Download macroeconomic effects of exchange rate volatility in zambia

EXCHANGE RATE VOLATILITY AND ITS EFFECTS ON MACROECONOMIC
MANAGEMENT IN ZAMBIA
Jonathan M. Chipili*
Final Draft Report
December 2014
Abstract
The impact of exchange rate volatility on selected macroeonomic indicators in Zambia is
analysed over the period 2007-2013. The extent of exchange rate volatility deemed disruptive
on the economy is also determined. A significant role for exchange rate volatility in
macroeconnomic management is established: volatility in the kwacha/US dollar exchange
rate tends to depress trade, increase inflation, discourage capital flows and dampen output
although its impact tends to be temporary; and exchange rate depreciation in excess of 1%
tends to generate negative effects on the economy. The results underscore the significance of
the exchange rate in trade and monetary policies and the transimission of exchange rate
impulses to the rest of the economy. The central bank must therefore seek to stabilize the
exchange rate; however, this requires an appropriate currency stabilization strategy that
ensures currency flexibility and competitiveness. Interventions in foreign exchange markets
should not be undertaken in isolation as this may significantly deteriorate the reserves
position and weaken the country’s ability to respond to adverse external shocks. Thus,
complementary policies are recommended to sustain a stable exchange rate.
JEL classification: F31; C22; C32
Keywords: Exchange rate volatility, macroeconomic indicators, GARCH, SVAR
*
Bank of Zambia, P.O. Box 30080, Lusaka, 10101. Zambia. E-mail: [email protected] Tel: +260 211 22888892. The views expressed in this paper do not in any way represent the official position of the Bank of Zambia. I
remain responsible for all the errors and omissions.
1
1.
Introduction
The study of persistent variability in the exchange rate post-1973 remains an active
subject of empirical investigation due to the adverse effects a volatile exchange rate can
potentially impose on the economy. Policymakers are interested in exchange rate volatility
for formulating suitable macroeconomic policies while corporate entities are concerned about
its implications on profits by designing appropriate risk management tools (Bollen, 2008;
Bauwens and Sucarrat, 2006).
Aggregate and sectoral output, price level, international trade and foreign investment
are typically affected by a volatile exchange rate (Mirdala, 2012; Arratibel, et al., 2011;
Takagi and Shi, 2011; Chipili, 2010). Volatility in the exchange rate introduces uncertainty
which in turn generates negative economic welfare effects (Bergin, 2004; Obstfeld and
Rogoff, 1998). Further, fluctuations in the exchange rate affect consumer goods prices which
in turn affect demand and consequently consumption. Monetary policy is also affected by
currency fluctuations especially where domestic growth is underpinned by exports as
authorities attempt to support the external sector through exchange rate stabilisation at the
expense of inflation stabilisation (Crosby, 2000). Echange rate uncertainty can also create
incentives for trade protectionist tendencies and sharp currency reversals which in turn
impose further costs on the economy (Sengupta, 2002; Bayoumi and Eichengreen, 1998).
Volatility in exchange rates can also restrict the flow of international capital by
reducing direct and portfolio investments. Speculative capital flows may also be induced by
exchange rate volatility under the flexible regime that could in turn contribute to instability in
economic conditions (Willett, 1982). Further, greater exchange rate volatility increases
uncertainty over the return of a given investment. Potential investors are attracted to invest in
a foreign location as long as the expected returns are high enough to compensate for the
currency risk. In view of this, foreign direct investment tends to be lower under higher
exchange rate volatility. Most developing economies are net debtors such that considerable
fluctuations in exchange rates may affect the real cost of debt service. Similarly, banks,
corporate entities and individuals exposed to foreign denominated debt are negatively
affected by exchange rate volatility (Schnabl, 2009).
Central banks globally therefore endeavour to stabilize exchange rates in order to
moderate the adjustment and uncertainty costs that a volatile exchange rate imposes on the
economy.
While a vast amount of literature on the impact of a country’s exchange rate volatility
on macroeconomic variables exists, limited empirical work has been undertaken on Zambia.
Notably, Chipili (2013), Musonda (2008) and Tenreyro (2007) have examined the exchange
rate volatility-trade link; Aghion et al. (2009) have analysed the impact of exchange rate
volatility on productivity growth; and Chipili (2013) examined the relevance of exchange rate
volatility in monetary policy setting as well as central bank foreign exchange intervention
decision. In all these studies, the revelance of exchange rate volatility is noted with varying
degree of impact on the sectors/variables studied. While a number of studies report negligible
exchange rate volatility effects on real economic activity, Aghion et al. (2009) find exchange
rate volatility to exert significant negative impact on productivity growth subject to a
country’s level of financial development: the impact is severe in thin and less developed
capital markets.
This study seeks to empirically examine the impact of the kwacha exchange rate
volatility on selected macroeconomic variables in Zambia over the period 2007-2013 due to
data limitations especially on capital flows at monthly interval. A brief perspective on the
macroeconomic policy evolution from early 1990 when economic reforms commenced is
provided. Specifically, the study analyses trends in the kwacha/US dollar exchange rate and
2
major macroeconomic variables. Possible causes of exchange rate volatility in the kwacha
exchange rate are reviewed from past studies. A formal econometric framework for
examining the impact of the kwacha/US dollar exchange rate volatility on the economy and
the threshold for exchange rate volatility deemed disruptive to the economy are provided.
The results reveal that exchange rate volatility tends to depress trade, increase
inflation, discourage short-term capital flows and dampen output although its impact tends to
be temporary. In addition, exchange rate depreciation in excess of 1% tends to generate
negative effects on the economy.
