Transcript of "11.a causal relationship between stock indices and exchange rates empirical evidence from india"
Research Journal of Finance and Accounting www.iiste.orgISSN 2222-1697 (Paper) ISSN 2222-2847 (Online)Vol 3, No 1, 2012 A Causal Relationship between Stock Indices and Exchange Rates-Empirical Evidence from India Amalendu Bhunia Fakir Chand College, Diamond Harbour South 24-Parganas-743331 West Bengal, India * E-mail of the corresponding author: email@example.comAbstractThis paper examines the causal relationship between stock prices and exchange rates, using data from 2April 2001 to 31 March 2011 about India. Macroeconomic variables are of crucial importance fordetermining the effects on stock prices and investment decisions. There are many empirical studies todisclose the relationship between macroeconomic variables such as interest rate, inflation, exchangerates, money supply etc. and stock indexes. However, the direction of causality still remains unresolvedin both theory and empirics. In the present study, national, services, financials, industrials, andtechnology indices are taken as stock price indices. The results of empirical study indicate that there isbidirectional causal relationship between exchange rate and all stock market indices. While thenegative causality exists from national, services, financials and industrials indices to exchange rate,there is a positive causal relationship from technology indices to exchange rate. On the other hand,negative causal relationship from exchange rate to all stock market indices is determined.Keywords: Stock Prices, Exchange Rate, Toda-Yamamoto, India1. IntroductionMany factors, such as enterprise performance, dividends, stock prices of other countries, grossdomestic product, exchange rates, interest rates, current account, money supply, employment, theirinformation etc. have an impact on daily stock prices (Kurihara, 2006). Especially, the continuingincreases in the world trade and capital movements have made the exchange rates as one of the maindeterminants of business profitability and equity prices (Kim, 2003).The relationship between stock prices and exchange rates has preoccupied the minds of economistssince they both play important roles in influencing the development of a country’s economy. In therecent years, because of increasing international diversification, cross-market return correlations,gradual abolishment of capital inflow barriers and foreign exchange restrictions or the adoption ofmore flexible exchange rate arrangements in emerging and transition countries, these two markets havebecome interdependent. These changes have increased the variety of investment opportunities as wellas the volatility of exchange rates and risk of investment decisions and portfolio diversification process.Thus, understanding this relationship will help domestic as well as international investors for hedgingand diversifying their portfolio. Also, fundamentalist investors have taken into account theserelationships to predict the future trends for each other (Phylaktis and Ravazzolo, 2005; Mishra et al.,2007; Nieh and Lee, 2001; Stavárek, 2005). Although, economic theory suggests that foreign exchangechanges can have an important impact on the stock price by affecting cash flow, investment andprofitability of firms, there is no consensus about these relationship and the empirical studies of therelationship are inconclusive (Joseph, 2002; Vygodina, 2006). However, the linkage between thesefinancial variables can be established through the instruments of wealth, demand for money, interestrates etc. (Mishra, 2004).According to traditional approach, exchange rates lead stock prices. On the other hand, portfoliobalance approach states that exchange rates are determined by market mechanism. In other words,changes in stock prices might have impact on exchange rate movements. This approach states thatstock price is expected to lead exchange rate with a negative correlation since a decrease in stock pricesreduces domestic wealth, which leads to lower domestic money demand and interest rates. Also, thedecrease in domestic stock prices leads foreign investors to lower demand for domestic assets anddomestic currency. These shifts in demand and supply of currencies cause capital outflows and thedepreciation of domestic currency. On the other hand, when stock prices rise, foreign investors becomewilling to invest in a country’s equity securities. Thus, they will get benefit from international 47
