Anxiety for the pandemic and trust in financial markets
AAnxiety for the pandemic and trust in financial markets ∗ Roy Cerqueti , † Valerio Ficcadenti School of BusinessLondon South Bank UniversityLondon, SE1 0AA, UK [email protected] (V. Ficcadenti); [email protected] (R. Cerqueti) Department of Social and Economic SciencesSapienza University of RomeRome, I-00185, Italy [email protected]
Abstract
The COVID-19 pandemic has generated disruptive changes in many fields. Here we focuson the relationship between the anxiety felt by people during the pandemic and the trustin the future performance of financial markets. Precisely, we move from the idea that thevolume of Google searches about “coronavirus” can be considered as a proxy of the anxietyand, jointly with the stock index prices, can be used to produce mood indicators – in termsof pessimism and optimism – at country level. We analyse the “very high human developedcountries” according to the Human Development Index plus China and their respective mainstock market indexes. Namely, we propose both a temporal and a global measure of pessimismand optimism and provide accordingly a classification of indexes and countries.The results show the existence of different clusters of countries and markets in terms of pes-simism and optimism. Moreover, specific regimes along the time emerge, with an increasingoptimism spreading during the mid of June 2020. Furthermore, countries with different gov-ernment responses to the pandemic have experienced different levels of mood indicators, sothat countries with less strict lockdown had a higher level of optimism.
Keywords:
COVID-19; coronavirus; Google Trends; Financial Stock Index; Mood.
JEL Codes:
D83, G15, G41. ∗ The authors sincerely thank Prof. Anna Maria D’Arcangelis for her contributions. † Corresponding author. a r X i v : . [ q -f i n . S T ] A ug Introduction
The world is experiencing the rapid and dramatic widespread of COVID-19 [14, 22] – a pandemicgenerated by a coronavirus – with millions of infected and a large number of deaths. Beyondthe sanitary aspects of such an infectious disease, one of the main concerns experienced by thecommunity regards the economic impact of the measures taken for contrasting the virus.The individuals’ behaviours are at the core of the interest of many scientific studies given that,those behaviours are the critical variables to understand the perspective of many economic activ-ities. Several businesses require the physical interactions among the involved actors – and suchinteractions have been reduced by the lockdown policies and by the natural attitude of peopleavoiding possible sources of contagion – while virtual connections allow another set of economicrelevant activities, such as investing in financial markets. Interestingly, [9] provides a brief discus-sion on the reactions of the financial markets to rare catastrophic events of non-financial nature.The author points the attention of the readers to the plausible parallelisms between pandemicsand natural disasters, terrorist attacks and even nuclear conflict. Less recently, [13] elaborate onhow aviation disasters can generate a decline in stock market prices. In general, empirical evidenceprove that prices collapse in concomitance to rare and unexpected disasters (see, e.g. [2, 7, 10]).On the same line, but in a broader perspective, several authoritative studies highlight that anxietyand negative mood might increase investors’ risk aversion, hence leading to the collapse of stockprices (see, e.g. [1, 11, 12, 4]).Therefore, the financial distress we are observing in the international stock markets can bereasonably interpreted through the anxiety of people, whose worries for the pandemic diseaseaffect the expectation of financial markets future performance.This paper enters this debate. Specifically, it explores how the anxiety for COVID-19 mirrorsthe strategies of investing/disinvesting money in financial markets. In particular, we discuss therelationship between anxiety for COVID-19 and the view of financial markets, with the particularaim of investigating optimism and pessimism. The analysis is carried out by dealing with thecountry-level moods. We explore the relationship mentioned above for a large set of countries,to derive the different behaviours of the populations. [6] and [3] are remarkably relevant forcontextualizing our study. The authors discuss the economic anxiety stemmed from coronavirus.[3] conducts a survey study of over 500 US consumers and shows that the serious concern aboutcoronavirus implications leads to pessimistic expectations about macroeconomic turnaround viadeterioration of the economic fundamentals. [6] complement [3]’s perspective by including alsothe time dimension and the causal effect of the pandemic on the increased economic anxiety. Themethodological ground of [6] lies on the meaningfulness of Google Trends data, which are assumed2o give in-depth information on the development of the anxiety in the specific context of theeconomic outcomes. We adopt [6]’s view and hypothesize that anxiety for COVID-19 is proxiedby the irrepressible persistence of related Google searches. In so doing, we also follow [15], where asurvey study over a large number of respondents confirms that media exposure and online searchesare good predictors of the increasing fear of coronavirus (in this, see also the review paper by [8]).In details, we collect and compare two different datasets over the same reference period whichgoes from January 6 th , 2020 to the June 19 th , 2020. By one side, we consider the daily GoogleTrends data. Specifically, we examine for the searches volumes of the word “coronavirus” alongwith its translations for different countries and respective most spoken languages. Data retrieved ata country level allows for sounding out similarities and discrepancies in the searching for informationpractised by users in need of awareness. In our approach, such a compulsive searching is intendedas a proxy for the anxiety generated by the pandemic. On the other side, we consider the dailylevels of the main stock indexes, which include companies related to the considered countries.The source of financial data is Datastream. In order to have a reliable and consistent dataset,countries are chosen by using the Human Development Index (HDI) embraced by the UnitedNations Development Programme (UNDP) in the Human Development Report Office to rankcountries on the basis of their human development. Specifically, we select the areas having anHDI index greater than 0.8 calculated with the 2018 information. The choice of 0.8 as a thresholdis appropriate because all the countries having at least that level can be considered as “veryhigh human developed countries”. It ensures a good enough level of connections between socio-financial entities within the countries, namely it guarantees the incorporation of the necessarylinks between citizens’ cognitions of the problems, ability to get informed about them and financialstrategies designer presence. To such a list of nations, we add China, which is ranked below the 0.8thresholds – specifically, 0.75. We reasonably do so because China is central in the phenomenonunder investigation. Moreover, the countries without data on stock exchanges in our source –which is Datastream – have been obviously excluded from the list.We move our steps from [6] in two main respects: first, the quoted paper deals with topics inGoogle Trends, and we deal with one crucial word. In so doing, we have a translation task to face –as also acknowledged by [6]. Nevertheless, the use of one word allows to obtain intuitive results andis far from being restrictive in our context. Indeed, a preliminary inspection of the Google Trendsdata shows that the considered word is the most relevant trend related to the current pandemic;second, the quoted paper derives information about economic anxiety directly by Google Trends.Differently, we here start from the idea that the anxiety is manifested through the Google searchesof the word “coronavirus” (and its translations), but then we move to the real performance of thefinancial markets, to assess the links with the trust on them.3ome distance measures between time series have