Threshold Effect of Exchange Rate Volatility on Exports: Evidence from

India

Sidheswar PandaInstitute of Economic Growth, University Enclave, University of Delhi, Delhi 11007, India

Abstract

This study empirically examines the existence of threshold effects in the relationship between

real exchange rate volatility and India’s bilateral export. The study has used data for the top 13

exported countries for the period from 1980-81 to 2010-11 by employing panel data regression

model, the results show that there is a significant negative effect between partner country’s real

exchange rate volatility and India’s export volume in the post liberalization period. The study has

also implemented Hansen (1999) threshold regression model to measure the threshold effects of

real exchange rate volatility on India’s bilateral exports. Per capita real income of partner

countries is identified as threshold variable and with the identified threshold value the countries

are classified as low income countries and high income countries. The results from the threshold

model indicate that partner country’s real exchange rate volatility adversely affects India’s export

volume only for low income countries whereas there is no effect for high income countries.

JEL classification: F31; F4; C23

Key words: Exchange rate volatility, Exports, Panel data, Threshold regression model

1. Introduction

From 2008 to August 2013, the Indian rupee to dollar exchange rate depreciates around 70%.

Meanwhile, the Indian rupee to other main currency exchange rate also experienced a huge

depreciation. The debate on the effect of exchange rate on trade has been raised again, although

this topic has been much discussed in literature. Some studies have addressed this topic by

building theoretical models. Dornbusch (1976) developed a simple macroeconomic framework

to analyze the exchange rate movement. He identified exchange rate as a critical channel for the

transmission of monetary policy in influencing the domestic output and argued that the

effectiveness of monetary policy on interest and exchange rates is significantly affected by the

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behavior of real output. Hooper and Kohlhagen (1978) gave a theoretical explanation on the

relationship between exchange rate volatility and international trade. They argued that the higher

exchange rate volatility increases the cost for the risk-averse traders and also reduces the foreign

trade due to unpredictable condition in the change of exchange rates, the profit becomes

uncertainty and thereby reduces the benefits of international trade. In developing countries, the

forward markets are not accessible to all the traders due to absence of hedging in the exchange

rate risk. If there is a hedging persists in the forward market then there will be more cost and

limitations in the market. De Grauwe (1988) found that the positive relationship between trade

and exchange rate variability is due to the income effect dominating the substitution effect. An

increase in exchange rate volatility raises the expected marginal utility of increased exports,

given that the exporters are sufficiently risk averse. The flexibility in the foreign exchange rate

market and exchange rate determination, there is excess volatility, which have an adverse impact

on price discovery, export performance, sustainability of current account balance and balance

sheet in view of dollarization.

Chowdhury (1993) argued that the influence of exchange rate volatility on trade is an empirical

issue. His results show that exchange rate uncertainty reduces the trade flows of the G-7

countries, using both error correction model and multivariate cointegration technique to measure

short run and long run dynamics. Similarly Arize (1998) and Chit et al. (2010) investigated that

higher exchange rate volatility is negatively affecting international trade. Umarani (1993)

examined the impact of exchange rate volatility on trade flows in India during the period January

1975 to December 1988. She also found that India’s bilateral exports and imports have been

negatively affected by the volatile nature of the exchange rate. De Grauwe (1992) and Broll and

Eckwert (1999) argued in favor of a positive relationship between exchange rate volatility and

trade flows. Oskooee and Mitra (2008) estimated the impact of exchange rate volatility in

commodity trade flows between the U.S. and India, during the period from 1960 to 2004 by

using the bound test approach to cointegration and error correction model. They have found

mixed results in the sense that 40% of sample industries have a positive impact on exchange rate

volatility on commodity trade and the rest have a negative impact. Oskooee et al (2013)

analyzed the exchange rate volatility on commodity trade between the U.S. and Brazil. They

found that the trade flows of many industries have no long-run relationship with their

macroeconomic determinants. Kiyataka et al (2013) investigated the impact of exchange rate

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volatility on intermediate goods exports in Asia. They found the exchange rate volatility has

negative effect on machinery industry and the impact on intra-regional trade differs across

industries.

Literature in the relationship between exchange rate volatility and volume of trade has so far

provided mixed evidence. These inconclusive results may be a result of non-linearity in the

relationship between exchange rate volatility and volume of trade. There are very few studies in

the recent times that focus on understanding this non-linearity. Zhang et al. (2006) found the

non-linear relationship between exports volume and exchange rate volatility, i.e., when exchange

rate volatility surpasses a certain threshold which tends to increase export volume using the time

series econometric technique on bilateral export volume to the US from the other six G-7

countries. Hsu and Chiang (2011) examined the effects of real exchange rate volatility on the

U.S. bilateral export flows by using panel data model and Hansen (1999) threshold regression

model. They found that real exchange rate volatility has a positive effect on bilateral exports in

the relative low income countries, but in the relative GDP per-capita of the trading partners over

the threshold value, it indicates a negative relationship between exchange rate volatility and the

bilateral exports from the U.S. to its trading partners.

