
(6)
3.8. Goodness of Fit Tests
Two common problems that can affect an ordinary least squares regression are heteroskedasticity
and autocorrelation of the error terms. Heteroskedasticity means that variances of error terms are
not constant from one observation to the next while autocorrelation refers to the presence of
series correlation between error terms (Khozan, 2010). In both these cases, the implications are
that the least squares regression is no longer an efficient estimator of the data. Hence four residual
diagnostic tests were used to test the model for misspecification.
3.8.1. Durbin-Watson Statistic
The Durbin-Watson test is the most common test for the presence of autocorrelation based on
estimated residuals (Khozan, 2010). By measuring the linear association between adjacent residuals
in a regression model, the DW statistic tests the hypothesis that error values for a regression have a
first-order autoregression component (Sherrod, 2010 and Johnson, 2000). The test is defined as per
Equation 7 (Khozan, 2010:38):
2

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DW = (7)
where are the estimated residuals, and refers to the number of observations. DW values close
to 2 indicate that errors are uncorrelated while values significantly less than or greater than 2
indicate that autocorrelation could be present (SAS User’s Guide, 1999).
3.8.2. Breusch-Godfrey Serial Correlation LM Test
The Durbin-Watson statistic has three limitations: (i) it can fail to identify errors that are non-
stationery (random walk); (ii) it only tests for serial correlation applied to first order processes; and
(iii) it is not valid if a lagged dependent variable is used in the regression model (Macrodados Help
File, 2006 and SAS User’s Guide, 2010). The Breusch-Godfrey test addresses these shortcomings.
After a regression model is fitted by ordinary least squares, a set of sample residuals is obtained.
The Breusch-Godfrey uses the following test model (Lott, 2010):
(8)
where the following test statistic is applied to test the null of errors not being autocorrelated:
(9)
3.8.3. Histogram and Jarque-Bera Normality Test
The Jarque-Bera test statistic measures the difference of the skewness to the normal distribution
(which has a skewness of 0) and quantifies whether the shape of the data distribution (kurtosis)
matches that of the Gaussian distribution which has a kurtosis of 0 (Graphpad (2007). The null
2

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hypothesis of the JB test is that the data follows the normal distribution based on the following test
statistic:
(10)
where is the sample size, is the skewness, and is the kurtosis. The intention is to determine
whether the residuals follow the normal distribution, otherwise the data will need to be
transformed (Ciuiu, 2008).
3.8.4. Breusch-Godfrey-Pagan Test for Heteroskedasticity
Breusch-Godfrey-Pagan tests the hypothesis of homoscedasticity in the regression by regressing the
squared residuals from the initial regression on a known set of variables (Khozan, 2010). One of the
assumptions for the least squares coefficient is homoscedasticity (AIAccess, 2010). Thus if the
results of the Breusch-Godfrey-Pagan test indicates that the data is not homoscedastic then further
data transformation is required to produce a correctly specified model.

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4. ECONOMETRIC ANALYSIS
This section discusses the econometric approach used to undertake the empirical investigation. In
the first stage, unit root tests were performed on the variables to test for stationarity. Thereafter,
an ARIMA model was produced using level-stationary or first-differenced data based on the results
of the unit root tests. The ARIMA model was also tested for mis-specification using the diagnostic
tests.
4.1. Test for Stationarity
Both Augmented Dickey-Fuller (1979) and Phillips-Perron (1988) unit root tests were performed on
the data. As shown in Table 2 provides, the null hypothesis of the presence of a unit root was
rejected at the 1% significant level for most variables. The logarithm of the exchange rate and gold
price were found to be stationery only at the first difference. Neither the difference nor the
logarithm of the M2/GDP was found to be stationery and so the first difference of the logarithm
was used to integrate the series.
There were no contradictions in the results of the unit root tests, although GDP growth
differentials, as well as the ratio of foreign exchange reserves to GDP were found to be stationery at
different levels of significance. The Kwiatkowski-Phillips-Schmidt-Shin test confirmed the variables
to be stationery.

