Minhaj Mahmud
email: m.mahmud@econ.keele.ac.uk

On the contingent valuation of mortality risk reduction in developing countries

Keywords: Bangladesh, contingent valuation, risk reduction, WTP, sensitivity to scope JEL Classification: I1, D6, D8, H4 Bangladesh, contingent valuation, risk reduction, WTP, sensitivity to scope JEL Classification: I1, D6, D8, H4

On the contingent valuation of mortality risk reduction in developing countries

Introduction

The contingent valuation (CV) method has been widely used to value mortality risk reduction but mostly in developed countries 1 (see Corso et. al., 200l;Krupnick et al., 2002;Alberini et al., 2004;Liu et al., 2005). The CV method involves eliciting individuals' willingness-to-pay (WTP) for a hypothetical reduction in the risk of dying during a given time period. The individual's WTP can then be transformed into the value of a statistical life (VSL) by dividing the WTP by the magnitude of risk change in question (see for a theoretical discussion, Weinstein et al., 1980;Viscusi, 1993;[START_REF] Johansson | The value of a statistical life: theoretical and empirical evidence[END_REF]. However, most previous CV studies have found unreasonably low sensitivity of WTP to the size of risk reduction. One likely reason for the lack of sensitivity is a poor understanding of probabilities and a lack of intuition about small risk changes (see Hammitt and Graham 1999). Recent evidence suggests that there are ways to increase the sensitivity by using visual aids in the presentation of risks in the survey (see Corso et. al., 200l;Krupnick et al., 2002;Alberini et al., 2004).

It is particularly challenging to use the CV method to elicit people's VSL in the context of developing countries. The main reason is the difficulty communicating probabilities and risk reduction to the respondents, since many either have very low levels of education or are illiterate. A brief training of the respondents in the survey regarding probability and risk concepts may enable the respondents to better process risk information. Thus the respondents will become more elaborate about their preferences for risk reduction; this should yield lower The enumerators used to conduct the survey were trained regarding the risk presentations and the CV methodology following the guidelines of conducting CV studies in developing countries(see Whittington, 1998;2002). Following several focus groups and two pilot studies, the final CV survey was conducted during October-November 2003 among 780 rural household heads in the selected 30 villages of the following five districts of Bangladesh: Netrokona, Mymensingh, Manikganj, Gazipur and Narayanganj. Table 1 presents the sample statistics.

>>> TABLE 1

Based on a t-test, we do not find any statistically significant differences (p-value>0.05) in terms of socio-economic characteristics between the populations of the two sub-samples, i.e. 'with training' and 'without training.' Therefore, differences in the WTP responses (relating to a specific risk reduction) between these two sub-samples could be attributed to receiving training in the survey. See the Appendix A1-A5 for detail on the CV questionnaire including how the respondents were trained in the survey.

The training regarding probability and risk concepts

The training involved concepts of probability of different events occurring, risks and implications of risk changes (see Appendix A1). In particular, we used coin flipping, dice throwing and a lottery example to introduce the concept of probabilities to the respondents. Mortality risk was discussed using the example of risk of dying from traffic accidents. The chance of winning in a lottery and the mortality risk examples were explained with the use of graph paper containing 100 and 1,000 squares, respectively(see Appendix A3). The respondents were asked test questions and a respondent is considered to have passed the entire test if 5 he/she provided the correct answer to the three probability questions on dice throwing, lottery winning, and mortality risk at the first attempt (Table 2). Only 24% of the respondents passed the entire test. The respondents were, however, allowed three attempts, following repetitions of the examples, to provide a correct answer.

>>> TABLE 2

The meaning of the risk reductions was explained to the respondents by giving published information about the average risk of dying for an adult in Bangladesh in the next five years i.e. 40 in 1,000 (WHO, 2004) (see Appendix A2). Then they were told that with appropriate public policy this mortality risk could be reduced to, for example, 35 in 1,000, implying that 5 out of 40 lives could be saved. Similarly, further risk reductions, i.e. reducing the risk to 20 in 100 and 10 in 1000, were explained to the respondents up to three times to facilitate their understanding of the risk reductions. Almost 95% of the respondents revealed that they had understood all risk reduction examples after the first explanation and almost 98 % of the respondents preferred the largest risk reduction.

2 Objective risk and risk perception

Before asking the CV questions, all respondents, including those without training, were first informed about the average mortality risk of persons aged 30-34 and persons aged 55-59 in the next five year period as 15 in 1,000 and 90 in 1,000, respectively. Then they were asked to mention their perceptions of their own risks of dying during the same period, taking into consideration particularly their ages, health and lifestyles (see Appendix A4). Siegel and Castellan (2000) for a description of the test), we can conclude that respondents' subjective and objective (age-related) risks are significantly different (p-value <0.001). As shown in Figure 1, people on average overestimate mortality risk at younger ages and underestimate it at older ages.

This supports earlier findings in economics and psychology that people tend to overestimate small risks and underestimate large risks (e.g. Viscusi, 1992;Tversky and Kahneman, 1992;[START_REF] Kahneman | Choices, Values and Frames[END_REF].

>>> FIGURE 1

The CV scenario

Finally, the respondents were asked about their WTP for either a 25% or 50% reduction in their perceived risk, which was to be achieved through a vaccination program. They were asked to state their maximum WTPs for obtaining the stated risk reduction (see Appendix A5). We choose the open-ended format as it provides more information than the closed-ended format2 . Moreover, there is also experimental evidence showing that dichotomous choice(close ended questions) overestimates values more than the open-ended questions in the case of private goods (see [START_REF] Balistreri | Can Hypothetical Questions Reveal True Values? A Laboratory Comparison of Dichotomous Choice and Open-Ended Contingent Values with Auction Values[END_REF]. Although vaccinations can be seen as a good with a positive externality, we find no indication that people consider this when deciding on their own vaccination. If the respondent stated zero WTP, he/she was asked several follow-up questions to ascertain possible scenario rejections. The

Analysis of WTP results

Ten percent of the respondents (79 individuals) stated zero WTP for risk reduction. Based on the responses to follow-up questions we can conclude that, of these 79 responses, 84% indicates a scenario rejection (see Appendix B: Table 1). We do not include these responses in our further statistical analysis of this paper. However, we analyze the probability of scenario rejection using a standard probit model and observed that the Muslim respondents are more likely to provide a protest zero and, although significant at 10% level, the respondents receiving training in the survey are less likely to reject the scenario (for detail result, see Appendix B: Table 2). To control for WTP outliers in relation to income, we drop responses with WTP greater than 50% of households' annual per capita income (although there is no a priori reason to assume that WTP for reducing the risk of dying should be a small part of income 3 , particularly since the payment for the risk reduction was to be made once for a five-year period). Table 3 reports the WTP distributions for different sub-samples.

>>> TABLE 3

We find that the mean WTP is TK. 487 (TK. 672) for a 25% (50%) risk reduction, for the no-training sub-sample. For the trained sub-sample, the mean WTP is TK. 671 (TK. 970) for a 25 % (50%) risk reduction. Using non-3 Alberini (2005) We calculate the individual VSL by dividing the individual WTP by the magnitude of risk change offered to the respondents. The magnitude of the VSL estimates (US$ 1,783 -$2,922) are very low compared to the available estimates for developing countries. For example, using results from several VSL studies from other countries, Miller (2000) predicts a VSL for Bangladesh in the range of US$ 30,000 to US $1,000,000. However, the lower absolute VSLs in our case may be attributed to the fact that unlike many other studies we had relatively large risk reductions, if we assume that there is inadequate sensitivity to scope. For example, [START_REF] Carlsson | Is Transport Safety More Valuable in the Air?[END_REF] suggests that VSL tends to decrease rapidly when the size of the risk reduction increases.

We estimate a truncated regression model where WTP is truncated at zero, assuming homoscedasticity concerning the training; dummy variables are included identifying the training and the risk reduction levels in pooling the data. 5 4 Wilcoxon-Mann-Whitney test. See Siegel and Castellan (2000) for a description of the test.

5 An appropriate econometric model for analyzing the WTP data that includes zeros would be Tobit with selection, which allows for modelling zero and positive WTP separately (see [START_REF] Carlsson | Willingness to Pay for Improved Air Quality in Sweden[END_REF]. However, with only 13 (non-protest) zero WTP observations in our case, it is doubtful whether a sample selection model can be justified; it is also unclear whether 4 reports the results. Assuming that people who passed the entire training would show higher sensitivity to scope compared to other respondents, separate interaction variables were included. Since several enumerators were used to conduct the CV survey, we control for the enumerators effects by including dummy variables for the enumerators; however, we do not present the enumerator coefficients in Table 4.

>>> TABLE 4

The training coefficient is highly significant and implies that WTP for a larger reduction in risk is 79% higher for the group receiving the training. The coefficient of 50% reduction is highly significant and indicates that WTP for the larger risk reduction is 45% higher than the WTP for the smaller reduction. The WTP difference concerning risk reduction is even higher for the training subsample (16% higher) in general and for the group who passed the tests (6% higher) in particular; however, none of these differences are statistically significant. We find that the more highly educated individuals have on average 7% higher WTPs compared to illiterate people; however, this difference is not statistically significant. Although it can be expected that people with higher levels of education might have higher values for risk reduction, other studies, e.g. Krupnick et al. (2002) and Alberini et al. (2004), have found that more highly educated people report lower WTPs.

there is any true negative WTP. Given that the distributions of WTP significantly differ between the sub-samples, we first estimated a model allowing for heteroscedasticty concerning training. However, we cannot reject the null hypothesis of homoscedasticity (p value 0.96). The estimated marginal effects for income 6 are positive and significant with an income elasticity of 0.43. The result that the income elasticity of the VSL is well below unity is also found in many other CV studies (e.g. [START_REF] Carlsson | Is Transport Safety More Valuable in the Air?[END_REF]Persson et al., 2001). In cross-country comparisons of VSL studies, Viscusi and Aldy (2003) found the income elasticity of VSL in the range of 0.5 to 0.6.

We find that the effect of age on WTP is negative at younger ages, until a minimum is reached at age 46, and then increases. Alberini et al. (2004) found, using a sample of over 40-year olds that WTP does not decline until age 70. We also find that having a chronic illness has no significant effect on WTP; this result is consistent with Alberini et al. (2004) that having a chronic condition does not reduce the WTP for mortality risk. Finally, all else remaining the same, overall individual happiness significantly and positively affects WTP for risk reduction.

