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This study an attempt to examine the long-run volatility and causality effects of Sri Lankan (LKR) currency and nine currency of emerging countries in Asia against USD over 17 years i.e., from 01st January, 2002 to 31st December, 2018 by using the Descriptive Statistics (Summary), GARCH (1,1) Model, Correlation and Granger Causality Test.

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  1. International Journal of Management (IJM)
    Volume 11, Issue 2, February 2020, pp. 191–208, Article ID: IJM_11_02_021
    Available online at http://www.iaeme.com/ijm/issues.asp?JType=IJM&VType=11&IType=2
    Journal Impact Factor (2020): 10.1471 (Calculated by GISI) www.jifactor.com
    ISSN Print: 0976-6502 and ISSN Online: 0976-6510

    © IAEME Publication Scopus Indexed

    EXCHANGE RATE VOLATILITY AND
    CAUSALITY EFFECT OF SRI LANKA (LKR)
    WITH ASIAN EMERGING COUNTRIES
    CURRENCY AGAINST USD
    Kasilingam Lingaraja
    Assistant Professor, Department of Business Administration
    Thiagarajar College (Autonomous), Madurai -09, India

    C. Jothi Baskar Mohan
    Associate Professor & Head, Department of Business Administration
    Thiagarajar College (Autonomous), Madurai -09, India

    Murgesan Selvam
    Professor & Head, Department of Commerce and Financial Studies
    Bharathidasan University, Trichy – 24, India

    Mariappan Raja
    Assistant Professor, Department of Business Commerce
    Bharathidasan University Constituent College, Lalgudi, Trichy.India

    Chinnadurai Kathiravan
    Research Scholar, Department of Commerce and Financial Studies
    Bharathidasan University, Trichy – 24, India

    ABSTRACT
    This study an attempt to examine the long-run volatility and causality effects of Sri
    Lankan (LKR) currency and nine currency of emerging countries in Asia against USD
    over 17 years i.e., from 01st January, 2002 to 31st December, 2018 by using the
    Descriptive Statistics (Summary), GARCH (1,1) Model, Correlation and Granger
    Causality Test. A descriptive statistics and Graphical model were specified and
    empirical results show a significant currencies movements and the Granger causality
    test indicates the strong evidence that the causation runs between Sri Lankan currency
    (LKR / USD) to nine Asian emerging countries currency price behavior against USD.
    The purpose of the study is to make a finer point with respect to relationship, volatility
    and causality effect between the Sri Lankan currency and Asian Emerging countries

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  2. Kasilingam Lingaraja, C. Jothi Baskar Mohan, Murgesan Selvam

    currency returns against USD. It is found that the significant uni-directional causality
    effects and relationships among the sample currency data series with LKR against USD.
    Hence, this result would help to international portfolio managers, multinational
    corporations, and policymakers for decision-making in the Asian region.
    Keywords: Foreign Exchange Market, Granger Causality, Correlation, Exchange Rate
    Volatility, Asian Emerging Countries and Sri Lanka (LKR/USD)
    Cite this Article: Kasilingam Lingaraja, C. Jothi Baskar Mohan, Murgesan Selvam,
    Mariappan Raja and Chinnadurai Kathiravan, Exchange Rate Volatility And Causality
    Effect Of Sri Lanka (Lkr) With Asian Emerging Countries Currency Against Usd,
    International Journal of Management (IJM), 11 (2), 2020, pp. 191–208.
    http://www.iaeme.com/IJM/issues.asp?JType=IJM&VType=11&IType=2
    JEL Classifications: C50; C58; F31; R15; O34

    1. INTRODUCTION
    Exchange rate volatility has been a constant feature of the International Monetary System ever
    since the breakdown of the Bretton Woods system of fixed parities in 1971 (Black, F and
    Scholes, M (1973). Many theories were that a change in the exchange rates would affect a
    firm’s foreign operation and overall profits. It is widely acknowledged that international
    financial markets and exchange rate value of countries currency have become substantially
    integrated in recent years. On the one hand, the collapse of the Bretton Woods system was
    followed by greater exchange rate fluctuations. On the other, the liberalization of markets and
    capital flows in the 1990s was followed by a huge increase in the volume of cross border
    transactions in both securities and currencies. Liu et al. (2019) & Lingaraja et.al. (2014 &
    2015) denotes that the merchandise trade and portfolio investment are most helpful in
    increasing the direct use of currency, while foreign direct investment (FDI) has a stronger effect
    on promoting vehicle use. Kathiravan et al., 2019, investigated the Causal effect among the
    three weather factors (temperature, humidity, and wind speed) and the returns of the Agriculture
    Commodity Index called Dhaanya, in India. Hence, the volatility and causality effect of foreign
    exchange markets has been a topic of interest of academic researchers and practitioners alike.

