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Applied EconomicsPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/raec20

Exploring the interaction between stock priceindex and exchange rates: an asymmetric thresholdapproachAthanasios Koulakiotisa, Apostolis Kiohosa & Vassilios Babalosb

a Department of International and European Studies, University of Macedonia, Thessaloniki54006, Greeceb Department of Accounting & Finance, Technological Educational Institute of Peloponnese,Kalamata, GreecePublished online: 06 Jan 2015.

To cite this article: Athanasios Koulakiotis, Apostolis Kiohos & Vassilios Babalos (2015) Exploring the interaction betweenstock price index and exchange rates: an asymmetric threshold approach, Applied Economics, 47:13, 1273-1285, DOI:10.1080/00036846.2014.990618

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Exploring the interaction between

stock price index and exchange

rates: an asymmetric threshold

approach

Athanasios Koulakiotisa, Apostolis Kiohosa and Vassilios Babalosb,*aDepartment of International and European Studies, University ofMacedonia, Thessaloniki 54006, GreecebDepartment of Accounting & Finance, Technological EducationalInstitute of Peloponnese, Kalamata, Greece

This article examines the impact of stock market news on the foreignexchange markets of USA, Canada and UK, employing an innovativeextension of the asymmetric threshold model of Apergis and Miller(2006). Under this framework we can disentangle the reaction of foreignexchange market to bad or good news and small or large news of stockreturns. Our comprehensive daily data-set spans the period from January1990 to June 2014. Using a cointegration and error correction model, wedocument the existence of a causal relationship between stock market andforeign exchange markets. Most interestingly, our results derived from theasymmetric threshold model confirm that the relationship between stockand foreign exchange markets is sensitive to short-term good or bad newsand short-term small or large news. Our findings entail significant impli-cations for policymakers, governments, risk managers and internationalinvestors.

Keywords: stock market; foreign exchange market; cointegration; asym-metric threshold model

JEL Classification: C32; G15; F21

I. Introduction

Rapid integration of capital and currency markets hascaught the attention of academics, researchers andpolicymakers since the late 1980s. From an investorpoint of view holding an internationally diversifiedportfolio, movements in the exchange rates are

crucial to balancing the demand and supply ofdomestic and foreign financial assets. On the otherhand, portfolio hedging needs are amplified wheninvestors engage in cross-border transactions due toexposure to foreign exchange risk. Hence, with theaim of establishing a causal relationship betweenequity prices and exchange rates, a number of studies

*Corresponding author. E-mail: vbabalos@teikal.gr

Applied Economics, 2015Vol. 47, No. 13, 1273–1285, http://dx.doi.org/10.1080/00036846.2014.990618

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have emerged, reaching contradictory results thatvary with the employed methodology, time periodand data sample.In particular, literature exploring the theoretical

foundations of the interaction between equity pricesand exchange rates maps itself into two distinctstrands that are ‘flow-oriented’ models and ‘stock-oriented’ or ‘portfolio-balance models’. When itcomes to the ‘flow-oriented’ models, proponents ofthis theory assert that a chain of macroeconomicevents triggered by a movement in a country’s cur-rency will result through the mechanism of the tradebalance or current account, in an increase in the coun-try’s real output and stock market values (Dornbuschand Fischer, 1980). Flow-oriented models predict apositive relationship between the foreign exchangerate and equity prices. A number of studies provideevidence in favour of the flow-oriented hypothesis ofexchange rates (see, inter alia, Chiang et al., 2000;Wu, 2000; Fang, 2002; Wongbangpo and Sharma,2002; Phylaktis and Ravazzolo, 2005). The oppositeunidirectional causality that runs from stock prices toexchange rates is grounded on the ‘portfolio balance’or ‘stock-oriented models’ and has also received thegrowing interest of many empirical studies (see interalia Branson, 1983; Frankel, 1983; Soenen andHennigar, 1988; Gavin, 1989; Kwona and Shinb,1999; Maysami and Koh, 2000; Ibrahim and Aziz,2003; Tai, 2007; Tsai, 2012). According to this expla-nation the exchange rate is determined through aportfolio balance effect as investors make investmentdecisions adjusting their portfolio exposure to alter-native assets including domestic currency, domesticbonds and equities, and foreign securities respectively.Broadly speaking, when stock prices surge/fall, thedemand for domestic assets and currency subse-quently grows/subsides, resulting in an appreciation/depreciation of domestic currency. At this point, it isworth mentioning that the empirical literature offerstwo different channels: a direct and an indirect one.According to the direct channel, international inves-tors – in the light of the stock market boom – will behighly motivated to increase their position on domes-tic assets and sell off foreign assets with the aim ofbuying more domestic assets and thus causing domes-tic currency to rise in value. According to Walid et al.(2011) the indirect channel works through the inter-dependence among stock market wealth, demand fordomestic assets and interest rates. Shortly speaking,when domestic stock market rises, the demand for