been suitably introduced to offer a wideperspective on the connections between the considered data. We consider concepts of distancefocusing on specific dates and offering global information on the entire reference period. All theproposed measures range in the unitary interval [0 , We now present the employed data. As we will see in detail below, the considered dataset isassociated with the Google Trends and to the financial markets at country level. As a premise, wehave to say that data on financial markets are not always available; moreover, the access to theweb is not a reliable issue is some realities. Thus, we focus on a set of countries whose data areunbiased and reliable. At this aim – and for providing a consistent analysis – we have used theHuman Development Index (HDI) adopted by United Nations Development Programme (UNDP)’sHuman Development Report Office. HDI is a composite index made of factors like life expectancy,education, per capita income indicators and other relevant factors whose details are recollected in[17] by Mahbub ul Haq, one of the two designers of the index. HDI is used to rank countries onthe basis of human development. We take all the countries defined as “very high human developedcountries”, namely those having an HDI index greater than 0.8. The selection is based on datafrom 2018, Table 1 of [18]. China is added to the considered countries – even if the HDI of Chinais 0.75 – because of its centrality in the COVID-19 propagation; the first known human infectionswere in China.The respective most used language is associated with each of these countries. Then, by means4f Google Translate, the word “coronavirus” is translated from English to each of those languages.In so doing, we obtain the translations reported in Table 1.The translated terms are employed to download the web searches indicator from Google Trends.Namely, for each country, one looks for the searches of the respective “coronavirus” translations.In this way, the magnitude of the searches by country is obtained employing the words translatedin the country most common language. The period investigated goes from January 6 th , 2020 toJune 19 th , 2020.At the end of this process, one gets a matrix of time series regarding 63 countries. In ouranalysis, we are interested in examining the time series of the searches from the first day in whicha relevant volume of researches is recorded in each country – i.e., in the first day in which GoogleTrends offers a nonnull value – for the respective translated terms. See columns one, two and threeof Table 1 and Figure 1 to have an idea of the main trends in the data. The most evident pointregards the high volume of searches occurred during the same days around mid-March 2020.We associate at least one stock market index with each country of the list mentioned above.Per each index, the closing prices are downloaded from Thomson Reuters Datastream. The timespan is defined by the same criterion adopted in collecting the Google Trends data (see Table 2 andFigure 2), so that one has the same time span. Andorra, Bahamas, Barbados, Belarus, Brunei,Liechtenstein, Palau, Seychelles and Uruguay do not have a stock market index of reference inour data source, so we exclude them. The final list of considered countries contains 54 elements.Furthermore, we align the Google Trends data and the financial data so that, for each day inwhich prices are recorded, the volume of web searches can be used in the analysis. Consequently,because the financial markets are closed during non-trading days, Google Trends data is reducedaccordingly. As a reference for the number of observation, one can look at column “N. Obs.” inTable 2. To face the problem, we design indicators able to capture the connection between anxiety forthe pandemic and expectations on the future outcomes of financial markets. The underling idearelates to the synchronicity between increments and decrements of Google searches and of stockindex levels, so that, increasing (decreasing) volumes of searches and decreasing (increasing) pricesare associate to pessimistic (optimistic) moods.To describe the employed methodology, some notation is needed.We denote the number of considered countries by J – and J is 54 for us, see Section 2 – and5igure 1: Heatmap representation of the Google search indicators of the word “coronavirus” andits translations in the respective most spoken language for each country. The indents give a clearview of the beginning of the interest in COVID-19 for each country.6abel the generic country by j = 1 , . . . , J . Each country hosts K financial markets. The numberof financial markets depends on the selected country, so that one should write K = K ( j ). Sucha dependence will be omitted when not necessary. Often, K > K = 1. The generic financial market is k = 1 , . . . , K .As already discussed in Section 2, we have daily data on prices and Google searches of theword “coronavirus” (and its translations) in a common reference period of T days. For country j ,we denote the available time series of the prices of the stock index k by p jk = ( p jk (1) , . . . , p jk ( T )).Analogously, the sample of the Google searches for country j is w j = ( w j (1) , . . . , w j ( T )).Notice that the range of variation of the components of p jk and w j are different. Indeed, p jk has nonnegative components, while the components of w j are integer numbers ranging in [0 , t such that w j (¯ t ) = 100. Time ¯ t represents the day with the maximum levelof searches over the period [1 , T ], and depends naturally on j . Also such a dependence will beconveniently omitted. The minimum value of the elements of w j is not necessarily null. Indeed,null search means absence of interest for the considered word in country j – i.e., null amount ofGoogle searches; such an occurrence does not necessarily appear over the period [1 , T ] . Assigningvalue 100 to the highest daily flow of Google searches over [1 , T ] and null value to null searchesallows the easy normalization – implemented directly by the Google Trends algorithm – of theGoogle search data in the range [0 , , p jk for each j and k through a simple normalization procedure. We denote the normalized series of the prices by¯ p jk .First of all, we identify ¯ t ∈ { , . . . , T } such that p jk (¯ t ) = max { p jk ( t ) : t = 1 , . . . , T } . Then, weset ¯ p jk (¯ t ) = 100. Null price is associated to zero value for the normalized series, so that we set¯ p jk ( t ) = 0 when p jk ( t ) = 0. Evidently, one can have p jk ( t ) > t = 1 , . . . , T , so that one has¯ p jk ( t ) > t .The entire series can be derived as follows¯ p jk ( t ) = (cid:34) × p jk ( t ) p jk (¯ t ) (cid:35) , ∀ t = 1 , . . . , T, (1)where [ • ] is the integer part of the real number • .The exploration and comparison of financial data and Google Trends will proceed at coun-try level; it will be implemented by conceptualizing suitable distance measures, under differentperspectives. In so doing, we provide several insights on countries regularities and discrepancies.7 .1 Time-dependent distance measures We first build a distance measures based on the comparison between the time-dependent normalizedaccumulations of prices and Google searches. We consider t , t ∈ { , . . . , T } with t ≤ t and define A j ([ t , t ]; k ) = 12 · t (cid:88) s = t (cid:34) ¯ p jk ( s )¯ P jk − w j ( s ) W j (cid:35) + 12 , (2)where W j = T (cid:88) t =1 w j ( t ) , ¯ P jk = T (cid:88) t =1 ¯ p jk ( t ) . By construction, it results A j ([ t , t ]; k ) ∈ [0 , A j ([ t , t ]; k ) means that [ t , t ] isa time period with a high percentage of price of market k and a low percentage of Google searches– where percentages have to be intended in terms of the total amount on the overall period. Thus, A j ([ t , t ]; k ) close to one means that [ t , t ] is an optimistic period. Differently, A j ([ t , t ]; k ) isclose to zero when prices are relatively low and Google searches of the word “coronavirus” arerelatively high. In this case, [ t , t ] is a time interval where country j has experienced anxietyabout COVID-19 and lack of trust in market k .Notice that the case t = 1 and t = T is trivial and not interesting, being A j ([1 , T ]; k ) = 1 / j and k – i.e., in the middle (fair) situation between optimism and pessimism. Indeed,[1 , T ] is the entire period, hence being associated to full percentage of prices and Google searches.More reasonably, the proper selection of t and t allows to explore elements of the consideredsample in relevant subperiods.At a country level, we can average the A j ’s in (2) with respect to the markets. In particular,we define A j ([ t , t ]) = 1 K ( j ) K ( j ) (cid:88) k =1 A j ([ t , t ]; k ) . (3)We observe that A j ([ t , t ]) ∈ [0 , We here compare the considered series on the basis of the signs of their daily variations. Specifically,we assess how often an increase (a decrease) of the Google searches is associated to a decrease (anincrease) of the prices of the financial markets. The entity of the daily variation is also taken intoaccount.Consistently with our framework, we refer hereafter to a generic series x = ( x (1) , . . . , x ( T )),whose components range in [0 , ζ ∈ [0 , t = 1 , . . . , T −
