Day of the Week Effect on European Stock Markets

Transcription

1 International Research Journal of Finance and Economics ISSN Issue (006) EuroJournals Publishing, Inc Day of the Week Effect on European Stock Markets Rosa María Cáceres Apolinario, Octavio Maroto Santana and Lourdes Jordán Sales Departament of Financial Economics and Accounting The University of Las Palmas de Gran Canaria Spain Alejandro Rodríguez Caro Departament of Quantitative Methods The University of Las Palmas de Gran Canaria Spain Abstract The day of the week effect implies that the stocks return is not independent of the day of the week in which they are generated. The existence of seasonal behaviour in return and volatility of different international stock exchanges may be considered as an indication of non integrated financial markets. Investment opportunities can therefore arise from this abnormal behaviour. This paper focuses on this type of opportunity, specifically on the analysis of the day of the week effect on the major European stock markets by means of GARCH and T-ARCH models. The findings indicate that abnormal behaviour is not present in the returns of these stock markets. In addition, evidence is obtained of the day of the week effect in the volatility of major European stock markets, using symmetric and asymmetric models. Key Words: Day of the week effect, volatility, GARCH, T-ARCH. JEL Classification: G, C3. I. Introduction The increasing internationalisation of the main economies from developed nations has given the investor additional choices when considering his portfolio. He is no longer obliged to focus his attention on the financial markets where the assets of his own country are listed in the stock market but instead may look towards other investment horizons whose markets offer opportunities to obtain greater results with respect to profit and risk. This scenery is characterised by significant relaxation of national barriers, thus allowing for the entrance of foreign capital, and its repercussions are seen in the considerable increase in international capital flows (see Climent and Meneu, 999). Torrero (999) recognised the same occurrences and commented that institutional investors caused greater internationalisation in investments due to growing influence of international investments in their portfolios. Nevertheless, it is necessary to remember that investment opportunities in international markets depend on the degree of integration or segmentation that said markets possess, even though the increasing international nature of these economies is quite evident. In this respect, Jacquillat and Solnik (978) stated that the advantages that are derived from international diversification result in the relative Harvey (99) claimed that if two assets are sold in two completely integrated financial markets at the same risk level, then they would have the same expected profitability, leading to a decision to avoid diversification within these markets.

2 4 International Research Journal of Finance and Economics - Issue (006) independence between the distinct national economies and the price behaviour of securities. Thus, if markets are highly integrated than opportunities of receiving profits from an international portfolio are not so high. The presence of anomalies in international financial markets can be a clear sign that a lack of integration among these markets exists, thus investment opportunities derived from different behaviours in the generation of returns are available. Several studies have centred on relative anomalies in the seasonality of distinct financial markets of developed countries as an explanation to why there is an absence of integration between international financial markets. The growing use of daily data has led to additional research in the financial literature, specifically extending the analysis of seasonal behaviour to include the day of the week effect, the weekend effect and the bank holiday effect. The financial literature on this topic has offered several justifications for these anomalies: the absence of negotiations during the weekends, Monday availability of information regarding responses to generated information during non-listing days; market transaction payment procedures, effects derived from liquidity, etc. This seasonality has been the subject of different studies which detected empirical evidence of abnormal yield distributions based upon the day of the week. The pioneering work was carried out using data from the U.S. market. The following authors, among others, made important contributions: Osborne (96), Cross (973), French (980), Gibbons and Hess (98), Lakonishok and Levi (98), Keim and Stambaugh (984) and Rogalski (984). This effect has also been analysed in security markets under an international setting in works by Jaffe and Westerfield (98a), (98b), Aggarwal and Rivoli (989), Solnik and Bousquet (990), Chang, Pinegar and Ravichandran (993), Athanassakos and Robinson (994), Corredor and Santamaría (996), Dubois and Louvet (996) and Kyimaz and Berument (00). The objective of this paper is to empirically contrast the day of the week effect in the major European stock markets from July 997 to March 004. We will study not only return but volatility as well. The day of the week effect under a volatility context has not received much attention in the literature. The motivation for this paper comes from the growing process of integration of the distinct world economies and European economies in particular, resulting in an increasing correlation and synchronization among financial markets from different countries. The paper is divided into the following sections. Section presents a brief review of the financial literature dealing with the anomaly commonly referred to as the day of the week effect. Section 3 then follows with a description of the database as well as the methodology employed in the paper. The estimations from the GARCH and T-ARCH models and the results are presented in Section 4. The paper ends with a summary of the main conclusions. II. Earlier Studies There is an extensive amount of financial literature which focuses on the day of the week effect. Osborne (96) and Cross (973) discovered empirical evidence demonstrating that Monday yields were lower than Friday ones for the S&P 00 Index. Similar results are presented in French (980), upon comparing Monday, Friday and weekly average returns for the same index. He observed that Friday returns were greater than the average while Monday returns were lesser than the average. Gibbons and Hess (98) also came to the conclusion that Mondays resulted in negative returns. Their study was based on a sample of 30 stocks from the Dow Jones Industrial Index. Lakonishok and Levi (98) have offered market transaction payment procedures as an explanation for the seasonal behaviour in the daily yields. Keim and Stambaugh (984) tried to explain the weekend effect in the American market as being related to the measurement errors in stock prices. These earlier studies of the day of the week effect were based on yield calculations at closing between two dates. Rogalski (984) approached the problem by dividing yields into non-trading periods (from close to opening) and trading periods (from opening to close). He came to the conclusion that negative Monday returns were generated between the Friday closing and Monday opening, thus