domestic assets will surge and consequently this willdrive interest rates to higher levels. As a result of thehigher interest rates the demand for the domesticcurrency will increase leading to domestic currencyappreciation.On the empirical front several researchers have

attempted to address the issue of stock market andcurrency market interplay all reaching contradictoryfindings see inter alia Aggarwal (1981), Jorion(1990), Ajayi et al. (1998), Chiang and Yang(2003), Pan et al. (2007). Recently, Tsagkanos andSiriopoulos (2013), employing a novel nonpara-metric econometric method, highlight the existenceof a positive, causal relationship from stock prices toexchange rates that is long-run in EU and short run inUSA during the recent financial turmoil. On the otherhand, Tsai (2012), relying on data for six Asiancountries, documents a negative relation betweenequity and currency markets with a larger magnitudewhen exchange rates are extremely high or low whileZhao (2010) fails to establish a long-term equili-brium relationship between renminbi real effectiveexchange rate and stock price. A different approachwas adopted byMoore andWang (2014), who exam-ined the causes of the dynamic correlation betweenstock market returns and real exchange rates ofdeveloped and emerging Asian markets in relationto the US market. Their results stressed the impor-tance of macroeconomic variables such as trade bal-ance in shaping a dynamic correlation between theAsian markets. In a related study, Liang et al. (2013)concluded that exchange rates impact stock pricesnegatively via capital mobility. Employing anadvanced dynamic conditional correlation modelLee et al. (2011) have documented significant pricespillovers from stock market to foreign exchangemarket for Indonesia, Korea, Malaysia, Thailandand Taiwan. Related to the above, two studies(Abdalla and Murinde, 1997; Hatemi-Ja and Roca,2005) have examined the interaction of stock marketand foreign exchange markets for a group of emer-ging financial markets. To sum up, the majority of theaforementioned studies examine the symmetric cor-relation between exchange rates and stock returnsthrough causality dynamic correlation techniquesand cointegration. On the contrary, Kanas (2000)and Yang and Doong (2004) examined the asym-metric volatility spillovers between these variables.However, to the best of our knowledge no study hasattempted to account for the impact of small and large

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news on the long-run equilibrium relationshipbetween exchange rate and stock returns. Thus, itwould be challenging to establish whether or nottransmissions between two asset markets, the cur-rency market and the equity market, exhibit differentpatterns in the advent not only of good or bad newsbut also of large or small news.There are a number of significant contributions to

the literature. With the above in mind, we opt for anasymmetric threshold approach in order to shed light,for the first time in the literature, on the underlyingmechanism that governs the response of exchangerate differentials to stock price returns. We extend themodel of Apergis and Miller (2006), adding thresh-old effects to their asymmetric approach which isconsistent with the suggestions of Longin andSolnik (1995).1 Our analysis spans an extensive per-iod of time, incorporating different market phasesand various stock market crashes.The overarching goal of this study is to disentangle