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Table 2: Results from stationarity tests
Augmented
Dickey-Fuller Test
Phillips Perron
Test
Variable Level
1st
difference
Level
1st
difference
ZAR_USD_LOG -1.624 -7.356 *** -1.593 -7.331 ***
0.466 0.000 0.481 0.000
BONDOGDP_RTO -7.798 *** -6.932 *** -7.798 *** -18.467 ***
0.000 0.000 0.000 0.000
EQUITIESOGDP_RTO -4.970 *** -10.287 *** -4.872 *** -19.432 ***
0.000 0.000 0.000 0.000
GOLDP_LOG 3.480 -6.909 *** 1.941 -6.846 ***
1.000 0.000 0.999 0.000
GDPGROWTH_SPR -4.069 *** -7.680 *** -3.240 *** -7.576 ***
0.002 0.000 0.021 0.000
RER_SPR_LOG -11.105 *** -8.825 *** -13.729 *** -52.227 ***
0.000 0.000 0.000 0.000
STIR_SPR_D -6.152 *** -9.719 *** -6.186 *** -27.469 ***
0.000 0.000 0.000 0.000
LTIR_SPR_D -7.483 *** -9.206 *** -8.559 *** -39.186 ***
0.000 0.000 0.000 0.000
M2OGDP_RTO_LOGDIFF -2.192 -6.002 *** -2.476 -6.003 ***
0.210 0.000 0.125 0.000
DCEOGDP_RTO_D -4.529 *** -7.698 *** -4.413 *** -14.675 ***
0.000 0.000 0.001 0.000
FORRESOGDP_RTO_D -3.292 ** -16.036 *** -8.096 *** -63.083 ***
0.018 0.000 0.000 0.000
*** signifies a unit root null is rejected at the 1% significance level
** signifies a unit root null is rejected at the 5% significance level
* signifies a unit root null is rejected at the 10% significance level

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4.2. ARIMA Model
The principle aim for developing a model in this research was to measure whether a statistically
significant relationship exists between the volatility of the explanatory variables and the volatility of
the Rand/U.S. Dollar exchange rate. Hence the first step was to perform an ordinary least squares
regression between the explanatory variables and the exchange rate (see Table 3).
Three dummy variables were included to compensate for extreme outliers in 1999:Q1, 2001:Q4 and
2002:Q2. In addition, the correlogram test suggested that there was autocorrelation by one lagged
period and thus the model includes an AR(1) term. The final model can be described as an
ARIMA(1,1,0) or a first-order autoregressive or AR(1) model with one order of non-seasonal
differencing and a constant term. The standard deviation of 0,014 is somewhat large compared to
the mean of 0,006. However, the model still explains 80,4% of the volatility of the Rand/U.S. Dollar
exchange rate (see Figure 13). Castrèn (2004:31) indicates that regressions of financial market data
with an adjusted R2
of greater than 50% can be considered good estimates. Hence the model
suggested in Table 3 provides a good match between expected and actual values.
4.3. Results and Discussion
The results of the model in Table 3 show that the gold price (proxy for commodity price
movements), long-term interest rate differential and the ratio of foreign exchange reserves to GDP
are significant at the 1% level. The ratio of money supply (M2) to GDP was significant at the 5%
level, and the ratio of net equities to GDP and short-term interest rates are significant at the 10%
level. None of the other variables were found to be statistically significant.
The estimation output can be represented as per Equation 10:
(11)

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Since the regression equation contains a lagged variable, it is not possible to use the Durbin-Watson
test to test for autocorrelation. Instead, the Breusch-Godfrey test was use to confirm the absence
of series autocorrelation up to the second order (p=0,491). The Breusch-Pagan-Godfrey
heteroskedasticity test indicates that the data is homoscedastic (p=0,996). The Jarque-Bera test
(p=0,673) indicates that the null hypothesis -that the sample is drawn from a normally distributed
population, cannot be rejected. Thus, these residual diagnostic tests combined indicate the model
is not mis-specified (see Table 4).

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Table 3: Output of regression analysis
Dependent Variable: ZAR_USD_LOGDIFF
Variable Coefficient Std. Error t-Statistic Prob.
BONDOGDP_RTO 0.022 0.093 0.233 0.817
EQUITIESOGDP_RTO -0.159 0.083 -1.909 0.063 *
GOLDP_LOGDIFF -0.240 0.079 -3.022 0.004 ***
GDPGROWTH_SPR 0.000 0.003 0.073 0.942
RER_SPR_LOG -0.003 0.004 -0.755 0.455
STIR_SPR_D 0.004 0.002 1.805 0.078 *
LTIR_SPR_D 0.013 0.003 4.404 0.000 ***
M2OGDP_RTO_LOGDIFF 0.442 0.220 2.009 0.051 **
DCEOGDP_RTO_D -0.051 0.035 -1.454 0.153
FORRESOGDP_RTO_D 0.436 0.053 8.175 0.000 ***
C -0.001 0.010 -0.133 0.895
I2001Q4 0.038 0.013 2.910 0.006 ***
I1999Q1 0.053 0.013 4.256 0.000 ***
I2002Q2 -0.036 0.014 -2.583 0.013 ***
AR(1) 0.650 0.127 5.138 0.000 ***
R-squared 0.851 Mean dependent var 0.005
Adjusted R-squared 0.804 S.D. dependent var 0.032
S.E. of regression 0.014 Akaike info criterion -5.434
Sum squared resid 0.009 Schwarz criterion -4.905
Log likelihood 175.289 Hannan-Quinn criter. -5.227
F-statistic 17.944 Durbin-Watson stat 1.831
Prob(F-statistic) 0.000
Inverted AR Roots 0.650
*** signifies null is rejected at the 1% significance level
** signifies null is rejected at the 5% significance level
* signifies null is rejected at the 10% significance level

