Is there any relation of Euro Area's trade with foreign exchange and SDR reserves?

 INTERNATIONAL JOURNAL OF BUSINESS,MANAGEMENT AND SOCIAL SCIENCES

VOL-IV,ISSUE-I(II),APRIL 2015,45-49

           (This paper was presented on 24th April,2015, at Sadguru Gadage Maharaj College,Karad Vidyanagar,Karad.Maharastra.)

Is there any relation of Euro Area’s trade with foreign exchange and SDR reserves?

Dr.Debesh Bhowmik 

Key words- SDR, foreign exchange reserves, cointegration, VAR

Introduction

It is noted that Euro Area has not shown the identical behavior like USA and the world in describing the relation of export and import with foreign exchange and SDR from the survey period during 1999-2013.

It may be that Euro Area depends on Euro for its intra-trade and has utilised foreign exchange and SDR for international trade. Therefore, the role of foreign exchange and SDR in analyzing foreign trade is rather different that other big blocs and nations.

In this paper, we will verify this notion through the econometric model from 1999 to 2013.

Methodology and data

We have used the Johansen model (1988,) for cointegration and also used Johansen model (1991,1996) for applying VAR model. We collected data of exports, imports, foreign exchange reserves and SDR from International Financial Statistics (IMF) for Euro area.

Assume x₁= exports of Euro Area, X₂= imports of Euro Area.X₃= foreign exchange reserves of Euro Area,X₄= SDR reserves of Euro Area.

We also used the semi log and double log linear model for trend values.

The major findings of the Econometric model

There is inverse relation between exports of Euro Area with the total reserves of foreign exchange (excluding gold) in which the double log regression model observed that one percent increase in total reserves per year would lead to a decrease of 1.3636% export per year of Euro Area during 1999 to 2013.This is statistically significant. On the other hand, the Johansen co-integration test proved that both are not co-integrated because the Trace and λ Max statistic are insignificant and have no cointegrating vectors.

The export of Euro Area has the positive relation with the SDR reserves of Euro Area from the period of 1999-2013 which is shown by the double log linear model where it is observed that one percent rise in SDR reserves of Euro Area per year leads to 0.40258% increase in export of Euro Area per year. This estimation is statistically significant.

The Johansen co-integration test suggests that Trace and λ Max statistics showed one cointegrating equation in relating export and reserves of SDR of Euro Area during 1999-2013.

The double log multiple regression model tells us that one percent rise in total foreign and SDR reserves per year leads to decrease in 1.1988% in export and 0.3060 percent increase exports respectively in Euro Area during 1999-2013.The t are significant for all coefficients, high value of F and R².

The regression equation is given below,

Logx₁=13.288-1.1988logx₃+0.3060logx₄

              (10.156)* (-5.15)*     (3.312)*

 R²= 0.789  , F= 22.497*  , DW=1.893  ,

The Johansen co-integration test suggest that the Trace and λ Max statistics confirmed only one cointegrating equation .

Table-1: Cointegration test

No. of hypothesized (CEs)	Eigen value	Trace statistic	0.05 c.v.	Prob.
None*	0.93937	47.778*	29.797	0.0002
At most 1	0.4999	11.338	15.494	0.1915
At most2	0.16398	2.3284	3.841	0.1217
		λ Max Statistic
None*	0.93937	36.440*	21.131	0.0002
At most1	0.4999	9.0096	14.264	0.2854
Atmost 2	0.16398	2.328	3.841	0.1270

Source- Calculated by author

 The import of Euro Area has inverse relation with the total reserves of foreign exchange during 1999-2013 where double log regression equation states that one percent rise in foreign exchange reserves of Euro Area per year leads to 1.388261% fall in imports of Euro Area per year during the survey period. This is statistically significant .

The Johansen cointegration test suggests that the Trace and λ Max statistics confirmed no cointegrating vector.But the imports of Euro Area has the positive correlation with the reserves of SDR during the period of 1999-2013 in which the double log regression model suggest that one percent increase in SDR reserves of Euro Area per year would lead to 0.40897% increase in imports of Euro Area per year during the specified period. This result is statistically significant .The Johansen cointegration test suggests that there is one cointegrating vector as observed by the Trace Statistics and λ Max statistics.

The double log multiple regression model confirms that one percent increase in SDR reserves would leads to 0.3107 % increase in imports of EU and one percent increase in total foreign exchange would lead to 1.22088% decrease in import of EU during 1999-2013 respectively where both are statistically significant with high R² and F values.

Logx₂=13.3835-1.220889logx₃+0.3107logx₄

              (10.845)*  (-5.415)*      (3.565)*

  R²= 0.8135  , F= 26.172*  , DW=1.839

Moreover, the Johansen cointegration test suggests that there is only one cointegrating vector as observed by the Trace and λ Max statistics which are given below in Table-2.

  Table-2: Cointegration test of imports, foreign exchange and SDR of Euro Area

No. of hypothesized (CEs)	Eigen value	Trace statistic	0.05 c.v.	Prob.
None	0.93335	45.9238*	29.797	0.0003
At most 1	0.48233	10.7150	15.494	0.2296
At most2	0.15279	2.1555	3.8414	0.1420
		λMax Statistic
None	0.93335	35.2088*	21.131	0.0003
At most1	0.48233	10.7150	14.264	0.9246
At most2	0.15279	2.1555	3.841	0.1420

Source- Calculated by author

Thus, it is proved that Euro Area export and import have no cointegration with total foreign reserves but have cointegration with the reserves of SDR during the period of 1999-2013.

Let us have a VAR model of x₁, x₃, x₄ respectively in one period lag to show the relationship explicitly. The estimated equations are given below,

X_1t=2290.512+0.4665X_1t-1 -6.093X_3t-1+35.4603X_4t-1

         (1.167)       (1.332)           (-0.963)         (1.359)

 R²= 0.769   ,  F= 11.137

X_3t= 207.936 -0.02692X_1t-1 +0.2094X_3t-1+0.8222X_4t-1

          (1.744)      (-1.265)           (0.529)             (0.518)

  R²= 0.514       F= 3.53  

X_4t=10.692+0.00142X_1t-1 -0.04409X_3t-1 + 0.71303X_4t-1

         (0.818)    (0.610)             (-1.017)              (4.104)*

 R²= 0.858   , F= 20.152*  

   Loglikelihood = -219.77 , SC= 33.65  , AIC=33.110,*= significant at 5% level.

Thus, the VAR model of Euro Area export with foreign exchanges and SDR is a good fit with high R² but the t values of the coefficients are not significant. Moreover, the Impulse Response Functions in all cases do not converge  to zero which concludes instability that are shown below in Fig-1.

Fig-1: Impulse Response Function of export, foreign exchange and SDR 

       Source- Calculated by author

Yet the unit root circle test showed that all the roots of Characteristic Polynomial are less than zero and lie inside the unit circle which means VAR satisfies the stability condition. The values of the roots are shown in the table and their positions are shown in the unit root circle in Fig-2.

    Table-3:Roots of Characteristic polynomial

roots	Modulus
0.967855	0.967855
0.435367	0.435367
-0.01265	0.014265

            Source- Calculated by author                       

                           Fig-2

Source- Calculated by author

Doornik-Hansen VAR residual normality test confirms that only joint kurtosis is significant at Chi square distribution but skewness, kurtosis and Jarque-Bera showed insignificant , that’s why the normality is rejected.

   Table-4:Doornik-Hansen normality test

component	Skewness	Chi-sq	df	prob
1	0.228856	0.205167	1	0.6506
2	-0.458241	0.805338	1	0.3695
3	0.071878	0.020371	1	0.8865
joint		1.030877	3	0.7938
component	Kurtosis	Chi-sq	df	prob
1	2.960292	1.611232	1	0.2043
2	3.068134	1.212961	1	0.2707
3	3.669513	5.310471	1	0.0212
joint		8.134664	3	0.0433
component	Jarque-Bera
1	1.816399		2	0.4032
2	2.018300		2	0.3645
3	5.330842		2	0.0696
joint

Source- Calculated by author

The VAR model of Euro Area imports with foreign exchange reserves and SDR is shown below by estimating the equations were found as before.

X_2t=2045.064+0.5089X_2t-1 – 5.4219X_3t-1+35.880X_4t-1

         (1.06)         (1.449)              (-0.846)           (1.383)

R²=0.794  , F= 12.606

X_3t=214.455 -0.0286X_2t-1+0.1857X_3t-1+0.959X_4t-1

          (1.77)      (-1.298)          (0.4608)           (0.587)

 R²=0.518 , F= 3.58

X_4t=10.852+0.00141X_2t-1-0.0443X_3t-1+0.7107X_4t-1

          (0.812)   (0.580)          (-0.998)           (3.950)*

 R²= 0.857   , F= 20.07* 

AIC=32.95  , SC= 33.501  , loglikelihood=-218.673,*= significant at 5% level.

This is good fit with high R² and F

The Impulse Response Functions of the VAR model showed that they are diverging and the VAR model is unstable. It is shown in Fig-3.

                      Fig-3: Impulse Response Function

     Source- Calculated by author

The unit root circle test assured that the roots of characteristic polynomial lie inside the unit circle and are less than one and satisfies the stability condition.

 Table-5: Roots of characteristic polynomial

Root	modulus
0.978754	0.978
0.407833	0.407833
0.017995	0.017995

Source- Calculated by author.

The unit circle is given below

  Fig-4

Source- Calculated by author

The Doornik-Hansen VAR residual normality test confirmed that the normality is rejected since the component values of skewness, kurtosis and Jarque-Bera are not significant.

Table-6: Doornik-Hansen normality test

Component	Skewness	Chi-sq	df	prob
1	0.352922	0.483147	1	0.4870
2	-0.441389	0.748688	1	0.3869
3	0.085350	0.028714	1	0.8654
joint		1.260549	3	0.7385
component	Kurtosis	Chi-sq	df	prob
1	3.018876	1.455512	1	0.2276
2	3.015199	1.106527	1	0.2928
3	3.644780	5.157678	1	0.0231
joint		7.719717	3	0.0522
component	Jarque-Bera		df	prob
1	1.938659		2	0.3793
2	1.855215		2	0.3955
3	5.186392		2	0.0748
joint	8.980266		6	0.1747

Source- Calculated by author

Conclusion

The paper concludes that the exports and imports of Euro Area have no cointegration with total foreign exchanges reserves showing unstable VAR but these have cointegration with SDR having unstable VAR during the study period of 1999-2013 and SDR have positive impact on international trade.

The shortcoming of the model is that the paper has taken only 15 years for analyzing VAR and cointegration which are too short but we were bound to take such data because the Euro Area and its currency Euro have been started since 1999.

References

[1]Bhowmik,Debesh, 2003, Essays on International Money, Deep and Deep, New Delhi

[2]……………, 2014, The Euro crisis and international liquidity problems, Synergy publications, New Delhi

[3]……………,2014, A new track of reform in international monetary system, D.N.Konar and A.K.Karmakar ed(2014)-Post reforms :Indian Economy, Regal Publications, New Delhi.

[4] Boorman,Jack T. and A.I.Card.,2011, Reform of the international monetary system,Sage Publications,NewDelhi

[5]Enders,Walter.,2011,Applied Econometric Time Series.Wiley Student Edition.

[6]Engle R.F. and Granger C.W.J., 1987, Cointegration and error correction: Representation, estimation and testing. Econometrica, 55:251-276.

[7]Johansen,S., 1988,Statistical Analysis of Cointegrating Vectors. Journal of Economic Dynamics  and Control, Vol. 12,231-254.

[8]……………….,1991,Estimation of Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models, Econometrica  59,Nov,1551-80

[9]……………..,1994, The role of the constant and linear terms in cointegration analysis of nonstationary variables. Econometric Reviews,13, 205-229

[10] Johansen S., 1995, Likelihood-Based Inference in Cointegrated Vector Autoregressive Models. Oxford University Press.

[11]………….,1996, Likelihood-Based Inference in Cointegrated Vector Autoregressive Models,2^nd edition, Oxford University Press.

[12]……….. and K.Juselius.,1990,Maximum likelihood estimation and cointegration with application to the demand of money. Oxford Bulletin of Economics and Statistics,52(2),169-210.

[13]Johnson.A.,2006,The effects of FDI inflows on Host Country Economic growth. CESIS Electronic working paper series no-58.

[14]I.M.F.- International Financial Statistics-Various Years.

[15] Ocampo,Jose Antonio.,2010,Building an SDR based Global Reserve system,Journal of Globalisation and Development,No-2.

[16] Williamson,John.,2005, Revamping the International Monetary System.Institute for International Economics.

GREEN FREIGHT AND LOGISTICS IN ASIA

The demand for freight and logistics in Asia and the Pacific is expected to grow significantly in the coming years, and it will continue to play a large role in driving economic growth and alleviating poverty in the region. Within the transport sector,
freight and logistics account for a significant portion of total energy use – in many countries upward of 40% – and a correspondingly large share of CO2 emissions.
The promotion of efficient, environmentally sustainable and safe freight transport is an issue of substantial importance and urgency.
Based on the need for countries across Asia and the Pacific to address this issue, the Asian Development Bank (ADB) and Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) organized this first regional workshop to take stock of best
practices and systematic approaches towards efficient logistics and green freight.
The event, held 25-27 June 2014 in Singapore, aimed to give recognition to and provide a platform for the exploration of the multiple initiatives currently being developed at different levels of governance (local, national, sub-regional and regional) and by actors from different sectors (public, private, NGOs).
The workshop was arranged as a mix of predefined presentations by global contributors to the green freight and logistics discussion, as well as through facilitated peer learning and exchanges among the key stakeholders active in freight and logistics. Specifically, it aimed to (i) to foster discussion on the potential and benefits of green freight policies in the region; (ii) to identify opportunities that can
be developed into actions and projects; (iii) to shape a broad work plan for national activities; and (iv) to identify training needs according to select target groups (thus establishing a knowledge base in support of green freight and logistics programs in the region). The outputs of the workshop will also provide inputs to the ASEAN Working Groups on Land Transport and Transport Facilitation as well as the United Nations Center of Regional Development (UNCRD)-led initiative to develop a Regional
Framework on Green Freight and Logistics.This document is meant to summarize the workshop, draw out the key messages from each session, and includes a short synopsis of the group work that was
undertaken during the workshop. It also provides recommendations for future action, by ADB and GIZ.

Panel discussions: Green freight and logistics in Asia: Current trends and future pathways
The second panel discussion built on the lessons learned from the first panel to describe the current state of affairs in green freight and logistics in Asia. The panel was chaired by Ms. Glynda Bathan, Deputy Executive Director of Clean Air Asia, who set the stage by requesting panelists to describe steps taken by the stakeholders to make the freight and logistics sector greener, identify “What” are
the key drivers that can facilitate this development; and on “Opportunities” and “Barriers”.The discussion began with a short speech by Mr. Tran Anh Duong, DeputyDirector General of the Ministry of Transport, Viet Nam. He noted that emissions
from transport are not well regulated in Asia in general, and Southeast Asia in particular. With around one million trucks in the country, monitoring stations indicate that levels of PM10 are 1.5 – 2 times greater along the ring road in Hanoi and Ho Chi Min City than legal standards permit. Viet Nam has designed a National
Green Growth Strategy that highlights tasks for the freight sector in the comingyears:
• Increase energy efficiency
• Utilize alternative or renewable fuels
• Implement coastal shipping and inland waterway transport.
Next, Mr. Karmjit Singh, Fellow and Chairman of the Chartered Institute of Logistics and Transport (CILT) Singapore, noted that even in Singapore, the industry is always learning. Singapore is a logistics hub that developed from nothing to become one of
the most efficient airport and seaports ranking globally, not only serving regional but also global trade. In order to improve building energy efficiency, Singapore has implemented incentives for green buildings, with a goal of 80% of buildings being “green” by 2020. The city also needs to deal with new traffic patterns related to movement of the port by 2025, and terminal 5 of the airport constructed around the same time.
Mr. Singh concluded that there are challenges related to the human aspect of freight and logistics. Habits are hard to change, even with incentives. Furthermore, companies and governments look at freight as trucks moving, rather than as drivers driving trucks. Without recognizing the importance of drivers, they are unlikely to
take a role in better driving and emissions reduction.

Mr. Thibodee Harnparasert, Executive Advisor of the Federation of Thai Industries (FTI) then described some of the green freight work being undertaken in Thailand. Thailand has about one million trucks, 140,000 buses, 6.5 million passenger cars, and 6 million diesel pickup trucks. Transport alone consumed 36% of energy in the country, 80% of that from inland transport. In order to deal with this situation, the FTI proposed to government an energy
efficiency program in logistics and transport management, which trained over 2500 professionals from 240 companies in four areas:
• Management: e.g. standardizing maintenance, utilizing GPS and RFID, and shortening loading cycles and space utilization
• Engineering and technologies: e.g. tires can reduce fuel consumption 3-5% if tire pressure is managed
• Middle management: e.g. understanding what load is suitable for what trucks
• Driving: e.g. implementation of daily check sheets, idle time reduction, and being familiar with normal operating performance of the vehicle – not to mention being alert and sober. The final effort discussed by Mr. Harnparasert was that by utilizing GPS and
monitoring truck movement, and standardizing data for logistics, load optimization and back loading could increase significantly. Industry is generating data through this system for decision making by government. The last speaker in the second panel was Mr.
Stephan Schablinski, Executive Director of Green Freight Asia, who advocated that the private sector is not just part of the problem rather, it can be an important part of the solution to freight emissions. He noted that 90% of trucks in Asia and the Pacific are
owned by individual drivers, and only 0.1% are owned by big fleets. Most companies don’t have the capacity to work in terms of
GHG emissions and other global issues related to their work, and therefore economic incentives are necessary. Companies want to
reduce costs and improve profits through more sustainable operations. At the same time, manufacturers would like to choose
greener carriers, but they have little information on which carriers are greener.Green Freight Asia aims to use a methodology to identify companies that are acting to improve their emission profiles, or have committed to act. The organization also
aims to develop a clear-cut matrix of technologies for trucking companies that can help quickly identify which technologies are appropriate for their businesses.
Key discussion points During the panel discussion, several key points were raised:
• Asian countries face a huge challenge in enforcement of fuel and vehicle standards. The reality is that governments need to become more committed to fuel quality standards enforcement, and need to financially support truck owners to upgrade their fleets. Singapore is leading in technology standards for trucks, aiming to shift to Euro VI emission standards by 2017, but challenges remain with cross-border traffic, in a context where each country has its own
standards
• In order to promote its private sector backed green freight labeling scheme, Green Freight Asia requires assistance from intermediaries in each country, and in many cases needs help in providing balanced information in local languages on technologies appropriate for trucks