Based on the regression coefficients (Table 4), we can reject the hypothesis that WTP is insensitive to the magnitude of risk reduction for both the no-training and training sub-samples. The sensitivity to scope is higher for the trained sub-sample, although not statistically significant.

Discussion and conclusions

As elicited in the CV survey, individuals on average overestimate the risk at younger ages and underestimate the risk at older ages; the result is consistent with previous studies in the context of developed countries. On average, the individuals receiving training regarding probability and risk stated significantly 6 As the distribution of income in our sample is highly skewed we estimated separate models excluding relatively high income. However, as the coefficient of income remains roughly the same, we decided to keep them all in our final model presented here. higher WTP for the risk reductions and the estimated WTP is sensitive to the size of the risk reduction. Although the implied VSL is higher for the trained subsample, it is still substantially lower compared to other studies, which may be attributed to the fact that compared to other studies we have used relatively large risk reductions. Moreover, stating WTP for a risk reduction is rather difficult for people unfamiliar with the idea of trading income for risk reduction. Therefore, it is likely that people would suffer from initial anchoring when constructing an answer as to how much they would be willing to pay (see Tversky and Kahneman, 1974;Kahneman et al., 1982). The respondents might anchor on the price of vaccination or on their other expenditures. In the context of a developing country, household consumption expenditures are usually low on average, particularly in the rural areas. Therefore, when placing a value on a desired and substantial risk reduction, the respondents might anchor initially to such low expenditures and adjust thereafter. In addition, a one-time payment rather than on-going monthly or yearly payments may result in more conservative estimates (see [START_REF] Carson | Contingent Valuation: A User's Guide[END_REF].

In general, it appears constructive to train the respondents regarding probabilities and risk concepts in the CV risk reduction surveys. Training largely reduces the extent of cognitive burden that the respondents face in evaluating risk reductions and thereby increases the ability of the respondents to value the risk reduction. However, there might also be some associated problems with training in that the respondents may get tired if they find it boring and this may cause fatigue effects. Besides, by talking a lot about uncertainties and probabilities, the respondent can get the impression that avoiding risks is very important. Hence, they will tend to state higher WTP in the training version; this is not because they Finally, using the CV method to elicit people's VSLs is not a "mission impossible" in the context of developing countries. A comprehensible training on probability and risk concepts, interspersing risk examples with questions to check understanding as wes to maintain respondent interest, should be given in the CV risk reduction surveys. There are remaining problems but most of these appear to be related to the CV methodology per se, rather than to CV studies being performed in developing countries. Now I will discuss the chances and risks of events occurring using some examples.

Example 1: Sometimes we toss a coin to decide which of two things to choose. When we toss a coin [Enumerator: show tossing a coin], we get either a head or a tail. We cannot be sure of the result of the toss. As there are two things that can happen from a coin toss, the chance of getting a head is 1 in 2. The same is true for getting a tail.

Similarly, when we roll a dice (chakka) [Enumerator: show throwing a chakka] we may either see on the top 1, 2, 3, 4, 5, or 6, but we don't know which one beforehand. Since there are six different numbers from 1 to 6, we may see any of them on the top. The chance of seeing a 5 on the top is 1/6.

3% 39%

a The lists of possible reasons were read to them and the respondents were allowed to choose more than one reason. They were also allowed to express other reasons. Respondents who had chosen any response than (i) or had chosen more than one responses are believed to have provided protest zeros when answering the WTP question.

Introduction

The contingent valuation (CV) method has been widely used to value mortality risk reduction but mostly in developed countries 1 (see Corso et. al., 200l;Krupnick et al., 2002;Alberini et al., 2004;Liu et al., 2005). The CV method involves eliciting individuals' willingness-to-pay (WTP) for a hypothetical reduction in the risk of dying during a given time period. The individual's WTP can then be transformed into the value of a statistical life (VSL) by dividing the WTP by the magnitude of risk change in question (see for a theoretical discussion, Weinstein et al., 1980;Viscusi, 1993;[START_REF] Johansson | The value of a statistical life: theoretical and empirical evidence[END_REF]. The theory predicts that WTP for mortality risk reduction should be positively associated with the magnitude of risk reduction. Furthermore, for sufficiently small risk changes, WTP should be nearly proportional to the magnitude of risk reduction.2 However, most previous CV studies have found unreasonably low sensitivity of WTP to the size of risk reduction. One likely reason for the lack of sensitivity is a poor understanding of probabilities and a lack of intuition about small risk changes (Hammitt and Graham 1999). Recent evidence suggests that there are ways to increase the sensitivity by using visual aids in the presentation of risks in the survey (see Corso et. al., 200l;Krupnick et al., 2002;Alberini et al., 2004).

1 There are some studies focusing on valuing health risks in developing countries but not particularly focusing on obtaining the VSL estimates (e.g. Cropper et. al., 2004). as well as experimental setting [START_REF] Paradiso | The effect of knowledge on the disparity between hypothetical and real willingness to pay[END_REF]. Recent evidence also shows that WTP answers from respondents with higher cognitive ability are less flawed by scale bias i.e., insensitivity and non-near proportionality of WTP to the size of the risk reduction [START_REF] Andersson | Cognitive Ability and Scale Bias in the Contingent Valuation Method[END_REF]. One would expect that training largely reduces the extent of cognitive burden that the respondents face in evaluating risk reductions and thereby increases the ability of the respondents to value the risk reduction. Thus the respondents will become more elaborate about their preferences for risk reduction; this should yield lower variation in the responses as well as an increase in the sensitivity to scope. An increase in the sensitivity to scope would mean an increase in the proportionality of WTP to the risk changes, i.e. an increase in the ratio of mean WTP for larger risk reduction to mean WTP for smaller risk reduction. However, there might be some problems associated with the training. By talking a lot about uncertainties Nevertheless, the validity and reliability of the CV method is intensely debated (see e.g. [START_REF] Kahneman | Valuing Public Goods: The Purchase of Moral Satisfaction[END_REF]Diamond and Hausman, 1994;Hanemann, 1994), some of the criticisms against the CV method such as "warm glow" or "the purchase of moral satisfaction" are not applicable when valuing individuals' own risk reductions. For example, Hackl and Pruckner (2005) did not find strong empirical evidence of "strategic bias" or "warm glow" in a CV study of health care. Still, it can be highly cognitively demanding for the respondents to compare expected welfare effects from risk reductions with the effects of monetary changes [START_REF] Beattie | On the Contingent Valuation of Safety and the Safety of Contingent Valuation: Part 1-Caveat Investigator[END_REF].

The remainder of the paper is organized as follows: Section 1 presents the CV survey, the training, the respondents' risk understanding and risk perception, and the CV scenario. Section 2 presents the analysis of WTP results and the Ideally, it would be better to train people for a longer period. However, we intend to see whether some brief training as part of the questionnaire makes any significant difference in the responses since such education is the most realistic kind that can be pursued. The complete CV survey was translated back to English from Bengali to ensure the exact meaning of the original English version. The enumerators used to conduct the survey were trained regarding the risk presentations and the CV methodology following the guidelines of conducting CV studies in developing countries (see Whittington, 1998;2002). Following several focus groups and two pilot studies, the final CV survey was conducted during October-November 2003 among 780 rural household heads in the selected 30 villages of the following five districts of Bangladesh: Netrokona, Mymensingh, Manikganj, Gazipur and Narayanganj. Table 1 presents the sample statistics.

>>> TABLE 1

Based on a t-test, we do not find any statistically significant differences

The training regarding probability and risk concepts

The training involved concepts of probability of different events occurring, risks and implications of risk changes (see Appendix A1). In particular, we used coin flipping, dice throwing and a lottery example to introduce the concept of probabilities to the respondents. Mortality risk was discussed using the example of risk of dying from traffic accidents. The chance of winning in a lottery and the mortality risk examples were explained with the use of graph paper containing 100 and 1,000 squares, respectively(see Appendix A3). The respondents were asked test questions and a respondent is considered to have passed the entire test if he/she provided the correct answer to the three probability questions on dice throwing, lottery winning, and mortality risk at the first attempt (Table 2). Only 24% of the respondents passed the entire test. The respondents were, however, allowed three attempts, following repetitions of the examples, to provide a correct answer.

>>> TABLE 2

The meaning of the risk reductions was explained to the respondents by giving published information about the average risk of dying for an adult in Bangladesh in the next five years i.e. 40 in 1,000 (WHO, 2004) (see Appendix A2). Then they were told that with appropriate public policy this mortality risk could be reduced to, for example, 35 in 1,000, implying that 5 out of 40 lives could be saved. Similarly, further risk reductions, i.e. reducing the risk to 20 in 100 and 10 in 1000, were explained to the respondents up to three times to

2 Objective risk and risk perception

Before asking the CV questions, all respondents, including those without training, were first informed about the average mortality risk of persons aged 30-34 and persons aged 55-59 in the next five year period as 15 in 1,000 and 90 in 1,000, respectively. Then they were asked to mention their perceptions of their own risks of dying during the same period, taking into consideration particularly their ages, health and lifestyles (see Appendix A4).

Therefore, we customize the mortality risk for each individual according to his or her own perception. Based on the non-parametric Wilcoxon matched-pairs signed-rank test (see Siegel and Castellan (2000) for a description of the test), we can conclude that respondents' subjective and objective (age-related) risks are significantly different (p-value <0.001). As shown in Figure 1, people on average overestimate mortality risk at younger ages and underestimate it at older ages.

This supports earlier findings in economics and psychology that people tend to overestimate small risks and underestimate large risks (e.g. Viscusi, 1992;Tversky and Kahneman, 1992;[START_REF] Kahneman | Choices, Values and Frames[END_REF].

>>> FIGURE 1

The CV scenario

Finally, the respondents were asked about their WTP for either a 25% or 50% reduction in their perceived risk, which was to be achieved through a We choose the open-ended format as it provides more information than the closed-ended format, although the later is the most favoured by CV researchers (see [START_REF] Bateman | Guidelines for the Use of Expressed Preferences Methods for the Valuation of Preferences for Non-market Goods[END_REF]Hanely et al., 2003). Moreover, there is also experimental evidence showing that dichotomous choice(close ended questions) overestimates values more than the open-ended questions in the case of private goods (e.g. Balistreri et al., 2001). 3 Although vaccinations can be seen as a good with a positive externality, we find no indication that people consider this when deciding on their own vaccination. If the respondent stated zero WTP, he/she was asked several follow-up questions to ascertain possible scenario rejections. The responses can be divided into the following categories: (1) training and a 50% risk reduction, ( 2) training and a 25% risk reduction, (3) no training and a 50% risk reduction, and (4) no training and a 25% risk reduction.