    1.1. THE CONCEPTUAL FRAMEWORK
    i) FRONTIER: It is a type of developing country which is more developed than the least
    developing countries, but too small, risky, or illiquid to be generally considered an emerging
    market. The term is an economic term which was coined by International Finance Corporation’s
    Farida Khambata in 1992. The frontier, or pre-emerging equity markets are typically pursued
    by investors seeking high, long-run return potential as well as low correlations with other
    countries economic variables. Some frontier market countries were emerging position in the
    past, but have regressed to frontier status. Frontiers are countries that because of demographics,
    development, politics and liquidity are considered less mature than Emerging countries
    (Source: MSCI)
    ii) EMERGING: The concept of “Emerging”, used in the beginning of the 1980s, was
    initially developed to designate financial markets located in developing countries. The tem
    “Emerging Markets” was first coined by World Bank economist, Antoine W. Van Agtmael,
    1981, to refer to nations undergoing rapid economic growth, currency value, and
    industrialization. The term is often used interchangeably with ’emerging and developing
    economies and describe it as economies with low to middle per capita income (Economy
    Watch, 2010). The emerging countries are differentiated from developed, with respect to
    several qualitative characteristics, such as institutional infrastructure, taxation of dividends and
    capital gains, capital controls, market regulations, currency value and available information

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  3. Mariappan Raja and Chinnadurai Kathiravan, Exchange Rate Volatility And Causality Effect Of Sri
    Lanka (Lkr) With Asian Emerging Countries Currency Against Usd

    flows. The quality of these factors is generally lower for emerging countries than for the
    developed. These conditions affect, to a large extent, trading activity, price formulation, and as
    a result, the risk-return properties of emerging countries stock markets (Mohamed E1 Hedi
    Arouri et al., 2010).
    iii) DEVELOPED: It is a country that is most developed in terms of its economy, currency
    and capital markets. The country must have high income, but this also includes openness to
    foreign ownership, ease of capital movement, and efficiency of market institutions. As well,
    they have highly developed capital and money markets with high levels of liquidity, meaningful
    regulatory bodies, large market capitalization, and high levels of per capita income. (Source:
    MSCI).
    According to the criteria adopted by the Morgan Stanley Capital International (MSCI), the
    world countries are classified under three categories such as Developed, Emerging and Frontier
    are grouped into three regional classification by continent wise i.e., 1) Americas, 2) Europe,
    Middle East & Africa and 3) Asia.
    It is clear that there are five counties under developed markets categories in Asia, Nine
    countries under emerging markets categories in Asia and eight countries under frontier markets
    categories in Asian continent. The list of Asian countries under three category of classification
    by MSCI is given in Figure – 1

    Source: Morgan Stanley Capital International (MSCI) http://www.msci.com as on 30.07.2019.

    Figure – 1: List of Countries in the Asian Region under Frontier, Emerging and Developed Categories

    http://www.iaeme.com/IJM/index.asp 193 editor@iaeme.com

  4. Kasilingam Lingaraja, C. Jothi Baskar Mohan, Murgesan Selvam

    2. LITERATURE REVIEW
    Yamani, E (2019), investigated the diversification role of currency momentum for carry trade
    crashes during the turbulent periods surrounding the 1997-1998 Asian financial crisis and the
    2007-2008 global financial crisis by used 24 global currencies from December 31, 1996 to May
    11, 2017. This study found that the combined strategy was a good hedge with desirable
    diversification merits in times of financial stress. Khademalomoom, S and Narayan, P
    (2019), inspected intraday patterns in the currency market for hourly exchange rates of the six
    most liquid currencies (i.e. the Australian Dollar, British Pound, Canadian Dollar, Euro,
    Japanese Yen, and Swiss-Franc) vis-à-vis the United States Dollar over the period 2004-2014.
    It was noted that currencies’ behaviour induced by these intraday effects had implications for
    investors. Liu et al. (2019), investigated the currency use in financial transactions using the
    SWIFT dataset from October 2010 to August 2014. Kunkler, M and MacDonald, R (2019),
    examines the multilateral relationship between oil and G10 currencies during from 31st
    December 1985 to 31st December 2017. It was found that that the global price of oil moves
    multilaterally with a group of “oil” currencies: the Norwegian krone, the Australian dollar, the
    Canadian dollar and the British pound and also it was clearly noted that the Japanese Yen and
    the Swiss Franc move multilaterally against the group of oil currencies and not against the
    global price of oil. McCauley, R and Shu (2019), investigated how variation in Chinese
    authorities’ renminbi management since the August 2015 exchange rate reform maps on to
    variation in the co-movement between the renminbi with regional and other emerging market
    currencies. An efficient market provides, on continues basis, a platform for no opportunities to
    engage in profitable trading activities. If a market is not efficient, the regulatory authorities
    normally take necessary steps to ensure that the stocks are correctly priced, leading to stock
    market efficiency. Kathiravan et al. (2018), investigated the effect of three weather factors
    (temperature, humidity and wind speed), on the returns of the Indian stock market indices (BSE
    Sensex and S&P CNX Nifty) and used granger causality and Correlation. Shu et al. (2015),
    examined the changes in the RMB/ USD rates in two markets have a statistically and
    economically significant impact on changes in Asian currency rates against the US dollar during
    the data between September 2010 (when quotes for the CNH rates became regular) and
    September 2013. It is suggested that China’s regional influence is increasingly transmitted
    through financial channels. The efficiency of emerging markets is characterized by regular and
    unexpected changes in variance. It is to be noted that national and international events in
    countries, pave the way for high volatility (Lingaraja et al., 2014). Ben Rejeb, A and
    Boughrara, A (2013), studied the impact of financial liberalization on the degree of
    informational efficiency in emerging stock markets while considering three types of financial
    crises, i.e. Banking, Currency and Twin crises. The study revealed that emerging markets were
    characterized by greater efficiency in recent years. Tudor, C and Popescu – Dutaa, C (2012),
    investigated the issue of Granger causality between stock prices and exchange rates movement
    for Developed (Australia, Canada, France, Hong Kong, Japan, United Kingdom, and United
    States) and Emerging financial markets (Brazil, China, India, Korea, Russia and South Africa)
    during the period from January 1997 to March 2012. This study employed tools like Descriptive
    Statistics and Granger Causality Tests for the analysis. Charoenwong et al. (2009),
    investigated volatility forecast and compare the predictive power of the implied volatility
    derived from currency option prices that are traded on the Philadelphia Stock Exchange
    (PHLX), Chicago Mercantile Exchange (CME), and over-the-counter market (OTC) with four
    currency pairs from October 1, 2001 to September 29, 2006. It was clearly noted that the implied
    volatility provides more information about future volatility–regardless of whether it is from the
    OTC, PHLX, or CME markets–than time series based volatility. Lagoarde-Segot, T and
    Brian M. Lucey (2008), examined the informational efficiency of seven emerging Middle-
    Eastern North African (MENA) stock markets. The study found that the extent of weak-form

