Dundee Discussion Papers
Inflation Uncertainty, Exchange Rate
Depreciation and Volatility:
Evidence from Ghana, Mozambique and
Hassan Molana &
Inflation Uncertainty, Exchange Rate Depreciation and Volatility:
Evidence from Ghana, Mozambique and Tanzania
Hassan Molana and Kwame Osei-Assibey
University of Dundee
While flexible exchange rates facilitate stabilisation, exchange rate fluctuations can cause real
volatility. This gives policy importance to the causal relationship between exchange rate
depreciation and its volatility. An exchange rate may be expected to become more volatile when the
underlying currency loses value. We conjecture that a reverse causation, which further weakens the
currency, may be mitigated by price stability. Data from Ghana, Mozambique and Tanzania support
this: depreciation makes exchange rate more volatile for all but volatility does not causes
depreciation in Tanzania which has enjoyed a more stable inflation despite all countries adopting
similar macro-policies since early 1990s.
Keywords: exchange rate, depreciation, volatility, causality, GARCH, VAR
JEL Classification: E3, F3, F4,
Corresponding Author: Hassan Molana, Economic Studies, University of Dundee
Dundee, DD1 4HN, UK. Email: firstname.lastname@example.org
The main purpose of this paper is to examine whether the time series behaviour of the exchange rate
differs across LDC’s with varying inflation uncertainty. We focus on the causality between
exchange rate depreciation and its volatility. The policy relevance of this exercise is best explained
in the context of the relationship between inflation and the exchange rate. Since Friedman’s
explanations on merits of floating versus pegging (Friedman, 1953), a country’s exchange rate
regime is believed to play a crucial role in transmitting the impact of real exogenous shocks. On the
whole, there now seems to be general agreement that adopting a flexible exchange rate regime
reduces the impact of shocks and enables monetary authorities to pursue their stabilisation policies
more effectively. This is particularly important for LDCs with less advanced capital, financial and
in particular currency markets and with more rigid demarcation between the composition of exports
and imports. Broda (2004) documents the steep rise in the number of LDCs switching to the flexible
rate regime and provides evidence that shows Friedman’s hypothesis holds: when exposed to a real
exogenous shock (i.e., a terms-of-trade shock), countries that float exhibit a significantly lower
to clear the currency market and therefore has the potential to become too volatile.1 As a result, the
possibility that this volatility could feed back into the real economy has raised considerable concern
and a large number of studies have examined the real impacts of exchange rate volatility. While the
accumulated evidence is not totally conclusive2, there is sufficient indication that exchange rate
volatility can have significant real effects which are mitigated by price level stability: using data
from 71 countries for the period 1979-2000, Choudhri and Hakura (2006) find that a significant
positive association between the average inflation rate and the exchange rate effect exists across
both countries and time.3
Under a flexible exchange rate regime, however, the nominal spot exchange rate fluctuates
above findings and mirrors the studies which examine the causal relationship between inflation and
Our study of the causality between the exchange rate and its volatility is motivated by the
1 In fact, unlike what Friedman had envisaged, once they were free to float most exchange rates fluctuated far beyond
what economists had initially anticipated. It is now widely accepted that this volatility phenomenon stems mainly from
the ‘asset price’ characteristic of this variable, especially when speculators perceive opportunities in exploiting a
2 While some studies find exchange rate fluctuations to have little real effects (e.g., Baxter and Stockman, 1989), others
report significant effects: Krugman (1991), Frankel and Wei (1993), Goldberg and Klein (1998) and Campa and
Goldberg (2005, 2006).
3 Taylor (2000), Devereux and Engel (2002) and Gagnon and Ihrig (2001), among others, show this theoretically; the
latter also present evidence in line with Choudhri and Hakura (2006).
inflation uncertainty.4 As Friedman (1977) explained, if policy makers show reluctance to reduce it,
a rising inflation can lead to high inflation volatility/uncertainty. The same type of argument is
pertinent for the relationship between the exchange rate and its volatility in those LDCs which have
been floating their exchange rate but have not yet managed to sufficiently reduce and stabilise their
inflation rate. The rationale is simple: the main reasons why a country floats its exchange rate are to
eliminate the inflationary pressure and exogenous shocks due to pegging, and to free its monetary
policy so as to better target inflation or implement stabilization policies. However, the mere
knowledge that there is going to be no immediate intervention in the currency market renders the
country’s exchange rate vulnerable and an incident of depreciation can easily trigger future
exchange rate volatility especially if there are doubts about the strength of country’s foreign
exchange reserves to stand speculative attacks. On the basis of what the literature suggests, one
would expect in such circumstances to observe expenditure switching effects of exchange rate
fluctuations and hence real volatility to ensue, which defeats the purpose behind floating the
exchange rate in the first place. What matters most in such a situation is for the policy authorities to
know whether the causation between exchange rate depreciation and volatility runs both ways, in
which case a credible intervention in the currency market may be necessary in the interest of
preventing a vicious circle of depreciation and volatility.