Research Journal of Finance and Accounting www.iiste.orgISSN 2222-1697 (Paper) ISSN 2222-2847 (Online)Vol 3, No 1, 2012diversification. This situation will lead to capital inflows and a currency appreciation. (Granger et al.,2000; Caporale et al., 2002; Stavárek, 2005; Pan et al, 2007).Exchange rate changes affect the competitiveness of firms through their impact on input and outputprice (Joseph, 2002). When the Exchange rate appreciates, since exporters will lose theircompetitiveness in international market, the sales and profits of exporters will shrink and the stockprices will decline. On the other hand, importers will increase their competitiveness in domesticmarkets. Therefore, their profit and stock prices will increase. The depreciation of exchange rate willmake adverse effects on exporters and importers. Exporters will have advantage against othercountries’ exporters and increase their sales and their stock prices will be higher (Yau and Nieh, 2006).That is, currency appreciation has both a negative and a positive effect on the domestic stock marketfor an export-dominant and an import-dominated country, respectively (Ma and Kao, 1990).Exchange rates can affect stock prices not only for multinational and export oriented firms but also fordomestic firms. For a multinational company, changes in exchange rates will result in both animmediate change in value of its foreign operations and a continuing change in the profitability of itsforeign operations reflected in successive income statements. Therefore, the changes in economic valueof firm’s foreign operations may influence stock prices. Domestic firms can also be influenced bychanges in exchange rates since they may import a part of their inputs and export their outputs. Forexample, a devaluation of its currency makes imported inputs more expensive and exported outputscheaper for a firm. Thus, devaluation will make positive effect for export firms (Aggarwal, 1981) andincrease the income of these firms, consequently, boosting the average level of stock prices (Wu, 2000).Nieh and Lee (2001) state that in an open economy, since the expectations of relative currency valuesaffect the domestic and foreign interest rate and these changes affect the present value of a firm’sassets, exchange rates play a crucial role on stock prices, especially for internationally held financialassets. Wu (2000) explains the positive and negative relationship between exchange rate and stockprices by a real interest rate and an inflationary disturbance. According to real interest rate disturbance,when the real interest rate rises, capital inflow increases and the exchange rate fall. However, sincehigher real interest rate reduces the present value of future cash flows, stock prices will decline. Aninflationary disturbance may explain negative relationship between exchange rate and stock price. Thatis, when inflation increases, the exchange rate rises and because of high inflation expectations,investors will demand a higher risk premium and high rate of return. As a result, stock prices willdecrease (Wu, 2000). On the other hand, the asset market approach to exchange rate determinationstates a weak or no association between exchange rates and stock prices and treats exchange rate to bethe price of an asset (price of one unit of foreign currency). That is, expected future exchange ratesdetermine the exchange rates and factors affecting exchange rates and stock price may be different(Muhammad and Rasheed, 2002).This paper attempts to examine how changes in exchange rates and stock prices are related to eachother for India over the period 2001-2011. The paper is organized as follows: Section 2 contains a briefliterature review. Methodology and empirical results are presented in Section 3 and 4. Concludingremarks take place in Section 5.2. Review of literaturesAggarwal (1981) examines the influence of exchange rate changes on U.S. stock prices using monthlydata for the floating rate period from 1974 to 1978. He finds that stock prices and exchange rates arepositively correlated. Nieh and Lee (2001) examine the relationship between stock prices and exchangerates for G-7 countries and take the daily closing stock market indices and foreign exchange rates forthe period from October 1, 1993 to February 15, 1996. They find that there is no long-run equilibriumrelationship between stock prices and exchange rates for each G-7 countries. While one day’s short-runsignificant relationship has been found in certain G-7 countries, there is no significant correlation in theUnited States. These results might be explained by each country’s differences in economic stage,government policy, expectation pattern, etc.Kim (2003) uses monthly data for the 1974:01-1998:12 periods in the U.S.A. and the empirical resultsof the study reveal that S&P’s common stock price is negatively related to the exchange rate. Ozair(2006) examines the causal relationship between stock prices and exchange rates in the USA usingquarterly data from 1960 to 2004. The results show no causal linkage and no co-integration betweenthese two financial variables. Vygodina (2006) empirically searches the exchange rates and stock pricesnexus for large-cap and small-cap stocks for the period 1987-2005 in the USA and used Granger 48