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Table 4: Diagnostics tests
Breusch-Godfrey Serial Correlation LM Test
F-statistic 0.520 Prob. F(2,42) 0.599
Obs*R-squared 1.425 Prob. Chi-Square(2) 0.491
Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic 0.213 Prob. F(13,45) 0.998
Obs*R-squared 3.420 Prob. Chi-Square(13) 0.996
Scaled explained SS 1.260 Prob. Chi-Square(13) 1.000
Jarque-Bera Test
JB-statistic 1.302
Prob. 0.521
Figure 13: Actual, residual and fitted graphs
-.08
-.04
.00
.04
.08
.12
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Residual Actual Fitted

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5. DISCUSSION OF EMPERICAL RESULTS
5.1. Significant Variables
5.1.1. Commodity Price Movements
The proxy for commodity price movements (the logarithmic difference of the gold price) was found
to be highly statistically significant (p=0,004). The negative sign of the coefficient indicates that an
increasing gold price has a dampening effect on the volatility of the exchange rate, which is in line
with findings by Broda and Romalis (2003). The model in this research indicated that ceteris
paribus, a 1% increase in the gold price leads to a 0,24% decrease in currency volatility. As an
example, from 2000 to 2004, the median value of the gold price was USD 313,99 an ounce, while
the standard deviation of the Rand/U.S. Dollar exchange rate was 160 cents. In the second half of
the decade, upward momentum in the commodity cycle and growing housing bubbles led to
greater purchases of gold as a hedge against inflation. In addition, gold also became a hedge
against risk after the 2008 financial crises, pushing the price even higher. From 2005 to 2009, the
median value of the gold price rose to USD 671,10 but the standard deviation of the exchange rate
fell to 108 cents. Hence it can be concluded that, to a certain extent, increases in the gold price
leads to a dampening of Rand volatility.
The policy implications of this finding are not immediately evident. Ngandu (2005) finds that South
Africa has suffered symptoms of the ‘Dutch Disease’, in that an increasing gold price has led to a
loss in competitiveness for the manufacturing sector as a result of real exchange rate appreciation.
Further, a decrease in the price of gold, as per this research, would lead to higher currency volatility
with its associated welfare costs. It would thus appear that South Africa does not benefit from
movements of the gold price in either direction. One solution could be upstream diversification,
since products with more value-add sometimes have more stable prices than their raw input. South
African jewellery production grew from 2 tons per annum in 1988 to just over 7 tons in 2006
(Edwards, 1990 and Gold in South Africa, 2007) which only represents 1,39% of total gold
production. The 2007 Gold In South Africa report indicates that despite South Africa’s endowment
in gold, expansion in production is hampered by relative labour costs (compared to Taiwan and
China) and skills shortages. In addition, The Economist indicates that rising production and