3 International Research Journal of Finance and Economics - Issue (006) not taking into account the differences in average returns on specific days of the week when considering the trading period. These studies were first carried out in U.S. Stock Market and later in other international financial markets. This approach allowed Jaffe and Westerfield (98a) to obtain evidence of the weekend effect for the markets in Canada, Australia, Japan and the United Kingdom. Negative Tuesday returns were also obtained for the Japanese market (see Jaffe y Westerfield (98b)). Similar results are presented in Condoyanni, O Hanlon and Ward, (987) for the Singapore, Japan and Australia markets. The French and Italian markets are studied by Solnik and Bousquet (990) and Barone (990), respectively. Mention must also be given to authors such as Connolly (989) and Chang, Pinegar and Ravichandran (993), who analysed the robustness of the utilized techniques for the study of seasonality, including adjustments in size, heteroskedasticity, autocorrelation and kurtosis. Evidence has also been found showing the disappearance of the day of the week effect in Belgium, Denmark, Germany and the U.S. for a sample of 4 national indexes. Athanassakos and Robinson (994) observed negative tuesday returns in the Canadian market which exceeded those from mondays. Nevertheless, Dubois and Louvet (996) did not arrive to any clear conclusions when they studied nine international markets using both parametric and non-parametric tests. Several conditional autoregressive heteroeskadistic models have been developed and applied to the analysis of financial series by several researchers since the work of Engle (98). This approach has also been widely used in the analysis of seasonality, as can be specifically seen in Copeland and Wang (994), Corhay and Rad (994), Theodossiou and Lee (99), Corredor and Santamaría (996), Miralles and Miralles (000), Amigo and Rodríguez (00) and Kyimaz and Berument (00). Corredor and Santamaría (996) studied the NYSE and five other European exchanges using a GARCH (,) model. They observed daily seasonality in London, Paris, Madrid and Milan. Miralles and Miralles (000) analyzed daily seasonality in the Lisbon Stock Exchange using the same model. Amigo and Rodríguez (00) used GARCH (,) and T-GARCH 3 (,) models on the Nuevo Mercado and found common seasonal structures based on the stock returns that made up the market index. Kyimaz and Berument (00) studied daily seasonality in five international markets through the use of different variations of the GARCH model. III. Data and Methodology a) Data The present paper used series of daily returns from the corresponding stock indices of the following European markets: Germany, Austria, Belgium, Denmark, Spain, France, The Netherlands, Italy, Portugal, The United Kingdom, The Czech Republic, Sweden and Switzerland. The sampling dates begin with July, 997 and end on March, 004. The returns for each market are expressed in local currency and have been calculated as first differences in natural logarithms according to the following expression: r t = p t ln p t where p t and p t- are the values for each index for periods t and t-, respectively. The analysis of the day of the week effect was carried out in the following manner. First we used five observation per week in order to avoid possible bias from the loss of information due to bank See, among others, French, Schwert, and Stambaugh (987), Baillie and Bollerslev (989), Hsieh (989), Baillie and DeGennaro (990), Hamao, Masulis and Ng (990), Nelson (99), Campbell and Hentschel (99), Glosten, Jagannathan and Rungle (993). 3 These authors use the term GJR for the T-ARCH model.