Analysis of WTP results

Ten percent of the respondents (79 individuals) stated zero WTP for risk reduction. Based on the responses to follow-up questions we can conclude that, of these 79 responses, 84% indicates a scenario rejection (see Appendix B: Table 1). We do not include these responses in our further statistical analysis of this paper. However, we analyze the probability of scenario rejection using a standard probit model and observed that the Muslim respondents are more likely to provide a protest zero and, although significant at 10% level, the respondents receiving 2). To control for WTP outliers in relation to income, we drop responses with WTP greater than 50% of households' annual per capita income (although there is no a priori reason to assume that WTP for reducing the risk of dying should be a small part of income 4 , particularly since the payment for the risk reduction was to be made once for a five-year period). Table 3 reports the WTP distributions for different sub-samples.

>>> TABLE 3 We find that the mean WTP is TK. 487 (TK. 672) for a 25% (50%) risk 5 See Siegel and Castellan (2000), for a description of the test. Miller (2000) predicts a VSL for Bangladesh in the range of US$ 30,000 to US $1,000,000. However, the lower absolute VSLs in our case may be attributed to the fact that unlike many other studies we had relatively large risk reductions, if we assume that there is inadequate sensitivity to scope. For example, [START_REF] Carlsson | Is Transport Safety More Valuable in the Air?[END_REF] suggests that VSL tends to decrease rapidly when the size of the risk reduction increases.

We estimate a truncated regression model where WTP is truncated at zero. 6 Given that the distributions of WTP differ between the sub-samples (Table 3), we first estimated a model allowing for heteroscedasticty concerning training.

However, we cannot reject the null hypothesis of homoscedasticity (p value 0.96);

the variances of WTP between the two sub-samples do not differ significantly.

Hence we estimate the model assuming homoscedasticity concerning the training; dummy variables are included identifying training and the risk reduction levels in pooling the data. Table 4 reports the results. Assuming that people who passed the entire training would show higher sensitivity to scope compared to other respondents, separate interaction variables were included. Since several enumerators were used to conduct the CV survey, we control for the enumerators 6 An appropriate econometric model for analyzing the WTP data that includes zeros would be a two-equation sample selection model that allows for modeling zero and positive WTP separately (e.g. [START_REF] Carlsson | Willingness to Pay for Improved Air Quality in Sweden[END_REF][START_REF] Yoo | Modelling willingness to pay responses from dichotomous choice contingent valuation surveys with zero observations[END_REF][START_REF] Strazzera | Modelling zero values and protest responses in contingent valuation surveys[END_REF]. However, with only 13 (non-protest) zero WTP observations in our case, it is doubtful whether a sample selection model can be justified; it is also unclear whether there is any true negative WTP. 4.

>>> TABLE 4

The training coefficient is highly significant and implies that WTP for a larger reduction in risk is 79% higher for the group receiving the training. The coefficient of 50% reduction is highly significant and indicates that WTP for the larger risk reduction is 45% higher than the WTP for the smaller reduction. The WTP difference concerning risk reduction is even higher for the training subsample (16% higher) in general and for the group who passed the tests (6% higher) in particular; however, none of these differences are statistically significant. We find that the more highly educated individuals have on average 7% higher WTPs compared to illiterate people; however, this difference is not statistically significant. Although it can be expected that people with higher levels of education might have higher values for risk reduction, other studies, e.g. Krupnick et al. (2002) and Alberini et al. (2004), have found that more highly educated people report lower WTPs.

The estimated marginal effects for income 7 are positive and significant with an income elasticity of 0.43. The result that the income elasticity of the VSL is well below unity is also found in many other CV studies (e.g. [START_REF] Carlsson | Is Transport Safety More Valuable in the Air?[END_REF]Persson et al., 2001). In cross-country comparisons of VSL studies, Viscusi and Aldy (2003) found the income elasticity of VSL in the range of 0.5 to 0.6.

7 As the distribution of income in our sample is highly skewed we estimated separate models excluding relatively high income. However, as the coefficient of income remains roughly the same, we decided to keep them all in our final model presented here. We find that the effect of age on WTP is negative at younger ages, until a minimum is reached at age 46, and then increases. Alberini et al. (2004) found, using a sample of over 40-year olds that WTP does not decline until age 70. We also find that having a chronic illness has no significant effect on WTP; this result is consistent with Alberini et al. (2004) that having a chronic condition does not reduce the WTP for mortality risk. Finally, all else remaining the same, overall individual happiness significantly and positively affects WTP for risk reduction.

From the estimated results, we formally test for the sensitivity to scope.

We calculate the mean WTPratio, i.e. ratio of mean WTP for a 50% risk reduction to mean WTP for a 25% risk reduction, using the regression coefficients. The WTP ratios for both the sub-samples, and the difference between two mean WTP ratios are presented along with 95% confidence intervals and standard errors in Table 4. We can reject the hypothesis that WTP is insensitive to the magnitude of risk reduction for both the no-training and training sub-samples. The sensitivity to scope is higher (higher mean WTP ratio) for the trained sub-sample; however the difference in the sensitivity to scope is not statistically significant.

Discussion and conclusions

As elicited in the CV survey, individuals on average overestimate the risk at younger ages and underestimate the risk at older ages; the result is consistent with previous studies in the context of developed countries. On average, the individuals receiving training regarding probability and risk stated significantly higher WTP for the risk reductions and the estimated WTP is sensitive to the size of the risk reduction. Although the implied VSL is higher for the trained sub-sample, it is still substantially lower compared to other studies, which may be attributed to the Moreover, stating WTP for a risk reduction is rather difficult for people unfamiliar with the idea of trading income for risk reduction. Therefore, it is likely that people would suffer from initial anchoring when constructing an answer as to how much they would be willing to pay (see Tversky and Kahneman, 1974;Kahneman et al., 1982). The respondents might anchor on the price of vaccination or on their other expenditures. In the context of a developing country, household consumption expenditures are usually low on average, particularly in the rural areas. Therefore, when placing a value on a desired and substantial risk reduction, the respondents might anchor initially to such low expenditures and adjust thereafter. In addition, a one-time payment rather than on-going monthly or yearly payments may result in more conservative estimates (see [START_REF] Carson | Contingent Valuation: A User's Guide[END_REF].

In general, it appears constructive to train the respondents regarding probabilities and risk concepts in the CV risk reduction surveys. Training largely reduces the extent of cognitive burden that the respondents face in evaluating risk reductions and thereby increases the ability of the respondents to value the risk reduction. However, we find no significant difference in the variance of WTP, but rather in the levels. As discussed earlier, besides the possibility of fatigue effects, respondents in the training version can get the impression that avoiding risks is very important and that a higher value is expected of them, which may bias their stated WTP. While some respondents may respond in this way, others are able to draw inferences about the risk reduction, and training facilitates a cognitive structure that is essential to draw such inference in such a situation. a The dependent variable is truncated at zero. Superscripts *** and ** denote statistical significance at the 1 % and 5% levels, respectively. b The mean WTP ratio is defined as the ratio of mean WTP for a 50% risk reduction to mean WTP for a 25% risk reduction. The ratio is calculated using the regression coefficients of the dummy variable for risk reduction and the mean values of other explanatory variables. Difference of mean WTP ratio between two sub-samples indicates that the sensitivity to scope is higher for the training sub-sample. The standard errors of mean WTP ratios are calculated using the Delta Method (see Greene 2000). 95% Confidence intervals are reported in the parenthesis.

F

  customize the mortality risk for each individual according to his or her own perception. Based on the non-parametric Wilcoxon matched-pairs signed-rank test (see

  divided into the following categories: (1) training and a 50% risk reduction, (2) training and a 25% risk reduction, (3) no training and a 50% risk reduction, and (4) no training and a 25% risk reduction.

  we can reject the hypothesis that for the specific risk reduction (either 25% or 50%), WTP for the two sub-samples (training and no training) comes from the same underlying distribution (in both cases, p-value<0.001).These imply that the training in the CV survey increases the mean WTP. The results also indicate a smaller variation in the WTP responses for the training subsample, implying that some brief training facilitates respondent ability to better process the risk information.

  but because they think that it is expected of them. While some respondents may respond in this way, others are able to draw inferences about the risk reduction, and training facilitates a cognitive structure that is essential to draw such inference in such a situation.

  .L., Halie, M., Lampietti, J.,Poulos, C. and Whittington, D. (2004), The demand for a malaria vaccine: evidence from Ethiopia, Journal of Development Economics, 75:303-318 Diamond, P. A. and Hausman, J.A. (1994), Contingent Valuation: Is Some Number Better Than No Number? The Journal of Economic Perspectives, 8(4), 45-64. Hackl, F. and Pruckner, G J.(2005), Warm glow, free riding and vehicle neutrality in a health-related contingent valuation study, Health Economics 14, 293-306. Hammitt, J.K. and Graham, J. D. (1999) Willingness to pay for health protection: Inadequate sensitivity to probability, Journal of Risk and Uncertainty 18, 33-62 Hanemann, W. M. (1994), Valuing the Environment through Contingent Valuation, The Journal of Economic Perspectives, 8(4), 19-43. Hanley, N., Ryan, M., and Wright, R. (2003), Estimating the monetary value of health care: lessons from environmental economics, Health Economics 12, 3-16. Johansson, P.O. (2002), The value of a statistical life: theoretical and empirical evidence, Applied Health Economics and Health Policy, 1(1), 33-41. Kahneman, D. and Knetsch, J.L. (1992), Valuing Public Goods: The Purchase of Moral Satisfaction, Journal of Environmental Economics and Management, 22(1), 57-70. Kahneman, D., and Tversky, A. (2000), Choices, Values and Frames, Cambridge, Cambridge University Press.

Figure 1 .

 1 Figure 1. Objective and subjective mortality risk during the next five years as a function of age

  challenging to use the CV method to elicit people's VSL in the context of developing countries. The main reason is the difficulty communicating probabilities and risk reduction to the respondents, since many either have very low levels of education or are illiterate. Moreover, most people, being unfamiliar with the concept of risk-money tradeoffs, might face greater uncertainty in placing a value for the risk reduction. A brief training of the respondents in the survey regarding probability and risk concepts may enable the respondents to better process risk information. The lack of understanding or unfamiliarity of the object of valuation can largely affect respondents' valuation behaviour in the context of CV studies. For example, variance of values is found to decrease with the degree of familiarity (knowledge) regarding the object of valuation both in hypothetical

  the CV survey were constructed: one version including some brief training vis-à-vis probability and risk, and the other without such training.