Research Journal of Finance and Accounting www.iiste.orgISSN 2222-1697 (Paper) ISSN 2222-2847 (Online)Vol 3, No 1, 2012causality methodology. The result of study reveals that there is Granger causality from large-cap stocksto the exchange rate. However there is no causality for small-cap stocks. Stock prices and exchangerates are affected by the same macroeconomic variables and changes in federal monetary policy in theUSA have an important effect on the nature of these relationship. In other words, the nature of therelationship between stock prices and exchange rate is changing over time.Hatemi-J and Irandoust (2002) study a possible causal relation between exchange rates and stock pricesin Sweden. They use monthly nominal effective exchange rates and stock prices over the period 1993-98. They find that Granger causality is unidirectional from stock prices to effective exchange rates.Tsoukalas (2003) examines the relationship between stock prices and macroeconomic factors inCyprus. The result of study shows strong relationship between stock prices and exchange rates. Thereason of this is that Cypriot economy depends on services (import sector) such as tourism, off shorebanking etc.Pan et al. (2007) take the data of seven East Asian countries over the period 1988 to 1998 to examinedynamic linkages between exchange rates and stock prices. The result of study reveals that there is abidirectional causal relation for Hong Kong before the 1997 Asian crises. Also, there is a unidirectionalcausal relation from exchange rates and stock prices for Japan, Malaysia, and Thailand and from stockprices to exchange rate for Korea and Singapore. During the Asian crises, there is only a causal relationfrom exchange rates to stock prices for all countries except Malaysia. Ibrahim and Aziz (2003) analyzedynamic linkages between stock prices and four macroeconomic variables for Malaysia and usemonthly data over the period 1977-1998. The empirical results show that the exchange rate isnegatively associated with the stock prices.Kurihara (2006) chooses the period March 2001-September 2005 to investigate the relationshipbetween macroeconomic variables and daily stock prices in Japan. He takes Japanese stock prices, U.S.stock prices, exchange rate (yen/U.S. dollar), the Japanese interest rate etc. The empirical results showthat domestic interest rate does not influence Japanese stock prices. However, the exchange rate andU.S. stock prices affect Japanese stock prices. Consequently, the quantitative easing policyimplemented in 2001 has influenced Japanese stock prices.Doong et al. (2005) investigate the dynamic relationship between stocks and exchange rates for sixAsian countries (Indonesia, Malaysia, Philippines, South Korea, Thailand, and Taiwan) over the period1989-2003. According to the study, these financial variables are not co-integrated. The result ofGranger causality test shows that bidirectional causality can be detected in Indonesia, Korea, Malaysia,and Thailand. Also, there is a significantly negative relation between the stock returns and thecontemporaneous change in the exchange rates for all countries except Thailand.Abdalla and Murinde (1997) investigate stock prices-exchange rate relationships in the emergingfinancial markets of India, Korea, Pakistan and the Philippines using monthly data from 1985 to 1994.The empirical results show unidirectional causality from exchange rates to stock prices in India, Koreaand Pakistan. On the contrary, the reverse causation was found for the Philippines. Muhammad andRasheed (2002) examine the exchange rates and stock price relationships for Pakistan, India,Bangladesh and Sri Lanka using monthly data from 1994 to 2000. The empirical results show that thereis a bi-directional long-run causality between these variables for only Bangladesh and Sri Lanka. Noassociations between exchange rates and stock prices are found for Pakistan and India. Smyth andNandha (2003) investigate the