    http://www.iaeme.com/IJM/index.asp 194 editor@iaeme.com

  5. Mariappan Raja and Chinnadurai Kathiravan, Exchange Rate Volatility And Causality Effect Of Sri
    Lanka (Lkr) With Asian Emerging Countries Currency Against Usd

    efficiency in the MENA stock markets was primarily explained by differences in stock market
    size. Alan T. Wang (2007), examined the volatility of currency futures options for Australian
    dollar (AD), British pound (BP), Canadian dollar (CD), Deutsche mark (DM), and Japanese
    yen (JY) and used the sample of daily exchange rates and options with maturities from the
    beginning of January 1998 to the beginning of September 2001. Dunis, C and Huang, X
    (2002), examined the use of non-parametric Neural Network Regres- sion (NNR) and Recurrent
    Neural Network (RNN) regression models for forecasting and trading currency volatility, with
    an application to the GBP/ USD and USD/JPY exchange rates for the period April 1999 – May
    2000. This study threw light on the currency option market was inefficient and/or the pricing
    formulae applied by market participants were inadequate.
    From the earlier studies it has been found that researchers examined Risk and Return,
    volatility and relationship between Foreign exchange market and Stock Market using currency
    exchange rates and stock market indices price. But no study has been carried out causality effect
    and volatility of Asian region’s currencies under emerging category countries with Frontier
    country like Sri Lanka (LKR) on long run period i.e. 17 years. In order to fill this gap, the
    present study has been undertaken.

    3. PROBLEM STATEMENT OF THE STUDY
    Reserve Bank of India (RBI) report indicates the foreign exchange markets experienced a
    substantial increase in volatility in August 2007 and most of the countries amongst Asian
    currencies, the US Dollar depreciated by 2.7 per cent against Chinese yuan, but appreciated by
    52.2 per cent against Korean won, 24.7 per cent against the Indian rupee, 13.6 per cent against
    the Malaysian ringgit and 11.9 per cent against Thai baht. The currencies of many emerging
    and developing economies suffered large depreciations with the onset of the global financial
    crisis during 2007-09. The exchange rate losses varied largely commensurate with the extent
    and nature of each country’s exposure to trade and global financial operations. Most of the Asian
    currencies underwent depreciation during 2011 and showed significant volatility, coinciding
    with the world economic and financial conditions. The international investor tolerance (or
    expectations) could put downward pressure on the US Dollar and upward pressure on many
    Asian currencies. In addition, Asia also faces the challenge of surges in short-term capital
    inflows and the consequent upward pressure on currency values. While some corporates and
    financial institutions in Asia may remain vulnerable to their home currency depreciations, in
    aggregate, these economies have moved from running current account deficits to surpluses and
    stockpiled reserves in US Dollars and Euros. Hence, this study.

    3.1. Significance and Importance of the Study
    Understanding the causes of exchange rate volatility provides valuable insight for policy makers
    to design appropriate measures or intervention strategies in mitigating a country’s vulnerability
    to risk in periods of uncertainty. The changes in exchange rates will have both favorable and
    unfavorable impacts on economic activities and living standard of the public because of the
    largely globalized trade and finance involving the exchange of currencies. In addition that,
    identifying the sources of exchange rate volatility is important, as maintaining a competitive
    and stable exchange rate is necessary for promoting private investment, domestic and foreign,
    needed to meet the growth and development targets in the country. Hence, this study an attempt
    to test Causality Effect and Volatility of Sri Lanka Currency (LKR) with Asian Emerging
    Countries Currency against USD.

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  6. Kasilingam Lingaraja, C. Jothi Baskar Mohan, Murgesan Selvam

    4. OBJECTIVES OF THE STUDY
    The objectives of this study are as follows:
     To analyse the summary statistics (Mean, Maximum, Minimum and SD) among the
    selected sample currencies against USD.
     To examine the exchange rate volatility among the selected sample currencies
    against USD.
     To analyse relationship between Sri Lanka (LKR) and Asian emerging currencies
    against USD
     To investigate the causality effect between Sri Lanka (LKR) and Asian emerging
    currencies against USD.

    5. HYPOTHESES OF THE STUDY
    In the light of the objective of this study, the following Null Hypotheses are developed and
    tested in the analysis.
    NH01: There is no long-run exchange rate volatility among the sample countries currency
    against USD during the study period.
    NH02: There is no long-run significant relationship (movements) between Asian emerging
    currency and Sri Lanka (LKR) against USD during the study period.
    NH03: There is no long-run causality (linkage) effect between Asian emerging currency and
    Sri Lanka (LKR) against USD during the study period.