The present study examines the effects of exchange rate volatility on India’s bilateral export

volume. This paper is different from earlier studies on two aspects: First, there is a time series

technology used in most papers, but it has implemented a balanced panel data approach. The

major advantage of using panel data is that it controls for time-invariant country heterogeneity.

Second, this paper examines the existence of the possibility of non-linear effects of exchange

volatility on exports and tests the effect by using threshold regression methods for non-dynamic

panels with the individual-specific fixed effects proposed by Hansen (1999). Two variables are

considered as the possible threshold variables: The first is the bilateral real exchange rate

volatility between India and its trading partners. The second is the relative real gross domestic

product (GDP) per capita of India export partners to the Indians. The reason for using these two

variable as threshold variable is as follows: according to De Grauwe (1988), Arize at al (2000),

the exchange rate volatility generates higher uncertainty and increases the level of risk. The

impact of exchange rate volatility on exports depends on the degree of risk aversion. McKee

(1989) investigated that income effects play an important role in individual risk attitude. The

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relative income of India’s export partners is considered as one of the threshold variables in

explaining the nonlinear effects of exchange rate volatility on exports.

2.Model Specification

2.1 Non-dynamic linear panel data model

This study estimate a Static, non-dynamic linear panel data regression with an individual-

specific fixed effect model:

EXit= µ1+1 Yit-1+2 Pt-1+ 3Vit-1 +4 Dit-1 +5 Dit-1 Vit-1 +5T+eit (1)

Where the subscript i indexes India trading partner i and the subscripts t indexes time. EXitis the

real exports volume from the India to country i.µ1denotes the unobservable individual specific

effects.Yit-1represents the real foreign economic activities for country i at timet-1.Pit-1is the

competitiveness of exporters and is measured by the ratio of India exports price to the world

exports price in US dollars.Vit-1is the real exchange rate volatility for country i at time t.D = 1 for

observations during post-liberalization period for India (1991 onwards) = 0 otherwise. T is the

trend and eit denotes disturbances and is assumed to be independent and identically distributed

with mean zero and finite variance.

2.2Non-dynamic non-linear panel data model

The non-dynamic balanced threshold regression with individual-specific fixed effects model is

EXit= µ1+1Yit-1+2 Pt-1+3T +β1 Vit-1 I(Qit-1≤γ) +β2 Vit-1 I(Qit-1>γ) +ei(5.2)

Where, I is the indicator function, Qit-1 is threshold variable, γ is the threshold value. β1captures

the effect of real exchange rate volatility on real bilateral exports from India while the threshold

variable (Qit-1)is less than or equal to γ and β2 captures the effect while the threshold variable (Q it-

1)is greater than to γ. This study considers two possible threshold variables are real exchange rate

volatility and real GDP per capita to India of exporting countries i.

Hansen (1999) found that for any given threshold variable, the slope coefficient and the

threshold value can be estimated by Ordinary Least Squares method after fixed-effect

transformations in a panel setup. The optimal threshold value is selected in two steps. First,

shorting the distinct values of the threshold variable and eliminating the largest and smallest 5%

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of the observations of threshold variables. Second, the optimal threshold value is identified as the

smallest sum of squared residuals.

3. Data

To examine the existence of non-linearity in the relationship between real exchange rate

volatility of the foreign countries on India’s bilateral export, panel data is used. The data are

collected for the top 13 exported countries for the period from 1980-81 to 2010-11. The sample

period is dictated by the rise in the growth of India’s exports during the Sixth Five-year Plan and

also the availability of consistent data. The data sources are the International Financial Statistics

(IFS) published by IMF, Direction of Trade (DOT) and World Economic Outlook published by

the IMF. The top 13 trading partners for India are China, United Arab Emirates, United States of

America, Japan, United Kingdom, France, Germany, Honk Kong Singapore, Netherlands, Italy,

Saudi Arabia and Bangladesh. The bilateral exports (EXit) is the natural logarithm of bilateral

export volume from India to country i (2004-05=100). The real economic activity of partner

country i (Yit-1) is measured by the natural logarithm of GDP volume of country i (2004-05=100).

The relative price of exports (Pt-1)is defined as the natural logarithm of the ratio of India exports

price to the world exports price in US dollars. The relative real GDP per capita of country i to

India (Yit-1) is the natural logarithm of the ratio of real GDP per capita of country i is expressed in

US dollars to real GDP per capita of India. This study uses a moving sample standard deviation

method to derive the exchange rate volatility. Hansen’s (1999) threshold regression model is

used to measure the threshold effects of real exchange rate volatility on India’s bilateral exports.

