Cointegration analysis of selected currency pairs traded in Indian foreign exchange market

The main purpose of this research paper is to explore and understand the nature of association and the possible existence of a short run and long run relationship between US Dollar, EURO, British Pound and Japanese Yen.

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Cointegration analysis of selected currency pairs traded in Indian foreign exchange market

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Nội dung Text: Cointegration analysis of selected currency pairs traded in Indian foreign exchange market

- International Journal of Management (IJM)

Volume 11, Issue 5, May 2020, pp. 476-485, Article ID: IJM_11_05_045

Available online at http://www.iaeme.com/ijm/issues.asp?JType=IJM&VType=11&IType=5

Journal Impact Factor (2020): 10.1471 (Calculated by GISI) www.jifactor.com

ISSN Print: 0976-6502 and ISSN Online: 0976-6510

DOI: 10.34218/IJM.11.5.2020.045© IAEME Publication Scopus Indexed

COINTEGRATION ANALYSIS OF SELECTED

CURRENCY PAIRS TRADED IN INDIAN

FOREIGN EXCHANGE MARKET

Rajesh Sadhwani

Assistant Professor, Indukaka Ipcowala Institute of Management, CHARUSAT

Off. Nadiad-Petlad Highway, Changa 388 421, Anand, Gujarat, India.ABSTRACT

The main purpose of this research paper is to explore and understand the nature

of association and the possible existence of a short run and long run relationship

between US Dollar, EURO, British Pound and Japanese Yen. To find out the

relationship among currencies USD/INR, EUR/INR, GBP/INR and JPY/INR pairs are

considered. The main idea is to know how these selected indicators are related to

each other. The daily basis 2781 observations for all four variables from year 2007 to

2018 are taken into consideration. Data are collected from website of Reserve Bank of

India. The stationarity of time series is checked and differentiated as per requirement.

Johansen cointegration test to know the long run relationship between variables is

used. The result shows that there is no cointegration equation among the variables.

The short run relationship is examined with help of Vector Autoregression (VAR)

model and the short run relationship within different lags of variables has been

identified. The correlation among variables has been examined with help of

correlation matrix and Granger cause test is also used to understand the causal effect.

Key words: Cointegration, Vector Autoregression, Correlation, Currencies

Cite this Article: Rajesh Sadhwani, Cointegration Analysis of Selected Currency

Pairs Traded in Indian Foreign Exchange Market. International Journal of

Management, 11 (5), 2020, pp. 476-485.

http://www.iaeme.com/IJM/issues.asp?JType=IJM&VType=11&IType=51. INTRODUCTION

Considering the high volatility in currency markets it becomes very important to understand

the relationship between the different currencies. All currencies offers different level of risk

and return in certain market conditions, it performs differently from each other. This study

proposes to explore the relationship between the currencies. The paper tries to understand the

long run and short run relationship between the variables. Study considers the selected major

currencies traded in Indian financial markets. We have considered the selected four currencies

considering the ease of trading on exchanges, liquidity etc.. to all the type of hedgers, traderhttp://www.iaeme.com/IJM/index.asp 476 editor@iaeme.com

- Rajesh Sadhwani
such as corporates, high net worth individuals, domestic and foreign institutional investors

and all other market participants. The performance of the variables can be understood with the

help of following graph.

110100

90

80

70

60

50

40

30

07 08 09 10 11 12 13 14 15 16 17 18EURO GBP USD YEN

Figure 1 Trend of EURO/INR, GBP/INR, USD/INR and JPY/INR pairs

All the four currencies are in uptrend for the period of the study or in other words it shows

the poor performance of Indian rupee. Indian rupee had persistently fallen against the major

currencies in the world. Most of the currencies of developed economies have appreciated

against Indian rupee while Indian rupee has failed to do so. US dollar has appreciated against

all the major currencies in the world post 2007-08 financial crisis in US. US dollar index

DXY is noted on all time high level nearly 100 against the basket of six major currencies

including Canadian dollar (CAD), Swedish krona (SEK) and Swiss franc (CHF) in other three

currencies. Hence understanding the relationship between major currencies against Indian

rupee is very important. The outcome of the study will be especially helpful to pair traders,

arbitragers and banks dealing in Indian foreign exchange markets.2. LITRATURE REVIEW