    6. RESEARCH METHODOLOGY
    6.1. Data
    For the purpose of the study, we use the MSCI system of nine emerging Asian countries and
    one Sri Lankan (frontier) country exchange rates (ten currencies) against the US Dollar
    (numeraire currency). The ten currency universe consists of the following ten currencies:
    Chinese Yuan Renminbi (CNY), Indian Rupee (INR), Korean Won (KRW), Taiwan New
    Dollar (TWD), Malaysian Ringgit (MYR), Thai Baht (THB), Indonesian Rupiah (IDR),
    Philippine Peso (PHP), Pakistani Rupee (PKR) and Sri Lankan Rupee (LKR). The details of
    sample Countries, Currencies and their Symbols are shown in Table – 1.

    Table – 1

    The Details of Sample Currencies and Symbols
    Nature Country Name of the Currency Symbols/ Sign
    China Chinese Yuan Renminbi CNY ¥
    Emerging Countries in Asia

    India Indian Rupee INR
    Korea Korean Won KRW ₩
    Taiwan Taiwan New Dollar TWD NT$
    Malaysia Malaysian Ringgit MYR M$
    Thailand Thai Baht THB ฿
    Indonesia Indonesian Rupiah IDR Rp
    Philippines Philippine Peso PHP
    Pakistan Pakistani Rupee PKR ₨
    Frontier Sri Lanka Sri Lankan Rupee LKR රු.

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  7. Mariappan Raja and Chinnadurai Kathiravan, Exchange Rate Volatility And Causality Effect Of Sri
    Lanka (Lkr) With Asian Emerging Countries Currency Against Usd

    Source: Morgan Stanley Capital International (MSCI) http://www.msci.com as on
    30.07.2019

    6.2. Data Collection
    The countries currency data have been collected from different data base such as FRED
    Exchange rate UK. The FRED is the Research Division of the Federal Reserve Bank of St.
    Louis is to discover international historical banking and economic data. The widely used
    database FRED (Federal Reserve Economic Data) is updated regularly and allows 24/7 access
    to regional, national and International financial and economic data (Website:
    https://fred.stlouisfed.org/). And Exchange Rates UK is a site devoted to bringing you the latest
    currency news, historical data, currency conversion and exchange rates, using mid-market rates
    updated minutely (22:00 Sun – 22:00 Fri) through the Website:
    https://www.exchangerates.org.uk/.

    6.3. Period of the Study
    This study was conducted for the purpose of test the long-run currencies behavior of sample
    countries. So, we have collected the daily currency exchange rate data against USD for more
    than 15 years i.e. from 01st January, 2002 to 31st December, 2018.

    6.4. Tools Used for Analysis
    For the purpose of the study, we used the following tools for analyzing the data such as
    Descriptive Statistics (Summary), GARCH (1,1) Model (Volatility), Correlation
    (Relationship), Granger Causality test (Linkages) Chart and Graphs.

    6.4.1. Descriptive Statistics
    Descriptive Statistics, the Mean, Minimum, Maximum, Standard Deviation, and Jarque-Bera
    were used (Gupta. S.P., 2008). The measures of central tendency include the mean, median
    and mode, while measures of variability include the standard deviation (or variance), the
    minimum and maximum values of the variables and Jarque-Bera. The use of logarithms makes
    graphs symmetrical and look similar to the normal distribution, making them easier to interpret
    intuitively (Nick, Todd G., 2007).

    6.4.2. GARCH (1,1) Model
    A deficiency of ARCH (q) models is that the conditional standard deviation process has high
    frequency oscillations with high volatility coming in short burst. GARCH models (p, q) permit
    a wider range of behavior, in particular more persistent volatility. Tim Bollerslev (1986)
    proposed a more generalized form of the ARCH (m) model appropriately termed as the GARCH
    model which has two equations. Numerous parametric specifications for the time varying
    conditional variance have been proposed in the literature. The following is formula to calculate
    the GARCH model:

    σ2t = α0 +α1u2t-1 + α2u2t-2 + … + αqu2t-q + β1σ2t-1 + β2σ2t-2 + … + βpσ2t-p

    6.4.3. Correlation Analysis
    According to Tripti Nashier (2015), correlation is a statistical tool which measures the degree
    of relationship between two and more variables. Here, by term relationship, it is meant that the
    tendency of variable to move together. In the sense, it denotes interdependency amongst
    variables. The movement of variable may be in positive or negative direction. The correlation

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  8. Kasilingam Lingaraja, C. Jothi Baskar Mohan, Murgesan Selvam

    analysis is used to find out the movements of currency exchange rate between the countries
    over the period of time. Correlation measures the strength of the linear association between two
    variables of two different countries. The formula for correlation (r) is:
    1  x  x  y  y 
    r   
    n  1  s x  s y 

    Computationally, the Descriptor systems uses what is sometimes referred to as the sum of
    squares formula for r.
     X Y
     XY  N
    r 

      X  2
    
      Y 2


      
    X 2
     Y2 
    N N 
      

    6.4.5. Pairwise Granger Causality Test
    According to Brooks, C. (2002), a variable X Granger-causes Y if the past changes in X can
    project current values of Y. If X Granger-causes Y, this is called unidirectional causality. If X
    Granger-causes Y and Y also Granger-causes X then this is considered to be a bi-directional
    causality linkages. Granger causality tests are conducted to test the significance and
    bidirectional/ unidirectional causality between the foreign exchange and stock market returns.
    According to Granger, C.W.J. (1969), a variable X is said to ‘Granger cause’ Y if past values
    of X help in the prediction of Y after controlling for past values of Y, or equivalently if the
    coefficients on the lagged values of X are statistically significant.
    The computation of daily currency data for this study is made by using E-views (Version –
    7.0), MS Excel and SPSS (Version – 21.0).