the impact of bad and good news as well as of smalland large news of stock returns on exchange rates. Tothis end, we have chosen to examine the asymmetricthreshold impact of stock returns on exchange ratedifferentials for three core country members of the G-7 group, namely USA, Canada and UK. Therefore,we investigate stock market and foreign exchangemarket causality under a different perspective, incor-porating the threshold effects in the standard cointe-gration analysis.Previewing our results, we provide evidence in

favour of a causal relationship between stock andforeign exchange markets. In particular the resultsreveal that at the 1% level, there is a negative relationbetween stock price and exchange rate for the USmarket whereas there is a positive relation betweenstock price and exchange rate for the Canadian andUK market. Our results confirm the existence of aportfolio balance effect in the markets under exam-ination and for the selected period. More interest-ingly, currency markets respond asymmetrically tostock market news in the short run, with the effectbeing concentrated in Canada and UK. When itcomes for the scale, and the sign of the news it shouldbe noted that both proved significant for all marketsincluded in the study.

The rest of the article is structured as follows. InSection II we briefly describe the data sources, thevariable definition and present some preliminary sta-tistics of the employed variables. Section III presentsand analyses the empirical results, while Section IVoutlines the main findings of the asymmetricresponse analysis. Section V provides conclusionsand policy implications.

II. Data

Following Tesar and Werner (1995) and Kanas(2000), we employ the effective exchange rateseries for USA, Canada and UK, collected fromthe Bank of England. For instance, the trade-weighted exchange rate series for the sterling is aweighted geometric average of bilateral exchangerates against sterling, with the weights mirroringthe relative importance of the other currencies, asmeasured by trade flows of manufactured goodsbetween the countries under consideration.2 As forthe stock markets we utilize the closing prices ofStandard and Poor’s 500 (USA), FT All SharePrice Index (UK) and S&P/TSX CompositeIndex (Canada), all denominated in local currency.Data on stock market indices were retrieved fromBloomberg.Our data-set consists of daily values of the afore-

mentioned variables and spans the period from 1January 1990 to 30 June 2014, incorporating differ-ent market phases and stock market crashes such asthe dot com bubble and the recent global financialcrisis. Continuously compounded returns andexchange rate changes were calculated as the differ-ence between the logarithms of the closing prices fortwo successive trading days, using the followingformula:

St ¼ log Ps; tð Þ � log Ps; t � 1ð Þ (1)

and

Et ¼ log Pe; tð Þ � log Pe; t � 1ð Þ (2)

1Longin and Solnik (1995) examined the correlation between stock dividends and stock price returns using a thresholdapproach in the context of a bivariate GARCH framework capturing the asymmetric dimension of the transmission of news.2 For more information visit Bank of England (www.bankofengland.co.uk)

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where Ps,t and Pe,t are the stock price index andexchange rate at period t respectively.

Descriptive statistics for stock price returnasymmetries-thresholds on exchange rates

The descriptive statistics for US, Canadian andBritish stock price returns are depicted in the threefigures below. Our goal in this section is to examinewhether there is an asymmetry threshold effect in thedata series of the stock markets under examination.This task is easily performed by examining some keydistributional measures such as the Skewness metric.In particular, we observe at Figs. 1–3 that skewness isnegative and close to zero for all the employed stockmarket indices. This means that the distribution ofstock price returns is skewed to the left. As a result,the distribution is thicker in the lower tail.In addition, due to the fact that Kurtosis is larger

than 3 for all the countries under study, the distribu-tion of stock price returns is thicker in the tails. Ascan be seen below the Anderson–Darling Normalitytest provides evidence against normality for the ser-ies under examination. Furthermore, the values ofmedian for USA (see Fig. 1) and UK (see Fig. 3)series are smaller than the values of mean respec-tively; in contrast, the value of median for Canadian

(see Fig. 2) series is larger than the values of mean.Due to the fact that the median is substantially belowthe mean, the series for USA and UK stock marketshave, in addition, a long upper tail.Considering these preliminary statistical results, it

is rational to expect some asymmetry in the employedseries. This asymmetry may be due to the thickness ofthe upper tail or the lower tail. Thus, we might detectnegative or positive impact of news (asymmetry) andalso small or large effects (threshold) on exchangerates. The latter is due to the fact that the confidenceinterval of variance (σ) overrides the confidence inter-val of mean (µ) and median (see Figs. 1– 3).

