The 16 reference contexts in paper Christopher F. Baum, Mustafa Caglayan, Neslihan Ozkan (2000) “Nonlinear Effects of Exchange Rate Volatility on the Volume of Bilateral Exports” / RePEc:boc:bocoec:488

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    This volatility has often been cited bytheproponentsofmanaged or-xedexchangerates asdetrimental,sinceintheir view exchangerateuncertaintywillinevitablydepressthevolumeofinternationaltradebyincreasingtheriskinessoftradingactivityandnegativelyaffectingtheoptimalallocationof resources. Several theoretical studies
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    (Ethier (1973), Clark (1973), Baron (1976),
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    Cushman (1986), Peree and Steinherr (1989) to mention a few) have shown that an increase in exchange rate volatilitywill haveadverseeffects on the volume of internationaltrade. Contrarily, other models (for example Franke (1991), Sercu and Vanhulle (1992)) have shown that exchange rate volatility may have a positive impact on international trade -ows, or ambiguous effects depending on aggregate ex
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    Several theoretical studies (Ethier (1973), Clark (1973), Baron (1976), Cushman (1986), Peree and Steinherr (1989) to mention a few) have shown that an increase in exchange rate volatilitywill haveadverseeffects on the volume of internationaltrade. Contrarily, other models (for example
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    Franke (1991),
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    Sercu and Vanhulle (1992)) have shown that exchange rate volatility may have a positive impact on international trade -ows, or ambiguous effects depending on aggregate exposure to currency risk (Viaene and deVries (1992)).
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    Theempirical results are, in general, sensitiveto the choicesof sample period, model speci-cation, form of proxies for exchange rate volatility, and countries considered (developed versus developing). , 2 23 Negativeeffects of exchange rate uncertaintyon trade -ows are reported by
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    Cushman (1983, 1986, 1988),
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    Akhtar and Hilton (1984), Thursby and Thursby (1987), Kenen and Rodrik (1986), and Peree and Steinherr (1989), among others, while Hooper and Kohlhagen (1978), Gotur (1985), Koray and Lastrapes (1989), and Gagnon (1993) -nd insigni-cant effects.
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    Theempirical results are, in general, sensitiveto the choicesof sample period, model speci-cation, form of proxies for exchange rate volatility, and countries considered (developed versus developing). , 2 23 Negativeeffects of exchange rate uncertaintyon trade -ows are reported by Cushman (1983, 1986, 1988), Akhtar and Hilton (1984), Thursby and
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    Thursby (1987),
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    Kenen and Rodrik (1986), and Peree and Steinherr (1989), among others, while Hooper and Kohlhagen (1978), Gotur (1985), Koray and Lastrapes (1989), and Gagnon (1993) -nd insigni-cant effects. Kroner and Lastrapes (1993), using a multivariate GARCH-in-mean model, report that the reduced-formeffectsofvolatilityonexportvolumeandpricesvarywidely.
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    period, model speci-cation, form of proxies for exchange rate volatility, and countries considered (developed versus developing). , 2 23 Negativeeffects of exchange rate uncertaintyon trade -ows are reported by Cushman (1983, 1986, 1988), Akhtar and Hilton (1984), Thursby and Thursby (1987), Kenen and Rodrik (1986), and Peree and Steinherr (1989), among others, while Hooper and Kohlhagen (1978),
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    Gotur (1985),
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    Koray and Lastrapes (1989), and Gagnon (1993) -nd insigni-cant effects. Kroner and Lastrapes (1993), using a multivariate GARCH-in-mean model, report that the reduced-formeffectsofvolatilityonexportvolumeandpricesvarywidely.
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    exchange rate volatility, and countries considered (developed versus developing). , 2 23 Negativeeffects of exchange rate uncertaintyon trade -ows are reported by Cushman (1983, 1986, 1988), Akhtar and Hilton (1984), Thursby and Thursby (1987), Kenen and Rodrik (1986), and Peree and Steinherr (1989), among others, while Hooper and Kohlhagen (1978), Gotur (1985), Koray and Lastrapes (1989), and
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    Gagnon (1993)
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    -nd insigni-cant effects. Kroner and Lastrapes (1993), using a multivariate GARCH-in-mean model, report that the reduced-formeffectsofvolatilityonexportvolumeandpricesvarywidely. TheestimatedeffectsofGARCHconditional variance of the nominal exchange rate on export-ows differ in sign and magnitude across the countriesstudied.
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    includes U.S., Canada, Germany,U.K., France, Italy, Japan, Finland, Netherlands, Norway,Spain, Sweden,andSwitzerland, consists ofbilateral real exports for the period 1980-1998 on a monthly basis in each direction. Hence it is possible to examine dozens of bilateral relationships,and avoidthe narrowfocus on U.S. or the G7 countries- data that has characterised muchof the literature. Second, as
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    Bini-Smaghi (1991)
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    has stressed: there could be methodological problems, as all empirical analysis incorporates a proxy to capture exchange rate volatility. Most of the previous research uses a moving average standard deviation of the past monthly exchange rates, while others use variants of ARCH models.
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    Our study improves upon muchof the literature in its method of quantifying exchange rate volatility. We utilize daily spot exchange rates to compute one month-ahead exchange rate volatility (via amethod based on Merton (1980), which is also exploited by
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    Klaassen (1999)
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    in the exchange rate context) from the intra-monthly variations in the exchange rate. This approach provides a more representative measure of the perceived volatility avoiding potentialproblems,suchasthehighpersistenceofrealexchangerateshockswhenmoving average representations are used, or low correlation in volatility when ARCH/GARCH models are applied to quantify exchange rate volatility.