The moving sample standard deviation of the real exchange rate as the proxy for volatility is

expressed as:

Vi,t+m= ¿¿

Where, V is the real exchange rate changes. M is the order of moving average. In this study m

equals 3. Rit denotes the natural logarithm of real bilateral exchange rate (rer) between India and

country i. The advantage of this measurement is being able to capture higher frequency

movement in the exchange rate and it uses every value in the group of the data being used. The

rer is

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Rit= Exp (lnExIndia – lnWPIIndia- ln Ex country i + ln CPI county i)

Where, Rit represents the natural logarithm of real bilateral exchange rate (rer) between India and

country i. ExIndia is the natural logarithm of Indian exchange rate. WPIIndia is the natural logarithm

of Wholesale Price Index in India. Ex country i is the natural logarithm of exchange rate of country i.

CPI county i. is the natural logarithm of consumer price index of country i.

4. Empirical Results

4.1Panel Unit Root Results

Table 1: Panel Unit Root Test Results

Common Unit

Root-LLC

Individual Unit

Root-IPS

Individual Unit

Root-Fisher ADF

EX@ -0.913 (0.18) -1.386 (0.08) 39.515 (0.04)

Yit-1@ -2.383 (0.00) -2.357 (0.00) 45.236 (0.01)

Pt-1 -9.652 (0.00) -7.479 (0.00) 103.009 (0.00)

Vit-1 -0.805 (0.21) -3.067 (0.00) 46.730 (0.00)

Note: Numbers in parentheses are P-values@ With intercept and trend

This study uses recently developed panel unit root test such as Levin, Lin, Chu (LLC, 2002), Im,

Pearson, and Shin (IPS, 2003) and Fisher ADF tests. If the order of integration is zero, the series

is considered to be stationary and thus free from unit root. All these tests are based on the null

hypothesis of a unit root against the alternative hypothesis of stationary of the series. From the

above table 4 it is evident that all the variables are stationary in their level. GDP and export are

stationary with trend and intercept

4.2Non-dynamic linear Panel data Regression Results

The results of the linear panel data regression model from the below table 2, we can infer that the

effects of real economic activity of partner countries are all positive and statistically significant

i.e. 1. The coefficient of dummy variable 4 and competitiveness of exporters 2 are

insignificant. The estimated coefficient of exchange rate volatility (3) is 0.122 and which is

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positive and statistically significant that is before liberalization. This implies that an increase in

real exchange rate volatility of foreign countries increases bilateral exports of India.

Table 2:Non-dynamic linear Panel data Regression Estimates

Variables Estimated Coefficient

lnYit-1 0.159**(0.020)

lnPt-1 0.078(0.250)

Vit-1 0.122*(0.043)

Dit-1 0.245(0.177)

Dit-1 Vit-1 -0.123*(0.043)

T 0.117**(0.009)

Constant 4.390**(0.276)

R2 0.68

Note: Numbers in parentheses are standard errors.* And ** denotes significance at 5 and 1 percent levels

The estimated net effect of volatility on exports after liberalization (3+5) is -0.001. This

implies a negative and statistically significant effect even though the estimated coefficient is

small, which means that real exchange rate volatility of foreign countries has stronger negative

effect on Indian bilateral exports after the liberalization. So after 1991, the partner country

exchange rate volatility affects negatively the exports from India. The positive coefficient in the

pre-liberalization period and very small coefficient in the post-liberalization period warrants a

deeper analysis to realize the non-linear effects of this relationship.

4.3Non-dynamic non-linear panel data Regression results

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This study uses two variables namely Exchange Rate Volatility and Per Capita Real Income of

exporting countries as the threshold variables. If Exchange Rate Volatility qualifies as a

threshold variable then the partner countries are classified as high volatility countries and low

volatility countries whereas if per capita real income qualifies as a threshold variable then the

partner countries are classified as high income countries and low income countries. Following

Hansen (1999) we test for existence of threshold effects with the above mentioned variables as

threshold variables. The estimations are derived using the code written by Hansen for the

statistical software package R.

4.2.1. Test of Threshold Effect Results

The panel threshold regression model warrants a test for the existence of threshold relationship.

So we have to test the null hypothesis of no threshold effect with the alternative hypothesis of at

least one threshold effect. The results are shown in Table 3.

Table 3: Test for Threshold Effects

Volatility Per Capita Real Income

Test for a single threshold

F Statistics

(10%, 5%, 1% critical value)

14.824

(32.369)

(40.218)

(42.234)

31.029

(27.315)

(29.766)

(33.445)

P-value 0.87 0.05

Note: Numbers in parentheses are standard errors.