Interpreting the relation between stock price andexchange rate returns of USA, Canada and UK

In order to get a preliminary view of the underlyingrelationship between the returns of the stock pricesand exchange rates we compare the trend componentof the two series. In the two figures below, weobserve that in the US market the stock price andthe exchange rate series are related negatively from1990 to 2008 and from 2010 to 2014, putting empha-sis on the portfolio balance effect theory until 2004.This means that the increase of the returns of thestock prices will decrease the domestic exchange

0,0460,0360,0260,0160,006–0,004–0,014–0,024–0,034–0,044

95% Confidence Interval for μ

0,00020,00010,0000

95% Confidence Interval for Median

Variable: USA

1,91E-05

4,82E-03

–5,1E-06

MaximumThird QuartileMedianFirst QuartileMinimum

NKurtosisSkewnessVarianceSDMean

p-Value:A2:

2,02E-04

4,99E-03

2,36E-04

4,76E-022,40E-031,12E-04–1,9E-03–4,1E-02

63819,11472

–2,4E-012,41E-054,91E-031,15E-04

0,000114,552

95% Confidence Interval for Median

95% Confidence Interval for σ

95% Confidence Interval for μ

Anderson–Darling Normality Test

Fig. 1. Descriptive statistics for USA stock price returnsSource: Authors’ estimations, Bloomberg.

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0,0360,0260,0160,006–0,004–0,014–0,024–0,034–0,044

95% Confidence Interval for μ

0,00020,00010,0000

95% Confidence Interval for Median

Variable: UK

0,00E+00

4,73E-03

–4,8E-05

MaximumThird QuartileMedianFirst QuartileMinimum

NKurtosisSkewnessVarianceSDMean

p-Value:A2:

1,00E-04

4,90E-03

1,88E-04

4,08E-022,50E-03

0,00E+00–2,3E-03–4,0E-02

63816,45984

–1,2E-012,32E-054,81E-036,99E-05

0,00072,321

95% Confidence Interval for Median

95% Confidence Interval for σ

95% Confidence Interval for μ

Anderson–Darling Normality Test

Fig. 3. Descriptive statistics for UK stock price returnsSource: Authors’ estimations, Bloomberg.

0,0420,0320,0220,0120,002–0,008–0,018–0,028–0,038

95% Confidence Interval for μ

0,00020,00010,0000

95% Confidence Interval for Median

Variable: Canada

6,29E-05

4,27E-03

–1,6E-05

MaximumThird QuartileMedianFirst QuartileMinimum

NKurtosisSkewnessVarianceSDMean

p-Value:A2:

2,36E-04

4,42E-03

1,97E-04

4,07E-022,20E-031,46E-04–1,7E-03–4,3E-02

638111,4351

–7,5E-011,89E-054,34E-039,04E-05

0,000129,738

95% Confidence Interval for Median

95% Confidence Interval for σ

95% Confidence Interval for μ

Anderson–Darling Normality Test

Fig. 2. Descriptive statistics for Canadian stock price returnsSource: Authors’ estimations, Bloomberg.

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rate, causing the respective country’s currency toappreciate. For the other two markets (Canada andUK) there is a similar trend between the exchangerate and stock price returns. In addition, we canclearly see in Figs. 4 and 5 that in the substantialfinancial crisis of 2008/10 there is a positive relationbetween the stock prices and the domestic exchangerates. However, in the above crisis period, this rela-tion is not very clear for the Canadian series, regard-less of the existence of a positive trend in the mostpart of the period under study.Therefore, the investigation of the relation

between stock prices and exchange rates in theshort-term context is worth to be examined withmodels that capture asymmetries and thresholds.One would expect that the short-term effects, whichdue to asymmetry or threshold effects are more pro-nounced when exchange rates are extremely high orlow in the period under study.Furthermore, we examine the long-term effects of

the two markets in order to clarify whether there is amore stable relation between our two data series

under study, and whether the portfolio balance effectpersists for the whole period.