  (p-value>0.05) in terms of socio-economic characteristics between the populations of the two sub-samples, i.e. 'with training' and 'without training.' Therefore, differences in the WTP responses (relating to a specific risk reduction) between these two sub-samples could be attributed to receiving training in the the Appendix A1-A5 for detail on the CV questionnaire including how the respondents were trained in the survey.

  of the risk reductions. Almost 95% of the respondents revealed that they had understood all risk reduction examples after the first explanation and almost 98 % of the respondents preferred the largest risk reduction.

  They were asked to state their maximum WTPs for obtaining the stated risk reduction (see Appendix A5).

  survey are less likely to reject the scenario (for detail result, see Appendix B: Table

  reduction, for the no-training sub-sample. For the trained sub-sample, the mean WTP is TK. 671 (TK. 970) for a 25 % (50%) risk reduction. Using nonparametric Wilcoxon-Mann-Whitney test, 5 we can reject the hypothesis that for the specific risk reduction (either 25% or 50%), WTP for the two sub-samples (training and no training) comes from the same underlying distribution (in both cases, p-value<0.001). These imply that the training in the CV survey increases the mean WTP. The results also indicate a smaller variation in the WTP responses for the training sub-sample, implying that some brief training facilitates respondent ability to better process the risk information. Further test concerning the difference in variances is followed in the econometric analysis. We calculate the individual VSL by dividing the individual WTP by the magnitude of risk change offered to the respondents. The magnitude of the VSL 4 Alberini (2005) discusses about the robustness of CV estimates and different types of outliers e.g. WTP outliers in relation to income.

  dummy variables for the enumerators; however, we do not present the enumerator coefficients in Table

  to other studies we have used relatively large risk reductions.

Figure 1 .

 1 Figure 1. Objective and subjective mortality risk during the next five years as a function of age

  discusses about the robustness of CV estimates and different types of outliers e.g.

WTP outliers in relation to income.

Table 1 . Sample Statistics (N=767)

 1

	Page 17 of 53	Submitted Manuscript
	Variable	Definition	Mean	Standard
				deviation
	Male	Dummy variable=1 , if Male	0.91	0.28
	Muslim religion	Dummy variable=1 if Muslim religion	0.66	0.47
	Hindu religion	Dummy variable=1 if Hindu religion	0.34	0.47
	Age	Age of the respondent	43.6	12.4
	Income per capita Total yearly household income was divided by	22594	29117
		[Number of adults + 0.5*number of children] 0.75
	Illiterate	Dummy variable01, if cannot read and write	0.31	0.46
	Low education	Dummy variable=1 , if not illiterate and/or	0.55	0.50
		education up to high school level)
	High education	Dummy variable=1, if has education above high	0.14	0.35
		school level
	Having chronic	Dummy variable=1, if the respondent has been	0.39	0.49
	illness	suffering from any of the chronic diseases: heart
		disease, high blood pressure, asthma, bronchitis,
		cancer, or diabetes.
	Currently	Dummy variable=1, if the respondent currently	0.56	0.50
	smoking	smoking
	Self reported	Responses, on an 11-point scale, to the question:	5	2.2
	happiness	"As a whole, how happy would you say you are?
		The scale is described as follows: 0 means
		"extremely unhappy," 10 means "extremely
		happy'

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

of probability and risk for the sub-sample with training

	Probability/	% of respondents
	Risk questions a	answered the test
		questions correctly
	Dice throwing question: "when we roll a dice we may either see on the top 1, 2, 3,
	4, 5, or 6, but we don't know which one beforehand. Since there are six different numbers from 1 to 6, we may see any of them on the top. The chance of seeing a 5	31
	on the top is 1/6. Now, if I throw this dice, what is the chance that 2 will be shown
	on top? "
	Lottery question: "Now, suppose there are two lotteries. The chance of winning in one lottery is 5 in 1000 and the chance of winning in the other lottery is 10 in	74
	1000. Which lottery has the larger chance of winning?"
	Mortality risk question: "Now, suppose there are two roads that are both very prone to accidents. The risk of dying on road A is 1 in 1000 and the risk of dying	83
	on road B is 3 in 1000. Which road is more risky to take?"

a See Appendix A for test questions and the preceding examples.

Table 3 .

 3 WTP results for different sub samples a

		No training	Training
		25% risk	50% risk	25% risk	50% risk
		reduction	reduction	reduction	reduction
	Sample size	162	168	175	189
	Mean WTP	487	672	671	970
	Standard deviation	1531	1934	1377	1324
	Median WTP	100	200	500	500
	Mean WTP ratio b	1.38	1.45
	Null Hypothesis:	p-value <0.001	p-value <0.001
	Mean WTP ratio=1 c
	VSL based on changes in subjective risk
	Mean VSL	103074	106585	168905	107697
	Median VSL	20000	13333	33333	30000
	95% confidence	43742 -162407	32164 -181005	103714 -134097 81167 -134228
	interval for mean
	VSL
	a WTP and VSL are expressed in Bangladesh Taka. 57.8 Taka =1 US $, at the time of survey
	(October 2003).

b Ratio of mean WTP for a 50% risk reduction to mean WTP for a 25% risk reduction. c Using both the non-parametric Wilcoxon -Man-Whitney test and the t test.

Table 4 .

 4 Estimated WTP by sub-samples: Truncated regression model a

	Dependent variable Log (WTP+1)
	Variable	Coefficient	Standard error
	Constant	15.12**	6.89
	Received training	0.583***	0.146
	Passed probability test	0.170	0.230
	50% risk reduction	0.371***	0.137
	Received Training × 50% risk reduction	0.146	0.203
	Passed probability test × 50% risk reduction	0.060	0.310
	Muslim religion	-0.005	0.092
	Log(age )	-7.02*	3.73
	Log(age)-squared	0.912*	0.502
	Log (subjective risk )	0.021	0.052
	High education	0.070	0.090
	Low education	-0.064	0.074
	Log(Income per capita)	0.425***	0.063
	Having chronic illness	-0.010	0.102
	Currently smoking	0.016	0.102
	Self reported happiness	0.070***	0.023
	Disturbance standard deviation	1.22	0.033
	Log-Likelihood	-1100.12
	Mean WTP ratio (No Training)	1.45	0.198
	Mean WTP ratio (Training)	1.51	0.160

a

The dependent variable is truncated at zero. Superscripts *** and ** denote statistical significance at the 1 % and 5% levels, respectively.

Training -Explaining risk reduction Example 4:

  Is this example clear to you? [Enumerator: If no, explain again and make sure that the respondent understands. Write down how many times you had to explain. If the respondent has not understood after three times, write" 4" and continue.] Question PT1.Now, if I throw this "chakka" (dice), what is the chance that 2 will be shown on top? Answer: [Enumerator: If the answer is wrong, explain with example until the correct answer is given. Write down how many times you had to explain. If the respondent did not have it right after a third explanation, explain the answer and write 4. If the answer is wrong, explain with example until the correct answer is given. Write down how many times you had to explain. If the respondent did not have it right after a third explanation, explain the answer and write 4. ] Example3/ Question PT3. Now, suppose there are two roads that are both very prone to accidents. The risk of dying on road A is 1 in 1000 and the risk of dying on road B is 3 in 1000. [Enumerator: Show the grid table-4 and grid table 5, when explaining]. If the answer is wrong, explain with example until the correct answer is given. Write down how many times you had to explain. If the respondent did not have it right after a third explanation, explain the answer and write 4. ] Suppose the average risk of dying for an adult person during the next 5 years is 40 in 1000. [Enumerator: show grid table 6 when explaining]. Suppose a reduction in mortality risk, through some kind of public measure, could reduce the mortality risk from 40 in 1000 to 35 in 1000 [Enumerator: show grid table 6 and grid table 7 together to explain the difference]. This means that, on average, 5 out of 40 would be saved by the measure. If no, explain again and make sure that the respondent understands and write down how many times you had to explain. If the respondent has not understood after three times, continue and write 4] Example 5: Similarly, if the risk was reduced from 40 in 1000 to 20 in 1000[Show grid table 6 and grid table 8 together to explain the difference], then 20 out of 40 would be saved on average. If no, explain again and make sure that the respondent understands and write down how many times you had to explain. If the respondent has not understood after three times, continue and write 4] Answer: Example 6: If the risk was reduced from 40 in 1000 to 10 in 1000[show grid table 6 and grid table 9 together to explain the difference], then 30 out of 40 would be saved on average. If no, explain again and make sure that the respondent understands and write down how many times you had to explain. If the respondent has not understood after three times, continue and write 4]

	Appendix A2: PT4. Do you understand this risk reduction? Appendix B
	Yes	1
	No	2
	lottery has [Enumerator: Which the larger chance of winning? 5 in 1000 1 10 in 1000 2 Answer: …………. road is more risky to take? Road A 1 Road B 2 o r [Enumerator: PT5. Do you understand this risk reduction? Yes 1 PT7. Which of the above risk reductions would you prefer? [Enumerator: Show the cards and let the respondent point] a) 40 in 1000 to 35 in 1000 b) 40 in 1000 to 20 in 1000 c) 40 in 1000 to 10 in 1000 F o r F o r F o r [Enumerator: F P e e r R e v i e w No 2 P e e r P e e r P e e r [Enumerator: PT6. Do you understand this risk reduction? Yes 1 No 2 R e v i e R e v i e R e v i e [Enumerator: Answer: Example7/ Question w w w
		22

]

Example 2: Consider buying a lottery ticket. Many people buy lottery tickets and most people do not win. Suppose that there is only 1 prize in a lottery and 100 people buy one lottery ticket each. [Enumerator: Show grid table 1]. In this case we say that the chance of winning the prize will be 1 in 100. Is this example clear to you? [Enumerator: If no, explain again and make sure that the respondent understands. Write down how many times you had to explain. If the respondent has not understood after three times, write" 4" and continue.] Question PT2. Now, suppose there are two lotteries. The chance of winning in one lottery is 5 in 1000 and the chance of winning in the other lottery is 10 in 1000.