1, we define the sign variation of theseries x between t and t + 1 at the threshold ζ as follows: δ ( ζ ) t ( x ) = , if x ( t + 1) − x ( t ) > ζ ;0 , if − ζ ≤ x ( t + 1) − x ( t ) ≤ ζ ; − , if x ( t + 1) − x ( t ) < − ζ . (4)The parameter ζ is fixed a-priori; it represents the entity of the daily variation to be crossedfor stating that the series have an increase (or a decrease, by taking the variation with negativesign) from time t − t . Evidently, the case ζ = 0 leads to δ (0) t ( x ) = 1 when x ( t + 1) > x ( t ), δ (0) t ( x ) = − x ( t + 1) < x ( t ) and δ (0) t ( x ) = 0 when x ( t + 1) = x ( t ).The comparison between the behaviors of the Google searches and of the financial markets canbe performed at country level by means of the δ ’s defined in (4).For each j = 1 , . . . , J , we compare the series w j with ¯ p jk , for each k = 1 , . . . , K ( j ).We define ∆ ( ζ ) ( t, j, k ) = δ ( ζ ) t ( w j ) − δ ( ζ ) t (¯ p jk ) . (5)By definition, the ∆’s in (5) can take values in {− , − , , , } . Such values have specific meaningsand deserve an interpretation.When ∆ ( ζ ) ( t, j, k ) = −
2, then we observe a decrease of the Google searches related to “coron-avirus” and an increase of the price of the financial market k . This case has a clear interpretationin terms of optimism. Indeed, people show a decreasing anxiety for the pandemic disease – theyweaken the amount of searches on the Google – and, simultaneously, exhibit an increasing interestin investing in the financial market. The value -1 is associated to constant Google searches andincrease of the price or decreasing level of Google searches and invariant price. The value 0 isrelated to the cases of identical behavior between Google searches and price, so that they can beinvariant between date t and t + 1 or both of them can increase/decrease. The value +1 relies toincreasing level of Google searches and invariant price or, alternatively, a constant level of Googlesearches and decreasing price. The value +2 describes the situation in which Google searches growand price decrease. This is the other corner case, in which anxiety and sadness for the spread ofthe disease – mirroring in the growth of Google searches – is associated to decreasing amount ofinvestments in the financial market.In general, the positive values of the ∆’s describe situations of pessimism, captured by anxietyfor the disease and decrease of investments in the financial markets. Conversely, the cases ofnegative ∆’s are related to optimism, with decreasing interest for COVID-19 and growing attentionfor the future evolutions of financial markets.Some distance measures with high information content can be derived by (5).9e measure the aggregated connection between the considered trend in Google and the priceof market k in country j over the considered period by defining H ( ζ ) j ( k ) = 14( T − (cid:34) T − (cid:88) t =1 ∆ ( ζ ) ( t, j, k ) + 2( T − (cid:35) . (6)By construction, H ( ζ ) j ( k ) ∈ [0 , j tend to the highest level of optimism – in the sense expressed when discussed the case of -2 asvalue of the ∆’s – when analyzing the Google searches of the considered word and its connectionswith the price of financial market k . The converse situation appears when H ( ζ ) j ( k ) is close to one,where we are in presence of a high level of pessimism and anxiety.By averaging the H j ’s in (6) with respect to k we obtain an indicator describing the realityat country level, for all the connections between the considered word and the prices of financialmarkets, as follows: H ( ζ ) j = 1 K ( j ) K ( j ) (cid:88) k =1 H ( ζ ) j ( k ) . (7)Clearly, H ( ζ ) j ∈ [0 ,
1] and the arguments above – opportunely rephrased at country level – remainvalid.We now provide a measure of how a specific country has experienced optimism versus pessimismover the considered period. At this aim, we consider a ratio indicator as follows: R ( ζ ) j ( k ) = 12( T − (cid:34) T − (cid:88) t =1 (cid:16) ∆ ( ζ ) ( t, j, k ) = 2 (cid:17) − T − (cid:88) t =1 (cid:16) ∆ ( ζ ) ( t, j, k ) = − (cid:17) + T − (cid:35) . (8)where ( • ) = , if • is true;0 , otherwise.By construction, R ( ζ ) j ( k ) ∈ [0 , j and when referring to market k , there is ahigh percentage of optimistic days with respect to pessimistic ones as the value of such indicatorapproaches zero, while we are in a substantial context of pessimism when the indicator in (8) isclose to one. The corner cases have a clear interpretation: when R ( ζ ) j ( k ) = 0, then all the daysin the considered period present a decreasing anxiety for COVID-19 coupled with an increasingtrust in market k ; differently, R ( ζ ) j ( k ) = 1 is associated to an entire period of increasing need ofawareness on COVID-19 and decreasing price of market k .Also in this case, we can focus on country j by averaging the R j ’s over the markets: R ( ζ ) j = 1 K ( j ) K ( j ) (cid:88) k =1 R ( ζ ) j ( k ) . (9)Evidently, R ( ζ ) j ∈ [0 ,
1] and the discussion reported above applies also in this more general case.10he global distance measures presented above capture two different aspects of the phenomenonunder analysis. H ( ζ ) j and H ( ζ ) j ( k ) provide information on the mood as an average of the ∆’s overall the days of the considered sample. Differently, R ( ζ ) j ( k ) and R ( ζ ) j focus only on the dates wherethe daily variations of searches volumes and stock index levels have had discordant behaviors.Namely, the indicators R ’s offer more details on the ratio between fully optimistic days and fullypessimistic ones, i.e. on the proportion of the days in which the Google searches have decreasedand the indexes prices have increased and those with an increase of searches and a decrease of theprices. The normalised time series of the stock indexes prices are obtained via Eq. (1). The outcome ofsuch a normalisation is presented in Figure 2 and the main statistical indicators of both the originaland the normalised time series are showed in Table 2. The visual inspection of this Figure allowsthe reader to confirm the general trends of the markets, with a decline inducted by incorporationof the pandemic effects. Figure 1 and Table 1 show the increased Google searches of the translated “coronavirus” in different countries. The searching activities started at a different time and witha general delay with respect to the decline recorded in the financial markets.As a preliminary comment, we notice that the Moreover, the A j ’s in Eq. (2) and (3) comparethe normalised values of Google searches and prices, while the H j ’s in Eq. (6) and (7) and the R j ’s in Eq. (8) and (9) compare their daily increments and decrements. Thus, the A j ’s offer aview of the snapshots of anxiety for COVID-19 and trust in financial markets; differently, the H j ’sand the R j ’s propose an evolutive perspective on the daily variations of the Google search and thestock market data.In computing the index A j ([ t , t ]; k ) in Eq. (2), we take t − t constantly equal to five days,hence studying the weekly behaviour of the index. The outcomes per each index are summarized inFigure 4 and Table 3. Moreover, the results of A j ([ t , t ]) across the stock indexes of each country– namely, those in Eq. (3) – are reported in Figure 5 and Table 4. From this view, some