The rest of the paper is organised as follows. The next section provides a brief
overview of the exchange rate policy in Zambia and discusses the evolution of selected
macroeconmic and financial indicators. Section 3 reviews relevant literature on
macroeconomic effects of exchange rate volatility on the economy. The estimation procedure
and empirical results are presented in section 4. Section 5 concludes and offers policy
recommendations.
2.
Overview of the Exchange Rate Policy and Trends in Selected Macroeconic
Indiactors
In recent years, the focus of economic policy has been on raising economic growth
and reducing poverty by consolidating the stabilisation gains achieved since the
commencement of economic reforms in early 1990s. Financial sector and private sector
development plus public financial management and accountability reforms are other areas of
priority concern to the Government.
Prior to economic reforms, the focus of Government policy was on industrialisation
built around import substitution adopted soon after gaining independence in 1964. The main
sector of the economy was mining which accounted for about half of GDP and about 90% of
total export earnings (UNDP, 2006)1. Strong copper revenues initially supported the
development strategy as copper prices and output steadily rose. Subsequently, revenue from
copper weakened due to the fall in copper prices in early 1970s and the decline in copper
output. Oil prices shot up in 19973/74 and again in 1979/80 (oil price shocks) causing the
terms of trade to decline.
The Government responded by imposing exchange and trade controls (foreign
exchange controls on current and capital account transactions plus import licensing) as well
as price controls on goods. Interest rate ceilings were also introduced. The exchange rate was
fixed while monetary and fiscal policies were loosened. Attempts to diversify the economy
away from copper dependence and limit the dominance of state owned enterprises (SOEs)
created to spur the industrialisation development strategy in the late 1980s failed due to
underlying distortions created by the policies in place (see Adam, 1995). Negative real GDP
growth rates became entrenched by the mid 1990s.
Consequently, the economy was liberalised as market-oriented reforms were adopted
in early 1990s with the support of the IMF/World Bank. The reform package included the decontrol of product prices, trade liberalisation and elimination of exchange controls in 1994
making both current and capital account fully convertible that subsequently gave way to a
market determined exchange rate. The removal of interest rate ceilings in 1991, introduction
of Treasury bills auctions in 1993 and adoption of indirect instruments of monetary policy in
1
Mining continues to be a dominant sector and underpins the performance and structure of the economy
(UNDP, 2006).
3
1995, privatisation of SOEs including mining companies and rationalisation of expenditures
(especially non-essential) and enhancement of domestic revenue collection were other reform
measures undertaken.
Zambia has maintained a liberal flexible exchange rate system since 1994. Prior to
that, the exchange rate was fixed from the time of independence in 1964. A fixed exchange
rate regime existed from 1964 to 1982 and 1987 to 1991 while a crawling peg was adopted
between 1983 and 1985. An initial float of the kwacha took place between 1985 and 1987. A
more flexible exchange rate regime was adopted in the early 1990s as part of the economic
reforms. The decision to choose each of these exchange rate regimes was largely influenced
by conventional economic and political arguments. A comprehensive review of the exchange
rate policy in Zambia is provided by Chipili (2010), Mungule (2004) and Mkenda (2001).
According to figure 1, the kwacha/US dollar exchange rate exhibited a rising trend
over the priod 1994-2013 with some volatility, particularly during the global financial crisis
of 2007/8 and the period following debt forgiveness (HIPIC Initiative and MDRI) in early to
mid-2000. Over the sample period, remarkable economic improvements have been posted,
evidenced by sustained growth in real GDP since early 2000, reflecting strong growth in
agricultural output supported by favourable weather and appropriate farmer input support
programme by government; recovery in the mining sector following huge re-invetsment after
privatising the state owned mining conglomerate; pick-up in tourism due to various
promotional activities; and steady growth in the contruction sector owing to the boom in
private commercial and housing properties, public investment in health and educational
facilities and road infrastucture by the government. In addition, inflation has constitently
declined to single digits in the recent past, reflecting the stabilisation programme embarked
on by government since the commencement of economic reforms in early 1990. Similarly,
commercial bank lending rates have declined in line with the trend in inflation. Money supply
growth, though volatile, has slowed down and stabilised around an annual growth rate of 20%
in recent years, reflecting the resolve by authorities to contain inflationary pressures while
supporting growth.
The current account has recovered since 2000 despite some fluctuations, reflecting
strong growth in exports (both copper and non-traditional exports) while imports have been
rising in recent years in line with the expansion in key economic sectors such as mining.
Despite exhibiting considerable volatility, foreign direct invetsmet has picked up attributed to
a stable and robust macroeconomic environment. The size of the capital market has expanded
over the years as reflected in the rising stock market index. Fiscal deficits (as a percent of
GDP) improved from around 8% in 1994 to around 1% in 2008; however, the trend reversed
in recent years reaching around 6% in 2013 largely due to the infrastructure investment
programme embarked on by Government. Finally, the declining trend in domestic revenue
collections was reversed in 2010, picked up sharply and exceeded the 1994 level by 2013,
reflecting steady economic expansion and various revenue broadening measures.