[Enumerator: Show the grid table-2 and grid table 3, when explaining]. Which

Table 1 . Follow-up questions for stating zero WTP (N = 79)

 1

	Sub-sample of	Sub-sample of
	respondents who	respondents who
	would want free	would not want
	vaccination (77%)	free vaccination
		(23%)

Table 2 Probit regression of scenario rejection (N=79)

 2

	Variable	Marginal effects	Standard error
	Received training in the	-0.120	0.085
	survey
	50% risk reduction	-0.067	0.081
	Age in years	0.005	0.002
	Low education a	0.144	0.093
	Muslim religion	0.416***	0.145
	Log (income per capita)	0.477	0.482
	Having chronic illness	-0.065	0.084
	Currently smoking	-0.138	0.109
	Superscripts ***, **, * denote statistical significance at the 1 %, 5%, and 10% level, respectively.
	a We cannot estimate the marginal effect of high education as all eight observations from this
	group are dropped because, for this group, all the zero WTPs imply a scenario rejection.

Page 34 of 53 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

	Submitted Manuscript
	estimates (US$ 1,783 -$2,922) are very low compared to the available estimates
	for developing countries. For example, using results from several VSL studies
	from other countries,
	F o r
	P
	e
	e r
	R e v
	i e
	w
	10

Table 1 . Sample Statistics (N=767)

 1 the CV method to elicit people's VSLs is not a "mission impossible" in the context of developing countries. A comprehensible training on probability and risk concepts, interspersing risk examples with questions to check understanding as well as to maintain respondent interest, should be given in the CV risk reduction surveys. There are remaining problems but most of these appear to be related to the CV methodology per se, rather than to CV studies being performed in developing countries.

	o r
	P
	e
	e r
	R e v
	i e
	w

Table 4 .

 4 Estimated WTP by sub-samples: Truncated regression model a

	Dependent variable Log (WTP+1)
	Variable	Coefficient	Standard error
	Constant	15.12**	6.89
	Received training	0.583***	0.146
	Passed probability test	0.170	0.230
	50% risk reduction	0.371***	0.137
	Received Training × 50% risk reduction	0.146	0.203
	Passed probability test × 50% risk reduction	0.060	0.310
	Muslim religion	-0.005	0.092
	Log(age )	-7.02*	3.73
	Log(age)-squared	0.912*	0.502
	Log (subjective risk )	0.021	0.052
	High education	0.070	0.090
	Low education	-0.064	0.074
	Log(Income per capita)	0.425***	0.063
	Having chronic illness	-0.010	0.102
	Currently smoking	0.016	0.102
	Self reported happiness	0.070***	0.023
	Disturbance standard deviation	1.22	0.033
	Log-Likelihood	-1100.12
	Sensitivity to Scope b
	Mean WTP ratio (No Training)	1.45	0.198
		(1.06-1.84)
	Mean WTP ratio (Training)	1.51	0.160
		(1.20-1.83)
	Difference of mean WTP ratios between two sub-	0.06	0.073
	samples	(-0.08-0.21)

However, closed-ended format is the most favoured by researchers (e.g.[START_REF] Bateman | Guidelines for the Use of Expressed Preferences Methods for the Valuation of Preferences for Non-market Goods[END_REF] Hanley et al., 2003).

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

This means e.g. that WTP should be twice as high for a two-fold reduction in risk. For substantially large risk changes, however, theoretical prediction of proportionality would not hold, i.e., WTP would increase but less than proportionally to the risk changes and hence the resulting VSL can be smaller compared to the case when small risk changes are valued.

For experimental results suggesting disparities between hypothetical and actual (real) WTP, also see[START_REF] Johannesson | An Experimental comparison of dichotomous choice contingent valuation questions and real purchase decisions[END_REF] Frykblom, 2000; Bothelo and Pinto, 2002.

Acknowledgements

The author wish to thank Fredrik Carlsson, Håkan Holm, Olof Johansson-Stenman, and Peter Martinsson for very useful comments and suggestions.

Financial assistance from the Swedish International Development Cooperation Agency (Sida) is highly acknowledged.

Acknowledgements

The author thanks Fredrik Carlsson, Håkan Holm, Olof Johansson-Stenman, Peter Martinsson, and an anonymous referee for very useful comments and suggestions.

Financial assistance from the Swedish International Development Cooperation Agency (Sida) is highly acknowledged.

D60 -General < D6 -Welfare Economics < D -Microeconomics, I10 -General < I1 -Health < I -Health, Education, and Welfare, H40 -General < H4 -Publicly Provided Goods < H -Public Economics, D80 -General < D8 -Information and Uncertainty < D -Microeconomics

Appendix A3: Grid table showing mortality risk of an adult in the next five years as 40 in 1000

Page 23 of 53 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Appendix A4: CV questionnaire: Risk perception It has been estimated that in Bangladesh, an average of 15 out of 1000 people in the 30-34 age group will die over the next five years from various causes, and 90 out of 1000 people in the 55-59 age group will die over the next five years from various causes Enumerator: [show grid table 10 and11]. R1. Thinking about your own life and the way you are living it, what do you think the risk of you dying in the next five years is? [Enumerator: Let the respondent also see the tables 10 and 11 again, at the same time].

Answer:

in 1000

[Enumerator: Use the grid table 12, which is an empty grid table to represent the respondent's subjective risk of dying in the next five years. Let the respondent look at it.]

Appendix A5: CV scenario

Preventative vaccines could reduce the risk of dying from many infectious diseases.

Suppose that you could participate in a program involving various kinds of vaccinations against infectious diseases. The vaccines, if received, would reduce your risk of dying during the next five years.

Assume that the vaccines would be completely safe and would have no side effects. However, the effects of the vaccines would not last beyond the five-year period.

If received, such vaccines would reduce the risk of you dying over the next five years by one quarter/ one half.

[Enumerator: Show grid table 12 in which the stated risk from question C10 has been included by filling in the number of squares representing this subjective risk. Split the filled in area into 25-75%/50-50%. Then while mentioning the risk reduction, point at the 25% / 50% part of the split box and while mentioning the remaining risk on the other part of the split box.]

CV1. What is the maximum, as a one-time fee, you would be willing to pay to obtain such vaccines for yourself? You should also remember that if you were to pay for the vaccines, you would have less money left for other purposes. Maximum ………………Taka Corso, P.S., Hammitt, J.K. and Graham, J. D. ( 2001

Appendix A1: Training on Probability and risk examples

Now I will discuss the chances and risks of events occurring using some examples.

Example 1: Sometimes we toss a coin to decide which of two things to choose. When we toss a coin [Enumerator: show tossing a coin], we get either a head or a tail. We cannot be sure of the result of the toss. As there are two things that can happen from a coin toss, the chance of getting a head is 1 in 2. The same is true for getting a tail.

Similarly, when we roll a dice (chakka) [Enumerator: show throwing a chakka] we may either see on the top 1, 2, 3, 4, 5, or 6, but we don't know which one beforehand. Since there are six different numbers from 1 to 6, we may see any of them on the top. The chance of seeing a 5 on the top is 1/6.

Appendix A4: CV questionnaire: Risk perception

It has been estimated that in Bangladesh, an average of 15 out of 1000 people in the 30-34 age group will die over the next five years from various causes, and 90 out of 1000 people in the 55-59 age group will die over the next five years from various causes Enumerator: [show grid table 10 and11

Appendix A5: CV scenario

Preventative vaccines could reduce the risk of dying from many infectious diseases.

Suppose that you could participate in a program involving various kinds of vaccinations against infectious diseases. The vaccines, if received, would reduce your risk of dying during the next five years.

Assume that the vaccines would be completely safe and would have no side effects. However, the effects of the vaccines would not last beyond the five-year period.

If received, such vaccines would reduce the risk of you dying over the next five years by one quarter/ one half.

[Enumerator: Show grid

3% 39%

a The lists of possible reasons were read to them and the respondents were allowed to choose more than one reason. They were also allowed to express other reasons. Respondents who had chosen any response than (i) or had chosen more than one responses are believed to have provided protest zeros when answering the WTP question.


José Luis Miralles-Marcelo

José Luis Miralles-Quirós

María Del Mar Miralles-Quirós

José Luis

Intraday linkages between the Spanish and the US stock markets: Evidence of an overreaction effect

Keywords: Intraday data, GARCH Models, Overreaction

come

 [START_REF] Blasco | Is bad news cause of asymmetric volatility response? A note[END_REF]or Blasco, Corredor, Del Rio and Santamaría (2005). However, there are no previous studies which analyze the behavior of the main Spanish stock index, IBEX-35, hereafter IBEX, in its early and final hours of trading and the influence of the main US index, Dow Jones Industrial Average, hereafter DOW, on that trading and on investor behavior. Finally, we shed some light on the behavior of the Spanish stock market by providing some clues in order to better understand how the US market affects it.

The Spanish stock market has combined from the earlier nineties, when its main stock index IBEX-35 was created, sharp rises with periods of losses. Additionally, the improvement on the technical, operational and organizational systems supporting the market has enabled it to channel large volumes of investment and have made it more transparent, liquid and effective.

The pooling of interests has enabled Spain to reach a significant size in the European context and a diversified structure that covers the whole chain of activities in the markets, from trading to settlement. That enables it to make better use of resources, reduced costs and streamline services.

The results corroborate the influence of the US market over the IBEX but they also suggest that the IBEX have usually a low price movement till DOW opening.

Additionally, they suggest that the main Spanish stock index reacts quickly to the news, basically in the first four minutes following the opening of the US market, and furthermore, the existence of an overreaction effect during the two following hours which precede to the closing of the Spanish market.

The remainder of the paper is organized as follows. Section 2 describes the methodology and presents the data, Section 3 shows the principal results and Section 4 provides the main conclusions. Secondly, in order to analyze the influence of the opening of the DOW, we calculate the daytime returns as the difference between the opening price and the price at 3:30 pm plus 10 min (increasing again the length in 1 min intervals until 3:40 pm and reporting also ten different daytime returns). Finally, we calculate other daytime returns as the difference between the closing price of the IBEX and the price at 3:30 pm plus 10 min (calculating once again ten different daytime returns).

1 Considering that there is not a substantial overlapping period of trading between the NYSE and the Spanish stock market, just two hours from 3:30 pm to 5:30 pm (CET time), it is not necessary to use intraday data from the DOW. For that reason we just use opening and closing prices for the DOW which reflect all the relevant information we need to analyze the relationship between these two markets. Based on the common procedure, all returns are computed as logarithm differences of the stock price indexes and if one market is closed while the other market is open we set the latter's returns as missing.

Methodology

There are different options to analyze the information transmission between two or more markets. Wei, Liu, Yang and Chaung (1995) test the volatility spillover effect from the US and Japanese markets to the emerging Asian markets by using an univariate GARCH estimator. They find no significant volatility spillover effects using open-to-close returns.