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exploration costs are also inhibiting South Africa’s gold output (“Store of Value,” 2010). Collectively,
these facts lead to the conclusion that South Africa’s presence as major gold producer is not
sustainable and may suggest that Government needs to refocus its effort on sustainable industries,
such as manufacturing.
5.1.2. Net Equity Flows
The ratio of net equity flows to GDP is significant at the 10% level (p=0,063). The influence of this
variable was expected to be stronger, since Castrèn (2004:32) indicates that equity flows are not
hedged against currency risk and thus are effective in explaining movements in the Dollar.
However, that study considered the effect on the exchange rate rather than the volatility of the
exchange rate. Over the sample period, South Africa’s net equity to GDP ratio had a standard
deviation of 0,03 and indicates that although the country has experienced significant inflows with
periodic outflows, volatility overall has not been persistently excessive. Consequently the negative
equity coefficient suggests that a 1% increase in equity flows will lead to 0,16% dampening of
currency volatility, ceteris paribus. This relationship can be explained by South Africa’s gradual
relaxation of exchange controls. In its 2006 country report, the IMF indicated that continued
relaxation of controls over portfolio flows, together with deepening of the foreign exchange
market, could help to reduce exchange rate volatility (IMF, 2006:15). It is also important to mention
that the introduction of a transaction tax or “Tobin Tax” would increase cost and even impede
providers of foreign exchange liquidity. Thus the policy implication here is that removing the
remaining capital controls and avoiding transaction taxes could lead to a less volatile Rand.
5.1.3. Foreign Exchange Reserves
A highly significant relationship was found between the dependent variable and the ratio of foreign
exchange reserves to GDP (p=0,000). However, in contrast to the literature, the coefficient is
positive and indicates that an increase in reserves will lead to additional currency volatility. This
result is unexpected but the reason for this may be specific to South Africa. The May 2007
Econometrix Ecobulletin stated that at the time, the Reserve Bank did not hold sufficient reserves in
order to reduce volatility in the Rand and that in fact, the process of buying up foreign reserves
may have been causing additional volatility (Econometrix, 2007:2). Another reason may be due to
the specification of the variable. Both Hviding et al. (2004) and Cady and Gonzalez-Garcia (2007)

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used the ratio of foreign exchange reserves to short term debt (i.e. forward book liability of the
central bank). For emerging economies, this ratio was found be significant and to have a negative
coefficient. In other words, the extent to which foreign exchange reserves affect the volatility of a
currency needs to be viewed in relation to the level of sovereign debt.
Thus in the case of South Africa, it is suggested that present levels of reserves are insufficient to
sterilise the Rand’s volatility and in fact, the accumulation process is contributing to currency
volatility. However, it is postulated that by accumulating its reserves beyond a certain level, SARB
will achieve its indented objective, in line with IMF research on central bank policies in developing
countries15
. Based on this research, it is suggested that the Reserve Bank continue to build its
currency reserve position, despite the ‘side-effect’ of heightened currency volatility.
5.1.4. Short-term Interest Rates
The differential of short-term interest rates was found to be weakly significant at the 10% level
(p=0,078) since the results show that ceteris paribus a 1% increase in the interest rate spread
between South Africa and the United States will lead to an insignificant (0,002%) increase in
currency volatility. The fact that this variable is not more significant is not entirely unsurprising in
the case of South Africa. Jordaan and Harmse (2001) find that the relationship between the two
variables is complex. Using a Granger causality test, they find a bidirectional relationship between
the two variables. During episodes of currency ‘crises’ the direction of that relationship flows from
the Rand to interest rates, as authorities attempt to stem the depreciation. Hence this bidirectional
relationship explains why the significance of short-term interest rates in the model is quite muted.
This result is further supported by the insignificance of bond flows as a determinant of currency
volatility.
5.1.5. Long-term Interest Rates
Long-term interest rate differentials were found to be highly statistically significant (p=0,000). The
coefficient indicates that a 1% increase in the change of the spread would result in a 0,01% increase
in currency volatility, ceteris paribus. A study by Kiani (2009) indicates that in addition to being
sensitive to inflation expectations, long-term interest rates are also influenced by budget deficits.
15
See Hviding, Nowak and Ricci (2004)

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Since 2000, inflation in South Africa was driven by rising commodity prices, while the budget deficit
was influenced by infrastructure expansion, particularly due to preparations for the 2010 World
Cup. At the same time, the U.S. Federal Reserve kept interest rates low in order to stimulate the
economy. The behaviour of 10 year treasury bills for the South Africa and the United States since
2000 was markedly different: whereas the standard deviation for quarterly interest rates for South
Africa was 1,89 it was only 0,79 for the United States. The results of the model would seem to
indicate, therefore, that volatility in South African long-term interest rates has contributed to a
degree of volatility of the Rand.
5.1.6. Money Supply
The money supply ratio was found to be significant at the 5% level (p=0,051). As expected, the
coefficient is positive and shows that a 1% increase in the M2 money supply relative to GDP would
result in a 0,44% increase in currency volatility, ceteris paribus. The strength of this relationship is
surprising: it is the largest coefficient of all the variables and has a stronger effect than the finding
on emerging economies by Cady and Gonzalez-Garcia (2007). Since money supply as a percentage
of GDP is an indicator of financial deepening (Mowatt, 2001:23), this finding links to the finding on
the dampening effect of equities. This implies that in the case of South Africa, money supply plays a
very important role in moderating movements of the Rand, which has implications for monetary
authorities.
5.2. Non-significant Variables
5.2.1. Net Bond Flows
The ratio of net bond flows to GDP was found to be insignificant (p=0,817). The cause of this may
be found in research by Hodge (2005:26) who indicates that the commonly accepted positive
relationship between interest rate differentials, portfolio flows and exchange rate may have to be
treated with caution in the case of South Africa since 1994. The study found that after taking
inflation into account (i.e. real rates), decreases in interest rates differentials did not always
translate into currency depreciation, because at times, expectations of higher returns on the JSE
would move the Rand in the opposite direction.