4 6 International Research Journal of Finance and Economics - Issue (006) holidays 4. A total of 74 yields were collected for each of the analysed markets. Table I shows the indices used for each country market in our sample. Table I: Description of the sample Country Index Country Index Germany DAX Italy MIB-30 Austria ATX Portugal PSI-0 Belgium BEL-0 U. Kingdom FTSE-00 Denmark KFX Czech Rep. PX-0 Spain IBEX-3 Sweden Stockholm General France CAC-40 Switzerland Swiss Market Holland AEX Table II summarises the main descriptive statistics from the yield series. The limited asymmetry of the indices in the table should be noted, being positive or to the right in 6% of the cases. The high kurtosis values in all of the yield series and its implications deserve attention, namely that the normality test is rejected for all the analysed cases. Table II: Descriptive Statistics Germany Austria Belgium Denmark Spain France Holland Mean 0,000 0,000 0,000 0,0003 0,000 0,000 0,0000 Median 0,0007 0,0006 0,000 0,0003 0,0008 0,0004 0,0003 Maximum 0,078 0,040 0,0978 0,00 0,0668 0,07 0,0998 Minimum -0,069-0,0833-0,046-0,0607-0,0707-0,0739-0,07 Stand. Dev. 0,079 0,006 0,09 0,03 0,06 0,060 0,07 Asymmetry 0,004-0,688 0,4-0,793-0,089 0,0076 0,086 Kurtosis 4,4037 8,334 7,6379 4,88 4,643 4,8880,880 J-B 44,00.06,7.6,48 9,489 99,8 60,30 49,646 Prob. 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 0,0000 Italy Portugal Czech R. Sweden Switzerland U. K. Mean 0,000 0,000 0,0004 0,000 0,000 0,0000 Median 0,0003 0,000 0,0003 0,0004 0,0003 0,000 Maximum 0,0808 0,079 0,099 0,038 0,077 0,0608 Minimum -0,06-0,094-0,0683-0,0666-0,06-0,044 Stand. Dev. 0,0 0,00 0,09 0,03 0,038 0,08 Asymmetry 0,077-0,0-0,436 0,3 0,040 0,003 Kurtosis 4,786 7,938 4,64,4738,9979 4,6467 J-B 33,6.88, 98, ,448 67,39 98,74 Prob. 0,0000 0,0000 0,0000 0,0000 0,0000 0, However, Rogalski (984), Keim and Stambaugh (984), Kim and Park (994) and Aggarwal and Schatzberg (997), among others, remove post -bank holiday trading from their studies. That is, they only use returns whose calculation is performed with one day of difference for tuesday, wednesday, thursday and friday and three days of difference for mondays.

5 International Research Journal of Finance and Economics - Issue (006) 7 b) Methodology One of the most common seasonality anomalies is the day of the week effect. This analysis is based on the hypothesis that the yields produced by each security are not independent of the day of the week. An initial approximation that could contrast the day of the week effect can be carried out with a regression model, similarly to Miralles and Miralles (000). They included five dummy variables, one for each day of the week. rit = βd t + βdt + β3d3t + β4d4t + βd t + ε t where: r it : is the daily yield of the financial asset D jt : are dummy variables which take on the value if the corresponding return for day t is a monday, tuesday, wednesday, thursday or friday, respectively and 0 otherwise. β j : are coefficients which represent the average return for each day of the week. ε t : is the error term. It is worth noting that even though the corresponding return on a specific day of the week is significantly different than zero, this does not imply seasonality. Thus it is necessary to perform a means test. This test verifies if the returns are independent of the day of the week that they are produced in, or on the contrary, they are characterised by statistically similar average returns. The rejection of the null hypothesis would imply that a day of the week effect is indeed present. Nevertheless two serious problem arise with this approach. First, the residuals obtained from the regression model can be autocorrelated, thus creating errors in the inference. The second problem is that the variance of the residuals are not constant and possibly time-dependent. A solution to the first type of problem was to introduce the returns with a one week delay into the regression model, as used in the works by Easton and Faff (994), Corredor and Santamaría (996) and Kyimaz and Berument (00), among others. r = β D + β D + β D + β D + β D + β r + ε it t t 3 3t 4 4t t j+ j= 4 t j t ARCH models are proposed in order to correct the variability in the variance of the residuals. Engle (98) used this approach and it has the advantage that the conditional variance can be expressed as a function of past errors. These models assume that the variance of the residual term is not constant through time and is distributed as εt ~ iid( 0, σ t ). The generalized version of these models was proposed by Bollerslev (986) and is expressed by the sum of a moving-average polynomial of order q plus an autoregressive polynomial of order p: σ = α + α ε + γ σ t 0 i t i q p Others works by Baillie and Bollerslev (989), Hsieh (989), Copeland and Wang (994) and Kyimaz and Berument (00) also include dummy variables which account for the possible stationary effects within the equation of variance. The result of this approach is that joint estimates of the day of the week effects are obtained, not only in the mean but also in the conditional variance. ( 0 σ ) r = β D + β D + β D + β D + β D + β r + ε it t t 3 3t 4 4t t j+ t j t j= ε ~ iid, t σ = α D + α D + α D + α D + α D + α ε + γ σ t i t i t i t i q 4 p i t i