4
Figure 1
Selected Macroeconomic Indicators
R EAL_GD P_GR OWTH
EXC H AN GE_R ATE_KU S$
6
8
5
6
4
4
3
2
2
0
1
-2
-4
0
94
96
98
00
02
04
06
08
10
94
12
96
98
00
02
04
06
08
10
12
M ON EY_SU PPLY_GR OWT H
IN F LATION
80
80
60
60
40
40
20
20
0
0
94
94
96
98
00
02
04
06
08
10
96
98
00
02
04
06
08
10
12
12
LEN D IN G_R ATE
C U R R _AC C _PER C EN T_GD P
80
8
4
60
0
-4
40
-8
-12
20
-16
0
-20
94
96
98
00
02
04
06
08
10
12
94
96
98
EXPOR T S_PER C EN T_GD P
00
02
04
06
08
10
12
IM POR TS_PER C EN T_GD P
50
48
45
44
40
40
35
36
30
32
25
28
94
96
98
00
02
04
06
08
10
12
94
96
98
00
FD I_PER C EN T_GD P
02
04
06
08
10
12
STOC K_M KT_IN D EX
12
6,000
10
5,000
8
4,000
6
3,000
4
2,000
2
1,000
0
0
94
96
98
00
02
04
06
08
10
12
94
96
98
00
02
04
06
08
10
12
FISC AL_D EFIC IT_PER C EN T_G
R EVEN U E_PER C EN T_GD P
0
22
-2
20
-4
18
-6
16
-8
-10
14
94
96
98
00
02
04
06
08
10
12
94
96
98
00
02
04
06
08
Note: Exchange_Rate_KUS$=absolute values of kwacha/US$; Real_GDP_Growth=%; Inflation=%;
Money_Supply_Growth=%; Lending_Rate=%; Curr_Acc_Percent_GDP=current account in % of GDP;
Exports_Percent_GDP=exports as % of GDP; Imports_Percent_GDP=imports as % of GDP; FDI_Percent_GDP=foreign
direct investment as % of GDP; Stock_Mkt_Index= stock market index (=1000); Revenue_Percent_GDP=revenue as % of
GDP; Fiscal_Deficit_Percent_GDP=fiscal deficit as % of GDP.
5
10
12
3.
A Brief Look at the Literature
There is no consensus in the literature regarding the factors that influence exchange
rate volatility due to divergent theoretical models of exchange rate determination. In addition,
the factors are numerous compounded by the inability to predict them and their effect on real
exchange rate volatility varies considerably to be quantified with certainty (Canales-Kriljenko
and Habermeier, 2004; Dungey, 1999; Korteweg, 1980).
A large body of empirical work has employed fundamentals-based models in
modelling exchange rate volatility (see Kočenda and Valachy, 2006; Devereux and Lane,
2003; Bayoumi and Eichengreen, 1998; Pozo, 1992). Most of these studies document real
shocks with large permanent effects as the dominant source of exchange rate volatility.
Notwithstanding this evidence, fundamentals tend to explain only a small proportion of
exchange rate volatility due to the weak link between the two (popularly referred to as the
‘disconnect argument’), thus lending support for the role of microstructure factors in
exchange rate fluctuations in the short-term (see Bjørnland, 2008; De Grauwe et al. 2005;
Obstfeld and Rogoff, 2000; Flood and Rose, 1995 for an extensive discussion). However,
Morana (2009) provides more recent support for fundamentals in terms of linkages (causes
and trade-off) between exchange rate volatility and volatility in macroeconomic factors (i.e.
output, inflation, interest rate and money supply). Although the direction of causality is bidirectional, it is however, stronger from macroeconomic factors to exchange rate volatility
than vice-versa. Thus, stability in the macroeconomic variables is recommended to reduce
exchange rate volatility in sharp contrast to Flood and Rose (1995).
In the case of Zambia, the influence of copper price - major commodity export - on
the exchange rate like for most developing countries whose currencies are commodity-based
is documented (Cashin et al. 2002). In addition, the positive influences of exchange rate
regime, money supply and openness on conditional volatility of the kwacha/US dollar is
established (see Chipili, 2010; Bangaké, 2008; Hausmann et al., 2006). Savvides (1990) finds
both real and monetary variables to be key determinants of exchange rate variability.
Thus, identifying sources of exchange rate volatility facilitates the design of
appropriate policy response. By and large, real (external) sources tend to constrain policy
response due to their unpredictability of occurrence and highly differential impact on the
exchange rate. This is in contrast to the domestically generated factors such as monetary
which authorities have reasonable control over. If the sources are monetary in nature, the
policy response is to reduce the trend rate in monetary expansion and/or raise trend rate in
real output growth. This entails adopting a predictable monetary policy rule that involves preannouncement of monetary targets, instruments and their timing.
Empirical evidence regarding the impact of exchange rate volatility on
macroeconomic variables is mixed. For instance, Baxter and Stockman (1989) do not find
evidence of exchange rate volatility impacting on macroeconomic aggregates under
alternative exchange rate regimes. Low exchange rate volatility is associated with greater
output growth and lower inflation (Robertson and Symons, 1992). Sapir and Sekkat (1995)
find no appreciable impact of exchange rate volatility on trade, investment and growth. On
the contrary, Chit (2008) argues that evidence of exchange rate volatility adversely affecting
investment, growth and trade exists. Schnabl (2009) finds exchange rate volatility to exert
negative effect on growth.
The theoretical prediction of the exchange rate volatility-trade relationship is
ambiguous: exchange rate volatility can either stimulate or depress trade (Hooper and
Kohlhagen, 1978; Baron, 1976; Clark, 1973; Ethier, 1973). Similarly, empirical evidence is
inconclusive (see Ozturk, 2006; Clark et al. 2004; McKenzie, 1999; Côté, 1994).
Nonetheless, there is growing and firm evidence that exchange rate volatility imposes
6
significant effects on the volume of trade (see Ozturk, 2006; Clark et al. 2004; UK Treasury,
2003; McKenzie, 1999; Côté, 1994; IMF, 1984; Farrell et al., 1983). Exchange rate
variability affects international specialisation in production which in turn leads to a reduction
in the welfare of people as output declines and consequently income and consumption (Clark,
1973). Volatility in the exchange rate can lead to the reduction in the volume of international
trade due to increases in the level of trade riskiness that creates uncertainty about profits. In
addition, it causes prices of tradables to rise due to the risk mark-up (risk premium) imposed
by sellers in order to protect profits. This tends to affect the competitiveness of exports. In
response to fluctuations in the exchange rate, firms shift resources from the risky tradable
sector to the less risky non-tradable sector in order to protect their profits. Further, a rise in
exchange rate uncertainty increases transaction costs as agents attempt to hedge against
exchange rate risk (Schnabl, 2009).