III. Methodology and Empirical Results

Integration analysis

Before establishing a cointegration relationship it iscrucial to check if the variables are integrated of thesame order. Thus, we perform the standard test ofDickey and Fuller (1981). Our results are indicative ofthe presence of a unit root in the logarithmic values ofER (exchange rate) and Si (Stock Index) at the 5% levelof significance. However, as expected when we con-sider the first differences, we can reject the hypothesisof a unit root for the above two variables. The results ofunit root tests based on the Akaike criterion for theproper number of model lags are reported in Table 1.

Cointegration analysis: identifying the exchangerate fluctuations

Before proceeding with our asymmetric thresholderror correction model (Equation 4) we performed astandard cointegration analysis test which considersthe long-term effect of stock price returns on exchangerate fluctuations as indicated by Johansen and Juselius(1990).Table 2 presents the results of cointegration tests

between stock and exchange rate markets for USA,Canada and UK. In the long-term basis, the resultsindicate the significance of Max-Eigen and Trace test

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 20140

2000

4000

6000

8000

10 000

12 000

USA

UK

Canada

14 000

16 000

Fig. 4. Stock index plots for the USA, UK andCanadaSource: Authors’ estimations, Bloomberg.

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 201470

80

90

100

110

120

130Canada

UK

USA

Fig. 5. Exchange rate plots for the USA, UK andCanadaSource: Authors’ estimations, Bank of England.

Table 1. Unit root tests

Without trend With trend

Levels Differences Levels Differences

For USAER −1.44(1) −57.64(1) −1.84(1) −57.64(1)Si −1.24(3) −60.28(1) −1.71(1) −60.28(1)For CanadaER −1.38(1) −57.95(1) −2.20(1) −57.96(1)Si −0.73(1) −57.75(1) −2.58(1) −57.74(1)For UKER −1.73(1) −55.32(1) −1.70(1) −55.32(1)Si −1.64(1) −59.25(1) −2.39(1) −55.25(1)

Notes: (1) The figures in parentheses denote the number oflags in the tests that ensure white noise residuals. Theyhave been estimated through the Akaike criterion.(2) ER symbolizes the domestic Exchange rate, Si symbo-lizes the stock index.

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statistic at 10% significance level for the UK marketunder study. Thus, the short-term impacts of asym-metric (positive and negative) and threshold (smalland large) stock price news on the domestic exchangerate returns may be more important than the long-termimpact of error correction factor. As a result, investi-gating thoroughly the significance of both long-termand short-term impacts of stock price news on theexchange rate returns is worthwhile.We run the cointegration equation as follows:

Exchange rate ¼ α0 þ α1 �Stock priceþ εt (3)

We also test for Autocorrelation and Normality onresiduals of the above model to decide on the validityof this equation. The cointegration results of Table 3are mixed with respect to the causal nexus betweenstock prices and exchange rates. In particular, theresults reveal that at the 1% level, there is a negativerelation between stock price and exchange rate for theUSmarket whereas there is a positive relation betweenstock price and exchange rate for the Canadian andUK market. The former clearly indicates that anincrease (decrease) of the returns of stock price indexin USA will decrease (increase) the exchange rate,resulting in domestic currency appreciation (deprecia-tion). Our results contradict those reported byTsagkanos and Siriopoulos (2013) who failed to detectany long-term linear relationship between stock mar-ket returns and the bilateral euro to $ exchange rate.Table 3 reports the real effect of stock price values(−0.04, 0.12 and 0.11) on exchange rates. The criteriaof the presence of autocorrelation and normality aresatisfied in all the three markets under examination.