factsemerge: • The paths have drastically changed between the 7 th and the 8 th weeks of the year, namelybetween 17/02/2020 and 01/03/2020. This is the period during which the internationalcommunity started to take the situation seriously despite the controversial statements ofnational governments’ heads. On 11/03/2020, WHO’s Director declared “WHO has beenassessing this outbreak around the clock and we are deeply concerned both by the alarminglevels of spread and severity, and by the alarming levels of inaction. We have therefore made11igure 2: Heatmap representation of the normalised prices recorded for each stock market index(see, Eq. 1). The time series starting points are different because the prices are stored from thefirst day in which relevant volumes of Google searches in that country are recorded.12he assessment that COVID-19 can be characterised as a pandemic.” [19]. • Greece and South Korea have spent more than 90% of the analysed weeks in a quite positivemood, namely reporting an A j ([ t , t ]) > . • Cyprus and Iceland have experienced mild pessimism on a quite large number of weeks. Theypresent A j ([ t , t ]) < . • Weeks 10 and 11 are characterized by the lowest average of A j ([ t , t ]). Their means acrossthe countries are respectively 0.485 and 0.483. • The highest number of countries experiencing a A j ([ t , t ]) < . A j ([ t , t ]). Montenegro holdsthe first position for five weeks. Similarly, we observe that Greece, Iceland and Malta seat on thefirsts four positions most of the times. This outcome suggests that these countries experiencedwaves of optimism and pessimism; interestingly, for the quoted countries, consecutive weeks mayhave a large discrepancy in the ranking positions. Thus, one can say that the waves are of impulsiveand compulsive nature – perhaps, they are driven by news on the pandemic or statements of theGovernments – and this leads to sudden changing of the people’s behaviour in searching on Googleand taking positions in the market.We also propose a focus of weekly rankings of some paradigmatic cases: Sweden, Iceland andSouth Korea – the countries with an easy lockdown, see [20, 21, 16] – and Italy, UK, USA andChina – which are countries having or having had a harder lockdown. By inspecting Figure 6,one can appreciate that the countries having experienced an easier lockdown have spent moreoptimistic moods during the recent weeks.The results show some regularities in the behavior across countries and indexes, as Figures 4and 5 clearly testify. An initial phase of optimism was probably induced by skeptic statementsfrom national governments and media agencies; in fact, the emergence has been underestimated bya large number of people at its inception, see [5]. Then, once the situation has escalated, Googlesearches have drastically increased (see Figure 3) and the markets have simultaneously reacted,plausibly also in the light of the lockdown policies implemented all over the world. The raisedpessimism is represented in Figure 4 and 5 by the blue bands in weeks 10-15. A general relief13ame in after that. In a few cases, the anxiety boosted from the very beginning. This is clearlythe case for Iceland, Malaysia, Malta and more mildly for Singapore, see Figure 5 and Table 5.Considering week 24 th , the stock indexes and so the countries reporting the highest level of A j in (3) are Greece, Iceland and Malta, with values 0.527, 0.524, 0.523, respectively. On the otherhand, those having the lowest values are Montenegro, Bahrain and Singapore with 0.508, 0.507and 0.504, respectively.Figure 6 offers a comparison of the weekly rank of the countries – on the basis of A j ([ t , t ]) –having experienced an easy (upper panel) and hard (lower panel) lockdown. In general, countrieswith a stricter lockdown seem to show globally a more pervasive pessimistic moods than thosewith a weaker lockdown. In particular, one can notice the presence of common waves of optimism(low rank) and pessimism (high rank) over the considered period. Importantly, there is an evidentcountertendency among some countries, with opposite moods in peculiar subperiods. Indeed,Iceland, South Korea and Sweden show pessimism at the beginning of the pandemic and optimismfor the rest of the period, with a spike of pessimism around week 15-16. For China, UK, Italy andUSA the situation is more scattered, but there is optimism at the beginning for UK, Italy andUSA, a substantial pessimism of all the considered countries in the last part of the period. Chinaand Italy seem to follow analogous patterns in the late part of the period; a possible explanationcan be found in the strict collaboration between such countries during the lockdown, which can beseen as the driver of a common mood.Eqs. (6) and (8) allows getting the global distance measures considering different levels of ζ , which is the threshold used to capture the variations of the observed series on a daily basis.Specifically, we use ζ = 0 , , . . . , H ( ζ ) j ( k ) (see Eq. 6) are reported in Figure 7 and Table 6.Financial markets show quite similar behaviours in their links with the Google Trends, mainlyin the maximum values of H ( ζ ) j ( k ). Indeed, the variation range in the maxima is 0.502-0.530,with Bahrain’s stock indexes being outliers with 0.551 and 0.567. However, there are noticeabledifferences in the minimum values of the H ( ζ ) j ( k ), with a variation range 0.400-0.498. Notice thedifferences appearing also within the same country, like for the minima of the H ( ζ ) j ( k ) for the US– with NYSE COMPOSITE at 0.468 and NASDAQ 100 and NASDAQ COMPOSITE at 0.403.The averaged results at country level of Eq. (7) are shown in Figure 8 and Table 7.Some cases are particularly interesting and can be noticed by visual inspecting the results: • Latvia, Montenegro, Norway, Denmark and Canada have a vast majority of H ( ζ ) j > . ζ s used in calculating H ( ζ ) j ( k ), such an occurrence appears at least in14he 90% of the cases. • Malta have 92% of H ( ζ ) j < .
5, representing an average low level of decreasing Google searchesand stock indexes increments at the same time. • The highest value of H ( ζ ) j occurs in Bahrain, with 0.559, for ζ = 0. This finding is inagreement with those discussed already for H ( ζ ) j ( k ) above. • The smallest value of H ( ζ ) j occurs in Italy, with 0.400, for ζ = 0.The R ( ζ ) j ( k ) in Eq. (8) are reported in Figure 9 and Table 8.The variation range in the maxima for the case of R ( ζ ) j ( k ) is 0.5 – 0.565, with Bahrain’s stockindexes having the highest values. Differences in the minimum values are noticeable as well, andthe variation range goes from 0.421 to 0.5. The lowest value is associated with Italy’s index onceagain. Remarkable differences appear for the markets within the same country, in the specific caseof R ( ζ ) j ( k ); the USA is again one of the most remarkable examples of a wide variation range at astock market level.The results at country level are presented in Figure 10 and Table 9; they have been calculatedthrough Eq. (9). The most relevant facts are listed below: • Qatar has the highest percentage of ζ s such that R ( ζ ) j > .
5, namely 19 . ζ s. Belgium, Spain and France follow, with17 .
6% of the ζ s leading to R ( ζ ) j in the range (0.5,1]. • Greece, Malaysia, Argentina and New Zealand have the highest percentages of ζ s such that R ( ζ ) j < .
5, with the first two countries having 11 .
8% of the observations falling within [0,0.5)and the latest two ones having a proportion of 9 . • The lowest value of R ( ζ ) j occurs in Italy, with 0.421, for ζ = 0. • The highest value of R ( ζ ) j occurs in Bahrain, with 0.565, for ζ = 0.By looking at the global distances measures, the case of ζ = 0 is the most relevant to becommented for the information carried out. In such a case, the proposed indexes are sensible to thesmallest daily variation. Bahrain, Malta, Israel, Cyprus, United Arab Emirates, Singapore, Omanand Japan have H ( ζ =0) j > .
5. Thus, these countries have experienced on average a great level ofanxiety for COVID-19 and a small trust in the financial markets future performances. Differently,Italy, Canada, Lithuania, Germany, United Kingdom and Spain have the lowest positions, with H ( ζ =0) j < .