The importance of exchange rate in monetary policy and foreign exchange
interventions is established. Most empirical work especially in developed countries is
dominated by estimations of the interest rate rule proposed by Taylor (1993) as most central
banks typically use the nominal short-term interest rate as the main operating policy
instrument (see Eleftheriou et al. 2006; Adam et al. 2005; Clarida et al. 1998). Central banks
mostly adjust the nominal short-term interest rate in response to deviations of output and
inflation from potential level and target, respectively. In Zambia, exchange rate volatility is
found to be an integral part of monetary policy setting and central bank foreign exchange
intervention decision (see Chipili, 2013).
4.
Estimation Method, Data and Empirical Results
To establish the impact of exchange rate volatility on the economy, we begin by
estimating exchange rate volatility using the GARCH model. Thereafter, the structural VAR
(SVAR) model that includes exchange rate volatility derived from the GARCH model and
selected macroeconomic variables is estimated and impulse responses and variance
decomposition results reported. Finally, the threshold for exchange rate volatility deemed
disruptive is determined using the non-linear effect apparoch.
4.1
Estimation Method
GARCH Model
Volatility in the kwacha/US dollar exchange rate is modelled using the GARCH
approach specified in equations 1 and 2 in order to characterise its time-varying conditional
variance path. The GARCH models introduced by Engle (1982) and extended by Bollerslev
(1986) take into account the distributional form of the exchange rate: leptokurtic, clustering
and leverage behaviour typically present in financial time series data (Brooks, 2001;
Bauwens and Sucarrat, 2006). The standard deviation, a widely used measure of exchange
rate volatility, is not adopted on account of the fact that it tends to overstate total risk, does
not distinguish between predictable and unpredictable elements of a variable and lastly
assumes normality distribution of a variable which is not always the case especially for
financial variables.
7
q
st   0   i st i   t
(1)
i 1
 t t 1 ~ N (0, t2 )
 t2  ht
p
q
i 1
j 1
ht   0    i  t2i    j ht  j
(2)
where st is the logarithm of the percentage change in the nominal kwacha/US dollar
exchange rate,  t is residuals2 which are used to test for ARCH effects based on the Engle
(1982) LM test statistic3. Equation 1, specified in log first difference, represents the
conditional mean and describes how s t evolves over time. Equation 2 captures the
conditional volatility of s t such that  i  0 , i  0,1,2,3,.... p ;  i  0 , i  0,1,2,3,....q ; and
 0 is the long-term average value of conditional variance.
The persistence of shocks to conditional variance is captured by the sum of  and  .
The closer the sum is to 1 the more persistent the shocks are: shocks exert a permanent effect
on volatility if     1 (i.e. a unit root in variance is obtained and this kind of GARCH
model is referred to as Integrated GARCH (IGARCH) model). Convergence of conditional
volatility to its long-term value is not achieved when     1 or     1 ; instead
volatility is explosive and tends to infinity as shocks persist forever. However, a stationary
GARCH that ensures model stability or convergence of conditional variance forecast to  0 as
the prediction horizon increases obtains if     1 holds.
SVAR Model
The SVAR approach is employed to determine the effect of exchange rate volatility
on selected macroeconomic indiactors. In the SVAR4 set up, all the variables are treated a
priori as endogenous and theoretically motivated restrictions imposed on contemporaneous
relations among variables are examined such that the marginal effect of a shock to any of the
2
t
can be generated either from an autoregressive (AR), autoregressive moving average (ARMA) or standard
regression model.
2
3
To test for the presence of ARCH effects, squared residuals
t
are regressed on a constant and q lags (set by
the researcher) of squared residuals and the Engle (1982)
H 0 :  0  0,  1  0,  2  0,.......,  q  0 against H 1 :  0  0 or
conducted.
LM test statistic under the null
1  0 or  2  0 or …. or  q  0 is
LM  TR 2 where LM approximate  2 (q) , T is the number of observations while R 2 is
2
computed from the  t equation.
4
SVAR models treat every variable as endogenous due to the difficulty of finding exogenous variables in
macroeconomics (Gottschalk, 2001).
8
variables in the system and on itself can be traced out over time using impulse response
analysis5 in a dynamic interaction form.
Thus, the relationship among variables can be set up in VAR form of order p
consisting of a system of equations equal to the number of variables (see Clarida and Gertler,
1996)
p
AX t  A0   A1 X t 1  B t
(3)
i 1
where A is an invertible
(nxn) matrix capturing contemporaneous relations among X t
variables; X t is an (nx1) vector of macroeconomic variables; A0 is a vector of constants; A1
to A p is (nxn) matrix of unknown parameters on lagged values of X t to be estimated; B is
an (nxn) matrix reflecting direct effects of some  t on more than one X t variable;  t is an
(nx1) vector of uncorrelated structural innovations or shocks corresponding to each element
of X t with covariance matrix E ( t  t' )    ; t  1,2,......, T ; and n is the number of variables
in the system. Further manipulation yields
p
X t  0   1 X t 1  et
(4)
i 1
where 0  A 1 A0 ; 1  A1 A1 , 2  A1 A2 ,..., p  A1 Ap ; and et  A 1 B t is an (nx1)
vector of white noise error term with zero mean and constant variance E (et et' )   e . Equation
4 is a reduced form representation of equation 3 as the latter cannot be estimated directly
since the structural model cannot be identified. Structural shocks are orthogonal to each other
while the reduced form errors, et , are not. et are a linear combination of orthogonalised
structural shocks.