Liu and Pan (1997) employ a similar univariate GARCH model and do not find significant volatility spillover from both the US and Japanese markets. Wang, Rui and Firth (2002), Wang and Firth (2004) and Lee, Rui and Wang (2004), among others, use a standard two-stage procedure. In the first stage, they use alternative GARCH models to estimate the unexpected returns for each index and each market that cannot be predicted based on public information. In the second stage, they use those unexpected returns to analyze the interdependence of returns and volatilities between the markets. Blasco, Corredor, Del Rio and Santamaría (2005) perform the two stage adjustment process proposed by Gallant, Rossi and Tauchen (1992) by using a set of dummy and time-trend variables to capture some systematic effects and then analyze the influence of basic news on returns, volatility and volume. French, Schwert and Stambaug (1987), [START_REF] Akgiray | Conditional heteroskedasticity in time series of stock returns: evidence and forecasts[END_REF], [START_REF] Conolly | An examination of the robustness of the weekend effect[END_REF], [START_REF] Baillie | Stock returns and volatility[END_REF], [START_REF] Bollerslev | ARCH modeling in finance: a review of theory and empirical evidence[END_REF], Kyriacou and Sarno (1999), [START_REF] Gonzalez | A contemporary analysis of Mexican stock market volatility[END_REF] and Franses, Van Dijk and Lucas (2004) The latter show that the intraday return volatility is best described by a GARCH (1,1) specification. Those results were consistent with [START_REF] Akgiray | Conditional heteroskedasticity in time series of stock returns: evidence and forecasts[END_REF] who estimated volatility using different ARCH and GARCH specifications and found the GARCH (1,1) performed the best. King and Wadhwani (1990) hypothesized that international participants simply focus on price movements in other countries, specifically the United States. Ling, Engle and Ito (1994) improve the approach of King and Wadhwani (1990) by decomposing close to close returns into daytime and overnight returns and by employing an aggregate shock model which is related to the GARCH in mean model employed by Hamao, Masulis and Ng (1990). [START_REF] Baur | Return and volatility linkages between the US and the German stock market[END_REF] adapted the aggregate-shock model proposed by Lin, Engle and Ito (1994) by assuming that the returns follow a simple GARCH (1,1) process. This may include additional variables which capture possible effects on the returns (e.g., day of the week or holiday dummy) in both mean and volatility equations.

In order to analyze the intraday return and volatility spillovers between the IBEX and the DOW we adapt the methodology used by [START_REF] Baur | Return and volatility linkages between the US and the German stock market[END_REF] by assuming that the returns in a market follow a GARCH model such as:

2 1 - t 2, 11 1 t 1, 11 2 1 t 1, 11 11 t 1, t 1, 1 t 2, 2 1 t 1, 1 1 t 1, r d h c b a h r r µ r + + + = + + + =
where r 1,t are the IBEX returns and r 2,t the DOW returns. We investigate three hypotheses using this methodology. The first one analyzes the impact of the previous Open-to-Close returns of the DOW and IBEX (DOWDR t-1 and IBEXDR t-1 respectively) on the overnight returns of the Spanish stock index (IBEXNR t ).

Additionally, we analyze the influence of the DOW daytime volatility by including its squared return lagged one period in the variance equation:

2 1 t 11 1 t 1, 11 2 1 t 1, 11 11 t 1, t 1, 1 t 2 1 t 1 1 t DOWDR d h c b a h DOWDR IBEXDR µ IBEXNR + + + = + + + =
In the mean equation of the second model we measure the return spillovers from DOW overnight (DOWNR t ) returns into the IBEX daytime returns from Open-to-3:30 pm (IBEXDR t ). Additionally, we try to identify possible spillovers from the IBEX overnight returns into the IBEX daytime returns. We also analyze if DOW overnight volatility, characterized by the squared overnight returns, contain any relevant information for the daytime returns from Open-to-3:30 pm by including it into the volatility equation.

2 t 11 1 t 1, 11 2 1 t 1, 11 11 t 1, t 1, t 2 t 1 1 t DOWNR d h c b a h DOWNR IBEXNR µ IBEXDR + + + = + + + =
Finally, we investigate the contemporaneous correlation between the DOW overnight (DOWNR t ) returns and the IBEX daytime returns from 3:30 pm-to-Close (IBEXDBR t ).

In this case, the daytime returns of the Spanish index (IBEXDR t ) from Open-to-3:30 pm is added as an exogenous variable in the mean equation. Furthermore, the DOW overnight squared returns lagged one period are included in the volatility equation in order to capture the volatility spillover from the American market into the Spanish market.

2 t 11 1 t 1, 11 2 1 t 1, 11 11 t 1, t 1, t 2 t 1 1 t DOWNR d h c b a h DOWNR IBEXDR µ IBEXDBR + + + = + + + =
In all cases we run several regressions using different proxies of the IBEX overnight and daytime returns by starting from the reference quote (Opening or 3:30 pm price) and extending the time span on a minute by minute basis until the reference quote plus 10 min. The main objective of these procedures is to analyze the behavior of the Spanish market as more and more real-time information is available. In all the regressions maximum likelihood estimation are obtained from the Berndt-Hall-Hall-Hausman algorithm.

III. Empirical Results

Table 1 reports basic statistics for daily daytime and overnight returns for the IBEX and DOW indexes. Tables 2 to 4 report intraday overnight returns (Previous Close-to-Open plus 10 min) and intraday daytime returns (Open-to-3:30 pm plus 10 min and 3:30 pm plus 10 min-to-Close) for the IBEX. All tables cover the sample period from January, 2003 to December, 2004.

The first notable findings are the differences between daytime and overnight returns and volatilities reported on Table 1. Daytime returns are higher than overnight returns (0.000419 versus 0.000396 for the IBEX and 0.000481 versus 3.49•10 -5 for the DOW).

Similar results are obtained for the volatilities. The daytime volatility for the IBEX and DOW indexes are 0.009444 and 0.008801 respectively, while the overnight volatilities are 0.005381 and 0.001798 respectively. These values reveal another important finding: daytime trading is similar in both markets while overnight trading is more intense on the Spanish market due to the higher values on its returns and volatility. This is due to the arrival of information and noise from the DOW which takes place overnight. Furthermore, these four series have significant skewness and kurtosis, which indicate that their empirical distributions have heavy tails relative to the normal distribution.

Relative to the results shown in Table 2, the most interesting fact is the difference between volatility and returns behavior. Volatility increases slowly but constantly during the first ten minutes of trading because it adjust better to news than do returns values which have a more heterogeneous behavior.

Tables 3 and4 report the basic statistics of the two different intraday daytime returns.

The analysis of the means of both daytime returns and their comparison with the mean of the whole period (open-to-close), reported on the first column of Table 1, lead us to suggest that the price movements in the Spanish stock market is concentrated in the last two hours just after the opening of the US markets.

Although, the mean of the daytime returns (Open-to-Close) for the whole sample is 4.19•10 -4 , the mean of the day returns from Open-to-3:30 pm is only 5.76•10 -5 . Whereas, the mean for the period from 3:30 pm-to-Close, 3.61•10 -4 , is significantly bigger and closer to the mean of the whole trading day. This fact leads us to think that the difference is caused by reduced price movements while the Spanish market waits for the arrival of new information (e.g., DOW overnight returns). returns contain any information from DOW or IBEX and their previous daytime returns, these coefficients should be significant. These coefficients show that there are significant transmissions of information from DOW and IBEX previous daytime returns. All the coefficients in the mean equation are significant, which means that the overnight returns of the Spanish main stock index are influenced by both previous daytime returns. These results are consistent with the previous empirical evidence that shows significant transmissions of information between the US markets and different developed and emerging markets (a few examples are [START_REF] Becker | The intertemporal relation between the U.S. and Japanese stock markets[END_REF]Peiró, Quesada and Uriel, 1998;and Lee, Rui and Wang, 2004). Additionally, we find significant and negative values for the previous IBEX daytime returns which show evidence of an overreaction effect into the IBEX overnight returns.

The most important findings with respect to the volatility equation are the existence of significant spillover effects from the DOW previous daytime returns into the Spanish overnight returns and the increasing of persistence values when the time span is extended.

In all cases, the results of the Ljung-Box statistics show, for the standardized and squared residuals with 10 lagged values, the inexistence of serial correlation in the mean equations or remaining ARCH effects in the variance equations. Additionally, the ARCH LM test results which provide no evidence of additional ARCH effects in the standardized residuals.

Table 6 reports the results of the second hypothesis, which investigates the contemporaneous correlation between the DOW and the IBEX overnight returns and IBEX daytime returns from Open-to-3:30 pm (IBEXDR t ).

We find no significant contemporaneous spillovers from the IBEX overnight returns to the IBEX daytime returns until 3:30 pm. However, all the estimated coefficients in the mean equation relative to the DOWNR variable are positive and significant. These results suggest that the events that take place from close to open in New York significantly affect the daytime returns of the Spanish index.

F

However, this "Opening Dow" effect has no apparent influence on the volatility equation. Coefficients relative to the overnight volatility spillovers from the DOW are not significant, as shown in columns 2 to 12 of Table 6, although persistence values are higher than on the first hypothesis which might be due to the great influence of US news on Spanish investors. Finally, there is no evidence of ARCH effects in the standardized residuals as shown in the ARCH LM test neither is there any for the Ljung-Box statistics which show no evidence of serial correlations or remaining ARCH effects.

We obtain some additional clues which enables us to understand the influence of the DOW on the IBEX from the results of the regressions relative to the third hypothesis, which are shown in Table 7. There is no information overlap between the DOW overnight returns and the IBEX daytime returns from 3:30 pm-to-Close. Therefore, if the Spanish market is efficiently processing the information contained in the overnight returns there should be no significant spillover effects in either mean or variance equations, as Baur and Jung (2005) pointed out.

None of the coefficients relative to the daytime returns of the IBEX are statistically significant. This means that the price movements of the first part of the trading day in the Spanish market have no influence from 3:30 pm to 5:30 pm. However, the DOW overnight returns coefficients are statistically significant. These values support the previous findings, which make it clear that the Spanish stock market waits for the opening of the US market before reacting. Additionally, the negative values suggest the existence of an overreaction effect where the previous losers outperforms the previous winners.

That overreaction behavior is consistent with the findings of [START_REF] Fung | Intraday price reversals for index futures in the US and Hong Kong[END_REF], who suggest the existence of intraday price reversals following large price changes at the opening of the S&P 500 Futures market and the HIS (Hong Kong) Futures market; and Lo and MacKinlay (1990) who argue that when some stocks react more Volatility coefficients relative to the DOW overnight corroborate the overreaction effect because all the coefficients are negative. However, the results reveal that the most important transmission of information is produced between 3:30 pm and 3:34 pm (reported in columns 2 to 6) as both the mean and volatility coefficients relative to the DOW are significant 2 . While in the following regressions (Columns 7 to 12) only the variable in the mean equation is statistically significant. This fact determines the reaction of the IBEX in the following two hours (until the closing of the market).