5. In such countries, an optimistic mood seems to be preponderant, on average. Notice15hat such a list of “optimistic moods” contains highly developed countries with a noticeable spreadof the pandemic. Reasonably, optimism is linked to the confidence of the citizens of the mostdeveloped countries either in finance as well as in the health care infrastructures, in the light ofsolving a so pervasive problem like a widespread pandemic.For the case of R ( ζ =0) j < .
5, the lowest positions are held by Russia, Switzerland, Lithuania,Romania, Germany and Italy. So, these countries that have experienced a large number of days ofcontemporaneous decreases in Google searches and increases in stock index prices. Bahrain, Israel,Japan, Singapore, Oman, Malta and Iceland are the countries with R ( ζ =0) j > .
5. Of course, resultsfor H ( ζ =0) j and R ( ζ =0) j are often overlapping, and some countries confirm their general mood whenthe comparison between fully optimistic days and fully pessimistic ones is performed. Interestingly,we find that in places where the pandemic has being managed quite brightly, the general feelingshave been more pessimistic than optimistic (see e.g. the case of Israel).Figure 3: Averaged Google searches of “coronavirus” – along with its translations in the differentlanguages – across countries with HDI > . A j ([ t , t ]; k ) in Eq. (2), at stock index level. Indents representthe differences in the starting date of the related Google Trends data – i.e., the first date with anonnull Google search volume. 17igure 5: Heatmap representation of A j ([ t , t ]) in Eq. (3), at country level. Also in this case,indents represent the differences in the starting date of the Google Trends data at country level.18igure 6: A comparison of the weekly mood of the countries – on the basis of the ranks of A j ([ t , t ])– having experienced an easy/hard lockdown. The lower is the rank, the higher is the optimismexperienced in that week by the respective country. The study investigates the relationship between the Google search volumes of “coronavirus” andthe stock index prices of different markets. The analysis is carried out at country level; thus,the word “coronavirus” has been opportunely translated, when needed. Such analysis allows formapping interrelationships between COVID-19 anxiety in nations and lack of trust in stock marketsfuture performance. These aspects are related to the uncertainty surrounding the evolution of thepandemic and expectations about its effects. In our framework, we follow [6] and hypothesize thatanxiety is manifested via the intensity of the searches run on Google related to the virus.The proposed indicators allow to capture changes in moods along the time – for the case ofthe A j ’s in (2) and (3) – and permit also classification of markets and countries under a moreglobal perspective on the overall period – see the H j ’s in (6) and (7) and the R j ’s in (8) and (9).Moreover, the A j ’s compare the values of Google searches and prices, while the H j ’s and the R j ’scompare the daily increments/decrements of such quantities.For a fair treatment of the considered dataset, we have taken only “very high human developed19igure 7: Heatmap representation of H ( ζ ) j ( k ) in Eq. (6), at stock index level and on the basis ofthe thresholds ζ s. 20igure 8: Heatmap representation of H ( ζ ) j in Eq. (7), at country level and on the basis of thethresholds ζ s. 21igure 9: Heatmap representation of R ( ζ ) j ( k ) in Eq. (8), at stock index level and on the basis ofthe thresholds ζ s. 22igure 10: Heatmap representation of R ( ζ ) j in Eq. (9), at country level and on the basis of thethresholds ζ s. 23ountries” – i.e., those with an HDI greater than 0.8 – and have reasonably added China. Somecountries with HDI greater than 0.8 but without a stock exchange have been removed from thelist.The study allows having a panoramic view of the evolution of the pandemic, its effects on thebehaviour of people and its impact on financial markets. Furthermore, the country-level approachgives insights on similarities and discrepancies of the different populations in respect of the linkbetween the anxiety for COVID-19 and the expectations about stock markets performance.24 ountry terms N. Obs. µ σ Skew Kurt µ/σ
Andorra coronavirus 151 21.993 19.640 1.794 3.867 1.120Argentina coronavirus 151 29.079 24.462 1.206 0.609 1.189Australia coronavirus 151 26.735 22.690 1.207 0.306 1.178Austria Coronavirus 151 20.430 20.572 1.928 3.838 0.993Bahamas coronavirus 155 22.303 20.927 1.528 2.039 1.066Bahrain انوروك سو ريف
151 11.768 9.360 5.880 51.919 1.257Barbados coronavirus 155 26.800 22.471 1.293 1.209 1.193Belarus каранавірус
148 1.973 11.097 7.449 58.031 0.178Belgium coronavirus 151 23.669 21.221 1.243 0.912 1.115Brunei koronavirus 149 3.651 17.847 4.746 20.878 0.205Bulgaria коронавирус
154 22.786 21.561 1.341 1.104 1.057Canada coronavirus 152 25.039 22.082 1.398 1.268 1.134Chile coronavirus 152 21.914 19.096 1.625 2.406 1.148China 新冠 病毒
150 30.513 24.353 0.677 -0.032 1.253Croatia koronavirus 154 22.539 23.752 1.169 0.157 0.949Cyprus κορωνοϊόσ