Thus, the structure linking the structural shocks and the reduced form residuals takes
the form
Aet  B t
(5)
To identify A and B and thus generate impulse response functions, at least
2n 2  n(n  1) / 2 additional restrictions are required in addition to n normalisation
restrictions.
A non-recursive (structural factorisation) identifying structure is adopted whereby a
priori restrictions are imposed on contemporaneous interactions among X t variables in order
to identify the coefficient matrix A . Thereafter, the dynamic impact of  t can be traced on
5
Impulse response functions are calculated from the estimates of the VAR. They show how current and future
values of each variable in the VAR respond to a one-off unit increase in the current value of one of the structural
shocks in the VAR holding other shocks constant.
9
the path of any element in X t (Bjørnland and Halvorsen, 2008)6. Thus, the following
identification scheme is employed
X t'  ( yt , pt , impt , xxt , kt , erv t )
(6)
such that elements of X t' are expressed as
1
𝑎21
𝑎31
0
0
(𝑎61
0
1
𝑎32
𝑎42
0
𝑎62
0
0
1
0
0
0
0
0
1
0
0
0
0
1
𝑎63
𝑎64
𝑎65
0
0
0
𝑦
𝑒𝑡
𝑝
𝑒𝑡
𝑏11
𝑖𝑚𝑝
𝑎36
𝑒𝑡
𝑎46
𝑒𝑡𝑥𝑥
𝑎56
𝑒𝑡𝑘
1 ) 𝑒 𝑒𝑟𝑣
( 𝑡 )
0
0
=
0
0
(0
0
𝑏22
0
0
0
0
0
0
𝑏33
0
0
0
0
0
0
𝑏44
0
0
0
0
0
0
𝑏55
0
0
0
0
0
0
𝑦
𝜀𝑡
𝑝
𝜀𝑡
𝑖𝑚𝑝
𝜀𝑡
𝜀𝑡𝑥𝑥
𝜀𝑡𝑘
𝑏66 ) 𝜀 𝑒𝑟𝑣
( 𝑡 )
yt is real output, p t is the price level – a measure of inflation, impt is imports, xxt
is exports, , k t is a measure of capital flows and ervt is a measure of exchange rate volatility.
Output, inflation, imports, exports, capital flows and exchange rate volatility shocks are
denoted as  ty ,  tp ,  timp ,  txx ,  tk and  terv , respectively.
Diagonal elements of A are normalised to 1 while zero (zero exclusion restriction)
implies no contemporaneous relationship between X t variables7.
The study focuses on identifying the impact of a one off shock to exchange rate
volatility on output, inflation, trade (exports and imports) and short-term capital flows.
Output adjusts sluggishly with a lag to financial and monetary variables (Brischetto
and Voss, 1999; and Bjørnland and Halvorsen, 2008). Thus zero exclusion restriction is
imposed on all variables in A corresponding to the yt row. Prices adjust slowly to all
variables except movements in output to which they react contemporaneously (Brischetto and
Voss, 1999), hence zero exclusion restriction is imposed on all variables corresponding to the
p t row except yt . Imports are driven by domestic income, inflation and exchange rate risk
while exports are influenced by domestic inflation and exchange rate volatility. It is further
assumed that capital flows only respond contemporaneously to exchange rate volatility hence
zero exclusion on all variables in the k t row except for exchange rate volatility. Finally, the
exchange rate depends upon innovations in macroeconomic variables as it reacts almost
instantaneously to all information. Hence there is no zero exclusion in the row corresponding
to ervt (Brischetto and Voss, 1999; and Bjørnland and Halvorsen, 2008)8.
Matrix A must be identified in order to derive impulse response functions and variance decomposition
(Bjørnland and Halvorsen, 2008).
6
7
a ij = variable j affects variable i instantaneously.
8
Allowing
fxpt
and
fxst
to contemporaneously affect
ervt
10
is rejected.
Exchange Rate Volatility Threshold: Non-linear Effect Approach
To examine the threshold for exchange rate volatility beyond which volatility is
deemed to be disruptive on the economy, a non-linear size-dependent effect speficification is
employed as follows
yt   0   1st   2 st .Z c
𝑤ℎ𝑒𝑟𝑒 𝑍 = {
1,
0,
(7)
𝑖𝑓 |∆𝑠| 𝑐 > 0
𝑖𝑓 |∆𝑠| 𝑐 < 0
such that c is a threshold representing a certain percentage rate of depreciation in the
kwacha/US dollar exchange rate, and st is the logarithm of the percentage change in the
nominal kwacha/US dollar exchange rate (an approximate measure of volatility).
The motivation here is to determine what happens to y when exchange rate
depreciation exceeds a certain level which defines the threshold of exchange rate volatility
beyond which undesirable effects on y are generated. Different values of c are used to
determine the threshold level of exchange rate volatility. The threshold value c is the one
that maximises R 2 . The choice of c is guided by the average rate of depreciation of the
kwacha/US dollar over the sample period, equal to 2.5%. The initial value of c is set at 0.5%
and subsequently increased at half a percent until 2.5% after which further values of c are set
at 3%, 4% and 5% (which is on the uppe limit of the rate of depreciation).
4.2
Data Sources and Description
Monthly data from 2007 to 2013 are used to estimate the impact of exchange rate
volatility on output, inflation, trade (exports and imports) and short-term capital flows due to
data limitations on macroeconomic and financial varaibles. All the data are sourced from the
Bank of Zambia.
As data on real output, GDP, are not available at monthly frequency, the quadraticmatch sum interpolation method is used to derive monthly real GDP ( y ) from the annual
series. This method ensures that monthly values sum up to the annual value and allows for
some variation in a year as opposed to alternative constant-linear and linear-match last
methods. The consumer price index (CPI ,2009  100) is used as a measure of inflation ( p ) .
Exports (xx) and imports (imp ) data enter separately in the estimated relationship as
exchange rate volatility may have differential impact on them. Short-term capital flows ( k ) is
measured by non-resident holdings of fixed income securities issued by the government
(govhold ) . Foreign direct investment data are not available at monthly interval hence the
use of
govhold . Exchange rate volatility ( erv ) of the kwacha/US dollar is derived from the
GARCH model.
11
4.3
Empirical Results
The empirical results from the GARCH model reported in table 1 and plotted in figure
2 reveal that the kwacha/US dollar exchange rate is characterised by episodes of high
volatility over the sample period. In particular, volatility was high soon after the foreign
exchange market was liberalised in 1994, a reflection of exchange rate adjustment following
a long period of misalignment. Volatility baceme more pronounced in 2001 and for most part
in 2003. Therefater, volatility receded until the global financial crisis in 2007 and persisited
until the end of 2010. A spike in volatility occurred towards the end of 2012. Over the same
period, GDP, exports and imports rose while inflation slowed down from double to single
digits. Non-resident holdings of fixed income securities varied considerably.
Table 1 GARCH Model Results
st = 0.503 + 0.442 st 1
(2.54)** (6.47)***
ht = 2.218 + 0.323  t21 + 0.516 ht 1
(5.27)*** (3.79)*** (7.36)***
Diagnostic tests
 t Q(5) =3.1992[0.669];  t2 Q (5) =0.9093[0.970]; J-B =108.0861[0.000]; ARCH LM
=0.0723[0.7883]; Log L =-607.221; AIC =-5.145; SBC =5.218
The lag length for
t Q
and
 t2 Q of 5 is determined according to Tsay (2002): k  ln( T ) where k is lag
length and T is the number of observations. z-statistics are reported in parenthesis while p-values are in square
brackets. ***,**,* refer to statistical significance at 1%, 5% and 10%, respectively.
Note: The GARCH model is well specified based on the diagnostic tests. Volatility in the
kwacha/US dollar exchange rate is also found to be persistent.
12
Figure 2
Plots of Kwacha/US dollar Exchange Rate Volatility and Selected
Macroeconomic Variables
ERV
Y
80
9.4
9.2
60
9.0
8.8
40
8.6
8.4
20
8.2
0
8.0
94
96
98
00
02
04
06
08
10
12
94
96
98
00
02
P
04
06
08
10
12
06
08
10
12
06
08
10
12
XX
5
7
4
6
3
5
2
4
1
3
94
96
98
00
02
04
06
08
10
12
94
96
98
00
IMP
02
04
GOVHOLD
7.0
7.5
6.5
7.0
6.0
6.5
5.5
6.0
5.0
5.5
4.5
4.0
5.0
94
96
98
00
02
04
06
08
10
12
94
96
98
00
02
04
Unit root tests precede the estimation of SVAR models. The unit root tests reported in
table 2 are conducted using ADF and P-P methods. All the variables are I(1) except exchange
rate volatility. Nonetheless, the SVAR model is estimated as its implementation requires at
least one variable to be I(1) (see Enders, 2004)9. All the variables enter the SVAR in log
levels except erv to allow for possible cointegration among them (Lewis, 1995). A lag of
one is used in the VAR based on AIC and SBC.
9
SVAR results are sensitive to underlying identifying assumptions, variations to sample length and lags in the
VAR.
13
Table 2 Unit Root Tests
ADF
level
erv
y
p
xx
imp
govhold
First
Diff
lags
Deterministic
terms
P-P
level
First
Diff
Deterministic
terms
-6.50***
-1.70
-1.05
-2.21
-2.61
-3.93**
-9.97***
-20.59***
-15.10***
0
12
2
1
1
C
C&T
C&T
C&T
C&T
-6.45***
-2.03
-1.64
-2.26
-3.79
-10.59***
-9.57***
-20.89***
-30.66***
C
C&T
C&T
C&T
C&T
-2.26
-5.14***
1
C&T
-1.90
-5.10***
C&T
Critical values for unit root tests are Mackinnon (1996) one-sided p-values. All variables are expressed in
natural logarithm except exchange rate volatility ( erv ) and non-resident purchase of equities at the stock
exchange ( stock ). *, ** and *** imply 1%, 5% and 10% levels of significance, respectively. C is constant
while T stands for trend.
As shown in table 3, diagnostic tests from the estimated SVAR model indicate the
absence of serial correlation of order two. The over-identification restrictions under the null
hypothesis that the restrictions are valid with a test statistic that has a chi-square distribution
with three degrees of freedom cannot be rejected at 5% significance level.
Table 3 SVAR Model Results: Estimated A Matrix
y
p
imp
govhold
xx
1
-0.13
(-0.37)
-77.77
(-0.04))
0
0
1
0
0
0
0
0
0
1
0
0
0
1
0
0
13.87
(0.05)
-3.97
(-1.15)
0
0
0
1
11108.39
(0.01)
-9035.72
(-0.01
-1516.24
(-0.01)
509.16
16.89
(0.01)
(0.01)
2
Test for over-identification restrictions:  (3)  6.361[0.0953]
erv
0
0
-0.14
(-0.03)
0.01
(0.26)
-0.001
(-0.46)
1
Sample period: 2007.10-2013.12. z-statistics are in parenthesis. VAR (1) diagnostics: serial correlation LM test
of order 2=30.179[0.7412]; ARCH  =723.9529[0.0000]; J-B normality test=1929.598[0.0000] with df=12.
Lag length 1 for the VAR was chosen on the basis of AIC and SBC.