relationship between exchange rates and stock prices for the samecountries over the period 1995-2001. They find that there is no long run relationship between variables.Also, the empirical results reveal unidirectional causality running from exchange rates to stock pricesfor only India and Sri Lanka. That is, changes in exchange rates affect stock prices through influencingfirms’ exports in India and Sri Lanka.Ajayi and Mougoue (1996) search the relationship between exchange rates and stock indices for eightadvanced economies using daily data from 1985 to 1991. According to results of study, there aresignificant short-run and long-run feedback relations between these two financial markets. An increasein stock price has a negative short-run effect as well as a positive long-run effect on domestic currencyvalue. Also, currency depreciation has a negative both short-run and long-run effect on the stockmarket. Ajayi et al. (1998) take daily market indexes and exchange rates to investigate causal relationsbetween stock returns and changes in exchange rates for seven advanced markets from 1985 to 1991and eight Asian emerging markets from 1987 to 1991. The empirical results show that there is aunidirectional causality between the stock and currency markets in all the advanced economies while 49
Research Journal of Finance and Accounting www.iiste.orgISSN 2222-1697 (Paper) ISSN 2222-2847 (Online)Vol 3, No 1, 2012no consistent causal relations exist in the emerging economies. They explained the different resultsbetween advanced and emerging economies with the differences in the structure and characteristics offinancial markets between these groups.Erbaykal and Okuyan (2007) examined exchange rates-stock price relations for 13 developingeconomies using different time period for each country. The findings provide evidence to indicatecausality relations for eight economies. While there is a unidirectional causality from stock price toexchange rates in the five of them, bidirectional causality exist for remaining three economies. Theyalso found no causality for these financial variables in Turkey and this finding is not consistent with ourresults. The reason of difference may be the time period used. On the other hand, Sevuktekin andNargelecekenler (2007) found positive and bidirectional causality between these two financial variablesin Turkey using monthly data from 1986 to 2006.3. MethodologyThe stationary status of series should be detected when investigating the relationship between exchangerate and stock market price. In this context, we perform Augmented Dickey-Fuller (ADF) (1979),Phillips- Perron (PP) (1988) and KPSS (1992) tests in order to determine the integrated level of eachseries. ADF test are performed as shown below:∆yt = α + ϕT + (1- β)yt -1 + Σλ∆yt-j + εtWhere yt is the variable tested for unit root; ∆ is the first difference operator; α is the constant term; T isa time trend; and p is the lag number. The null hypothesis is H0: (1-β) =0, β=1, implying the non-stationary of yt. Rejecting the null hypothesis points that yt has no unit root. Lag length is selected byminimizing AIC. Also, whether residuals are white noise is taken into consideration in selecting properlag length. Rejecting null hypothesis requires that the calculated test value is greater than critical valuescalculated from MacKinnon (1991). Performing PP test we use ADF equation without non-augmentedform (∆yt-j, j=1,2… are not included, in the DF equation).In the event that series are integrated of order one, Johansen’s procedure should be used to determinewhether any co-integrating vector among variables exists or not. In this procedure, trace (λtrace) andmaximum eigen value (λmax) statistics are computed, proposed by Johansen (1988) and Johansen andJuselius (1990). When performing λtrace and λmax test, the null hypothesis that there is r or fewer co-integrating vectors are tested against at least r + 1 co-integration vectors and r + 1 co-integratingvectors, respectively. After applying co-integration test, if it is concluded that two series are co-integrated, error correction models are appropriate to investigate causality relationship. In case seriesare integrated different orders, for example I(0) and I(1) or are not co-integrated, it is not possible toinvestigate the causality via error correction model. In this situation the one way to determine causalityrelationship between series is use of Toda-Yomamoto (TY) (1995) method. The integrated properties ofseries are not important in TY method, providing that the risk of misspecification of the order ofintegration of the series is minimized. Thus, the causality relationship between series which areintegrated different orders can be investigated. In order to apply TY method, firstly, the VAR order, k,and the maximum order of integration of the variables, dmax, should be determined in the VAR model.The sum of k and dmax, is taken into consideration as the total order of VAR, that is (k+dmax)th orderof VAR is estimated. Then, in order to employ causality test, modified Wald test (MWALD), proposedby Toda and Yamamoto (1995), is applied to the first k VAR coefficients to investigate causality. Thistest has an asymptotic χ2 distribution when a VAR (k + dmax) is estimated. A Monte Carlo experiment,presented in Zapata and Rambaldi (1997), provides evidence that the MWALD test has a comparableperformance in size and power to the likelihood ratio and WALD tests if (i) the correct number of lagsfor estimating k + dmax is identified and (ii) no important variables are omitted, provided a sample of50 or more observations is available. According to Zapata and Rambaldi (1997), the advantage of thisprocedure is that it does not require the knowledge of co-integration properties of the system.To analyze Granger causality between exchange rate and stock market price by using TY procedure, thefollowing VAR system should be estimated.lext = α0 + Σα1lext-i + Σα2lext-j + Σλ1jlstpt-i + Σλ2jlstpt-j + +µ1tlstpt = β0 + Σβ 1lstpt-i + Σβ 2lstpt -j + Σφ 1j lext -i + Σφ 2j lextt-j + +µ2twhere lext is log of nominal exchange rate and lstpt is log of stock market price indices includinginfrastructures (lnfr), services (lser), financials (lfin), industrials (lind), and technology (ltec) indices.All data obtained from various stock exchanges of India, websites of Ministry of Finance, India and 50
Research Journal of Finance and Accounting www.iiste.orgISSN 2222-1697 (Paper) ISSN 2222-2847 (Online)Vol 3, No 1, 2012MCX, India. The data are daily and were collected daily from 2 April 2001 to 31 March 2011. Thereason of selecting this period is that exchange rate regime is determined as floating in 2001.4. Empirical results and analysisIn order to apply the MWALD test, it is necessary to determine the maximum order of integration ofeach of the series used in the study. Therefore, ADF, PP, and KPSS tests are employed to determineintegrated status of series. The test results shown in table-1 indicate that the stock market indices haveintegrated of order one for all unit root tests. On the other hand, there are different results about theintegrated levels of lex. Although ADF and PP tests are indicate that lex is integrated of order zero,KPPS test shows that this result is not valid, that is, lex is integrated of order one.Unit root test results indicate that the maximum order of integration is one, I(1). To apply TYprocedure, we selected the optimal lag length of the VAR determined by AIC and SBC. Thus, theoptimum order of the VAR (k) is 7 in order to detect the causal relationship between lex and lnat, lfin,lind, and ltec. On the other hand, optimum order of the VAR (k) is taken as 3 when the causalrelationship is investigated between lex and lser. Also, as seen from unit root tests, maximum order ofintegration (dmax) is equivalent to one. After specifying VAR (k) and dmax, VAR (k+ dmax) model canbe estimated by using MWALD test statistic. The results of causality tests are shown in table-2.The results showed in the table-2 indicate that there is bi-directional causal relationship between lexand all stock market indices. As regards the sign of causality, while the negative causality exists fromlnfr, lser, lfin, lind to lex, there is a positive causal relations from ltec to lex. In addition, negativecausality is found from lex to all stock market indices.5. ConclusionEconomists have tried to explain exchange rates-stock price nexus for a long time. There have beenmany empirical and theoretical studies to define the direction of causality between these two financialvariables. However, the direction of causality still remains unresolved in both theory and empirics.While some empirical studies find some relations and causality, other studies show no causalitybetween these two variables. Moreover, direction of causality changes from one economy to another.Also, the empirical studies for a specific economy may show different results for this relation. Thereason for these differences can be explained by time period used for data, econometric models usedand economic policies of countries.In this study, we investigated the relationship between mentioned variables in India using daily datafrom 2 April 2001 to 31 March 2011. Infrastructures, services, financials, industrial, and technologyindices are taken as stock price indices. The results of empirical study indicate that there is bi-directional causal relationship between exchange rate and all stock market indices. While the negativecausality exists from infrastructures, services, financials and industrials indices to exchange rate(supporting portfolio balance approach), there is a positive causal relationship from technology indicesto exchange rate. On the other hand, negative causal relationship from exchange rate to all stock marketindices is determined.ReferencesAbdalla, I. S. A. and Murinde, V., (1997), “Exchange rate and stock price interactions inemerging financial markets: Evidence on India, Korea, Pakistan and the Philippines”, AppliedFinancial Economics, 7, pp. 25–35.Aggarwal, R., (1981), “Exchange rates and stock prices: A study of the United States capital marketsunder floating exchange rates”, Akron Business and Economic Review, 12, (Fall), pp. 7-12.Ajayi, R. A., and Mougoue, M., (1996), “On the dynamic relation between stock prices andExchange rates”, Journal of Financial Research, 19, pp. 193–207.Ajayi, R. A., Friedman, J., and Mehdian, S. M., (1998), “On the relationship between stockreturns and exchange rates: Test of granger causality”, Global Finance Journal, 9(2), pp. 241–251.Caporale, G.M., Pittis, N., and Spagnolo, N., (2002), “Testing for causality-in-variance: anapplication to the East Asian markets”, International Journal of Finance & Economics, 7(3), pp. 235-245.Dickey, D.A., and Fuller, W.A., (1979), “Distribution of the Estimators for Autoregressive Time Series 51
Research Journal of Finance and Accounting www.iiste.orgISSN 2222-1697 (Paper) ISSN 2222-2847 (Online)Vol 3, No 1, 2012with a Unit Root”, Journal of the American Statistical Association, 74, pp. 427–31.Doong, Shuh-Chyi, Yang, Sheng-Yung and Wang, Alan T., (2005), “The Dynamic Relationship andPricing of Stocks and Exchange Rates: Empirical Evidence from Asian Emerging Markets”, Journal ofAmerican Academy of Business, Cambridge, 7(1), pp. 118-123.Erbaykal, E. and Okuyan, H.A., (2007), “Hisse Senedi Fiyatları ile Döviz Kuru ilişkisi:Gelişmekte Olan Ülkeler Üzerine Ampirik Bir Uygulama”, BDDK Bankacılık ve Finansal PiyasalarDergisi, 1(1), pp. 77-89.Granger, Clive W.J., Huang, Bwo-Nung, and Chin-Wei, Yang., (2000), “A bivariate causality betweenstock prices and exchange rates: evidence from recent Asian flu”, The Quarterly Review of Economicsand Finance, 40, pp. 337–354.Hatemi-J, A. and Irandoust, M., (2002), “On the Causality between Exchange Rates and Stock Prices:A Note”, Bulletin of Economic Research, 54(2), pp.197-203.Ibrahim, H and Aziz, H., (2003), “Macroeconomic variables and the Malaysian equity market: A viewthrough rolling subsamples”, Journal of Economic Studies, 30(1), pp. 6-27.Joseph, N.L., (2002), “Modelling the impacts of interest rate and exchange rate changes on UK stockreturns”, Derivatives Use, Trading & Regulation, 7(4), pp. 306-323.Kim, K., (2003), “Dollar Exchange Rate and Stock Price: Evidence from MultivariateCointegration and Error Correction Model”, Review of Financial Economics, 12, 301-313.Kurihara, Yutaka., (2006), “The Relationship between Exchange Rate and Stock Prices during theQuantitative Easing Policy in Japan”, International Journal of Business, 11(4), pp.375-386.Ma, C.K. and Kao, G.W., (1990), “On Exchange Rate Changes and Stock Price Reactions”,Journal of Business Finance & Accounting, 17 (3), pp. 441-449.MacKinnon, James G.., (1991), “Critical Values for Cointegration Tests In Long-Run EconomicRelationships: Readings in Cointegration”, (ed), R. F. Engle and C. W. J. Granger., Oxford: OxfordUniversity Press.Mishra, Alok Kumar., (2004), “Stock Market and Foreign Exchange Market in India: Are theyRelated?”, South Asian Journal of Management, 11(2), pp. 12-31.Mishra, Alok Kumar, Swain, Niranjan and Malhotra, D.K., (2007), “Volatility Spillover between Stockand Foreign Exchange Markets: Indian Evidence”, International Journal of Business, 12(3), pp. 343-359.Muhammad, Naeem and Rasheed, Abdul., (2002), “Stock Prices and Exchange Rates: Are TheyRelated? Evidence from South Asian Countries”, The Pakistan Development Review, 41(4), pp. 535-550.Nieh, Chien-Chung and Lee, Cheng-Few., (2001), “Dynamic relationship between stock prices andexchange rates for G-7 countries”, The Quarterly Review of Economics and Finance, 41, pp. 477–490.Nieh, Chien-Chung and Wang, Yu-Shan., (2005), “ARDL Approach to the Exchange RateOvershooting in Taiwan”, Review of Quantitative Finance and Accounting, 25, pp. 55–71.Ozair, Amber., (2006), “Causality Between Stock prices and Exchange Rates: A Case of The UnitedStates”, Florida Atlantic University, Master of Science Thesis.Pan, Ming-Shiun, Fok, Robert Chi-Wing and Liu, Y. Angela., (2007), “Dynamic linkagesbetween exchange rates and stock prices: Evidence from East Asian markets”, International Review ofEconomics and Finance, 16, pp. 503-520.Phillips, Peter C. B., and Perron, Pierre., (1988), “Testing for a Unit Root in Time SeriesRegression”, Biometrika, 75(4), pp. 335–59.Phylaktis, Kate and Ravazzolo, Fabiola., (2005), “Stock prices and exchange rate dynamics”, Journalof International Money and Finance, 24, pp. 1031-1053.Sevuktekin, Mustafa and Nargelecekenler, Mehmet., (2007), “Turkiyede IMKB ve Doviz KuruArasındaki Dinamik İlişkinin Belirlenmesi”, 8. Turkiye Ekonometri ve Istatistik Kongresi, InonuUniversitesi, Malatya. 52
Research Journal of Finance and Accounting www.iiste.orgISSN 2222-1697 (Paper) ISSN 2222-2847 (Online)Vol 3, No 1, 2012Smyth, R. and Nandha, M., (2003), “Bivariate causality between exchange rates and stock prices inSouth Asia”, Applied Economics Letters,10, pp. 699–704.Stavárek, Daniel., (2005), “Stock Prices and Exchange Rates in the EU and the USA: Evidence of theirMutual Interactions”, Finance a úvûr–Czech Journal of Economics and Finance, 55, pp. 141-161.Tsoukalas, Dimitrios., (2003), “Macroecomoic factors and stock prices in the emerging Cypriot equitymarket”, Managerial Finance, 29(4), pp. 87-92.Vygodina, Anna V., (2006), “Effects of size and international exposure of the US firms on therelationship between stock prices and exchange rates”, Global Finance Journal, 17, pp. 214– 223.Wu, Ying., (2000), “Stock prices and exchange rates in a VEC model-the case of Singapore in the1990s”, Journal of Economics and Finance, 24(3), pp. 260-274.Yau, Hwey-Yun and Nieh, Chien-Chung., (2006), “Interrelationships among stock prices ofTaiwan and Japan and NTD/Yen exchange rate”, Journal of Asian Economics, 17, pp. 535–552.Zapata, H. O. and Rambaldi, A. N., (1997), “Monte Carlo evidence on co-integration andCausation”, Oxford Bulletin of Economics and Statistics, 59, pp. 285-298. Table-1: Unit Root TestsSeries ADF PP KPSS Series ADF PP KPSS lex -3,14 (20)** -3,57(9)*** 0,40* (25) ∆ lex 0,33 (12) lnat -0,48 (1) -0,54(2) 4,14*** (25) ∆ lnat -31,22 (0)*** -31,22 (0)*** 0,05 (2) lser -0,20 (0) -0,19(4) 4,13 (25)*** ∆ lser -33,64(0)*** -33,65 (4)*** 0,07 (4) lfin -0,50 (1) -0,56 (2) 4,04 (25)*** ∆ lfin -31,20 (0)*** -31,20 (0)*** 0,07 (4) lind -0,75 (1) -0,79 (5) 4,29 (25)*** ∆ lind -31,10 (0)*** -31,09 (4)*** 0,03 (5) ltec -1,95 (2) -2,11 (10) 2,44 (25)*** ∆ ltec -22,20 (1)*** -32,51 (9)*** 0,04 (9)PP is the Phillips-Perron, ADF is the Augmented Dickey-Fuller, and KPSS is Kwiatkowski, Phillips,Schmidt, and Shin test.***, **, and * indicate rejection of the null hypothesis of non-stationary at the 1%, 5%, and 10%levels, respectively.The proper lag order for ADF test is chosen by considering Akaike Information Criteria (AIC) andwhite noise of residuals, representing in parenthesis.For KPSS and PP tests, the bandwidth is chosen using Newey–West method and spectral estimationuses Bartlett kernel, representing in parenthesis.The 1% and 5% critical value for the ADF and PP tests is -3,43 and -2,86, respectively.The 1%, 5%, and 10% critical value for the KPSS test is 0, 74, 0,46, and 0,35 respectively. 53
Research Journal of Finance and Accounting www.iiste.orgISSN 2222-1697 (Paper) ISSN 2222-2847 (Online)Vol 3, No 1, 2012 Table-2: Causality Results from Toda-Yamamoto Procedure The sing of the dmax and VAR P values for Direction of sum lagged (k) MWALD statistics causality coefficients From lnat to lex (1,7) 60.89*** (-) lex ↔ lnat From lex to lnfr (1,7) 38.42*** (-) From lser to lex (1,3) 54.08*** (-) lex ↔ lser From lex to lser (1,3) 8.11** (-) From lfin to lex (1,7) 57.69*** (-) lex ↔ lfin From lex to lfin (1,7) 36.10*** (-) From lind to lex (1,7) 56,20*** (-) lex ↔ lind From lex to lind (1,7) 41.98*** (-) From ltec to lex (1,7) 53.11*** (+) lex ↔ ltec From lex to ltec (1,7) 44.06*** (-)lnfr, lex, lser, lfin, lind, and ltec are the logs of infrastructures, nominal exchange rate, services,financials, industrials and technology, respectively.k is the lag length used in the system and dmax is the maximum order of integration (1).↔ denotes the bi-directional causality.*** and ** indicate significance at the 1% and 5% level, respectively. 54
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