115 13.322 21.840 1.587 2.013 0.610Czech Republic koronavirus 150 18.880 20.863 1.877 3.188 0.905Denmark coronavirus 154 20.994 20.459 1.595 2.090 1.026Estonia koroonaviirus 163 17.773 22.791 2.068 3.554 0.780Finland koronaviirus 152 12.974 15.485 2.597 10.577 0.838France coronavirus 151 23.060 21.109 1.465 2.102 1.092Germany Coronavirus 152 22.296 19.562 1.411 1.765 1.140Greece κορωνοϊόσ
115 3.774 14.466 6.049 35.634 0.261Hong Kong 新冠 病毒
154 32.175 20.736 0.780 0.649 1.552Hungary koronavírus 151 26.543 24.531 1.189 0.361 1.082Iceland kórónaveira 148 6.128 17.692 2.926 8.299 0.346Ireland coronavirus 151 27.245 23.121 1.114 0.584 1.178Israel הנורוק ףיגנ
154 29.182 21.834 1.166 0.811 1.337Italy coronavirus 150 25.960 22.112 1.130 0.554 1.174Japan コロナウイルス
163 25.540 19.700 1.113 1.137 1.296Kazakhstan коронавирус
151 32.086 24.145 0.727 -0.504 1.329Kuwait انوروك سو ريف
151 12.695 10.626 3.974 29.089 1.195Latvia koronavīruss
150 15.533 22.113 2.207 3.816 0.702Liechtenstein Coronavirus 151 19.927 15.596 1.817 5.039 1.278Lithuania koronavirusas 159 23.572 26.291 1.369 0.775 0.897Luxembourg coronavirus 153 22.529 21.246 1.482 1.884 1.060Malaysia koronavirus 155 5.884 11.544 5.493 36.443 0.510Malta koronavirus 146 4.192 14.699 3.795 15.588 0.285Montenegro вирус Корона
115 10.765 21.945 1.909 2.946 0.491Netherlands coronavirus 152 23.072 22.431 1.236 0.857 1.029New Zealand coronavirus 152 26.013 22.373 1.360 1.025 1.163Norway koronavirus 150 11.373 22.099 3.053 8.220 0.515Oman انوروك سو ريف
152 11.776 10.691 4.038 29.401 1.102Palau coronavirus 150 17.420 21.531 1.012 0.542 0.809Poland koronawirus 150 23.940 22.218 1.448 1.644 1.078Portugal coronavírus 149 5.383 11.303 7.275 57.320 0.476Qatar انوروك سو ريف
160 16.350 12.382 2.138 11.681 1.321Romania coronavirus 151 22.338 22.240 1.513 1.619 1.004Russia коронавирус
151 16.762 15.348 1.776 5.187 1.092Saudi Arabia انوروك سو ريف
149 12.409 12.271 2.984 16.307 1.011Seychelles coronavirus 149 31.342 20.223 0.993 0.979 1.550Singapore 新冠 病毒
148 23.757 18.333 0.892 1.760 1.296Slovakia koronavírus 149 16.201 18.168 2.181 5.867 0.892Slovenia koronavirus 152 18.901 20.257 1.743 2.969 0.933South Korea 코로나 바이러스
152 8.967 16.827 3.654 13.159 0.533Spain coronavirus 151 22.490 21.035 1.601 2.516 1.069Sweden coronavirus 155 22.271 20.375 1.306 1.197 1.093Switzerland Coronavirus 151 22.093 20.035 1.472 2.050 1.103Turkey koronavirüs 151 28.934 22.757 0.923 0.533 1.271United Arab Emirates انوروك سو ريف
164 15.878 13.302 2.037 8.827 1.194United Kingdom coronavirus 151 27.576 23.148 1.238 0.748 1.191United States coronavirus 151 25.397 23.879 1.329 0.896 1.064Uruguay coronavirus 151 19.570 20.028 1.809 3.270 0.977
Table 1: Google Trends data. The table contains country name, translation of “coronavirus” fromEnglish to the most used language in the respective country and statistical summary of the relatedtime series. The different number of observations depends on the first date in which a positivevalue for the search volumes is recorded. 25 ountry Index N. Obs. µ σ µ norm σ norm Skew norm
Kurt norm µ norm /σ norm Argentina S&P MERVAL INDEX 109 35486.915 6392.100 72.598 13.077 -0.399 -0.920 5.552S&P/ASX 200 109 5911.601 747.938 82.535 10.442 0.465 -1.120 7.904S&P/ASX 300 109 5871.308 746.514 82.512 10.491 0.451 -1.122 7.865Austria ATX - AUSTRIAN TRADED INDEX 109 2430.447 451.639 75.656 14.059 0.602 -1.060 5.381Belgium BEL 20 109 3329.824 468.996 79.313 11.171 0.510 -0.971 7.100Bulgaria BULGARIA SE SOFIX 110 483.159 57.005 82.488 9.732 0.662 -1.198 8.476S&P/TSX COMPOSITE INDEX 110 15309.834 1702.964 85.320 9.490 -0.070 -0.765 8.990S&P/TSX 60 INDEX 110 922.101 95.774 86.235 8.957 -0.130 -0.672 9.628Chile S&P/CLX IGPA CLP INDEX 110 19958.315 2275.439 82.770 9.437 -0.061 -0.485 8.771SHANGHAI SE A SHARE 108 3019.785 91.141 93.812 2.831 -0.056 -0.572 33.133SHENZHEN SE B SHARE 108 874.845 52.208 88.023 5.253 0.589 -1.001 16.757Croatia CROATIA CROBEX 110 1695.045 214.439 82.471 10.433 0.577 -1.112 7.905Cyprus CYPRUS GENERAL 83 50.145 5.027 76.969 7.715 2.246 3.667 9.976Czech Republic PRAGUE SE PX 108 924.318 115.479 80.871 10.104 0.376 -0.834 8.004OMX COPENHAGEN (OMXC20) 110 1158.501 82.213 91.520 6.495 -1.038 0.293 14.091OMX COPENHAGEN (OMXC) 110 931.722 71.290 90.021 6.888 -0.872 -0.073 13.070Estonia OMX TALLINN (OMXT) 117 1191.718 118.272 86.711 8.606 0.021 -1.203 10.076Finland OMX HELSINKI (OMXH) 110 8951.857 1069.305 83.364 9.958 -0.048 -1.037 8.372FRANCE CAC 40 109 4926.751 683.725 80.618 11.188 0.532 -1.070 7.206SBF 120 109 3889.479 543.649 80.484 11.250 0.516 -1.082 7.154DAX 30 PERFORMANCE 110 11498.195 1463.513 83.387 10.614 -0.023 -1.031 7.857MDAX FRANKFURT 110 24631.730 3074.107 83.909 10.472 -0.102 -1.037 8.013PRIME ALL SHARE (XETRA) 110 4716.334 611.388 83.047 10.766 0.026 -1.048 7.714ATHEX COMPOSITE 83 619.443 62.320 76.348 7.681 0.483 0.903 9.940FTSE/ATHEX LARGE CAP 83 1508.975 168.287 73.491 8.196 0.901 1.558 8.967HANG SENG 110 24983.880 1691.153 86.762 5.873 0.525 -0.811 14.773HANG SENG CHINA ENTERPRISES 110 10024.493 551.686 88.466 4.869 0.211 -0.231 18.171HANG SENG CHINA AFFILIATED CORP 110 3894.232 302.801 85.101 6.617 0.286 -0.434 12.861Hungary BUDAPEST (BUX) 109 