IV. Conclusions

This paper differs from the previous methodologies by focusing on short-term information transmission following the approaches of Lin, Engle and Ito (1994), Susmel

and Engle (1994) and [START_REF] Baur | Return and volatility linkages between the US and the German stock market[END_REF]. It analyzes the behavior of the main Spanish stock index, IBEX, in its early and final hours of trading and the influence of the main US index, DOW, on the stock index prices.

We have contrasted in this paper three hypotheses concerning the transmission of information from the US market to the Spanish market. The results of the first hypothesis show that the previous daytime returns of the DOW index have a great influence on the overnight returns of the IBEX.

However, the most important findings are relative to the second and third hypotheses which firstly suggest that the Spanish stock market usually has a low price movement till DOW opening and, secondly, that the Spanish market reacts quickly to DOW news, 2 With the exception of the volatility coefficient in the fifth column where the endogenous variable is the IBEX daytime return from 3:30 pm plus 3 min-to-Close.

  the influence of the NYSE on the Spanish stock market in its early hours of trading (9:00 am CET) and at 3:30 pm CET, which is the opening hour of the NYSE. Starting on January 2, 2003 we have compiled intraday data of the IBEX, the main Spanish stock index, through December 31, 2004. Starting from the first quote at 9:00 am the data are available on the basis of a minute by minute interval until the first quote plus 10 minutes, repeating the same procedure at 3:30 pm. Additionally, opening and closing prices of both IBEX and DOW indexes are also computed in order to calculate daytime and overnight returns 1 . We divide daily (close-to-close) returns into overnight (previous close-to-open) returns, NR, and daytime (open-to-close) returns, DR for both markets. Additionally, we calculate three different returns for the IBEX. Firstly, overnight returns are calculated starting on the previous closing price of the IBEX and ending on the opening of the day after increasing the length in 1 min intervals until open plus 10 min is reached (reporting ten different overnight returns).

  than others they lead the investors to use a contrarian strategy that may produce profits.

  first four minutes following the opening of the US market. Finally, we find the existence of an overreaction effect during the two hours before the closing of the Spanish stock market. ., C.F. Lee and K.P. Ang (2002) Intraday return volatility process: evidence from Nasdaq stocks, Review of Quantitative Finance and Accounting, 19, 155-180. Stoll, H.R. and R.E. Whaley (1990) Stock market structure and volatility, Review of Financial Studies, 3, 37-71. Susmel, R. and R.F. Engle (1994) Hourly volatility spillovers between international equity markets, Journal of international Money and Finance, 13, 3-25. Wang, S.S. and M. Firth (2004) Do bears and bulls swim across oceans? Market information transmission between greater China and the rest of the world, International Financial Markets, Institutions and Money, 14, 235-254. Wang, S.S., O.M. Rui and M. Firth (2002) Return and volatility behavior of dually-traded stocks: the case of Hong Kong, Journal of International Money and Finance, 21, 265-293. Wei, K.C., Y. Liu, C. Yang and G. Chaung (1995) Volatility and price change spillover effects across the developed and emerging markets, Pacific-Basin Finance Journal, 3, 113-136.

Table 5

 5 reports the regression results for the first hypothesis. The first three rows show the coefficients relative to the mean equation which measure the impact of DOW's and IBEX's previous open-to-close returns (DOWDR t-1 and IBEXDR t-1 respectively) on the overnight returns of the Spanish stock index (IBEXNR t ). In this case, if IBEX overnight

	F o r
	P
	e
	e r
	R e v
	i e
	w

Table 1 . Descriptive Statistics

 1

	Submitted Manuscript		Page 16 of 22
		IBEXDR IBEXNR DOWDR DOWNR
	Mean	0.000419 0.000396 0.000481 3.49•10 -5
	Median	0.000407 0.000806 0.000593 -2.06•10 -5
	Maximum 0.054157 0.023751 0.035499 0.020044
	Minimum -0.033971 -0.027487 -0.035885 -0.020167
	Std. Dev. 0.009444 0.005381 0.008801 0.001798
	Skewness 0.324171 -0.700756 0.166136 2.105963
	Kurtosis	6.129889 6.481102 4.470254 77.54050
	Jarque-Bera 213.2706 293.9681 47.42906 116357.8
	Probability 0.000000 0.000000 0.000000 0.000000
	Sum	0.209873 0.198399 0.240822 0.017493
	Sum Sq. Dev. 0.044590 0.014479 0.038724 0.001616
	Observations	501	501	501	501

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Table 2 . Descriptive Statistics Overnight Returns (Previous Close-to-Open plus 10 min)

 2

	Page 17 of 22					Submitted Manuscript
		IBEXNR IBEXNR1 IBEXNR2 IBEXNR3 IBEXNR4 IBEXNR5 IBEXNR6 IBEXNR7 IBEXNR8 IBEXNR9 IBEXNR10
	Mean	0.000396 0.000256 0.000275 0.000299 0.000309 0.000293 0.000310 0.000327 0.000351 0.000333	0.000343
	Median	0.000806 0.000869 0.000764 0.000804 0.000766 0.000727 0.000757 0.000794 0.000880 0.000836	0.000662
	Maximum	0.023751 0.023703 0.023238 0.024472 0.025483 0.027145 0.025809 0.027790 0.027954 0.028577	0.029426
	Minimum	-0.027487 -0.026565 -0.026261 -0.027202 -0.025771 -0.025675 -0.024736 -0.024784 -0.025423 -0.027028 -0.028271
	Std. Dev.	0.005381 0.005485 0.005746 0.005864 0.005974 0.006017 0.006047 0.006103 0.006175 0.006163	0.006186
	Skewness	-0.700756 -0.697061 -0.602100 -0.519654 -0.470275 -0.377262 -0.393491 -0.357962 -0.359583 -0.384927 -0.393635
	Kurtosis	6.481102 5.916518 5.399824 5.470381 5.314850 5.148016 4.918544 5.035500 5.104222 5.276363	5.410024
	Jarque-Bera	293.9681 218.1366 150.4931 149.9439 130.3261 108.2009 89.76570 97.19001 103.2258 120.5427	134.1847
	Probability	0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000	0.000000
	Sum	0.198399 0.128279 0.137562 0.149637 0.155029 0.146714 0.155330 0.163912 0.175969 0.166855	0.171967
	Sum Sq. Dev. 0.014479 0.015043 0.016510 0.017195 0.017843 0.018103 0.018284 0.018622 0.019068 0.018994	0.019135
	Observations	501	501	501	501	501	501	501	501	501	501	501

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Table 3 . Descriptive Statistics Daytime Returns (Open-to-3:30 pm plus 10 min)

 3

						Submitted Manuscript					Page 18 of 22
		IBEXDR30 IBEXDR31 IBEXDR32 IBEXDR33 IBEXDR34 IBEXDR35 IBEXDR36 IBEXDR37 IBEXDR38 IBEXDR39 IBEXDR40
	Mean	5.76•10 -5	5.34•10 -5	4.36•10 -5	7.15•10 -5	9.60•10 -5	5.98•10 -5	6.71•10 -5	7.64•10 -5	6.53•10 -5	4.34•10 -5	1.69•10 -5
	Median	0.000306	0.000305	0.000246	0.000220	0.000347	0.000348	0.000268	0.000185	0.000309	0.000260	0.000294
	Maximum	0.022553	0.022901	0.022577	0.022287	0.022663	0.022959	0.023023	0.023583	0.022592	0.022824	0.022585
	Minimum	-0.021651 -0.021647 -0.021845 -0.021269 -0.021517 -0.021697 -0.022699 -0.021583 -0.021847 -0.020996 -0.020328
	Std. Dev.	0.007013	0.007041	0.007068	0.007090	0.007160	0.007187	0.007196	0.007181	0.007181	0.007159	0.007128
	Skewness	-0.126022 -0.133227 -0.141078 -0.139476 -0.120285 -0.139934 -0.141887 -0.145246 -0.127240 -0.127445 -0.131107
	Kurtosis	3.518160	3.459085	3.434486	3.358452	3.406715	3.395566	3.456821	3.433453	3.429987	3.391500	3.366807
	Jarque-Bera	6.930827	5.881667	5.602641	4.306563	4.661197	4.901419	6.037329	5.683579	5.211428	4.555792	4.243976
	Probability	0.031260	0.052822	0.060730	0.116103	0.097238	0.086232	0.048866	0.058321	0.073850	0.102500	0.119793
	Sum	0.028871	0.026761	0.021867	0.035832	0.048092	0.029947	0.033633	0.038298	0.032735	0.021741	0.008453
	Sum Sq. Dev. 0.024588	0.024785	0.024980	0.025131	0.025629	0.025830	0.025894	0.025787	0.025785	0.025624	0.025405
	Observations	501	501	501	501	501	501	501	501	501	501	501

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Table 4 . Descriptive Statistics Daytime Returns (3:30 pm plus 10 min-to-Close)

 4

	Page 19 of 22					Submitted Manuscript
		IBEXDRB30 IBEXDRB31 IBEXDRB32 IBEXDRB33 IBEXDRB34 IBEXDRB35 IBEXDRB36 IBEXDRB37 IBEXDRB38 IBEXDRB39 IBEXDRB40
	Mean	0.000361	0.000365	0.000375	0.000347	0.000323	0.000359	0.000352	0.000342	0.000354	0.000376	0.000402
	Median	0.000360	0.000384	0.000413	0.000504	0.000459	0.000458	0.000497	0.000443	0.000499	0.000458	0.000562
	Maximum	0.043577	0.044572	0.045122	0.044877	0.045134	0.045619	0.045973	0.044441	0.043767	0.043428	0.043435
	Minimum	-0.017323	-0.017970	-0.017996	-0.017970	-0.017961	-0.018032	-0.018555	-0.018644	-0.018770	-0.018511	-0.018234
	Std. Dev.	0.005690	0.005657	0.005640	0.005596	0.005578	0.005559	0.005562	0.005534	0.005526	0.005519	0.005513
	Skewness	1.046544	1.093814	1.152943	1.137797	1.155958	1.207630	1.234903	1.147517	1.143184	1.148471	1.149078
	Kurtosis	10.53984	11.27672	11.79973	11.94369	12.20207	12.70301	13.05367	12.13326	11.79935	11.67387	11.59007
	Jarque-Bera 1278.181	1529.924	1727.454	1777.881	1879.232	2087.122	2237.302	1851.269	1725.445	1680.686	1650.603
	Probability	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
	Sum	0.181001	0.183112	0.188005	0.174041	0.161780	0.179926	0.176240	0.171574	0.177138	0.188131	0.201420
	Sum Sq. Dev. 0.016190	0.016004	0.015906	0.015660	0.015558	0.015451	0.015469	0.015313	0.015266	0.015228	0.015197
	Observations	501	501	501	501	501	501	501	501	501	501	501
			Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Table 5 . Hypothesis 1. IBEX Overnight Returns (Close to Open plus 10 min)

 5 Box statistics for the standardized residuals and squared residuals, respectively, with 10 lagged values included. ARCH-LM test is the test statistic for the presence of ARCH effects in the standardized residuals with one lagged value included. P-values are reported.