The results indicate that exchange rate volatility is not qualified as a threshold variables as the

null hypothesis of no threshold effect is not rejected based on its F statistics. The per capita real

income of partner country relative to India qualifies as a threshold variable with the null

hypothesis of no threshold effects is rejected based on F statistics at 5% level of significant.

4.2.2. Panel Threshold Regression Result

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Per capita real income of partner country as the threshold variable. The results are presented

from the below table 4, it is found that the threshold value (γ) is – 0.633. This implies that the

countries are classified as high income countries where their per capita real income is greater

than -0.633 and low income countries where their per capita real income is less than -0.633.

Since all the variables are in natural logarithmic form, the threshold value is negative and small.

The estimates from the threshold regression model also pointed out that there is a positive

relationship between real exports and foreign countries income (1). The competitiveness index

of the exporters and trend variable (2 and 3) turns out to be negative and statistically

significant.

Table 4: Estimates of Panel Threshold Regression Model (Threshold Variable: Per Capita

Real Income)

Note:Numbers in parentheses are standard errors.* And ** denotes significance at 5 and 1 percent levels

The very important result from the above estimation is that the effect of real exchange rate

volatility of low income countries on India’s real export captured by β1 is negative and highly

statistically significant. Interestingly, the effect of real exchange rate volatility of high income

countries on India’s real export captured by β2 is positive but statistically insignificant. This

implies that the exchange rate volatility of the high income countries has no impact on the

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Estimated Coefficient Estimated Coefficient

lnYit-1 0.015

(0.051)

lnPt-1 - 0.866*

(0.301)

T -0.017*

(0.008)

Vit-1 I(Qit-1≤γ) - 5.327**

(0.983)

Vit-1 I(Qit-1>γ) 0.0005

(0.0006)

γ -0.633

exports from India, whereas there is a strong evidence of exports negatively affected by the

exchange rate volatility of low income countries. This also indicates the existence of

non-linear/threshold effect of exchange rate volatility on India’s bilateral exports.

Figure 1: Confidence Interval Construction in Single Threshold Model

The above figure provides the confidence interval construction in single threshold model. It also

points out that at -0.633 the likelihood ratio attains the minimum level. This study also identifies

only two countries (China and Bangladesh) out of the 13 countries considered in this analysis has

per capita real income less than or equal to γ.

5. Conclusion

The empirical literature so far on the relationship between exchange rate volatility and volume of

trade has provided mixed evidence. These inconclusive results may be a resultant of non-

linearity in the relationship between exchange rate volatility and volume of trade. In the context

of a developing country’s perspective these empirical studies are very limited and to our

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knowledge there is no study on testing this non-linearity. This study tries to fill the gap by

providing the existence of non-linearity in this relationship between exchange rate volatility and

volume of trade from a developing country’s perspective. To understand the relationship

between exchange rate volatility of export partner countries on India’s export, top 13 exported

countries are considered in a panel set up for the period from 1980 to 2009. This study applied

two procedures to understand this relationship. To realize the relationship in the pre and post

liberalization period a simple panel regression model is used and a panel threshold regression

model of Hansen (1999) is implemented to understand the existence of non-linearity in the

relationship. Bilateral Exchange rate volatility is measured by using a moving sample standard

deviation method.

Panel regression model result shows that there is a significant negative effect between partner

country’s real exchange rate volatility and India’s export volume in the post liberalization period.

There exists a non-linear relationship between partner country’s real exchange rate volatility and

India’s exports. Per capita real income of partner countries is identified as threshold variable and

with the identified threshold value the countries are classified as low income countries and high

income countries. The results from the threshold model indicate that partner country’s real

exchange rate volatility adversely affects India’s export volume only for low income countries

whereas there is no effect for high income countries. This study focuses only on exchange rate

volatility and its effect on exports. There is an ample scope to have a bigger model of exports

with all the other determinants and test for non-linearity in the relationship.

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Appendix A. Percentage of countries above and below threshold value (γ)

Year ≤γ >γ Year ≤γ >γ

1983 0 100 1997 15.384 84.615

1984 0 100 1998 15.384 84.615

1985 0 100 1999 15.384 84.615

1986 0 100 2000 7.692 92.307

1987 0 100 2001 7.692 92.307

1988 0 100 2002 7.692 92.307

1989 0 100 2003 7.692 92.307

1990 0 100 2004 7.692 92.307

1991 0 100 2005 7.692 92.307

1992 7.692 92.307 2006 0 100

1993 15.384 84.615 2007 7.692 92.307

1994 15.384 84.615 2008 7.692 92.307

1995 15.384 84.615 2009 7.692 92.307

1996 15.384 84.615 2010 7.692 92.307

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