IV. Asymmetric and Threshold Effectsbetween Stock Prices and ExchangeRates

In this section we set out to explore potential asym-metric threshold response of exchange rates to stockmarket news. In their seminal study Longin andSolnik (1995) have included four dummies in theirunivariate GARCH model examining the impact ofpositive, negative, small and large news on volatility.In the approach of this article, the good and bad news

are defined as the positive and negative stock price

Table 2. Cointegration tests for USA, Canada and UK

Rank Eigen value Max-Eigen statistic 0.05 critical value Trace Trace-95%

For USA0 0.000581 3.704 (0.88) 14.264 5.118 (0.79) 15.4941 0.000222 1.414 (0.23) 3.841 1.414 (0.23) 3.841For Canada0 0.000312 3.904 (0.86) 14.264 5.155 (0.79) 15.4941 0.000196 1.250 (0.26) 3.841 1.250 (0.26) 3.841For UK0 0.000708 4.517 (0.801) 14.264 7.445 (0.526) 15.4941 0.000459 2.927 (0.087) 3.841 2.927 (0.087) 3.841

Notes: Rank = number of cointegrating vectors, Maximum eigen value statistic = λ-max, Trace = trace statistic and numbers inparentheses are the p-values, p-values based on MacKinnon-Haug-Michelis test (MacKinnon et al., 1999), p-value < 0.10denotes rejection of the hypothesis of cointegration. Trace test andmax-eigen tests indicate cointegration only in theUKmarket.

Table 3. Cointegration between exchange rate andstock prices

Exchange rate ¼ α0 þ α1 �Stock priceþ εt

Exchange rate/stock price Model 1

For USAConstant 4.85 (0.01)*Stock price −0.04 (0.01)*Autocorrelation (LM-T test) 75979.57*Normality (p-value) 52.28*For CanadaConstant 3.41 (0.02)*Stock price 0.12 (0.01)*Autocorrelation (LM-T test) 76041.19*Normality (p-value) 90.178*For UKConstant 3.49 (0.03)*Stock price 0.11 (0.03)*Autocorrelation (LM-T test) 75646.21*Normality (p-value) 246.85*

Note: (*) denotes statistical significance at the 1% level.

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returns respectively. Small and large news are definedas the stock price returns which are smaller or greaterthan a threshold value. The threshold value in ourmodelis the negative SD of returns for the small news and thepositive SD of returns for the large. In our model, thesenews are segregated without producing problemsbecause positive and negative returns and small andlarge news do not contradict to each other. This meansthat our model is not subject to a dummy problem andfor this reason it is not necessary to exclude from ourmodel the constant or one out of the four dummies.Following the above suggestion, we included the

above four dummy variables in our error correctionmodel (ECM) examining the impact of stock pricecharacteristics (news) on exchange rate responses.Our contribution here is that we augment the specifi-cation that accounts for asymmetric effects by Apergisand Miller (2006) to incorporate not only short-termpositive and negative but also short-term small andlarge news of stock price returns. Therefore, ouradvanced model takes the following form:

EC ¼ Exchange rate� a0 � a1 �Stock price

ΔER ¼ α0 þ β1 �ΔERðiÞ þ β2 �Δs�þ β3 �Δsþ þ β4 �Δss þ β5 �Δslþ β6 �ECð�1Þ þ v (4)

where,

ΔER is the difference of exchange rate termβ1 measures the effect of long-term lagged exchangerate returns differenceβ2 and β3 measure the effect of short-term negativeand positive news on exchange rates difference,respectivelyβ4 and β5 measure the effect of short-term small andlarge news on exchange rates difference, respectivelyβ6 measures the effect of long-term lagged level errorcorrection term based on the equation (EC =Exchange rate + α0 + α1* Stock price).EC is the error correction termσ is the SD of the stock price return termΔs are the dummy variables that take the values:ΔsS = 1 if Δst is equal or less than −σΔs� = 1 if Δst is equal or less than 0Δsþ = 1 if Δst is greater than 0Δsl = 1 if Δst is equal or greater than σ