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When studying the relationship between the U.S. Dollar and the Pound, Deutschemark and Yen,
Siourounis (2004) finds evidence to support an important proposition made by Hau and Rey (2002).
That is that bond flows can be poor indicators of contemporaneous U.S. Dollar exchange rates since
cross-border transactions are usually hedged against currency risk, which counters any impact on
exchange rates16
.
Since not all cross border bond holdings are hedged, another reason for the insignificance of these
variables may have to do with specification. Brooks et al. (2004) found that net bonds flows did not
have a significant effect on the Euro/U.S. Dollar exchange rate, however, when these bond flows
were separated into their components, the study found that agency bond flows17
had increased by
a factor of four between 1995 and 2000, and thus had a statically significant effect on the currency
(unlike government and corporate bonds). Thus, taking a more heterogeneous view of South
African net bond flows may result in a more statistically significant result.
5.2.2. Domestic Credit Extension
The domestic credit extension ratio was found to be insignificant. However, with the removal of the
autoregressive term and the dummy variables included to compensate for crises periods (i.e. 1999,
2001, 2002), the ratio became weakly significant at the 10% level (p=0,099). It is interesting to note
that despite a significance of the M2/GDP variable, and somewhat significant equities variable,
domestic credit extension does not have a significant relationship (i.e. the expansion of the money
supply has had a greater effect on currency volatility compared to credit extension). This can be
explained by the composition of the domestic credit extension. Within the major component, ‘Total
loans and advances’, 35% went to home loans as at June 2010 (Banking Association of South Africa,
2010). Since these were domestic purchases, they would not have affected the currency.
16
Siourounis (2004) also reviews industry data to verify this finding. Using data from a fund of funds that invests in over
200 funds a year, it was found that from 1993 to2003 approximately 90% of cross border bond holdings were hedged,
while this was only the case for 12% of equity transactions.
17
Agency bonds are bonds issued by a corporation that is either owned or sponsored by the U.S. Government
(Morningstar, 2010).

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5.2.3. GDP Growth Differential
The GDP growth differential between South Africa and the United States was found to be
insignificant (p=0,942). While GDP growth rate was found to be a cause of exchange rate volatility
in both developed and emerging economies (Cady and Gonzalez-Garcia, 2007), the results in this
research indicate that the GDP growth differential between South Africa and the United states is
not a cause of currency volatility.
5.2.4. Relative Equity Returns
Relative equities returns were found to be insignificant (p=0,455) despite net equity flows being
significant at the 10% level. This was unexpected, since there are numerous academic studies which
use the variable to investigate exchange rate dynamics. In the 2006 Euromoney Foreign Exchange &
Treasury Management Handbook, the point was made that correlations between exchange rates
and relative equity returns had become steadily weaker in the preceding 10 years (Tessier, 2006:4).
In a global financial system that is becoming ever more integrated, volatility in bilateral equity
returns alone may be insufficient to cause volatility in bilateral exchange rates. A different
explanation can be due to the increase in the M2/GDP ratio from 0,91 in 1995:Q2 to 4,27 in
2009:Q4 which suggests that monetary expansion has stimulated economic activity, and thus
pushed up share prices. Thus it is postulated that the flow of equities is being driven by monetary-
induced growth as opposed to relative equity returns.

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6. CONCLUSIONS AND RECOMMENDATIONS
6.1. Conclusions
The objective of this research was to determine whether volatility in bonds, equities, commodity
prices and macroeconomic factors have a statistically significant relationship with the volatility of
the nominal Rand/U.S. Dollar exchange rate. Following the methodology by Cady and Gonzalez-
Garcia (2007) a modified-ARIMA (1,1,0) model was constructed which explained 80,4% of the
volatility in the currency from 1995:Q2 to 2009:Q4. The results from the model are used to answer
the following research objectives:
6.1.1. To determine the degree to which volatility in portfolio flows is associated with volatility in
the nominal Rand/U.S. Dollar exchange rate
Equity flows were found to have a moderate dampening effect on the Rand’s volatility. On
the other hand, bond flows were not found to affect the currency’s stability. While
numerous explanations are offered in the literature, the most plausible appears to be that
cross border bond holdings are mostly hedged while this is generally not the case with
equities. Based on these findings, equity flows are found to be the most significant
component of portfolio flows that influence the Rand/U.S. Dollar exchange rate volatility.
6.1.2. To ascertain whether volatility in commodity prices induces volatility in the exchange rate
Using the gold price as a proxy for commodity prices movements, the results indicate that
increases in commodity prices have a dampening effect on currency volatility, suggesting
that South Africa would appear to benefit from increases in the gold price. However, this
finding needs to be kept in the context of other literature, which states that such increases
also lead to Dutch Disease symptoms. In addition, a fall in the gold price would lead to
currency instability. Therefore it is evident that South Africa does not benefit from
movements in the gold price in any direction.

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6.1.3. To investigate whether volatility in key macro-economic variables is associated with volatility
in the Rand
Short-term interest rates were found to be weakly significant, which the literature indicates
may be caused by bi-directionality of the two variables. Long-term interest rates were found
to be highly significant but led to small levels of exchange rate volatility. The money
supply/GDP ratio and foreign exchange reserves/GDP were both found to be significant and
have positive relationships with volatility of the Rand/U.S. Dollar exchange rate. However, it
is postulated that once the Reserve Banks builds its reserves beyond a certain level, this will
lead to a reduction in currency volatility in line with research on other emerging economies.
Thus, this research suggests that monetary policy can play an important role in smoothing
out episodes of high currency volatility.
6.2. Recommendations
Based on the findings in this report, five key recommendations emerge:
i) In order to promote the dampening effect of equity flows, dismantling capital controls and
not imposing transaction taxes should be considered by fiscal authorities.
ii) While increases in the gold price lead to a dampening of the Rand’s volatility, other
literature points to the negative effect on exports: the Dutch Disease. Further, this research
finds that decreases in the gold price will lead to greater currency instability. In addition,
gold production in South Africa is facing falling levels of profitability. These findings
combined suggest that South Africa’s role as a large gold producer is not sustainable, and
investments in other export industries, primarily manufacturing, are recommended.
iii) Monetary authorities also have a role to play in moderating movements in the Rand. Of all
the variables studies, money supply had the largest effect on the currency volatility and
hence needs to be taken into consideration during episodes of monetary easing or economic
expansion.

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iv) While the process of accumulating foreign exchange reserves is currently inducing greater
volatility in the Rand, it is suggested that the Reserve Bank continue with this strategy, and
build up reserves beyond the point where they can influence the Rand’s movements.
v) Long-term interest rate differentials influence the Rand’s movements. Since these rates are
influenced by long-term inflation outlooks, the Reserve Bank’s inflation targeting regime are
contributing to lower currency volatility and should be continued.
6.3. Proposed Future Research
A number of potential areas for future research have been highlighted during the course of this
study:
i) Greater insight into the exchange rate can be gained by exploring leading and lagging
indicators (see Castrèn, 2005). This would allow monetary and fiscal authorities to plan for
future fluctuations in order to mitigate some of the adverse welfare effects that currency
volatility induces.
ii) In order to gauge the extent to which exports as whole (and not just commodity prices
movements) influence the Rand, trade variables should be measured against the currency’s
volatility. For example, Total Exports/GDP or Degree of Openness.
iii) Expansionary monetary policy induces economic growth, but this research also indicated
that this may lead to currency volatility, which has adverse welfare consequences. Further
research is suggested to determine the extent to which such policies have a negative impact
on the economy, and whether these effects can be mitigated to a certain extent.
iv) Econometric models different to the one used in this research are also available to measure
currency volatility. One example is GARCH, though in order to use this model, an ‘ARCH
effect’ must be present amongst the residuals (Engle, 2001). This was not the case for the

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data used for this research, though this may be resolved through the use of different
variables, which may lead to other useful findings.
v) The positive relationship between the ratio of foreign exchange reserve to GDP and the
Rand’s volatility suggests that further research could be undertaken to understand the
critical level where reserve accumulation results in decreased exchange rate volatility.

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