To understand and examine the long run relationship between the variables cointegration test

is been used by many central bankers and researchers. Jose A. Lopez (1999) examined the

cointegration between the foreign exchange rates for different time period; he concluded the

relationship between currencies changes over a period of time and relationship is also affected

by central banks activities. Cointegration analyses of exchange rates in foreign exchange

market were also checked by Chinese researcher between selected pair of currencies. He

identified the long and short run relationship among them. Recursive cointegration analysis

was used to examine the relation between foreign exchange markets by Mei-Se Chien and

other two researchers. Gupta and Agarwal in 2011 examined the correlation between the

Indian stock market and five other major Asian economies and found a weak correlation

among the stock exchanges. This proved to have benefits of diversification to institutional and

international investors. Similarly Sharma and Bodla in 2011 studied the linkages between

Indian, Pakistan and Sri Lankan stock exchanges. The outcome suggested that Indian stock

exchange Granger cause the Karachi Stock Exchange of Pakistan and the Colombo Stock

Exchange of Sri Lanka. In similar fashion Ismail Aktar has done a study on Co-movementhttp://www.iaeme.com/IJM/index.asp 477 editor@iaeme.com

- Cointegration Analysis of Selected Currency Pairs Traded in Indian Foreign Exchange Market
between Stock Markets of Turkey, Russia and Hungary. The study investigated the long run

relationship and Granger Causality between Turkish, Russian and Hungarian stock indices for

the period of January 5, 2000 to October 22, 2008. A study was done by Michalis Glezakos,

Anna Merika & Haralambos Kaligosfiris on, “Interdependence of Major World Stock

Exchanges: How is the Athens Stock Exchange Affected?” The paper investigates and

examines the short and long-run relationships between major world financial markets with

particular attention to the Greek stock exchange. Paper covered the data from 2000 to 2010

using monthly data. Murali Batareddy in 2012, Hoque in 2007 and Ibrahim 2005 found that

the US market has an impact on the Asian markets. Sam Agyei Ampomah in 2011 examined

the nature and extent of linkages among African stock markets and the relationships between

the regional and global stock markets. Prof. Ritesh Patel paper published in 2012 examined

the causal relationship among equity markets to better understand how shocks in one market

are transmitted to other markets. He also examined the causal relationship, comovement and

dependency among equity markets to understand shocks in one market are transmitted to

other markets. More recently in 2015 Thangamuthu Mohanasundaram and Parthasarathy

Karthikeyan examined thelong run and short run relationship between stock-market indices of

South Africa, India and the USA. The paper applied the granger cause test. After testing the

Granger cause relationship, the existence of a long run and short run relationship is tested.

The long run relationships among the stock market indices were analysed, following the

Johansen and Juselius multivariate cointegration test. The outcome suggested the absence of

cointegrating equation among variables. The vector auto regression suggested the short run

relationship among variables.3. RESEARCH METHODOLOGY

3.1. Research Gap

Most of the research is carried out to study the relationship between different assets classes

and currencies of various developed countries etc… Hence we have examined the relationship

between endogenous variables in Indian context. This will be helpful for all types of

participants in Indian forex market.3.2. Objectives

The main objective of the research is to understand the long run and short run relationship

between variables, also to check if any of it is useful to forecast other variables within the

group.3.3 Sample and Data Collection

The research is carried out through secondary data sources. The data for USD/INR,

EUR/INR, GBP/INR and JPY/INR are collected Reserve bank of India and Indian

government websites and some and some other authenticated sources are used for data

collection. The 2781 observations based on closing price from January 2007 to January 2018

were taken into consideration.3.4. Research Tools & Techniques

3.4.1. Unit Root Test

The unit root test is used to examine the stationarity and non-stationarity of time series. The

presence of the unit root test in all four variables is checked with the help of Augmented

Dickey Fuller (ADF) test.http://www.iaeme.com/IJM/index.asp 478 editor@iaeme.com

- Rajesh Sadhwani
3.4.2. Linear Correlation

The linear correlation shows the association between two variables. Correlation analysis is

used to understand how two variables move in relation to each other.3.4.3. Granger Causality Test