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    Third, there could be a problem of model misspeci-cation, such as inadequate dynamics and omitted variable bias. Our model uses a -exible Poisson lag speci-cation 4 andtrade-ows,seeFarelletal. (1983),IMF(1984),and
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    Willett(1986)
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    regardingtheliteraturethroughthemid-1980s, and Côté (1994) regarding more recentworks. Severalauthorsinthe-nanceliteraturehaveusedhighfrequencydatatoobtainvolatilitymeasures(e.g.,Anderson et al. (2001), French et al. (1987)).
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    Our model uses a -exible Poisson lag speci-cation 4 andtrade-ows,seeFarelletal. (1983),IMF(1984),andWillett(1986)regardingtheliteraturethroughthemid-1980s, and Côté (1994) regarding more recentworks. Severalauthorsinthe-nanceliteraturehaveusedhighfrequencydatatoobtainvolatilitymeasures(e.g.,
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    Anderson et al. (2001),
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    French et al. (1987)). Klaassen (1999), using G7 data, demonstrates that proxies obtained from both ARCH models and moving standard deviation measures have con-icting implications for the evolution of risk over time. 4 4 to allow the data to determine the appropriate dynamic speci-cation of the time form of explanatory variables- impacts.
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    Our model uses a -exible Poisson lag speci-cation 4 andtrade-ows,seeFarelletal. (1983),IMF(1984),andWillett(1986)regardingtheliteraturethroughthemid-1980s, and Côté (1994) regarding more recentworks. Severalauthorsinthe-nanceliteraturehaveusedhighfrequencydatatoobtainvolatilitymeasures(e.g.,Anderson et al. (2001), French et al. (1987)).
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    Klaassen (1999),
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    using G7 data, demonstrates that proxies obtained from both ARCH models and moving standard deviation measures have con-icting implications for the evolution of risk over time. 4 4 to allow the data to determine the appropriate dynamic speci-cation of the time form of explanatory variables- impacts.
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    In our extension, we seek to capture the potential effect of the volatilities of exchange rates and foreign income on exporters- supply decisions. The strands of literature which consider entry/exit costs and evaluate -real options- to participate in export markets give rise to additional factors determining medium-runsupply (see, for example,
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    Franke (1991)).
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    The value of a real option, like that of any option, is enhanced by volatility in the underlying relationship, and in this case exporters will be sensitive to both the volatility of foreign income and volatility of the exchange rate.
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    itself to generate such a proxy (as has often been done in the literature), we chose not to use industrial production in that context, since it provides a limited measure of overall economic activity. As an alternative, we apply the -proportional Dentonbenchmarkingtechnique toquarterly real GDP series in order to producemonthlyGDP estimates. The proportional Denton benchmarking technique
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    (Bloem et al., 2001)
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    uses thehigher-frequencymovementsofanassociatedvariable-inourcase,monthlyindustrial production-asaninterpolatorwithinthequarter,whileenforcingtheconstraintthatthe sum of monthly GDP -ows equals the observed quarterly total.
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    ) uses thehigher-frequencymovementsofanassociatedvariable-inourcase,monthlyindustrial production-asaninterpolatorwithinthequarter,whileenforcingtheconstraintthatthe sum of monthly GDP -ows equals the observed quarterly total. From the constructed monthlyGDP series, the volatility of foreign income is estimated for each countryusing amoving window technique similar to that employed by Thursby and
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    Thursby (1987,
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    p.491) in which the logarithm of monthly real GDP is regressed on a quadratic trend for a six-month moving window. The root mean squared error of the moving-window regression over observationsis used as the estimate of income volatility for period Although this is an adaptive measure of volatility, we believe that it is appropriateforareal-sectorvolatilityseries, whichisobservedonlyinfrequentlycom
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    daily data, to de-ne the risk measure: (5) ∈ ,. (1 5) √ ∑ tt T t d ttt s,t - tt t tt i iti t tt s&-s -s.s -s- - s-, ---s -[ ]=[] []-[] []= + (-[] ) -[ ] [] =1 -1 2 =1 ∑ wheretheparameters , , areestimatedfromtheempiricaldistribution ofthe 12 12 --s s,t - t values. A period-aheadforecast ofisthen computedbygeneratinga standard multi-period-ahead AR(2) forecast from equation (5), following
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    Hamilton (1994,
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    pp. 80-81). 2.3 Modeling the dynamics of the export relationship Giventhattheinclusionofexchangeratevolatilityinequation(3)arisesduetotimelags betweenagents-decisionstopurchaseandthecompletion ofthattransaction,aneclectic exchange rates, which are in many cases much larger than monthly changes in consumer prices.
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    , in Table 6 we summarize the prevalence of these effects from the exporter-s perspective: that is, from each exporting country how many of the bilateral relations to importersexhibit sensitiv21 ˆ --. ijij -ˆ 0079 0235 , .,. ij i,j ,..., =1 13 18 testnl The test of this hypothesisfrom the estimated nonlinear model is computed by Stata-s procedure,which is based on a statistic described in
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    Greene (2000,
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    p. 153-154). --,ijij ij ij ij ij ij ij ij ij 5. Estimation Results: Exporters G7 G7 nonG7 nonG7 ERM All Importers G7 nonG7 G7 nonG7 ERM All median 0.015 0.014 0.025 0.003 0.018 0.014 mean 0.103 0.144 0.296 0.041 0.251 0.157 std. error 0.060 0.084 0.089 0.062 0.130 0.040 95% conf. -0.019 -0.025 0.116 -0.086 -0.014 0.079 interval0.224 0.313 0.477 0.168 0.51
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