    7. LIMITATIONS OF THE STUDY
    The present study has the following limitations.
     The sample currencies consist of only ten from 9 Asian emerging countries and one
    frontier (Sri Lanka).
     The study is based on secondary data and the period is limited to 17 years from 2002
    to 2018.
     The Global Financial Crisis which occurred during September- 2008 is not removed
    in this data set.
     The study is confined to only foreign exchange rate of samples countries against
    USD.
     The study does not analyze or consider the economic and political risk factors of the
    sample countries.

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  9. Mariappan Raja and Chinnadurai Kathiravan, Exchange Rate Volatility And Causality Effect Of Sri
    Lanka (Lkr) With Asian Emerging Countries Currency Against Usd

    8. ANALYSIS OF LONG-RUN RELATIONSHIP, EXCHANGE RATE
    VOLATILITY AND CAUSALITY EFFECT BETWEEN THE SRI
    LANKA (LKR) AND ASIAN EMERGING CURRENCIES AGAINST USD
    Table -2

    The Results of Descriptive Statistics for the Sample Emerging Asian Countries Currency and Sri
    Lanka Currency Returns against USD during the Study Period from 01st January, 2002 to 31st
    December, 2018

    Descriptive
    Statistics
    Jarque-
    Mean Median Maximum Minimum Std. Dev. Obs.
    Bera
    Countries
    Currency
    CHY / USD 7.09 6.83 8.28 6.04 0.80 505.18 4412

    INR / USD 52.42 48.44 74.33 38.48 9.27 470.28 4412
    Emerging Countries in Asia

    KRW / USD 1113.19 1121.40 1570.10 903.20 102.89 334.83 4412

    TWD / USD 31.73 31.84 35.21 28.50 1.70 209.85 4412

    MYR / USD 3.60 3.64 4.50 2.94 0.39 162.37 4412

    THB / USD 34.99 33.56 44.24 28.60 4.08 464.72 4412

    IDR / USD 10490.81 9481.48 15305.29 8097.35 1902.80 631.31 4412

    PHP / USD 48.20 47.41 62.27 40.32 4.61 284.60 4412

    PKR / USD 82.72 84.85 139.85 56.95 20.64 266.49 4412

    Frontier Country (Sri Lanka)

    LKR / USD 119.71 113.60 182.70 93.13 19.68 418.44 4412

    Source: https://fred.stlouisfed.org/ and Computed using E-Views (Version – 7).
    The results of descriptive statistics for the Sample Emerging Asian Countries Currency and
    Sri Lanka Currency Returns against USD during the Study Period from 01st January, 2002 to
    31st December, 2018 are shown in Table – 2. It is clear from the above Table that during the
    study period, the currency exchange rate of Malaysia (MYR) earned high mean value of 3.60,
    followed by China (7.09), Taiwan (31.73) and Thailand (34.99) against USD. At the same time
    Indonesia (10490.81) and Korea (1113.19) earned low mean value compare with Sri Lankan
    currency (119.71) against USD during the study period. In terms of foreign exchange rate
    unpredictability as measured by the standard deviation of daily returns, only two sample
    currencies namely Indonesia (IDR/USD) assumed the highest risk value (1902.80), followed
    by Korea (KRW/USD) with the value (102.89) during the study period. This indicates the fact
    that there was high risk (in the order of currencies, namely, IDR and KRW). It is significant to
    note that high degree of risk is useful for speculators but the investors may study the country
    risk and carefully watch the currency value before taking investment decision. We also compute

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  10. Kasilingam Lingaraja, C. Jothi Baskar Mohan, Murgesan Selvam

    the Jarque-Bera statistics to test whether the returns are normally distributed. Besides, the
    Jarque-Bera (JB) values of all ten sample currency were more than 5. Hence, it clearly implied
    that all the sample were normally distributed. In other words, all the sample currencies were
    less volatile except Indonesia and Korea during the study period.

    Table : 3

    Results of Volatility using GARCH (1, 1) Model for Sample Emerging Asian Countries Currency and
    Sri Lanka Currency Returns against USD during the Study Period from 01st January, 2002 to 31st
    December, 2018

    List of Sample Countries
    Currency
    C α β α+β P Value

    China
    0.0000000 0.01661 0.97985 0.99646 0
    (CHY / USD)
    India
    0.0000000 0.07155 0.93670 1.00825 0
    (INR / USD)
    Emerging Asian Countries Currency

    Korea
    0.0000003 0.06647 0.92787 0.99434 0
    (KRW / USD)
    Taiwan
    0.0000001 0.06522 0.93289 0.99811 0
    (TWD / USD)
    Malaysia
    0.0000000 0.08219 0.92854 1.01073 0
    (MYR / USD)
    Thailand
    0.0000003 0.09711 0.88480 0.98190 0
    (THB / USD)
    Indonesia
    0.0000467 0.22908 0.28190 0.51098 0
    (IDR / USD)
    Philippines
    0.0000098 0.19771 0.36787 0.56559 0
    (PHP / USD)
    Pakistan
    0.0000000 0.02661 0.97049 0.99711 0
    (PKR / USD)

    Frontier Country (Sri Lanka)

    Sri Lanka
    0.0000001 0.15805 0.71627 0.87432 0
    (LKR / USD)

    Source: https://fred.stlouisfed.org/ and Computed using E-Views (Version – 7).