Estimation yields the results reported in Table 4.The parentheses of the above terms include the SEs,while the F-test shows the holistic significance of themodel. Also, Table 4 presents some tests for theresiduals of the models. In particular, we refer tothe Autocorelation test of Ljung-Box (12) lags, theNormality test of Darling–Anderson and the hetero-scedastic test of Breusch–Pagan. The presence ofheteroscedasticity and normality for all the threecases of the markets of USA, Canada and UK issatisfied. There is absence of serial correlation onlyin the US market. This means that, in the future,ECM could account for heteroscedasticity in a var-iance latent equation as developed by Bollerslev(1990) and could be further expanded.Following the development by Bollerslev (1990)

and our expanded EC model the short-term positiveand negative values are compared with the short-termsmall and large values of coefficients of the ΔERmodel and found to be not statistically equal (see inTable 4: β2 + β3 = β4 + β5) for all the countries understudy using an F-test. Also, in the short-term, thepositive values are tested to see if there were equalto negative values (β2 = β3) using an F-test.Moreover, in the short term the small values areexamined if there were equal to large values(β4 = β5) using an F-test.The procedure applied successfully for positive and

negative values and also for small and large values ofstock price returns in the UK market (β2 − β3 < 0 andβ4 − β5 < 0) and for positive and negative values ofstock price returns in Canadian market (β2 − β3 < 0)using a T-test. The results of F-test and T-test arereported in the last two columns of Table 4 indicatinga statistically significance difference. Thus, exchangerates respond to positive and small values of stockprice returns differently than negative and large valuesonly in the UK market. In the other two markets thelarge news of stock price returns found not to besignificant. In addition, positive values were alsofound insignificant in the US market.Furthermore, the results in Table 4 (Column 2)

indicate that negative news (0.04) of stock pricereturns is significant while positive news found notto affect the exchange rate fluctuations in the USmarket. A significant result found for the smallvalues (−0.06) of stock price returns with an oppositesign. This means that short-term asymmetric andthreshold effects on US exchange rate returns arepartly different in both significance and magnitude.

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In Canada, positive (0.05) and negative (0.06)news of stock price returns are significant withalmost equal magnitude and also there are significantresults for the small scale news (0.08) of stock pricereturns on exchange rate returns. This means that theimpact of small news is more statistically significantthan of large news. Furthermore, in the UK market,negative (−0.03) and positive (−0.03) as well as small(0.04) and large (0.03) news found to be statisticallysignificant and this indicates the importance of a fullimpact of asymmetric and threshold effects of stock

price returns on exchange rate fluctuations.Therefore, in the short-term, threshold effects ofstock returns have a full impact on exchange ratereturns only in the UK market whereas asymmetriceffects of stock returns have a full impact onexchange rate returns in the two out of the threemarkets (Canada and UK).Lastly, Table 4 indicates that long-run effects (EC)

found to be significant for Canada (0.01) and UK(−0.01) at the 5% level. Therefore, both short-termand long-termeffects between stock price and exchange

Table 4. Cointegration between exchange rate and stock prices with contemporaneous dummy variables

EC ¼ Exchange rate� a0 � a1 �Stock priceΔER ¼ α0 þ β1 �ΔERðiÞ þ β2 �Δs� þ β3 �Δsþ þ β4 �Δss þ β5 �Δsl þ β6 �ECð�1Þ þ v