37429.623 4776.098 81.048 10.342 0.450 -1.224 7.837Iceland OMX ICELAND ALL SHARE 106 1395.103 96.631 89.616 6.207 -0.328 -0.894 14.437Ireland ISEQ ALL SHARE INDEX 109 5918.513 831.809 81.577 11.465 0.249 -1.153 7.115Israel ISRAEL TA 125 110 1427.398 154.637 84.756 9.182 0.114 -0.792 9.231Italy FTSE MIB INDEX 108 19418.503 3093.527 76.218 12.142 0.658 -1.027 6.277TOPIX 117 1531.508 139.025 87.808 7.971 -0.024 -1.064 11.016NIKKEI 225 STOCK AVERAGE 117 21131.002 2114.473 87.741 8.780 -0.260 -1.016 9.994TSE SECOND SECTION 117 6170.147 774.153 82.443 10.344 0.229 -1.201 7.970Latvia OMX RIGA (OMXR) 108 1003.015 54.737 94.273 5.145 -1.477 1.249 18.324Lithuania OMX VILNIUS (OMXV) 115 695.252 47.960 92.656 6.392 -1.025 -0.131 14.497Luxembourg LUXEMBOURG SE GENERAL 110 482.199 95.839 72.382 14.386 0.760 -1.002 5.031Malaysia FTSE BURSA MALAYSIA KLCI 111 1447.520 91.933 90.708 5.761 -0.273 -0.895 15.745Malta MALTA SE MSE 105 4165.910 324.601 88.641 6.907 0.598 -1.311 12.834AEX INDEX (AEX) 110 535.687 55.943 85.134 8.891 -0.077 -0.561 9.576AEX ALL SHARE 110 765.096 83.728 84.605 9.259 -0.025 -0.706 9.138New Zealand S&P/NZX 50 110 4622.743 351.083 89.366 6.787 -0.451 -0.401 13.167Norway OSLO EXCHANGE ALL SHARE 108 876.964 98.544 83.512 9.384 0.132 -0.898 8.899Oman OMAN MUSCAT SECURITIES MKT. 110 3705.503 288.851 88.283 6.882 0.617 -1.422 12.828Poland WARSAW GENERAL INDEX 108 48442.017 5980.053 82.655 10.204 0.315 -0.984 8.101PORTUGAL PSI-20 107 4473.655 517.801 82.299 9.526 0.538 -0.945 8.640PORTUGAL PSI ALL-SHARE 107 1293.501 131.589 83.217 8.466 0.374 -0.871 9.830Romania ROMANIA BET (L) 109 8725.098 922.946 85.375 9.031 0.272 -1.180 9.454RUSSIA RTS INDEX 109 1237.167 204.970 75.699 12.542 0.361 -0.938 6.036MOEX RUSSIA INDEX 109 2739.974 252.593 85.378 7.871 0.054 -0.561 10.847Singapore STRAITS TIMES INDEX L 106 2741.389 281.596 84.610 8.691 0.638 -1.057 9.735Slovakia SLOVAKIA SAX 16 107 342.573 14.839 94.204 4.081 -0.188 -1.647 23.085Slovenia SLOVENIAN BLUE CHIP (SBI TOP) 110 846.764 85.949 86.072 8.737 0.206 -1.087 9.852KOREA SE COMPOSITE (KOSPI) 110 1989.641 187.604 87.756 8.275 -0.563 -0.192 10.606KOREA SE KOSPI 200 110 266.169 24.970 86.961 8.158 -0.319 -0.510 10.659IBEX 35 109 7729.301 1252.259 76.652 12.419 0.727 -1.107 6.172MADRID SE GENERAL (IGBM) 109 765.974 126.374 76.468 12.616 0.736 -1.119 6.061OMX STOCKHOLM 30 (OMXS30) 111 1621.549 156.619 85.332 8.242 0.113 -0.924 10.353OMX STOCKHOLM (OMXS) 111 621.746 64.915 84.860 8.860 -0.101 -0.879 9.578Switzerland SWISS MARKET (SMI) 109 9889.044 736.210 87.801 6.537 -0.042 -0.310 13.432Turkey BIST NATIONAL 100 109 104932.181 10880.241 84.927 8.806 0.035 -1.023 9.644FTSE 100 109 6286.916 736.578 82.606 9.678 0.515 -0.950 8.535FTSE ALL SHARE 109 3478.904 425.013 82.373 10.063 0.492 -0.980 8.185FTSE 250 109 17623.833 2586.914 80.597 11.830 0.401 -1.037 6.813FTSE TECHMARK FOCUS (£) 109 5226.403 573.801 85.119 9.345 -0.100 -0.668 9.108S&P 500 COMPOSITE 109 2958.381 282.570 87.367 8.345 -0.373 -0.505 10.470DOW JONES INDUSTRIALS 109 25186.118 2723.498 85.228 9.216 -0.057 -0.677 9.248NASDAQ COMPOSITE 109 8841.200 826.366 88.232 8.247 -0.741 -0.367 10.699RUSSELL 2000 109 1379.807 202.794 81.353 11.957 0.090 -1.086 6.804NASDAQ 100 109 8871.472 773.012 87.886 7.658 -0.732 -0.243 11.477NYSE COMPOSITE 109 11880.292 1407.200 84.037 9.954 0.178 -0.895 8.443MSCI BAHRAIN 109 88.203 17.119 76.635 14.874 0.658 -1.358 5.152MSCI BAHRAIN $ 109 87.436 17.452 75.904 15.150 0.676 -1.352 5.010MSCI KAZAKHSTAN 109 502.812 83.111 75.749 12.521 0.409 -1.129 6.050MSCI KAZAKHSTAN U$ 109 405.035 66.949 75.749 12.521 0.409 -1.129 6.050Montenegro MONTENEGRO SE MONEX 83 10439.545 411.055 92.385 3.638 1.122 0.039 25.397MSCI QATAR 115 742.311 55.968 86.851 6.548 0.715 -0.730 13.263MSCI QATAR $ 115 742.235 55.970 86.849 6.549 0.715 -0.729 13.261MSCI SAUDI ARABIA 107 857.739 75.097 84.553 7.403 0.152 -0.823 11.422MSCI SAUDI ARABIA $ 107 856.837 75.450 84.485 7.439 0.161 -0.836 11.356MSCI UAE 118 280.885 40.723 81.191 11.771 0.394 -1.410 6.897MSCI UAE $ 118 280.876 40.722 81.191 11.771 0.394 -1.410 6.897Kuwait DJ Islamic Market Kuwait 109 658.603 80.377 83.045 10.135 0.610 -1.235 8.194AustraliaCanadaChinaDenmarkFranceGermanyGreeceHong KongJapanNetherlandsPortugalRussiaSouth KoreaSpainSwedenSaudi ArabiaUnited Arab EmiratesUnited KingdomUnited StatesBahrainKazakhstanQatar
Table 2: The statistical summary of the stock indexes closing prices is reported. The last fourcolumns regard the normalized time series, according the Eq. (1).26 ountry Index µ σ Skew Kurt µ/σ