    Table-3 shows the results of volatility, using GARCH (1.1) model, for daily (closing value)
    currency returns of Asian emerging countries and frontier country (Sri Lanka) against USD,
    during the study period from 01st January, 2002 to 31st December, 2018. As stated earlier, the
    sample of nine currency exchange rate against USD from emerging countries in Asia while the
    one sample from frontier country, namely, Sri Lanka (LKR/ USD). From the Table, it is clearly
    observed that value of the probability (P-Value) was zero at 99% confidence level. It is worth
    noting that the values (α+ β) for eight currencies were close to one. The values (α+ β) of ten
    sample Countries currency exchange rate against USD were 1.01073 (for Malaysia – MYR/
    USD), 1.00825 (for India – INR/ USD), 0.99811 (for Taiwan – TWD/ USD), 0.99711 (for

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  11. Mariappan Raja and Chinnadurai Kathiravan, Exchange Rate Volatility And Causality Effect Of Sri
    Lanka (Lkr) With Asian Emerging Countries Currency Against Usd

    Pakistan – PKR/ USD) 0.99646 (for China- CHY/ USD), 0.99434 (for Korea – KRW/ USD),
    0.98190 (for Thailand – THB/ USD), and 0.87432 (for Sri Lanka – LKR/ USD). According to
    the analysis of GARCH Model, the α+ β values of ten currencies, Seven out of Nine Asian
    emerging Countries Currency and one Frontier country currency were close to one. At the same
    time, the two Asian emerging countries currency i.e., Indonesia (IDR/ USD) was 0.51098 and
    Philippines (PHP/ USD) was 0.56559 were recorded low volatility during the study period. This
    indicates the fact that the data of sample currency against USD, for eight countries currency
    (China, India, Korea, Taiwan, Malaysia, Thailand, Pakistan and Sri Lanka) out of ten were
    highly volatile, during the study period from 01st January 2002 to 31st December, 2018. Thus
    the null hypothesis (NH01), there is no long-run exchange rate volatility among the sample
    countries currency against USD during the study period from 01st January, 2002 to 31st
    December, 2018, was rejected.
    The overall results of GARCH (1, 1), for the returns of ten sample currencies against USD,
    showed that all the parameters in the GARCH (1, 1) were highly significant at 1% significance
    level. The high degree of significance of α (GARCH term) and β (GARCH term) implied that
    past volatility highly influenced the current volatility of all the series under study. As both α
    and β were significant, it revealed that the lagged conditional variance and lagged squared
    variance had impact on current volatility. From the sum values of co-efficient of α+β of the
    series, it was clearly evident that eight countries currency showed a value which was close to
    unity or one. At the same time, the currencies like Philippines (PHP) and Indonesia (IDR) were
    not highly volatile among the sample currencies.

    Source: Data taken from Table-3 and Computed using MS office Excel – 2007

    Chart –1: Results of Volatility (α+β) for Sample Emerging Asian Countries Currency and One
    Frontier (Sri Lanka) Currency against USD during the Study Period from 01st January, 2002 to 31st
    December, 2018
    The results of volatility (both α+β value), of all the Nine Asian emerging Countries currency
    and Frontier Sri Lanka (LKR) exchange rate against USD, during the study period from 01st
    January, 2002 to 31st December, 2018, are shown in Chart – 1. The Chart clearly explains the
    high rate of volatility in sample emerging counties currency of Asia and Frontier countries

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  12. Kasilingam Lingaraja, C. Jothi Baskar Mohan, Murgesan Selvam

    currency. The values of both risk and return (α+ β) were close to one and the Chart represents
    both high and low volatility of sample currency. It implies that the volatility among the sample
    currencies, except for Philippines (PHP/ USD) and Indonesia (IDR/ USD) were Low persistent
    at 1% and 5% significant levels. In other words, less volatility (risk and return) may be
    transmitted among the sample currency returns. Out of ten sample currencies, Eight currencies,
    namely, Malaysia – MYR/ USD, India – INR/ USD, Taiwan – TWD/ USD, Pakistan – PKR/
    USD, China- CHY/ USD, Korea – KRW/ USD, Thailand – THB/ USD, Sri Lanka – LKR/USD
    were highly volatile, with more than 98 percent of risk with return (α+ β), during the study
    period from 01st January, 2002 to 31st December, 2018.

    Table – 4

    Results of Correlation between Emerging Asian Countries currency and one Frontier (Sri Lanka)
    Currency against USD during the Study Period from 01st January, 2002 to 31st December, 2018

    Countrie
    s CHY/U INR/U KRW/ TWD/ MYR/ THB/U IDR/U PHP/U PKR/U
    Currenc SD SD USD USD USD SD SD SD SD
    y
    CHY /
    1
    USD

    INR /
    0.5968 1
    USD
    43

    KRW / 0.1974
    0.0443 1
    USD 21
    44

    TWD / 0.7612 0.33607
    0.3640 1
    USD 31 3
    17
    MYR / 0.3283 0.4561 0.21170 0.41360
    1
    USD 68 28 6 9

    THB / 0.8484 0.79961 0.52651
    0.2516 0.26342 1
    USD 68 5 2
    07
    – – –
    IDR / 0.9099 0.58194
    0.5089 0.13816 0.25578 0.2145 1
    USD 82 9
    22 8 75
    – –
    PHP / 0.7718 0.13365 0.59095 0.68266 0.8108
    0.0983 0.0020 1
    USD 15 8 5 9 81
    88 24
    – – – –
    PKR / 0.8841 0.12931 0.11296 0.8003
    0.8493 0.65229 0.6207 0.4021 1
    USD 01 1 8 21
    59 3 74 84

    – – – –
    LKR / 0.9022 0.34023 0.8812 0.9329
    0.6955 0.00843 0.54340 0.5206 0.2316
    USD 57 6 88 44
    49 5 82 68
    Source: https://fred.stlouisfed.org/ and Computed using SPSS (Version – 21)