Exchange rate/stock price Values Tests F-test values T-test values

For USAExchange rate returns(−1) −0.01(0.01) β2 + β3 = β4 + β5 274454.27*Stock price returns(−) 0.04(0.01)* β4 = β5 = 0 73708.95*Stock price returns (+) −0.01(0.01) β2 = β3 = 0 29481.28*Stock price returns (s) −0.06(0.01)*Stock price returns (l) −0.01(0.01)Error correction(−1) −0.01(0.01)Autocorrelation (LM-T test) 10.63Normality (p-value) 31.03Heteroscedasticity(p-value) 106.76*For CanadaExchange rate returns(−1) −0.01(0.01) β2 + β3 = β4 + β5 187651.29*Stock price returns(−) 0.06(0.02)* β4 = β5 = 0 45957.39*Stock price returns (+) 0.05(0.01)* β2 = β3 = 0 19005.50*Stock price returns (s) 0.08(0.02)* β2 − β3 < 0 −59.06*Stock price returns (l) 0.02(0.02)Error correction(−1) 0.01(0.01)**Autocorrelation (LM-T test) 38.68*Normality (p-value) 65.62*Heteroscedasticity(p-value) 30.78*For UKExchange rate returns(−1) 0.05(0.01)* β2 + β3 = β4 + β5 297340.27*Stock price returns(−) −0.03(0.01)* β4 = β5 = 0 84012.47*Stock price returns (+) −0.03(0.01)** β2 = β3 = 0 38103.80*Stock price returns (s) 0.04(0.01)** β2 − β3 < 0 −63.51*Stock price returns (l) 0.03(0.01)** β4 − β5 < 0 −35.45*Error correction(−1) −0.01(0.01)**Autocorrelation (LM-T test) 20.75***Normality(p-value) 53.75*Heteroscedasticity(p-value) 200.19*

Notes: Exchange rate returns(−1) = ΔΕR(i)Stock price returns(−) = Δs−

Stock price returns(+) = Δs+

Stock price returns(s) = Δss

Stock price returns(l) = Δsl

(*)(**)(***) denotes significance at the (1%) (5%) (10%) levels respectively.

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rate returns play a substantial role in our analysis, withshort-term effects being superior to long-term effects.This adds value to the importance of ‘asymmetricthreshold’ effects in our methodology, investigatingthe response of exchange rate fluctuations to fourshort-term stock price return proxies (‘bad’, ‘good’,‘small’ and ‘large’ news). Note that our EC specifica-tion in Equation 4 accommodates one long-term effectfor the Canadian and UK market which is significant.

V. Conclusions

The aim of this article was to investigate the impactof the direction and scale of stock market news onexchange rates for three major G-7 countries, namelyUSA, Canada and UK. We extend the model ofApergis and Miller (2006), adding threshold effectsto their asymmetric approach which is consistentwith the suggestions of Longin and Solnik (1995).Our preliminary findings favour the existence of aportfolio balance effect theory that manifests itselfthrough a causal relationship between stock and for-eign exchange markets.Turning to the existence of short-term asymmetric

threshold response of the foreign exchange markets,the empirical results reveal that short-term small newsaffect exchange rates of the above mentioned stockmarkets more than large news, in general. In particu-lar, short-term small news of stock market returns forUSA and Canada affects exchange rates more inten-sely than short-term large news of stock price returnsof these markets. In the UK both short-term small andlarge news found to be significant. Regarding theimpact of the direction of the news in the short-term,we conclude that positive and negative news are sig-nificant but not similar in Canada and UK, whereasonly negative news is significant in the USA.The long-term impact of error correction factor on

the domestic exchange rate return found to be signifi-cant only in Canada and in the UK but not in the USA.This finding might be explained by the existence ofprice bubbles in Canada and in the UKwhich are wellcaptured by the short-term proxy of asymmetricthreshold impact analysis through our model. Thisfinding is in accordance with Tsai (2012) study, whofound that the relationship between stock prices andexchange rates is negative in Asian markets and it ismore pronounced when exchange rates are extremely

high or low. In the same vein we document a positiverelationship between stock prices and exchange ratesfor Canada and UK which appears more solid, whenthe stock prices are extremely high or low, small orlarge. Our results entail implications for internationalportfolio management In particular, the documentedasymmetric causal nexus between stock markets andexchange rates could be useful for international inves-tors and managers when it comes to hedging anddiversification strategies.

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