6 8 International Research Journal of Finance and Economics - Issue (006) This model is characterised by its symmetric behaviour since the volatility is invariant during gains and losses of the stock quotations. Nevertheless, it is well known that the impacts in the volatility in positive and negative yields need not have the same effect. Kiymaz and Berumet (00) and Amigo and Rodríguez (00) have argued that on many occasions the obtained volatility from a negative return is usually greater than the corresponding one during a gain in the stock quotation that is being analysed. The asymmetric T-ARCH model is used in this case to confirm the existence or absence of any asymmetric behaviour, which is known as the leverage effect. The T-ARCH model introduced by Zakoian (990) and Glosten and Jagannathan and Runkle (993) contains a structure which is similar to the symmetric GARCH model with one exception. They include a term where the λ parameter is used to indicate the existence of differentiated behaviour in the volatility against positive and negative shocks. The generalised structure of the T-ARCH model follows: r = β D + β D + β D + β D + β D + β r + ε it t t 3 3t 4 4t t j+ j= ε ~ iid, t ( 0 σ ) t j t σ = α D + α D + α D + α D + α D + α ε + γ σ + λε d t i t i where d t- is a dicotomic variable which takes on value when the stock quote falls in a period and 0 for increments of the stock quotation. IV. Estimation of the Models and Empirical Results The study of seasonality in the returns and volatility for the European stock markets that are included in our sample is carried out based on obtained estimates from the daily returns of each one of the stock markets considered. A) The Study of Day of the Week Effect on Returns Four dummy variables have been used to account for seasonality in each of the stock exchanges for each work day except wednesday. The regression model follows: t q 4 p r = α + β D + β D + β D + β D + β r + ε it t t 4 4t t j+ j= i t i t j t The individual meaning for each one of the dicotomic variables could reveal the presence of an atypical yield during a day of the week with respect to that of wednesday. Not only is the statistical significance of each dummy variable studied but also possible structure in the autoregressive portion and in the moving average which includes the regression model. The obtained results are summarised in Table III and indicate that the day of the week effect is not evident in most European stock markets since the yield for each day of the week is not especially different than that of other days. This fact tells us that the return for the most important representative European markets is independent of the day of the week. Nonetheless, a stationary effect can be observed on mondays for the representative indexes of France and Sweden since the yields on this day are greater than the rest of the week. This result does not coincide with those obtained in most empirical studies where average monday returns are usually significantly less than the average returns for the other days of the week. A similar finding is observed in Sweden where friday yields are much greater than those for the other days of the week, thus recalling the friday effect for this specific market. Table AI from the Appendix summarises the values of the significant coefficients in the yield equation. 4 t t