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  13. Mariappan Raja and Chinnadurai Kathiravan, Exchange Rate Volatility And Causality Effect Of Sri
    Lanka (Lkr) With Asian Emerging Countries Currency Against Usd

    Table – 4 exhibits the results of correlation matrix, for sample between Emerging Asian
    Countries currency and one Frontier (Sri Lanka) Currency against USD during the Study Period
    from 01st January, 2002 to 31st December, 2018. It is clear that the Sri Lanka (LKR) currency
    was significant positive correlated with Pakistan Rupee (PKR), Indian Rupee (INR) and
    Indonesian Rupiah (IDR) with the values (correlation coefficient) of 0.932944, 0.902257 and
    0.881288 respectively. It is to be noted that out of nine sample currencies of Asian emerging
    countries three countries currency (Chinese Yuan –CHY, Taiwan New Dollar – TWD and Thai
    Baht – THB) were significant negative correlation with Sri Lanka (LKR) with the correlation
    coefficient values of -0.695549, -0.543405 and -0.520682, respectively. At the same time, the
    currencies like Malaysian Ringgit (MYR), Korean Won (KRW) and Philippine Peso (PHP) did
    not have significant correlation with Sri Lanka (LKR) during the study period. In addition to
    the above fact, there was only minor relationship (interdependence) between Asian emerging
    countries currency and Sri Lanka (LKR). However, out of nine currencies only three (Malaysia,
    Korea and Philippines) currencies did not reach significant correlation during the study period.
    But, remaining six currencies (CHY, INR, TWD, THB, IDR and PKR) were attained significant
    correlation with Sri Lanka (LKR) against USD during the study period. Hence the null
    hypothesis (NH02), namely, there is no long-run significant relationship (movements)
    between Asian emerging currency and Sri Lanka (LKR) against USD during the period
    from 01st January, 2005 to 31st December, 2005, was rejected.
    The results of Pairwise Granger Causality, for testing the Causality effect between Sri Lanka
    (LKR) and nine sample currencies of Asian emerging countries against USD, during the period
    from 01st January, 2002 to 31st December, 2018, are shown in Table – 5. The analysis of Asian
    sample currencies with Sri Lanka (LKR) against USD, reveals that only one currency, namely,
    Thai Baht -THB (Thailand) recorded no causality linkage (—) in the both way during the study
    period. A pair currency, namely, Sri Lanka (LKR/USD) on Chinese Yuan (CHY/USD) earned
    a value of 4.36797, Indian Rupee (INR/USD) on Sri Lanka (LKR/USD) recorded a value of
    6.79359, Korean Won (KRW/USD) on Sri Lanka (LKR/USD) earned a value of 4.15625,
    Taiwan New Dollar (TWD/USD) on Sri Lanka (LKR/USD) recorded a value of 4.57597,
    Malaysian Ringgit (MYR) on Sri Lanka (LKR/USD) earned a value of 4.51862, Sri Lanka
    (LKR/USD) on Indonesian Rupiah (IDR/USD) recorded a value of 5.29903, Philippine Peso
    (PHP/USD) on Sri Lanka (LKR/USD) with a value of 4.24211 and Sri Lanka (LKR/USD) on
    Pakistan Rupee (PKR/USD) earned a value of 5.49767 were registered unidirectional (→ and
    ←) or one way causality linkage during the study period on the basis of F- Statistics.

    Table – 5

    The Results of Pairwise Granger Causality of SRI LANKA (LKR/USD) with Emerging Asian
    Countries Currency Exchange Rate against USD during the study period from 01st January 2002 to
    31st December 2018

    Null Hypothesis: Obs F-Statistic Prob. Result

    LKR / USD does not Granger Cause CHY / USD 4410 4.36797 0.0127 Rejected
    CHY / USD does not Granger Cause LKR / USD 4410 2.05784 0.1279 Accepted

    LKR / USD does not Granger Cause INR / USD 4410 1.69649 0.1834 Accepted
    INR / USD does not Granger Cause LKR / USD 4410 6.79359 0.0011 Rejected

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  14. Kasilingam Lingaraja, C. Jothi Baskar Mohan, Murgesan Selvam

    LKR / USD does not Granger Cause KRW / USD 4410 0.12073 0.8863 Accepted
    KRW / USD does not Granger Cause LKR / USD 4410 4.15625 0.0157 Rejected

    LKR / USD does not Granger Cause TWD / USD 4410 0.73626 0.479 Accepted
    TWD / USD does not Granger Cause LKR / USD 4410 4.57597 0.0103 Rejected

    LKR / USD does not Granger Cause MYR / USD 4410 1.99023 0.1368 Accepted
    MYR / USD does not Granger Cause LKR / USD 4410 4.51862 0.011 Rejected

    LKR / USD does not Granger Cause THB / USD 4410 0.37717 0.6858 Accepted
    THB / USD does not Granger Cause LKR / USD 4410 2.89075 0.0556 Accepted

    LKR / USD does not Granger Cause IDR / USD 4410 5.29903 0.005 Rejected
    IDR / USD does not Granger Cause LKR / USD 4410 1.73326 0.1768 Accepted

    LKR / USD does not Granger Cause PHP / USD 4410 0.11659 0.89 Accepted
    PHP / USD does not Granger Cause LKR / USD 4410 4.24211 0.0144 Rejected

    LKR / USD does not Granger Cause PKR / USD 4410 5.49767 0.0041 Rejected
    PKR / USD does not Granger Cause LKR / USD 4410 0.51015 0.6004 Accepted
    Source: https://fred.stlouisfed.org/ and Computed using E-Views (Version – 7).
    It is interesting to note that out of nine sample Currencies of Asian emerging countries, only
    one currency, namely, Thailand Baht (THB) registered no causality linkages with Sri Lanka
    (LKR) against USD. At the same time, the other eight currencies, namely, Chinese Yuan
    Renminbi, Indian Rupee, Korean Won, Taiwan New Dollar, Malaysian Ringgit, Thai Baht,
    Indonesian Rupiah, Philippine Peso and Pakistani Rupee experienced unidirectional linkages
    with Sri Lanka (LKR) against USD. Hence the null hypothesis (NH03) – there is no is no long-
    run causality (linkage) effect between Asian emerging currency and Sri Lanka (LKR)
    against USD during the study period from 01st January, 2002 to 31st December, 2018, was
    partially rejected.

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  15. Mariappan Raja and Chinnadurai Kathiravan, Exchange Rate Volatility And Causality Effect Of Sri
    Lanka (Lkr) With Asian Emerging Countries Currency Against Usd

    Source: The results of Table – 5
    NOTE
    One way – Unidirectional causality

    No causality relation
    Figure – 2: The Dynamic Linkages of SRI LANKA (LKR/USD) with Emerging Asian Countries
    Currency Exchange Rate against USD during the study period from 01st January 2002 to 31st
    December 2018
    Figure – 2 displays the graphical demonstration of two forms of dynamic linkages, for
    sample currencies of nine Asian emerging countries currency, with the frontier currency of Sri
    Lankan Rupee (LKR), during the period from 01st January 2002 to 31st December 2018. The
    above Figure, formulated with the help of Table 5 are given at the above Figure. According to
    Figure – 2, out of nine emerging countries currency, seven currencies namely, Chinese Yuan
    Renminbi (CHY), Indian Rupee (INR), Korean Won (KRW), Taiwan New Dollar (TWD),
    Malaysian Ringgit (MYR), Indonesian Rupiah (IDR), Philippine Peso (PHP) and Pakistani
    Rupee (PKR) registered significant degree of unidirectional linkages with Sri Lanka (LKR)
    against USD during the study Period. At the same time, one Asian emerging country currency,
    namely, Thai Baht (THB), registered no causality linkage with the Sri Lanka (LKR) against
    USD during the study Period.
    Graph 1 shows the evolution of the Nine Asian emerging countries and Sri Lankan
    currency exchange rates against the U.S. Dollar since the beginning of this century i.e., from 1st
    January, 2002 to till 31st December, 2018. It also shows the paths of the China (CHY/USD),
    Korean (KRW/USD), Taiwan (TWD/USD), and Thailand (THB/USD) currencies were
    performed better than U.S. Dollar during the study period. At the same time, the following
    currencies India (INR/USD), Indonesia (IDR/USD), Malaysia (MYR/USD) Philippines
    (PHP/USD), Pakistan (PKR/USD) and Sri Lanka (LKR/USD) were equally performed with
    U.S Dollar till 2014, the graph depicts very large variation in all ten sample currencies over this
    long data set for 17-year horizon, with broad trends emerging and disappearing, occasional

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  16. Kasilingam Lingaraja, C. Jothi Baskar Mohan, Murgesan Selvam

    sharp turns, and quite a few ups and downs. The sample currencies were trajectories do not look
    qualitatively different from that of a freely floating Chinese Yuan renminbi (CHY). The overall
    performance of the currency values of sample countries were good and the currencies like
    Pakistan Rupee (PKR), Sri Lankan Rupee (LKR) and Indonesian Rupiah (IDR) were equally
    moved from start to end of the study period. It is to be noted that the countries like Indonesia,
    Malaysia, Pakistan and Sri Lanka were highly affected their currency values from the year 2015
    to 2017 against USD.

    Source: https://fred.stlouisfed.org/ and Computed using E-Views (Version – 7)

    Graph – 1: Graphical Expression for SRI LANKA (LKR/USD) and Emerging Asian Countries
    Currency Exchange Rate against USD during the study period from 01st January 2002 to 31st
    December 2018 9.0 Conclusion and Recommendations
    The present paper empirically investigated the relationship between the volatilities and
    causality effect between Asian emerging countries and Frontier (Sri Lanka) currency exchange
    rate against USD for 17 year from 01st January 2002 to 31st December, 2018. In the sample
    currency pairs namely, CNY/USD, INR/USD, KRW/USD, TWD/USD, MYR/USD,
    THB/USD, IDR/USD, PHP/USD, PKR/USD and LKR/USD; it is found that the results of
    GARCH Model only two sample currencies i.e., Indonesia (IDR/ USD) was 0.51098 and
    Philippines (PHP/ USD) was 0.56559 were recorded low volatility during the study period. At
    the same time, the remaining 8 counties currency were highly volatile and it good for
    speculators to make their better investment. The results of Granger causality test show a
    unidirectional relationship between the exchange rate of Asian emerging countries and LKR
    against USD except Thailand Baht (THB). Hence, the Sri Lankan currency market investors
    would focus their portfolio investment plan to Thailand baht. These results, apart from offering
    a much better understanding of the Volatility, Causality effect in the sample countries may have
    important implications for currency market efficiency to the selected sample countries. Finally,
    this study results would help to international portfolio managers, multinational corporations,
    and policymakers for decision-making in the Asian region.

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  17. Mariappan Raja and Chinnadurai Kathiravan, Exchange Rate Volatility And Causality Effect Of Sri
    Lanka (Lkr) With Asian Emerging Countries Currency Against Usd

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