causation from exchange rate depreciation to its volatility, the causality in the opposite direction is
likely to vary with a country’s inflationary experience. More specifically, we expect the reverse
causality to occur in countries with a higher inflation uncertainty. To ascertain this, we analyse
monthly time series data on the price level and the exchange rate (CPI and nominal spot rate) for the
period 1990-2009 for Ghana, Mozambique and Tanzania. These countries have gone through
comparable policy engagements with the IMF, have followed similar floating exchange rate regimes
during this period and currently all adhere to the IMF convention of free current account
convertibility and transfer.5 However, Tanzania has had a relatively lower and more stable inflation
in the last decade and we find that this is reflected in its exchange rate behaviour: Tanzanian shilling
has depreciated much less and there is no significant causation from exchange rate volatility to its
depreciation. In Section 2 we describe the main features of the series used in the analysis, apply the
GARCH methodology to estimate the volatility of inflation and exchange rate, and explain the
results of Granger-causality tests. Section 3 concludes the paper.
4 Ball (1992) provides a theoretical explanation for this phenomenon on which now ample evidence exists: see, among
others, Grier and Perry (1998), Nas and Perry (2000), Fountas (2001), Hwang (2001), Apergis (2004), Kontonicas
(2004) and Daal et al. (2005).
5 Ghana and Tanzania accepted Article VIII of IMF “Articles of Agreement” in 1994. Mozambique began floating in
1992 under the SAP reforms of IMF; Article IV consultation was completed in 2009 and acceptance of Article VIII
Our conjecture is that while it is not so uncommon to observe a significant positive
2. Data, volatility estimates and causality tests
We use monthly time series data for the period January-1990 to December-2009. The first row of
Table 1 illustrates the behaviour of inflation series. Given our emphasis on stability, inflation rates,
t π , are the monthly percentage changes in domestic CPI relative to the US CPI. There is
a clear change in the way inflation has fluctuated in these countries over the period, with the last
decade showing more stable patterns in all countries; sample means (standard deviations) of
t π for
2000-2009 are: Ghana, 0.011796 (0.014530); Mozambique, 0.006198 (0.014142); Tanzania,
0.003624 (0.013268). To provide a better idea of inflation behaviour we examined its volatility,
, as follows:6 (i) We tested the time series properties of
t π and found it to be stationary but
highly autocorrelated in all countries. (ii) We experimented with different ARMA specifications
( )( ) ()
( )( )
where α ,
α , β and
s β are polynomials of adequate order in the lag operator L, subscript s denotes
the seasonal component that can be factorised and together with m=12 captures any dynamics due
to the months’ effects,
tμ is a deterministic component which captures any mean-shifting across
months with month-dummies, and
tε is an independent zero-mean disturbance term. We selected a
statistically robust and parsimonious empirical representation of the generating process for
t π for
each country. (iii) The empirical representations obtained thus are conditional on the assumption of
tε being homoscedastic. We tested the homoscedasticity assumption which was strongly rejected in
all cases. We therefore generalized the selected models by augmenting them with a first order
exponential GARCH process,
which allows for an asymmetric uncertainty effect captured by
3 φ : ceteris paribus, a positive shock
is assumed to increase (reduce) volatility if
3 φ >0 (
3 φ <0) via strengthening (dampening) the ARCH
effect. We found that in all cases estimates of the augmented model confirmed strong presence of
ARCH effects and that (2) adequately captured the variations.7 The estimated variances,
6 Statistical details are not reported here but are available upon request.
7 Raising the order of the process in (2) or using other GARCH processes (power, threshold or component GARCH) did
not lead to any improvement in any of the cases. For details on the formulation of GARCH models see, e.g., Ding et al.
(1993) and Hentschel (1995).
also plotted in the same figures in the first row of Table 1 and show that inflation in Tanzania has
been relatively more stable in the last decade; sample means (standard deviations) of
2009 are: Ghana, 0.000165 (0.000321); Mozambique, 0.000221 (0.000199); Tanzania, 0.000111
With this background, we examined the time series properties of the exchange rate whose
respectively. The second row in Table 1 shows plots
level and volatility we denote by
, x t
autocorrelated in all countries. It is worth noting that whilst there has been persistent depreciation in
all countries, Tanzania’s shilling has depreciated much less and has enjoyed a more stable pattern
over the last decade; sample means (standard deviations) of
unit root tests revealed that log
tx is I(1) and
is stationary but highly
for 2000-2009 are: Ghana,
0.011657 (0.021859); Mozambique, 0.005057 (0.033068); Tanzania, 0.004244 (0.016131). We
followed the same steps described above to obtain estimates of
, x t
which we plot in the third row
of Table 1 and in the row below it we report the GARCH effects which show that whilst strong
GARCH and ARCH effects are present in all countries, the latter effect is milder and is further
dampened by a negative asymmetric effect in Tanzania.
autoregressive model (VAR) for (
In order to examine the nature of causality, for each country we estimated a vector
. Starting with a sufficiently long lag-
length we used the sequential likelihood ratios and chose the appropriate lag-length for each
country’s VAR. We then tested the exclusion restrictions in each VAR implied by the
corresponding Granger-non-causality null hypotheses “
does not Granger-cause
σ does not Granger-cause
”, respectively. The test results together with the
impulse responses (not reported but available upon request) showed that depreciation makes the
exchange rate more volatile in all three countries, and that volatility feeds back to cause further
depreciation in Ghana and Mozambique but not in Tanzania. The same conclusions were obtained
from bivariate VARs involving only (
well as daily series for the period 1-January-2001 to 31-December-2009.8
and using both the above monthly series as
8 This result is encouraging since daily series convey a more robust measure of volatility. These additional results are
not reported here but are available upon request.
Table 1: Inflation Rate Volatility and Exchange Rate Behaviour (i)
Ghana Mozambique Tanzania
% Δ in CPI and its Volatility
90 9294 9698 00 0204 0608
σ , on the right axis
90 929496 980002 040608
σ , on the right axis
909294 969800020406 08
σ , on the right axis
Level and % Δ
level, 1990 =1, on the right axis
level, 1990 =1, on the right axis
level, 1990 =1, on the right axis
% Δ and its Volatility
90 92949698000204 0608
σ , on the right axis
909294 9698000204 06 08
σ , on the right axis
90 92 949698 00 0204 06 08
σ , on the right axis
EGARCH Coefficient Estimates EGARCH Coefficient Estimates EGARCH Coefficient Estimates
GARCH Effects for
(i) Data, originally provided by the IMF, were obtained from the US Department of Agriculture.
(ii) Inflation rates measure the percentage changes in CPI relative to the US CPI and inflation volatility series were
estimated using the GARCH technique as described above.
(iii) Exchange rates are nominal spot rates corresponding to units of: Ghana cedi, Mozambique metical and Tanzania
shilling per US dollar. Their volatility series were estimated using the GARCH technique as described above.
(iv) The GARCH coefficients correspond to those in equation (2); ***, ** and * denote significance at 1%, 5% and
3. Summary and conclusion
It is known that flexible exchange rate regimes facilitate macroeconomic stabilisation policies. But
there is also evidence that exchange rate fluctuations cause real volatility, e.g., via their relative
price and expenditure switching effects. Thus, any information pertaining to the nature of causality
between a depreciation of exchange rate and its volatility can prove useful to monetary policy
authorities. In general, it is not unfeasible to expect even a mildly persisting depreciation of
exchange rate to raise its volatility. But the existence of the reverse causation is not so
straightforward and we conjecture that it can be mitigated by price stability. To examine this
conjecture we use data from three LDCs: Ghana, Mozambique and Tanzania. These countries were
chosen because they adopted similar macroeconomic policies since early 1990s but Tanzania has
enjoyed a relatively stronger currency and more stable inflation in the last decade. We find that,
whilst depreciation makes exchange rate more volatile in all three countries, volatility causes
depreciation only in Ghana and Mozambique. The intuition for this evidence may lie in the fact that
policies resulting in more stable inflation establish credibility. As for the policy conclusion, it is
tempting to draw a bold one: unless the policy freedom gained by floating the exchange rate is fully
channelled to achieve a more credible and lasting price stability, exchange rate fluctuations could
become too persistent and even a vicious circle of depreciation and volatility may ensue.
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