7 International Research Journal of Finance and Economics - Issue (006) 9 Table III: Day of the week effect on returns Country Significant variables Country Significant variables Germany Italy MA(4) Austria MA(), MA(3), MA(4) Portugal AR(), AR(3) Belgium AR() U. Kingdom MA(3) Denmark AR() Czech Rep. AR() Spain Sweden D, D, AR() France D Switzerland AR() Holland b) Day of the week effect on volatility The importance of an analysis for the anomalies for distinct stock markets with respect to yields encountered for the day of the week can not be ignored. The aim of each investor is to maximize the binomial yield-risk from his investment. Thus it is especially important to analyse fluctuations which are produced in the same markets. That is why both symmetric and asymmetric models have also been used to study their variance. We have included the earlier dummy variables to the equation of variance, similarly to Kyimaz and Berument (00) in order to collect possible stationary effects which may arise. b.) GARCH Model The structure for the equation of estimated variance follows: σ = α + α D + α D + α D + α D + α ε + γ σ t i t i q p Table IV presents the results derived from the day of the week effect on volatility for each stock market index, as well as the GARCH structure for each series. Table IV: Day of the week effect on variance: GARCH model GARCH Significant GARCH Significant Country structure variables Country structure variables Germany (,) D, D Italy (,) D, D4 Austria (,) D, D Portugal (,) Belgium (,) D4, D U. Kingdom (,) D Denmark (,) D, D Czech Rep. (,) Spain (,) D, D4 Sweden (,) D, D France (,) D4 Switzerland (,) D, D4 Holland (,) D, D4 i t i The table shows that the resultant structure for all markets except Germany is GARCH (,). This structure is the most appropriate for studying financial time series according to Lamoreux and Lastrapes (990). The case of Germany is characterised by a GARCH (,) structure.

8 60 International Research Journal of Finance and Economics - Issue (006) With regards to volatility during each day of the week, we did not find common behaviour in the day of the week effect in the equation of conditional variance. This finding is in agreement with Kyimaz and Berument (00). There is, however, presence of abnormal volatility on mondays and fridays in Denmark. Other observations include significantly distinct volatility on Mondays and Thursdays, with respect to Wednesday, in Spain, Holland, Italy and Switzerland. The case is different for abnormal volatilities for the United Kingdom and France, where the days are Tuesdays and Thursdays, respectively. Seasonal behaviour is also apparent on Tuesdays and Fridays for the cases of Germany, Austria and Sweden. Abnormal volatility occurs on Thursdays and Fridays in Belgium. Finally, Portugal and the Czech Republic show no changes with regards to the day of the week. Table AII in the Appendix summarises statistical coefficients for the equation of variance. A general statement can be made for all of the markets that exhibit seasonal behaviour in the volatility. Mondays and Thursday are always greater than Wednesdays, while the opposite is true for Tuesdays and Fridays, that is, the yields are lesser than those experienced on Wednesday. Tables AIV and AV show the corresponding values for the ARCH-LM test and the Q statistic of the standardised residuals, respectively with lags of,0 and 0 in order to verify that ARCH effects on the residuals are not present. The results derived from these tests reveal that an ARCH effect is not present in the corresponding residuals of the estimates for these financial markets. Thus, there is no problem of specification in these models. Consequently the day of the week effect in volatility in distinct European financial markets is present even though no common behaviour is noted among the respective countries. b.) T-ARCH model As pointed out earlier, volatility can differ significantly, depending upon the sign of the obtained yield for each period. For this reason we estimate volatility using a T-ARCH model which incorporates possible asymmetric behaviour. The structure for the equation of variance follows: σ = α D + α D + α D + α D + α D + α ε + γ σ + λε d t i t i q Table V presents the obtained results from the analysis of the volatility in the day of the week for each stock market index in addition to the T-ARCH structure for each series. Table V: Day of the week effect on variance: T-ARCH model Country GARCH structure Significant Variables Assymetry Country GARCH structure Significant variables Assymetry Germany (,) D SI Italy (,) D, D4 SI Austria (,) D, D SI Portugal (,) D SI Belgium (,) D SI U.Kingdom (,) D SI Denmark (,) D, D SI Czech Rep. (,) NO Spain (0,) D, D4 SI Sweden (0,) D, D, D4, D France (0,) SI Switzerland (,) D, D4 SI Holland (0,) D, D4 SI p i t i t t SI The inclusion of a parameter which accounts for asymmetric behaviour produces clear results in Table V. The most common structure in all of the markets is a GARCH (,), whereas Spain, France, Except friday in the Belgian market.

9 International Research Journal of Finance and Economics - Issue (006) 6 Holland and Sweden follow a GARCH (0,). Finally it should be noted that Germany resembles a GARCH (,) structure. The asymmetric behaviour in all markets except the Czech Republic needs to be pointed out. Thus the gains and losses in each one of the stock markets in our sample affect in volatility in a different way. The use of an additional parameter in the T-ARCH model for asymmetric behaviour leads to different results than those from the symmetric GARCH model, with the expected exception in the Czech Republic, whose results were the same for both models. Table AIII in the Appendix displays all of the significant coefficients for the equation of variance in this model. The day of the week effect reveals a similar behaviour pattern in the equation of variance as in the earlier model, that is, greater volatility on mondays and thursdays with respect to wednesdays, and lesser volatility on tuesdays and fridays 6. Tables AVI and AVII from the Appendix show the corresponding values from the ARCH-LM test and the Q statistic from the standardized residuals, respectively, using lags of,0 and 0. They are shown to confirm the absence of ARCH effects on the residuals. The results from these tests indicate that no effect is present in the corresponding remainders of the estimates of the financial markets. Thus, we do not encounter specification problems in this model. The following observations can be made regarding the day of the week effect based on the estimation of variance with an asymmetric model. First, a Monday effect takes place in Portugal and the United Kingdom, while a Tuesday effect occurs in Germany and Belgium. Secondly, all other countries except Sweden present seasonal behaviour in two days of the week. Thirdly, this behaviour is seen on Mondays and Thursday in Spain, Holland, Italy and Switzerland. On the other hand, Tuesdays and Fridays are statistically significant in Austria, as opposed to Mondays and Fridays in Denmark. Finally, the Swedish market demonstrates volatility each day of the week with respect to Wednesday. IV. Conclusions Investors that are interested in including international markets in their portfolio need to know if these markets are integrated or not. We pursued the answer to this question by studying possible seasonality in international markets. Our analysis focused on an empirical comparison of the day of the week effect in the major European markets from July 977 to March 004, and included not only returns but volatility as well. To begin with, we should note that most European markets do not reflect a day of the week effect since the results for each day do not differ significantly from the other days of the week. The returns in these markets are based on representative indexes and reveal independence concerning which day of the week the return is calculated on. Nevertheless a seasonal effect can be observed on Mondays for the French and Swedish markets. The Swedish markets also reflects a significantly higher return on Fridays as opposed to the remaining days of the week. With respect to the existence of abnormal volatility in the equation of conditional variance in the European markets, the following can be observed. A day of the week effect is present in all of the financial markets except in Portugal and the Czech Republic, where a symmetric model is applied. Exceptions are found in France and the Czech Republic, using an asymmetric T-ARCH model. Nevertheless, this effect does not agree with other analysed financial markets. However if we introduce a parameter which accounts for different behaviour in the volatility of the stock market indexes, then continuity in the day of the week effect becomes evident, differentiating the rise and fall of prices. Its presence is unlike that of the GARCH model because the statistical significance of the day of the week in the symmetric model in some cases could have been affected by asymmetric effects that were considered in the structure of the variance in the model. Seasonality in conditional volatility in specific markets follow a similar behaviour pattern independent of the type of model that is being used. Mondays and Thursdays are more uncertain than on Wednesdays, while the Wednesday measure is lower than that of Tuesdays and Fridays. 6 Except on mondays in the United Kingdom.

10 6 International Research Journal of Finance and Economics - Issue (006) Even though initially there does not seem to be a day of the week effect in yields from different European markets, an analysis of the conditional variance verifies that the extreme shifts observed in the major stock markets of each country indicate the absence of complete integration among all markets. This finding can be useful for an investor who is looking for investment instrument opportunities based on the change in volatility of these financial markets during specific days of the week.

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12 64 International Research Journal of Finance and Economics - Issue (006) [4] Hamao, Y., R.W. Masulis y V.K. Ng (990): Correlations in Price Changes and Volatility across International Stock Markets, Review of Financial Studies, 3, pp [] Harvey, C. (99): Predictable Risk and Returns in Emerging Markets, Review of Financial Studies, 8, 3, pp [6] Hsieh, D. A. (988): The statistical properties of daily foreign exchange rates: , Journal of International Economics, 4, pp [7] Jacquillat, B. y B. Solnik (978): Multinational are Poor Tools for Diversification, Journal of Porfolio Management, 4,, Winter. [8] Jaffe, J. y R. Westerfield (98a): The week-end effect in common stock returns: The international evidence, Journal of Finance, 40, pp [9] Jaffe, J. y R. Westerfield (98b): Patterns in Japanese common stock returns, Journal of Financial and Quantitative Analysis, 0, pp [30] Keim, D.B. y F. Stambaugh (984): A further investigation of weekend effects in stock returns, Journal of Finance, 39, pp [3] Kim, C.K. y J. Park (994): Holidays Effects and Stock Returns: Further Evidence, Journal of Financial and Quantitative Analysis,, pp [3] Kyimaz, H. y H. Berument (00): The day of the week effect on Stock Market Volatility, Journal of Economics and Finance,,, pp [33] Lakonishok, J. y M. Levi (98): Weekend effect in stock return: A note, Journal of Finance, 37, pp [34] Lamoreux C. y W. Lastrapes (990): Persistence in variance, structural change, and the GARCH model, Journal of Business and Economic Statistics,, pp [3] Miralles, J.L. y M.M. Miralles (000): An Empirical Analysis of the Weekday Effect on the Lisbon Stock Market over Trading and Non-Trading Periods, Portuguese Review of Financial Markets, 3,, pp. -4. [36] Nelson, D.B. (99): Conditional Heteroskedasticity in Asset Returns: A New Approach, Econometrica, 9, pp [37] Osborne, M. (96): Periodic structure in the brownian motion of stock prices, Operations Research, 0, pp [38] Rogalski, R.J. (984): New findings regarding day of the week returns over trading and nontrading periods: A note, Journal of Finance, December, pp [39] Solnik, B. y L. Bousquet (990): Day of the week effect on the Paris Bourse, Journal of Banking and Finance, 4, pp [40] Theodossiou, P. y U. Lee (99): Relationship between volatility and expected returns across international stock markets, Journal of Business Finance & Accounting,, pp [4] Torrero, A. (999): La Importancia de las Bolsas en la internacionalización de las finanzas, Análisis Financiero, 79, pp [4] Zakoian, J. M. (990). Threshold Heteroskedasticity Models, manuscript, CREST, INSEE.

13 International Research Journal of Finance and Economics - Issue (006) 6 Appendix Table A: Significant Coefficients: return equation D D D4 D France 0,003 (0,0748) Sweden 0,008 (0,07) 0,009 (0,00) Note: Statistical probability shown in parentheses Table AII: Significant Coefficients: variance equation (GARCH) D D D4 D Germany -7,69E-0-7,4E-0 (0,0000) (0,0000) Austria -,E-0 -,96E-0 (0,0333) (0,0948) Belgium,33E-0,6E-0 (0,007) (0,086) Denmark,87E-0 -,87E-0 (0,0039) (0,09) Spain 4,70E-0,0E-0 (0,007) (0,0048) France 4,6E-0 (0,0707) Holland 3,4E-0 4,83E-0 (0,0860) (0,0077) Italy,9E-0 4,8E-0 (0,0043) (0,034) Portugal U. K. -,40E-0 (0,0679) Czech Rep. Sweden -,3E-0-4,7E-0 (0,00) (0,037) Switzerland 3,4E-0 3,84E-0 (0,07) (0,004) Note: Statistical probability shown in parentheses

14 66 International Research Journal of Finance and Economics - Issue (006) Table AIII: Significant Coefficients: variance equation (T-ARCH) Germany Austria Belgium Denmark Λ D D D4 D ,9E-0 (0.0000) (0,0000) ,E-0 -,9E-0 (0.0009) (0,033) (0,09) ,9E-0 (0.00) (0,039) 0.084,4E-0 -,0E-0 (0.000) (0,0809) (0,083) Spain ,40E-0 3,88E-0 (0.0000) (0,04) (0,06) France 0.3 (0.000) Holland ,30E-0 4,9E-0 (0.0069) (0,068) (0,0033) Italy ,4E-0 4,74E-0 (0.008) (0,0037) (0,003) Portugal 0.04,78E-0 (0.000) (0,0960) U. K ,3E-0 (0.0000) (0,0777) Czech Rep. Sweden ,79E-0-7,49E-0,34E-0-6,7E-0 (0.0000) (0,0797) (0,000) (0,0749) (0,0034) Switzerland 0.479,67E-0 3,0E-0 (0.0000) (0,086) (0,069) Note: Statistical probability shown in parentheses

15 International Research Journal of Finance and Economics - Issue (006) 67 Table AIV: ARCH-LM Test (GARCH) Lags Germany Austria Belgium Denmark Spain,93 0,734 0,40 0,4639 -,9077 (0,9) (0,664) (0,6730) (0,648) (0,066) 0 -,79 0,6848-0,33-0, -0,88 (0,009) (0,493) (0,739) (0,8799) (0,3776) 0 0,64 0,360 0,7467,8406 -,67 (0,870) (0,70) (0,43) (0,068) (0,4) Lags France Holland Italy Portugal U. K. -,3 -,686-0,48-0,678 0,36 (0,9) (0,099) (0,873) (0,368) (0,96) 0-0,79 -,439 -,463 -,670 -,4 (0,484) (0,46) (0,039) (0,090) (0,44) 0 0,93 -,0976-3,3 0,000-0,9 (0,847) (0,7) (0,007) (0,960) (0,34) Lags Czech Rep. Swedewn Switzerland,0673-0,977-0,8796 (0,860) (0,769) (0,379) 0 -,87-0,899 -,403 (0,064) (0,3687) (0,60) 0-0,8-0,7089-0,44 (0,608) (0,4784) (0,88) Note: Statistical probability shown in parentheses

16 68 International Research Journal of Finance and Economics - Issue (006) Table AV: Standardised residuals: Q statístic (GARCH) Lags Germany Austria Belgium Denmark Spain,068,3 4,868 4,349,40 (0,840) (0,4) (0,30) (0,36) (0,369) 0 4,337,434 8,47 0,0 9,3 (0,93) (0,607) (0,49) (0,346) (0,48) 0 7,68 4,6,4 8,66 3,6 (0,608) (0,) (0,64) (0,478) (0,849) Lags France Holland Italy Portugal U. K. 7,08 4,7,830,896,080 (0,86) (0,447) (0,) (0,7) (0,79) 0,03,44 0,83 6,,0 (0,3) (0,34) (0,87) (0,03) (0,08) 0 0, 7, 8,8 3, 8,69 (0,4) (0,67) (0,466) (0,08) (0,477) Lags Czech Rep. Swedewn Switzerland,34,63,30 (0,3) (0,6) (0,8) 0 8,060,637 8, (0,8) (0,776) (0,483) 0 4,66 4,77 0,70 (0,7) (0,737) (0,33) Note: Statistical probability shown in parentheses

17 International Research Journal of Finance and Economics - Issue (006) 69 Table AVI: ARCH-LM Test (T-ARCH) Lags Germany Austria Belgium Denmark Spain 0,044 0,006 0,06 0,09-0,049 (0,3) (0,76) (0,6) (0,0) (0,00) 0-0,037 0,0-0,003-0,00-0,03 (0,04) (0,49) (0,87) (0,93) (0,498) 0 0,0 0,00 0,0 0,047-0,00 (0,63) (0,8) (0,448) (0,) (0,4) Lags France Holland Italy Portugal U. K. -0,03-0,030-0,00-0,009 0,09 (0,0) (0,076) (0,8) (0,7) (0,3) 0-0,00-0,0-0,033-0,0-0,06 (0,644) (0,) (0,089) (0,338) (0,9) 0 0,009-0,03-0,049 0,06-0,08 (0,694) (0,8) (0,003) (0,444) (0,400) Lags Czech Rep. Swedewn Switzerland 0,00 0,00-0,006 (0,8) (0,907) (0,77) 0-0,03-0,0-0,04 (0,09) (0,93) (0,477) 0-0,008-0,0-0,0 (0,660) (0,686) (0,) Note: Statistical probability shown in parentheses

18 70 International Research Journal of Finance and Economics - Issue (006) Table AVII: Standardised residuals: Q statístic (T-ARCH) Lags Germany Austria Belgium Denmark Spain -0,007-0,08-0,0 0,09 0,00 (0,684) (0,60) (0,96) (0,334) (0,) 0 0,00 0,044 0,04 0,03 0,007 (0,893) (0,87) (0,33) (0,34) (0,34) 0 0,06 0,09 0,03-0,00 0,036 (0,00) (0,09) (0,33) (0,7) (0,689) Lags France Holland Italy Portugal U. K. -0,03-0,04-0,0 0,08-0,039 (0,086) (0,446) (0,68) (0,44) (0,46) 0 0,003 0,0-0,06 0,044-0,030 (0,33) (0,7) (0,93) (0,08) (0,99) 0 0,0 0,03 0,033 0,03-0,007 (0,47) (0,7) (0,366) (0,07) (0,09) Lags Czech Rep. Swedewn Switzerland -0,04-0,0-0,044 (0,44) (0,80) (0,) 0 0,0 0,003 0,03 (0,463) (0,883) (0,74) 0-0,033 0,00 0,060 (0,48) (0,87) (0,96) Note: Statistical probability shown in parentheses