	Regressor Mean Constant IBEXDR(-1) DOWDR(-1) Variance Constant ARCH GARCH DOWNR 2 (-1) Skewness Kurtosis LB(10) LBS(10) ARCH-LM Test Notes: Robust t-statistics in parentheses. 0 min 4.01•10 -4 (2.833) 2.90•10 -4 1 min (1.955) -0.210 (-8.277) -0.222 (-8.583) 0.407 (17.075) 0.418 (17.298) 3.31•10 -8 (0.171) 2.46•10 -7 (0.906) 0.101 (1.656) 0.106 (1.754) 0.721 (8.252) 0.743 (8.180) 0.040 (2.643) 0.033 (2.076) -0.238 -0.364 4.066 4.116 15.472 7.2832 25.399 18.977 0.014 0.066 Skewness and Kurtosis coefficients for standardized residuals 2 min 3.52•10 -4 (2.228) -0.217 (16.350) 0.428 (16.350) 4.11•10 -7 (0.865) 0.105 (1.698) 0.756 (7.717) 0.033 (1.827) -0.386 4.286 5.6287 13.696 0.170 LB(10) and LBS(10) are the Ljung-Page 3 min 4 min 5 min 6 min 7 min 8 min 9 min 10 min 3.82•10 -4 (2.357) 3.98•10 -4 (2.453) 3.91•10 -4 (2.381) 3.72•10 -4 (2.201) 3.82•10 -4 (2.229) 4.10•10 -4 (2.393) 3.93•10 -4 (2.279) 4.06•10 -4 (2.328) -0.218 (-7.759) -0.214 (-7.428) -0.217 (-7.365) -0.222 (-7.416) -0.221 (-7.308) -0.222 (-7.251) -0.216 (6.937) -0.216 (-6.989) 0.435 (16.008) 0.438 (16.156) 0.442 (16.158) 0.443 (15.748) 0.441 (15.542) 0.439 (15.249) 0.434 (15.087) 0.431 (15.159) 3.35•10 -7 (0.776) 3.29•10 -7 (0.773) 1.36•10 -7 (0.387) 3.21•10 -7 (0.738) 2.76•10 -7 (0.613) 2.16•10 -7 (0.477) 2.98•10 -7 (0.574) 2.35•10 -7 (0.532) 0.093 (1.619) 0.099 (1.835) 0.088 (1.940) 0.080 (1.937) 0.074 (1.935) 0.081 (1.941) 0.081 (1.892) 0.077 (1.898) 0.763 (7.730) 0.759 (8.153) 0.783 (9.615) 0.777 (9.415) 0.787 (10.502) 0.781 (10.318) 0.781 (10.086) 0.788 (10.569) 0.037 (1.845) 0.040 (1.849) 0.039 (1.968) 0.041 (1.931) 0.041 (1.936) 0.043 (1.872) 0.041 (1.847) 0.041 (1.887) -0.403 -0.473 -0.420 -0.434 -0.431 -0.437 -0.439 -0.412 4.485 4.943 4.732 4.662 4.757 4.931 4.833 4.520 8.1719 7.4107 7.2993 8.1776 7.9398 8.7496 8.6892 8.3109 9.7710 5.8705 6.7754 7.7173 8.9851 7.5621 8.8397 8.0951 0.336 0.461 0.525 0.709 0.698 0.736 0.707 0.629

20 of 22 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

	Submitted Manuscript
	F o r P e e r R e v i e w
	1

Table 6 . Hypothesis 2. IBEX Daytime Returns (Open-to-3:30 pm plus 10 min)

 6 Robust t-statistics in parentheses. Skewness and Kurtosis coefficients for standardized residuals LB(10) and LBS(10) are the Ljung-Box statistics for the standardized residuals and squared residuals, respectively, with 10 lagged values included. ARCH-LM test is the test statistic for the presence of ARCH effects in the standardized residuals with one lagged value included. P-values are reported.

	Regressor Mean Constant IBEXNR DOWNR Variance Constant ARCH GARCH DOWNR 2 Skewness Kurtosis LB(10) LBS(10) ARCH-LM Test Notes: Page 21 of 22	0 min 1.93•10 -4 (0.702) 0.014 (0.242) 0.335 (2.223) 6.72•10 -7 (1.495) 0.072 (2.825) 0.910 (29.753) 0.007 (0.156) -0.041 3.076 10.456 12.367 0.980	1 min 2.02•10 -4 (0.734) 0.011 (0.190) 0.337 (2.179) 7.77•10 -7 (1.489) 0.077 (2.897) 0.903 (27.634) 6.13•10 -4 (0.012) -0.060 3.076 10.968 10.843 0.920	2 min 1.88•10 -4 (0.682) 0.008 (0.135) 0.338 (2.203) 7.57•10 -7 (1.441) 0.077 (2.844) 0.903 (26.917) 0.002 (0.049) -0.067 3.033 10.447 10.970 0.941	3 min 2.20•10 -4 (0.798) 0.002 (0.036) 0.352 (2.245) 8.99•10 -7 (1.456) 0.085 (2.879) 0.892 (23.944) 0.010 (0.199) -0.064 2.991 11.210 12.382 0.888 Submitted Manuscript 4 min 5 min 2.35•10 -4 (0.841) 2.27•10 -4 (0.806) -0.004 (-0.073) -0.006 (-0.107) 0.362 (2.367) 0.367 (2.411) 8•10 -7 (1.419) 7.65•10 -7 (1.365) 0.076 (2.777) 0.074 (2.753) 0.903 (26.177) 0.905 (26.761) 0.013 (0.261) 0.016 (0.325) -0.057 -0.047 3.056 3.094 11.165 10.441 12.125 12.294 0.993 0.978	6 min 2.19•10 -4 (0.771) -0.004 (-0.067) 0.358 (2.347) 6.65•10 -7 (1.405) 0.065 (2.674) 0.916 (30.431) 0.022 (0.448) -0.054 3.114 11.300 13.797 0.962	7 min 2.37•10 -4 (0.842) -0.006 (-0.103) 0.367 (2.326) 8.04•10 -7 (1.433) 0.075 (2.714) 0.904 (26.221) 0.017 (0.324) -0.064 3.111 11.521 13.560 0.932	8 min 2.19•10 -4 (0.778) -0.008 (-0.128) 0.378 (2.380) 7.54•10 -7 (1.434) 0.072 (2.807) 0.908 (28.363) 0.015 (0.297) -0.068 3.100 12.368 13.067 0.986	9 min 2.10•10 -4 (0.745) -0.010 (-0.163) 0.386 (2.418) 7.79•10 -7 (1.420) 0.075 (2.839) 0.904 (27.146) 0.018 (0.355) -0.067 3.105 13.211 12.546 0.986	10 min 1.75•10 -4 (0.623) -0.006 (-0.095) 0.391 (2.415) 8.17•10 -7 (1.424) 0.073 (2.736) 0.905 (26.303) 0.021 (0.403) -0.086 3.127 12.488 13.885 0.853

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Table 7 . Hypothesis 3. IBEX Daytime Returns (3:30 pm plus 10 min-to-Close)

 7

	Regressor	0 min	1 min	2 min	3 min	4 min	5 min	6 min	7 min	8 min	9 min	10 min
	Mean Constant	3.62•10 -4 (2.020)	4.01•10 -4 (2.319)	3.83•10 -4 (2.251)	3.82•10 -4 (2.237)	3.54•10 -4 (2.088)	3.66•10 -4 (2.207)	3.26•10 -4 (1.896)	3.60•10 -4 (2.140)	3.91•10 -4 (2.337)	3.79•10 -4 (2.276)	4.16•10 -4 (2.512)
	IBEXDR	0.012 (0.383)	0.021 (0.670)	0.012 (0.378)	0.005 (0.163	2.51•10 -4 (0.007)	4.22•10 -4 (0.131)	-2.44•10 -4 (-0.007)	0.002 (0.074)	0.005 (0.190)	0.006 (0.221)	0.006 (0.208)
	DOWNR	-0.133 (-2.583)	-0.223 (-4.086)	-0.122 (-2.374)	-0.122 (-1.973)	-0.180 (-3.388)	-0.251 (-3.760)	-0.148 (-2.164)	-0.180 (-2.839)	-0.154 (-2.154)	-0.218 (-3.323)	-0.188 (-2.846)
	Variance Constant	2.90•10 -7 (5.536)	2.46•10 -7 (4.628)	2.70•10 -7 (4.990)	2.60•10 -7 (3.590)	2.67•10 -7 (3.532)	2.48•10 -7 (3.508)	1.73•10 -7 (1.713)	2.00•10 -7 (2.187)	2.05•10 -7 (2.188)	1.95•10 -7 (2.244)	1.93•10 -7 (2.215)
	ARCH	-0.019 (-2.034)	-0.020 (-1.481)	-0.018 (-1.673)	-0.015 (-1.620)	-0.018 (-1.801)	-0.015 (-1.539)	-0.001 (-0.082)	-0.005 (-0.453)	-0.007 (-0.745)	-0.005 (-0.461)	-0.006 (-0.565)
	GARCH	1.000 (133.709)	1.003 (94.997)	1.000 (114.163)	0.997 (118.408)	0.999 (116.547)	0.996 (115.315)	0.981 (72.637)	0.985 (80.918)	0.988 (96.491)	0.986 (88.651)	0.987 (85.297)
	DOWNR 2	-0.052 (-3.139)

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

Acknowledgements

We are grateful to Jorge Yzaguirre and José Antonio Pérez from Sociedad de Bolsas for providing us with the data necessary to perform this study.

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Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript