Pinar Ozbay

English

Research Papers in Economics

1
THE EFFECT OF EXCHANGE RATE UNCERTAINTY
ON EXPORTS
A CASE STUDY FOR TURKEY
MARCH 1999
Pýnar ÖZBAY
Research Department
The Central Bank of The Republic of Turkey
Ýstiklal Cad. No:10
06100 Ulus- Ankara
TURKEY
Pinar.Ozbay@tcmb.gov.tr
Tel: (90 312) 311 14 22
Fax: (90 312) 324 23 03
Abstract
This study investigates possible effects of exchange rate uncertainty on exports in the
context of the GARCH (Generalized Autoregressive Conditional Heteroscedasticity) model.
The empirical evidence for the 1988:II-1997:II period indicates that exports are adversely
affected by the real exchange rate uncertainty while emprical evidence does not indicate
statistically significant relationship between imports and real exchange rate uncertainty.
1. INTRODUCTION
The high degree of volatility of exchange rate movements since the beginning of the
generalized floating exchange rate regime has led policymakers and researchers to
investigate the nature and extent of such movements on trade flows. There is conflicting
evidence in the literature about the relationship between exchange rate volatility and trade
flows.2
On the one hand, a number of studies have argued that if market participants are risk-
averse, exchange rate uncertainty causes market participants to reduce their activities in
order to minimize their exposure to the effects of exchange rate volatility. Most international
transactions are realized after a time lag, and contracts are denominated in terms of the
currency of either the exporting or importing country. Unanticipated changes in exchange
rates may adversely affect the volume of trade through their effects on profits. The exchange
rate risk may increase exporter’s profit risk. If exchange exchange rate volatility increases,
then profit risk rises. Since exporters are risk-averse and hedging against exchange rate risk
is costly or impossible, the increase in profit risk reduces the benefits and therefore the
volume of international trade. Akhtar and Hilton (1984), Coes (1981), Cushman (1983,1986),
Kenen and Rodrick (1986), Koray and Lastrapes (1989), Thursby and Thursby (1987),
Chowdhury (1993), Doroodian and Caporale (1994), Arize (1995), Peree and Steinherr
(1988) provided empirical evidence.
On the other hand, Franke (1992), Giovannini (1988),and Sercu and Vanhullle (1992)
showed that trade benefits from exchange rate volatility or risk. These studies suggest that
trade can be considered as an option held by firms. Like any other option, such as, stocks
the value of trade can rise with volatility. According to the model developed by Franke
(1992), a firm evaluates the exit (entry) costs associated with leaving (entering) a foreign
market against losses (profits) created by exports. Firms with a comparative disadvantage in
international trade benefit from an increase in exchange rate volatility since their expected
cash flows from exports grow at a higher rate than their entry and exit costs.
The purpose of this paper is to assess which of two factors are more important for the
effect of exchange rate uncertainty on exports. We expect that exchange rate volatility may
have adverse effects on trade flows because trade is not an option for firms in Turkey. As far
as it is observed, they produce to export. We apply the Generalized Autoregressive
Conditional Heteroscedasticity (GARCH) technique and use quarterly data for the period3
from 1987:I to 1997:II. This technique allows us to capture time-varying conditional variance
as a parameter generated from a time series model of conditional mean and variance of the
exchange rate. Some other alternative technologies may be used. Arize (1995) used
cointegration and error-correction techniques as well as conditional and unconditional
measures of exchange-rate uncertainty in order to test the hypothesis that exchange rate
uncertainty impedes trade. Also, Chowdhur (1993) used the moving standard deviation of
growth rate of the exchange rate to measure volatility. It is assumed that current exchange
rate is known; however it is in fact not known and it has to be forecasted. So in this study, the
GARCH technique is used in order to forecast the current exchange rate and to perceive
volatility.
Liberal economic policies started to be implemented after the 1980’s in Turkey but the
exchange rate policy  was not fully liberalized. Adjustable peg policy was implemented after
1981. The Turkish lira (TL) was daily adjusted in the form of devaluations.Then the exchange
policy was liberalized after 1988. Department of money market was established at the
Central Bank of the Republic of Turkey with the aim of determining the exchange rate in the
market and bringing stability to the foreign exchange market. Role of the Central Bank was to
regulate the market and avoid volatilities.
The monetary policy implemented by the Central Bank has aimed to achieve stability in
the financial markets since 1996. It has tried to reduce uncertainities in the money markets. It
has avoided short-term and fast movements in prices both in the foreign exchange and TL
markets. Achieving stability in the foreign exchange market has been an important part of the
stability in financial markets in Turkey.
The remainder of this paper proceeds as follows: Section 2 introduces GARCH models,
Specification of the model is presented in Section 3. The empirical results are discussed in
Section 4. Section 5 provides a brief conclusion.6
Exports demand is determined by time trend, the first four lagged values of percentage
change in the real exchange rate, foreign income and exports. Last, three additive dummies
are included for 1994:1,1994:2 and 1994:3 periods to account the 1994 financial crisis. The
real exchange rate is a trade weighted real exchange rate based on consumer price indices
(CPI) of major 6 trade partners and Turkey. Foreign income is the sum of gross national
product of Germany and the USA.
The prices of domestic products become cheaper in terms of foreign currency as home
currency depreciates, so the demand for domestic products increases. Also as foreign
income rises, the demand for domestic products increases. Therefore, the expected sign of
real exchange rate and foreign income is to be positively associated with export demand.
Furthermore, in order to assess the predictable part of the real exchange rate, We
estimate the following models for the real exchange rates used in export  demand equation.
p6t= a0 + a1*P1t + a2*P2t + a3*P3t + a4* p6 (t-1)+ a5*p6 (t-2) + et  (6)
where et ~  (0,ht2 )  (7)
P1t,P2t and P3t are additive crisis dummies included for 1994:1,1994:2 and 1994:3
periods to control the 1994 financial crisis. p6t  denotes the percentage change in the real
exchange rate. The lagged order is determined by the final prediction error criterion. Final
prediction error criterion sets the lagged order such that it eliminates the autocorrelation in
the error term. This is crucial since autocorrelated errors indicate the presence of the ARCH
effect even if the ARCH effect is not present.
We allow the variance of the et is time dependent. By using the GARCH methodology,
we model the conditional variance for the equation above as7
ht2 =  g0+ g1*h2
t-1 +g2*h2
t-2 + g3* e
2 
t-1 + g4* e
2
t-2  (8)
ht  is used as the measure of the uncertainty.
Later, the following linear models to assess the effect of exchange rate uncertainty on
exports are estimated.
Xt = f( t, P1t,P2t,P3t, p6 (t-1) to (t-4), X(t-1)  to (t-4), YF(t-1) to (t-4), ht), (9)
where t denotes the trend, P1t,P2t,P3t are quarterly dummies used to control the effect of
the 1994 crisis. p6 denotes the percentage change in the trade-weighted real exchange rate
based on CPI’s of major 6 trade partners of Turkey. Increase in the real exchange rate
denotes depreciation of the Turkish Lira. Xt denotes the percentage changes in exports and
YF denotes the percentage change of foreign income. Numbers in subscript parentheses
indicate the lagged order.8
4. EMPIRICAL EVIDENCE
The equations 6,8 and 9 are estimated by using the quasi-maximum likelihood method.
The estimated coefficients of the equations  and t-ratios are reported below.
p6t= -0.0176 + 0.170P1t + 0.306 P2t + -0.011 P3t + -0.016 p6(t-1) + -0.183 p6(t-2) + et
        (-2.687  )  (19..617 )   (29.927)       (-0.358 )        (-0.185)             (-9.309)    (10)
ht
2 = 0.0002 + 0.174h2
t-1 + 0.0166 h2
t-2 + 0.591 e
2 
t-1 + 0.914 e
2
t-2
          ( 4.852 )    (4.150)           (0.243)              (1.851 )           ( 1.607)   (11)
Xt = 0.055 +0.002t +- 0.036P1t + - 0.140 P2t+-0.004 P3t +0.682 p6(t-1)+-0.204 p6(t-2) +-0.064 p6(t-3)+0.198 p6(t-4)
       (1.828)    (3.677)      (-1.432)      (-2.107)       (-0.043)      (3.330)            (-2.223)            (-0.291)          (1.505)
 + - 0.873X(t-1) + -0.991X(t-2) + -1.023X(t-3) + -0.026 X(t-4) + 0.627YF(t-1) + 6.469 YF(t-2) + -0.150YF(t-3)
         (-6.050)           (-5.675)           (-5.864)          (-0.176)           (1.252)             (6.646)                (-0.166)
  + 0.339YF(t-4) + -2.207ht                                                                                
      (0.780 )             ( -3.260)   (12)
The estimated coefficient of the trend indicates that the Turkish exports tend to
increase over time. Furthermore, exports tend to decrease in the first three quarters compare
to the fourth quarter. Real exchange rate is positively associated with the exports (the sum of
the real exchange rate coefficients is positive) even if in the second and third lagged, real
depreciation decreases the exports.
The current exports are negatively affected by its previous values. This is quite
important for the stability of the econometric model. This indicates that an adverse shock to
exports is going to die out rather than persist forever. Last, foreign income increases our
exports after its second quarter in a statististically significant fashion. Even if the estimated
coefficient is negative for the third quarter, it is individually statistically insignificant and the9
sum of the last four lagged coefficients is positive.
The estimated coefficient for the uncertainty variable  ht is negative and statistically
significant. This suggests that exchange rate uncertainty decreases the exports.
Two basic types of residual based robustness statistics are applied (Q statistics and
ARCH-LM test). Then, whether the residuals and standardised residual terms are
autocorrelated or not is tested. The probability values of autocorrelation coefficients for the
standardised residuals are 0,67, 0,62 and 0,61 for the 4
th ,8
th and 12
th lags so there is no
autocorrelation. Also the Engles’ ARCH LM test fails to detect autocorrelations for the 4
th, 8
th,
and 12
th lags. Hence, all the robustness statistics were satisfactory on the specification of our
model. The sum of the coefficient estimates of equation 14 is greater than 1. This suggests
that the conditional variance is explosive.  This could be due to small sample that we had to
use.  The results are also robust for different ARCH specifications.
The same method for the imports demand equation is used and found that the
estimated coefficient for the uncertainty variable ht is positive and statistically insignificant.
This suggests that exchange rate uncertainty increases the imports however, empirical
evidence does not indicate statistically significant relation.10
5. CONCLUSION
In this study main determinants of export demand and the impact of adverse effects of
the real exchange rate uncertainty on exports in Turkey for the period of 1988-1997 are
tested.
The basic finding of this paper is that real exchange rate and foreign income are
significant in determining exports demand. For the imports demand, real exchange rate,
domestic income and exports are significant. While real exchange rate uncertainty
significantly reduces the exports, it is not significantly effective on the imports.
The adopted monetary policy during the last two years that targets the real exchange rate is
very important in terms of reducing the exchange rate volatility and improving the trade
performance.11
REFERENCES
Akhtar, M.A., and R. Spence Hilton, “Effects of Exchange Rate Uncertainty on German and
U.S. Trade”, Federal Reserve Bank of New York Quarterly Review, 9, (Spring 1984), 7-
16.
Altýnkemer, M., ''The Foreign-Exchange Markets in the Central Bank of the Republic of
Turkey and the Determination of Exchange-Rate”, Economic Conjunkture and
Statistical Studies, The Central Bank of the Republic of Turkey (1988-1992), 177-182.
Arize, Augustine C.,”The Effects of Exchange-Rate Volatility on US Exports: An Emprical
Investigation”, Southern Economic Journal, 62, (July 1995), 34-41.
Berument, H.,and N. Güner, “Inflation, Inflation Risk and Interest Rates: A Case Study for
Turkey”, Metu Studies In Development ,24, (1997), 319-327.
Caporale,T., and K. Doroodian, “Exchange Rate Variability and The Flow of International
Trade”, Economic Letters, 46, (1994) 49-54.
Coes, Donald V., “The Crawling Peg and Exchange Rate Uncertainty” in John Williamson
(ed.), Exchange Rate Rules: The Theory, Performance and Prospects of the Crawling
Peg, (New York: St.Martins 1981).
Cushman, David O., “The Effects of Real Exchange Rate Risk on International Trade”,
Journal of International Economics (Aug.1983), 45-63.
Chowdhury, Abdur R., “Does Exchange Rate Volatility Depress Trade Flows?: Evidence
From Error-Correction Models”, The Review of Economics and Statistics (Aug
1993),700-705.
Franke,G., “Exchange Rate Volatility and International Trade”, Journal of International Money
and Finance, 10, (June1991), 292-305.12
Giovannini,A., “Exchange Rates and Traded Goods Prices”, Journal of International
Economics ,24, (1988), 45-68.
Kenen, P. and D.Rodrick, “Measuring and Analyzing the Effects of Short-term Volatility in
Real Exchange Rates”, this REVIEW, 68, (1986), 311-315.
Koray, Faik, and William D. Lastrapes, “Real Exchange Rate Volatility and U.S. Bilateral
Trade : A VAR Approach”,  this REVIEW, 71, (Nov. 1989), 708-712.
Peree, E., and A. Steinherr, “Exchange Rate Uncertainty and Foreign Trade”, European
Economic Review, 33, (1989), 1241-1264.
Sercu, P., and C. Vanhulle, “Exchange Rate Volatility, Exposure and the Value of Exporting
Firms”, Journal of Banking and Finance,16, (Feb.1992), 155-182.
Thursby, Jerry G. and Marie C. Thursby, “Bilateral Trade Flows, the Linder Hypothesis, and
Exchange Risk”,  Review of Economics and Statistics ,69, (Aug.1987), 488-495.13
APPENDIX
The same method  used for the estimates of export demand equation is also used for
the import demand equation.
Equations 1,2 and 3 are estimated by using quasi-maximum likelihood method for
import demand. The coefficient estimates of these equations are reported below.
p2t= 0.005 + 0.151P1 + 0.234 P2 + -0.119 P3 + 0.234 p2(t-1)+ -0.151 p2(t-2) + et
      ( 1.027)   (26.076 )    (14.926)        (-3.994 )      (3.583)            (-1.564)  (1)
ht
2 = 0.001 + -0.108h2
t-1 + 0.006h2
t-2 + -0.061 e
2 
t-1 + 0.824 e
2
t-2
          (4..372 )     (-2.248)          (0.115)          (-1..338)           (9.472) (2)
Mt =  0.008 + -0.230 P3+ -0.885 p2t+ 0.146 YTt+ 0.462Xt+ -0.300M(t-1) + 0.449ht
    (0.462)       (-5.757)       (-4.208)      (1.638)          (6.241)       (-5.832)          (1.335) (3)
Mt denotes  percentage change in imports. P3 is the third quarter dummy used to
control the effect of 1994 crisis. First and second quarter dummies are not statistically
significant. p2t denotes the percentage change in weighted real exchange rate based on
consumer price indices of the USA, Germany and Turkey. Increase in the real exchange rate
denotes depreciation of the TL. YT denotes the percentage change of domestic income and
Xt denotes the percentage changes in exports. Number in subscript parentheses indicates
the first lagged order.
First of all, imports tend to decrease in the third quarter compare to the fourth quarter in
1994. Real exchange rate is negatively associated with  imports, meaning that depreciation
of the TL decreases imports. Domestic income and exports increase our imports. Last, the14
current import is negatively affected by its previous value.
The estimated coefficient for the uncertainty variable ht is positive and statistically
insignificant. This suggests that exchange rate uncertainty increases the import but empirical
evidence is not statistically significant. The test statistics for robustness performed
reasonably well. The probability values of autocorrelation coefficients for the standardised
residuals are 0,52, 0,34 and 0,32 for the 4
th ,8
th and 12
th lags so there are is autocorrelation,
and  the  Engles’s ARCH LM test fails to detect autocorrelations for the 4
th, 8
th, and 12
th lags.

Augustine C.,”The Effects of Exchange-Rate Volatility on US Exports: An Emprical Investigation”,

Bilateral Trade Flows, the Linder Hypothesis, and Exchange Risk”, Review of Economics and Statistics ,69, (Aug.1987), 488-495.13 APPENDIX The same method used for the estimates of export demand equation is also used for the import demand equation.

Does Exchange Rate Volatility Depress Trade Flows?: Evidence From Error-Correction Models”, The Review of Economics and Statistics

Effects of Exchange Rate Uncertainty on German

Exchange Rate Uncertainty and Foreign Trade”,

Exchange Rate Variability and The Flow

Exchange Rate Volatility and International Trade”,

Exchange Rate Volatility, Exposure and the Value of Exporting Firms”,

Inflation, Inflation Risk and Interest Rates: A Case Study for Turkey”, Metu Studies In Development ,24,

Measuring and Analyzing the Effects of Short-term Volatility in Real Exchange Rates”,

Real Exchange Rate Volatility and U.S. Bilateral Trade : A VAR Approach”,

The Crawling Peg and Exchange Rate Uncertainty”

The Effects of Real Exchange Rate Risk on International Trade”,

The Foreign-Exchange Markets in the Central Bank of the Republic of Turkey and the Determination of Exchange-Rate”, Economic Conjunkture and Statistical Studies, The Central Bank of the Republic of Turkey

The effect of exchange rate uncertainty on exports a case study for Turkey

http://www.tcmb.gov.tr/research/discus/dpaper36.pdf

The effect of exchange rate uncertainty on exports a case study for Turkey

Abstract

Similar works

Full text

Available Versions

Research Papers in Economics