2
The impulse response function results of one standard deviation innovation in
exchange rate volatility over a five -year horizon are presented in figure 3. The results
suggest that shocks to the kwacha/US dollar exchange rate volatility have an immediate
negative effect on exports, output and inflation but imports respond with a lag. Non-residents
response countercyclically initially to the exchange rate volatility shock by increasing their
holdings of fixed income securities; however, the portfolio holdings gradually reduce as the
exchange rate risk is assimilated in investment portfolio decision. Overall, the effect on
exchange rate volatility shock on the variables understudy tends to be temporary. As exports
recover from a shock to exchange rate volatility, output too improves and gradually returns to
steady-state. Imports fall some months after the shock to exchange rate volatility has
14
occurred, but later recover and return to steady-state. Inflation increases on impact in
response to unexpected increase in exchange rate volatility which generates higher uncertainty
and subsequently affects inflation expectations. Inflation continues to rise, peaking after about
six months and then gradually tends towards its equilibrium, reflecting that short-lived shocks
are not incorporated in underlying inflation (Taylor, 1981).
Figure 3
Impulse Response Analysis
Response to Generalized One S.D. Innovations ± 2 S.E.
Response of Y to ERV
Response of P to ERV
.0020
.004
.0015
.003
.0010
.002
.0005
.001
.0000
.000
-.0005
-.0010
-.001
5
10
15
20
25
30
35
40
45
50
55
5
60
10
15
Response of IMP to ERV
20
25
30
35
40
45
50
55
60
50
55
60
Response of XX to ERV
.04
.02
.02
.00
.00
-.02
-.02
-.04
-.04
-.06
-.06
-.08
-.08
-.10
5
10
15
20
25
30
35
40
45
50
55
60
50
55
60
5
Response of GOVHOLD to ERV
.12
.08
.04
.00
-.04
-.08
5
10
15
20
25
30
35
40
45
15
10
15
20
25
30
35
40
45
The proportion of the error of forecast for 1, 6, 12, 24, 36 and 60 months forecast
horizon attributed to shocks to exchange rate volatility is reported in table 4. This determines
the fraction of the variation in output, inflation, imports, exports and capital flows due to
exchange rate volatility shocks. The results reveal that shocks to exchange rate volatility have
a stronger influence on trade (both at short and long horizons) followed by inflation, output
and lastly capital flows. This underscores the importance of the exchange rate in trade and
monetary policies and the transimission of exchange rate impulses to the rest of the economy.
Table 4 Variance Decomposition
Forecast Horizon
(months)
1
6
12
24
36
60
Variance decomposition of selected
macrovariables in response to exchange rate
volatility shock
Nonresident
holdings
of govt
Output Inflation Imports Exports securities
0.000
0.000
0.000
0.000
0.000
1.404
1.659
8.493
9.354
0.983
0.753
2.899
8.376
9.284
0.755
0.922
3.048
8.423
8.971
0.933
1.178
2.757
8.339
8.860
1.070
1.162
2.430
8.212
8.778
1.076
A summary of the size-dependent results is presented in table 5 while detailed
estimations are reported in the appendix. The results in table 5 reveal that, by and large,
persistent depreciation in exchange rates in excess of 1% per month have a tendency to
generate undesirable effects on the economy. Exchange rate depreciation in excess of 1% will
have a depressing effect on exports which in turn impacts on output at the same rate of
exchange rate depreciation of 1%. It is however, noted that import demand is less sensitive to
exchange rate depreciation than exports, responding strongly when exchange rate
depreciation exceeds 4% consistent with the SVAR model result. Thus, with a high rate of
depreciation of the kwacha, import prices expressed in domestic currency rise and
consequently lead to higher domestic inflation. The inelasticity of imports demand could be
attributed to uncertainity regarding persistence in exchange rate depreciation: agents are
unlikely to change their behavior if they believe that the currency depreciating trend is
transistory. The high sensistivity of exports to exchange rate depeciation could reflect the risk
averseness of exporters seeking to protect profits. Finally, short-term capital flows tend to
respond negatively to exchange rate volatility once exchange rate depreciation exceeds 1%.
16
Table 5 Exchange Rate Volatility Threshold Results
% change
in the
kwacha/US
dollar Non-resident
exchange
holdings of
rate fixed income
deemed (government)
disruptive
securities
Exports
Output
0.5%
1.0%
x
x
x
1.5%
2.0%
2.5%
3.0%
4.0%
5.0%
5.
Imports
x
Inflation
x
Conclusion
The study analysed the impact of exchange rate volatility on output, inflation, trade
and short-term capital flows in Zambia over the period 2007-2013. The extent of exchange
rate volatility deemed disruptive on the economy was also determined.
The results reveal that volatility in the kwacha/US dollar exchange rate tends to
depress trade by reducing both total exports and imports and subsequently impact on output
and inflation. Short-term capital flows are also discouraged by exchange rate fluctuations.
Further, exchange rate depreciation in excess of 1% per month tends to generate negative
effects on the economy.
The results from this study underscore the importance of the exchange rate in trade
and monetary policies and the transimission of exchange rate impulses to the rest of the
economy. Exchange rate volatility forms an essential part of trade, monetary and exchange
rate policy formulation and implementation and in attracting portfolio investments to ensure
that a stable macroeconomic environment is achieved and maintained.
Thus, the key lesson from this study is that central banks must stabilize exchange
rates; however, the choice of the currency stabilization strategy matters. Attempts by the
central bank to stabilize the exchange rate or avoid sharp currency movements through the
direct use of official reserves to intervene in the market may lead to significant deterioration
of reserves which in turn erode the country’s ability to respond to adverse external shocks.
Thus, complementary policies are recommended to achieve and maintain a stable exchange
rate: fiscal policy that ensures sustainable expenditure; monetary policy to remain focused on
inflation control of which exchange rate is a relevant factor in inflation dynamics; and a
general stable macoeconomic, political and social environment as the exchange rate is
influenced by expectations especially in the short-term in response to economic, political and
social news.
17
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20
Appendix
Exchange Rate Volatility Threshold for GDP ( yt )
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0
0.46
(20.8)
0.47
(21.5)
0.47
(22.4)
0.47
(23.1)
0.47
(23.9)
0.47
(24.5)
0.48
(25.9)
0.48
(26.6)
 1st
-0.01
(-2.9)
-0.01
(-3.0)
-0.01
(-2.9)
-0.01
(-2.9)
-0.01
(-2.9)
-0.01
(-2.9)
-0.01
(-2.7)
-0.01
(-2.7)
 2 st .Z 0.5
0.01
(1.8)
 2 st .Z1.0
0.01
(1.9)
 2 st .Z1.5
0.01
(1.9)
 2 st .Z 2.0
0.01
(1.7)
 2 st .Z 2.5
0.01
(1.6)
 2 st .Z 3.0
0.01
(1.8)
 2 st .Z 4.0
0.005
(-2.7)
 2 st .Z 5.0
R2
0.006
(1.1)
0.0400217
0.0416666
0.040615
0.037969
21
0.037239
0.04018
0.029
0.031
Exchange Rate Volatility Threshold for Inflation ( p t )
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0
1.29
(11.0)
1.30
(11.5)
1.30
(11.8)
1.29
(12.1)
1.31
(12.6)
1.32
(12.9)
1.31
(13.5)
1.3
(14.0)
 1st
0.06
(2.4)
0.06
(2.4)
0.06
(2.4)
0.06
(2.4)
0.06
(2.4)
0.06
(2.4)
0.06
(2.4)
0.06
(2.4)
 2 st .Z 0.5
0.03
(1.0)
 2 st .Z1.0
0.03
(0.9)
 2 st .Z1.5
0.03
(1.0)
 2 st .Z 2.0
0.03
(1.2)
 2 st .Z 2.5
0.03
(1.0)
 2 st .Z 3.0
0.03
(0.9)
 2 st .Z 4.0
0.04
(1.2)
 2 st .Z 5.0
R2
0.03
(1.1)
0.034773
0.034018
0.0348
0.036591
22
0.035275
0.03431
0.036976
0.035911
Exchange Rate Volatility Threshold for Exports ( xxt )
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0
2.41
(2.2)
2.37
(2.3)
2.24
(2.2)
2.15
(2.2)
2.11
(2.2)
2.05
(2.2)
1.83
(2.0)
1.84
(2.1)
 1st
-0.71
(-3.1)
-0.71
(-3.1)
-0.71
(-3.1)
-0.72
(-3.1)
-0.71
(-3.1)
-0.72
(-3.2)
-0.74
(-3.2)
-0.73
(-3.2)
 2 st .Z 0.5
-0.33
(-1.1)
 2 st .Z1.0
-0.33
(-1.2)
 2 st .Z1.5
-0.29
(-1.0)
 2 st .Z 2.0
-0.26
(-1.0)
 2 st .Z 2.5
-0.26
(-1.0)
 2 st .Z 3.0
-0.25
(-0.9)
 2 st .Z 4.0
-0.15
(-0.5)
 2 st .Z 5.0
R2
-0.20
(-0.7)
0.054204
0.054596
0.053519
0.53008
23
0.053137
0.05276
0.05047
0.051371
Exchange Rate Volatility Threshold for Imports ( imp t )
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0
1.50
(1.3)
1.52
(1.3)
1.55
(1.4)
1.48
(1.4)
1.41
(1.3)
1.42
(1.4)
1.44
(1.5)
1.54
(1.6)
 1st
-0.85
(-3.4)
-0.84
(-3.4)
-0.84
(-3.4)
-0.85
(-3.4)
-0.86
(-3.4)
-0.86
(-3.5)
-0.87
(-3.5)
-0.85
(-3.4)
 2 st .Z 0.5
0.07
(0.2)
 2 st .Z1.0
0.07
(0.2)
 2 st .Z1.5
0.06
(0.2)
 2 st .Z 2.0
0.10
(0.3)
 2 st .Z 2.5
0.15
(0.5)
 2 st .Z 3.0
0.16
(0.5)
 2 st .Z 4.0
0.18
(0.6)
 2 st .Z 5.0
R2
0.12
(0.4)
0.047633
0.047611
0.047566
0.047843
24
0.048397
0.048553
0.048897
0.048088
Exchange Rate Volatility Threshold for Short-Term Capital Flows: Govt Securities Holdings
by Non-Residents ( govhold )
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0
2.97
(1.1)
3.11
(1.2)
2.60
(1.1)
2.24
(1.0)
1.94
(0.9)
1.77
(0.8)
1.27
(0.6)
1.35
(0.6)
 1st
0.02
(0.0)
0.06
(0.1)
0.02
(0.0)
-0.01
(-0.0)
-0.04
(-0.1)
-0.07
(-0.1)
-0.11
(-0.2)
-0.04
(-0.1)
 2 st .Z 0.5
-0.98
(-1.2)
 2 st .Z1.0
-1.11
(-1.5)
 2 st .Z1.5
-0.94
(-1.3)
 2 st .Z 2.0
-0.85
(-1.2)
 2 st .Z 2.5
-0.73
(-1.0)
 2 st .Z 3.0
-0.64
(-0.9)
 2 st .Z 4.0
-0.40
(-0.5)
 2 st .Z 5.0
R2
-0.62
(-0.8)
0.023264
0.031385
0.024964
0.021658
25
0.016784
0.013805
0.006465
0.011368