Granger cause test is used to examine the causality between two variables in time series. The

test check the particular variable come before another, Hence this help in determining whether

a time series X is helpful in forecasting another Y.3.4.4. Cointegration Test

If time series variables are integrated of order d, and linear combination of those variables is

integrated of order less than d, then the collection is said to be cointegrated. It means if two or

more series are individually integrated but the linear combination of them has a lower order of

integration then series are said to be cointegrated.3.4.5. Vector Autoregression Model

Vector autoregressin explores interrelationship between the endogenous variables. The

relationship between the variables depend on previous change in one variables on current

change. Model includes lagged values of the existing variables as repressor. This allows for

estimating not only the instantaneous effects but also dynamic effects in the relationships up

to n lags.4. RESULTS AND DISCUSSION

During the period of January 2007 to January 2018 there were no outliers identified, in Table

1 summary statistics results for Mean, Median, Standard deviation, minimum and maximum

are computed for closing prices.Table 1 Summary Statistics, using the observations 2007 to 2018

EURO/INR GBP/INR USD/INR JPY/INR

Mean 69.5446 85.1660 54.6242 54.6219

Median 69.7000 83.9990 54.3885 55.7600

Standard Deviation 7.8119 9.7946 9.4299 9.1135

Kurtosis 2.3362 2.0652 1.5005 3.1100

Skewness 0.0755 0.2176 -0.0509 -0.7608

Minimum 54.32 65.64 39.27 32.69

Maximum 91.46 106.02 69.05 72.12

Sum 193403.5 236846.7 151910.0 151903.6

Count 2781 2781 2781 27814.1. Stationarity Check of Data

Time series diagram is firstly used for all four currency pairs data based on closing price for

January 2007 to January 2018. The clear non stationary trend can be identified from figure 2

(a) the fluctuation trend breaks the hypothesis of weaker stationary. In Box-Jenkins method, a

first order defferencing is computed for the time series data. The time plot of the same

differencing data is shown in figure 2(b) The differencing data shows the stationarity of the

data and hence the value of d(I) is 1.http://www.iaeme.com/IJM/index.asp 479 editor@iaeme.com

- Cointegration Analysis of Selected Currency Pairs Traded in Indian Foreign Exchange Market
EURO GBP

100 11090 100

80 90

70 80

60 70

50 60

07 08 09 10 11 12 13 14 15 16 17 18 07 08 09 10 11 12 13 14 15 16 17 18USD YEN

70 8065

70

6055 60

50 50

45

40

4035 30

07 08 09 10 11 12 13 14 15 16 17 18 07 08 09 10 11 12 13 14 15 16 17 18Figure 2 (a) Time plot of EURO/INR, GBP/INR, USD/INR and JPY/INR pairs at level

DEURO DGBP

4 43 2

2

0

1

-2

0

-4

-1-2 -6

-3 -8

07 08 09 10 11 12 13 14 15 16 17 18 07 08 09 10 11 12 13 14 15 16 17 18DUSD DYEN

3 43

2

2

1 10 0

-1

-1

-2-2 -3

07 08 09 10 11 12 13 14 15 16 17 18 07 08 09 10 11 12 13 14 15 16 17 18Figure 2 (b) Time plot of first order differencing on EURO/INR, GBP/INR, USD/INR and JPY/INR

pairs

Further on, unit root also has been tested using Augmented Dickey Fuller test ADF test.

The output of the same has been shown below,http://www.iaeme.com/IJM/index.asp 480 editor@iaeme.com

- Rajesh Sadhwani
Table 2 Result of Augmented Dickey Fuller Unit Root Test

At Level At First Difference

Variable Result

T Value Probability T Value Probability

EURO -1.5448 0.5108 -51.4368 0.0001* I(1)

GBP -1.5308 0.5179 -51.4995 0.0001* I(1)

USD -0.3969 0.9074 -39.3598 0.0000* I(1)

YEN -1.9298 0.3187 -53.4397 0.0001* I(1)

*Rejection of null hypothesis at 5 per cent and therefore data series is stationary

The unit root is present at the level of existing series and series is non-stationary in nature,

but the series is found to be stationary at first level of difference as suggested in above table.

The p value of both constant and constant & trend is below 0.05 at first level of difference.

Hence first order of differencing is considered to make the series stationary.Table 3 Correlation Matrix

EURO GBP USD YEN

EURO 1.0000 – – –

GBP 0.7520 1.0000 – –

USD 0.8199 0.7058 1.0000 –

YEN 0.6512 0.2747 0.6431 1.0000

Most of the currencies have positive correlation with each other GBP and EUR have a

positive association 0.7520, USD and GBP has also also strong positive correlation 0.7058

between them indicates both variables are following almost same trend over applicable period

of time. Also there is positive correlation between USD and EUR 0.6431. Overall there is

positive association among these variables. Here it can be observed that all the variables are in

positive correlation and following almost same trend over a given period of time. However

the value of all foreign currencies are against Indian rupee, hence possible effect of

weakening Indian rupee against all the major currencies can not be rejected.

The positive correlation among the variables leads to the further examination of variables, to

know if one variable is useful to predict the present or future value of the other variable. This

is evaluated with the help of the Granger causality test. The Granger causality test is highly

sensitive to the order of the lags selection. The optimal levels of lags are selected through

VAR lag order selection criteria.Table 4 VAR Lag order selection

Lag LogL LR FPE AIC SC HQ

0 -36031.44 – 2277781 25.99022 25.99877 25.99331

1 -4558.96 62831.46 0.000319 3.302535 3.345288* 3.317975*

2 -4541.46 34.8860 0.000319* 3.301453* 3.378408 3.329245

*Indicates the lag order selected by the criterion

LR: sequential modified LR test statistic (each test at 5% level)

FPE: final prediction error

AIC: Akaike information criterion

SC: Schwarz information criterion

HQ: Hannan-Quinn information criterion

Optimal Lag selection based on AIC = 2http://www.iaeme.com/IJM/index.asp 481 editor@iaeme.com

- Cointegration Analysis of Selected Currency Pairs Traded in Indian Foreign Exchange Market
The optimal level of lag length is 2 and it is based on the lowest value of Akaike

informationi criterion (AIC) which is 3.3014 in our case. It is also supported by FPE values,

which are lowest at lag 2, suggest the same level of lags.Table 5 VAR Granger Causality / Block xxogeneity Wald test

Null Hypothesis Observations F-Statistics P-value Decision on H0

GBP does not Granger cause EURO 0.2524 0.7770 Not Rejected

2779

EURO does not Granger cause GBP 0.8826 0.4138 Not Rejected

USD does not Granger cause EURO 1.2721 0.2804 Not Rejected

2779

EURO does not Granger cause USD 3.6093 0.0272 Rejected

YEN does not Granger cause EURO 0.8088 0.4455 Not Rejected

2779

EURO does not Granger cause YEN 2.7117 0.0666 Not Rejected

USD does not Granger cause GBP 1.2965 0.2736 Not Rejected

2779

GBP does not Granger cause USD 4.1128 0.0165 Rejected

YEN does not Granger cause GBP 3.3842 0.0340 Rejected

2779

GBP does not Granger cause YEN 0.9474 0.3879 Not Rejected

YEN does not Granger cause USD 1.1613 0.3132 Not Rejected

2779

USD does not Granger cause YEN 0.9260 0.3962 Not Rejected

Granger causality test results shows that null hypothesis EURO does not Granger cause

USD, GBP does not granger cause USD and YEN does not granger cause GBP are rejected,

whereas all other hypothesis are not. This indicates that EURO can be used to forecast USD,

GBP can be used to forecast USD and YEN causes GBP. The correlation and Granger

causality can be further verified for long run movement among the variables by cointegration

test. Time series data for the all variables are non-stationary at the level but stationary at first

order difference. Johansen cointegration test is applied to know the long run relationship

between the variables.Table 6 Johansen Cointegration Test

Unrestricted Cointegration Rank Test (Trace)

Hypothesized No. 0.05 critical

Eigenvalue Trace Statistics Prob.**

of CE(s) value

None 0.0039 21.1410 47.8561 0.9834

At most 1 0.0021 10.2146 29.7970 0.9770

At most 2 0.0014 4.1778 15.4947 0.8887

At most 3 6.1000 0.1694 3.8414 0.6806

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized No. 0.05 critical

Eigenvalue Trace Statistics Prob.**

of CE(s) value

None 0.0039 10.9263 27.5843 0.9673

At most 1 0.0021 6.0368 21.1316 0.9822

At most 2 0.0014 4.0083 14.2646 0.8584

At most 3 6.1000 0.1694 3.8414 0.6806

**MacKinnon, Hug and Michelis (1999) p-value

Cointgration Rank Test at 0.05

Trace No cointegration equation

Maximum Eigen Value No cointegration equation

Johansen cointegration test indicates there is no cointegration equation between the

variable. The same is supported by trace statistics and Maximum Eigenvalue of rank test as

shown in above table. Hence there is no long run relationship between the variables. As therehttp://www.iaeme.com/IJM/index.asp 482 editor@iaeme.com

- Rajesh Sadhwani
is no cointegration between the variables, to understand the short run relationship vector

autoregressin (VAR) can be used.Table 7 Vector Autoregression Estimates

Variable / Lag Parameter EURO GBP USD YEN

Coefficient 1.0040 0.0285 0.0257 0.0496

Standard error (0.0256) (0.0327) (0.0153) (0.0270)

EURO(-1)

t-statistics [39.1038] [0.8719] [1.6834] [1.8337] p-value 0.0000* 0.3833 0.0923 0.0667

Coefficient -0.0074 -0.0254 -0.0228 -0.0499

Standard error (0.0256) (0.0327) (0.0153) (0.0270)

EURO(-2)

t-statistics [-0.2884] [-0.7781] [-1.6222] [-1.8487] p-value 0.7730 0.4365 0.1048 0.0645

Coefficient 0.0090 1.0224 0.0181 -0.0050

Standard error (0.0198) (0.0252) (0.0118) (0.0208)

GBP (-1)

t-statistics [0.4561] [40.5472] [1.5381] [-0.2414] p-value 0.6483 0.0000* 0.1240 0.8092

Coefficient -0.0097 -0.0265 -0.0181 0.0042

Standard error (0.0197) (0.0252) (0.0118) (0.0208)

GBP (-2)

t-statistics [-0.4921] [-1.0537] [-1.5371] [0.2047] p-value 0.6227 0.2920 0.1243 0.8378

Coefficient 0.0195 0.0149 1.0041 0.0423

Standard error (0.0442) (0.0563) (0.0264) (0.0466)

USD(-1)

t-statistics [0.4426] [0.2654] [38.0268] [0.9090] p-value 0.6581 0.7907 0.0000* 0.3634

Coefficient -0.0175 -0.0139 -0.0049 -0.0413

Standard error (0.0442) (0.0563) (0.0263) (0.0466)

USD(-2)

t-statistics [-0.3955] [-0.2476] [-0.1870] [-0.8877] p-value 0.6924 0.8044 0.8516 0.3747

Coefficient 0.0140 -0.0719 -0.0283 0.9506

Standard error (0.0242) (0.0390) (0.0144) (0.0255)

YEN(-1)

t-statistics [0.5773] [-2.3270] [-1.9597] [37.1858] p-value 0.5637 0.0200* 0.0501* 0.0000*

Coefficient -0.0137 0.0709 0.0282 0.0471

Standard error (0.0242) (0.0309) (0.0144) (0.0255)

YEN(-2)

t-statistics [-0.5669] [2.2968] [1.9508] [1.8460] p-value 0.5708 0.0216* 0.0511* 0.0649

Coefficient 0.1719 0.1323 -0.0072 0.1654

Standard error (0.0923) (0.1176) (0.0550) (0.0972)

C

t-statistics [1.8622] [1.1254] [-0.1317] [1.7010] p-value 0.0626 0.2604 0.8952 0.0890

*Significant at 5 per cent level

The vector auto regression estimates shows that all four variables EURO, GBP, USD and

YEN are function of their own lags EURO(-1), GBP(-1), USD(-1) and YEN(-1). Apart from

it GBP EURO can be defined with 1st and 2ns lag lags of YEN, While USD can be defined

with 1st and 2nd lag of YEN along with its own lag. While the YEN cannot be influenced with

any of the variables, it is the function of its own lag YEN(-1) only. EURO cannot be defined

by any of these variables in short run, even though it is highly correlated with USD.http://www.iaeme.com/IJM/index.asp 483 editor@iaeme.com

- Cointegration Analysis of Selected Currency Pairs Traded in Indian Foreign Exchange Market
5. CONCLUSION OF THE STUDY

All the variables found to be non-stationary in nature. Hence we have made time series

stationary, using first order difference. There is a strong positive correlation among the

variables. There is strong positive correlation between USD/INR and EURO/INR and

GBP/INR and comparatively weaker positive correlation with YEN/INR. The correlation

direction is further verified using Granger Causality test; this shows that EURO granger cause

on USD, GBP can be used to forecast USD and YEN granger cause causes GBP at 5 per cent

level. Johansen cointegration test shows that there is no cointegration equation between the

variables hence there is no long run relationship between variables. We have also examined

the short run relationship between the variables using vector auto-regression test, this

indicates there is short run relationship between the variables with different legs of variables.

Hence we conclude that regardless of strong correlation between variables USD, EURO and

YEN there is no long run relationship between all four variables. All the four variables are

independent in the long run.REFERENCES

[1] Dickey, D.A. & Fuller W.A. (1979). Distribution of the estimators for autoregressive time

series with a unit root. Journal of the American Statistical Association, 74(366), 427-431.

[2] Johansen, S. (1988). Statistical analysis of cointegrating vectors. Journal of Economic

Dynamics and Control, Vol. 12 pp. 213-254.

[3] Gonzallo, J. & Granger, C.W.J. (1995). Estimation of common long-memory components

in cointegrated systems. Journal of Business and Economic Statistics, 13(1), pp. 27-35.

[4] Joze A. Lopez (2005). Exchange Rate Cointegration Across Central Bank Regime Shifts.

Research in Finance, Vol. 22, pp. 327-356.

[5] Mei-Se Chien, Te-Chung Hu and Meng-Huei, Su (2014). Recursive cointegration analysis

of foreign exchange market stability: An application for ASEAN countries. International

Journal of Science Commerce and Humanities, Vol-2, Iss 2, pp 39-54

[6] Rajesh Sadhwani and Tanvi Pathak (2019). A study and forecast of MCX comdex

commodity index using arima model. International Journal of Research in Engineering

and Management, Vol-05, Iss 5, pp. 211-216

[7] Hoque, Hafiz Al Asad Bin (2007). Co-movement of Bangladesh stock markets:

Cointegration and error correction approach. Managerial Finance, 33(10), pp. 810-820.

[8] Rajiv Meno, N. Subha, M.V. & Sagaran S. (2009). Cointegration of Indian stock markets

with other leading stock markets. Studies in Economics and Finance, 26(2), pp. 87-94.

[9] Raja Sethu Durai, & Bhadurai. (2011). Correlation dynamics in equity markets-

evidencefrom India. Research in International Business and Finance, Elsevier, 25(1), pp.

64-74. Available at: http://ideas.repec.org/a/eee/riibaf/v25y2011i1p64-74.html [accessed

2020-03-17].

[10] Sowmya Dhanaraj. Goplaswamy, A.K. & Suresh Babu M. (2013). Dynamics

interdependence between US and Asian markets: An empirical study. Journal of Financial

Economic policy, 5(2), pp. 220-237.

[11] Valadkhani, A. & Chancharat, S. (2008), Dynamic linkages between Thai and

international stock markets. Journal of Economic Studies, 35(5), pp. 425-441.

[12] Chakrabarty, A., & Ghosh, B.K. (2011). Long run financial market cointegration and its

effect on international portfolio diversification. Indian Journal of Finance, 5(4), pp. 27-37http://www.iaeme.com/IJM/index.asp 484 editor@iaeme.com

- Rajesh Sadhwani
[13] Qadan, M., & Yagl, J. (2015). Are international economic and financial co-movement

characterized by asymmetric co-integration?” Review of Accounting and Finance 14(4),

398-412. DOI: http://doi.org/10.1108/RAF-02-2015-0026

[14] Vohra, P.S. (2016). A study of co-movement among indices of Bombay stock exchange.

Indian Journal of Finance, 10(9), 11-29. DOI:10.17010/ijf/2016/v10i9/101476

[15] Patel, R. J. (2017). Co-movement and integration among stock markets: A study of 14

countries. Indian Journal of finance, 11(9), 53-66. DOI:10.17010/ijf/2017/v11ip/118089

[16] Data source www.rbi.org.in,http://www.iaeme.com/IJM/index.asp 485 editor@iaeme.com