Argentina S&P MERVAL INDEX 0.507 0.016 -1.075 0.147 31.047S&P/ASX 200 0.507 0.015 -1.018 -0.112 33.143S&P/ASX 300 0.507 0.015 -1.018 -0.113 33.086Austria ATX - AUSTRIAN TRADED INDEX 0.507 0.018 -1.405 1.891 28.855MSCI BAHRAIN 0.506 0.010 -1.064 2.100 52.517MSCI BAHRAIN $ 0.506 0.010 -1.032 2.024 52.041Belgium BEL 20 0.507 0.016 -0.837 -0.387 31.297Bulgaria BULGARIA SE SOFIX 0.507 0.016 -0.849 -0.382 31.820S&P/TSX 60 INDEX 0.507 0.016 -1.150 0.231 32.262S&P/TSX COMPOSITE INDEX 0.507 0.016 -1.138 0.201 32.048Chile S&P/CLX IGPA CLP INDEX 0.507 0.015 -1.561 2.619 32.734SHANGHAI SE A SHARE 0.508 0.007 -0.291 -0.900 69.022SHENZHEN SE B SHARE 0.508 0.007 -0.224 -0.917 73.086Croatia CROATIA CROBEX 0.507 0.017 -0.703 -0.895 29.432Cyprus CYPRUS GENERAL 0.506 0.018 0.139 -1.095 28.743Czech Republic PRAGUE SE PX 0.507 0.018 -1.595 2.014 27.678OMX COPENHAGEN (OMXC) 0.506 0.017 -1.314 1.049 29.865OMX COPENHAGEN (OMXC20) 0.506 0.017 -1.327 1.093 30.002Estonia OMX TALLINN (OMXT) 0.506 0.017 -1.601 2.413 29.180Finland OMX HELSINKI (OMXH) 0.506 0.017 -2.163 5.441 29.680FRANCE CAC 40 0.507 0.016 -1.062 0.903 31.220SBF 120 0.507 0.016 -1.064 0.905 31.176DAX 30 PERFORMANCE 0.507 0.015 -0.906 0.235 34.239MDAX FRANKFURT 0.507 0.015 -0.896 0.160 34.282PRIME ALL SHARE (XETRA) 0.507 0.015 -0.895 0.212 34.235ATHEX COMPOSITE 0.516 0.023 -3.681 14.420 22.250FTSE/ATHEX LARGE CAP 0.516 0.023 -3.673 14.390 22.210HANG SENG 0.508 0.008 -0.370 -0.183 60.783HANG SENG CHINA AFFILIATED CORP 0.508 0.009 -0.393 -0.195 58.726HANG SENG CHINA ENTERPRISES 0.508 0.008 -0.418 -0.269 61.537Hungary BUDAPEST (BUX) 0.506 0.017 -1.029 0.254 30.412Iceland OMX ICELAND ALL SHARE 0.505 0.025 -1.059 -0.222 20.333Ireland ISEQ ALL SHARE INDEX 0.507 0.016 -0.824 -0.299 32.556Israel ISRAEL TA 125 0.507 0.010 -0.959 -0.123 50.712Italy FTSE MIB INDEX 0.507 0.014 -0.803 0.127 35.194NIKKEI 225 STOCK AVERAGE 0.506 0.012 -0.537 -0.722 43.446TOPIX 0.506 0.011 -0.538 -0.722 44.073TSE SECOND SECTION 0.506 0.012 -0.516 -0.663 42.277MSCI KAZAKHSTAN 0.507 0.014 -0.252 -1.378 36.482MSCI KAZAKHSTAN U$ 0.507 0.014 -0.252 -1.378 36.482Kuwait DJ Islamic Market Kuwait 0.506 0.013 -1.209 2.126 39.475Latvia OMX RIGA (OMXR) 0.506 0.020 -1.713 2.434 25.431Lithuania OMX VILNIUS (OMXV) 0.506 0.017 -1.185 0.339 29.350Luxembourg LUXEMBOURG SE GENERAL 0.507 0.017 -0.934 0.297 29.277Malaysia FTSE BURSA MALAYSIA KLCI 0.507 0.017 -2.683 8.680 29.648Malta MALTA SE MSE 0.504 0.028 -1.119 -0.072 18.248Montenegro MONTENEGRO SE MONEX 0.505 0.033 -2.264 6.125 15.321AEX ALL SHARE 0.507 0.017 -0.961 -0.322 30.288AEX INDEX (AEX) 0.507 0.017 -0.970 -0.293 30.392New Zealand S&P/NZX 50 0.507 0.015 -1.291 0.794 33.972Norway OSLO EXCHANGE ALL SHARE 0.508 0.023 -2.237 5.170 21.625Oman OMAN MUSCAT SECURITIES MKT. 0.507 0.013 -1.375 2.646 37.753Poland WARSAW GENERAL INDEX 0.507 0.016 -1.093 0.449 30.770PORTUGAL PSI ALL-SHARE 0.510 0.012 -1.266 1.360 43.377PORTUGAL PSI-20 0.510 0.012 -1.202 1.268 42.707MSCI QATAR 0.507 0.010 -0.075 -1.102 51.130MSCI QATAR $ 0.507 0.010 -0.074 -1.102 51.130Romania ROMANIA BET (L) 0.507 0.017 -1.189 0.333 29.378MOEX RUSSIA INDEX 0.507 0.016 -1.033 0.462 32.642RUSSIA RTS INDEX 0.507 0.017 -0.888 0.119 29.871MSCI SAUDI ARABIA 0.507 0.016 -1.609 3.124 32.374MSCI SAUDI ARABIA $ 0.507 0.016 -1.607 3.117 32.346Singapore STRAITS TIMES INDEX L 0.507 0.008 0.491 0.214 61.183Slovakia SLOVAKIA SAX 16 0.507 0.016 -1.096 -0.001 32.341Slovenia SLOVENIAN BLUE CHIP (SBI TOP) 0.507 0.017 -1.300 1.403 29.858KOREA SE COMPOSITE (KOSPI) 0.507 0.026 -3.395 12.267 19.488KOREA SE KOSPI 200 0.507 0.026 -3.395 12.262 19.467IBEX 35 0.507 0.017 -1.066 0.902 30.213MADRID SE GENERAL (IGBM) 0.507 0.017 -1.052 0.872 30.194OMX STOCKHOLM (OMXS) 0.506 0.015 -1.008 0.216 33.044OMX STOCKHOLM 30 (OMXS30) 0.506 0.015 -1.026 0.291 33.372Switzerland SWISS MARKET (SMI) 0.507 0.015 -1.188 0.720 33.293Turkey BIST NATIONAL 100 0.506 0.014 -0.759 0.214 35.367MSCI UAE 0.507 0.012 -0.223 -0.665 43.329MSCI UAE $ 0.507 0.012 -0.223 -0.665 43.329FTSE 100 0.506 0.015 -1.009 0.359 32.823FTSE 250 0.506 0.016 -0.971 0.350 31.828FTSE ALL SHARE 0.506 0.016 -1.003 0.360 32.642FTSE TECHMARK FOCUS (£) 0.506 0.015 -1.097 0.529 32.673DOW JONES INDUSTRIALS 0.506 0.017 -1.095 0.172 30.084NASDAQ 100 0.506 0.017 -1.151 0.227 30.388NASDAQ COMPOSITE 0.506 0.017 -1.139 0.193 30.092NYSE COMPOSITE 0.506 0.017 -1.070 0.116 29.925RUSSELL 2000 0.506 0.018 -1.030 -0.002 28.860S&P 500 COMPOSITE 0.506 0.017 -1.116 0.175 30.276United KingdomUnited StatesSaudi ArabiaSouth KoreaSpainSwedenUnited Arab EmiratesKazakhstanNetherlandsPortugalQatarRussiaFranceGermanyGreeceHong KongJapanAustraliaBahrainCanadaChinaDenmark
Table 3: Main statistical indicators of A j ([ t , t ]; k ) from Eq. (2) at stock index level.27 ountry µ σ Skew Kurt µ/σ
Argentina 0.507 0.016 -1.075 0.147 31.047Australia 0.507 0.015 -1.018 -0.112 33.115Austria 0.507 0.018 -1.405 1.891 28.855Bahrain 0.506 0.010 -1.048 2.062 52.279Belgium 0.507 0.016 -0.837 -0.387 31.297Bulgaria 0.507 0.016 -0.849 -0.382 31.820Canada 0.507 0.016 -1.144 0.216 32.155Chile 0.507 0.015 -1.561 2.619 32.734China 0.508 0.007 -0.262 -0.908 71.104Croatia 0.507 0.017 -0.703 -0.895 29.432Cyprus 0.506 0.018 0.139 -1.095 28.743Czech Republic 0.507 0.018 -1.595 2.014 27.678Denmark 0.506 0.017 -1.321 1.071 29.935Estonia 0.506 0.017 -1.601 2.413 29.180Finland 0.506 0.017 -2.163 5.441 29.680France 0.507 0.016 -1.063 0.904 31.198Germany 0.507 0.015 -0.900 0.203 34.254Greece 0.516 0.023 -3.678 14.411 22.232Hong Kong 0.508 0.008 -0.394 -0.215 60.339Hungary 0.506 0.017 -1.029 0.254 30.412Iceland 0.505 0.025 -1.059 -0.222 20.333Ireland 0.507 0.016 -0.824 -0.299 32.556Israel 0.507 0.010 -0.959 -0.123 50.712Italy 0.507 0.014 -0.803 0.127 35.194Japan 0.506 0.012 -0.532 -0.702 43.270Kazakhstan 0.507 0.014 -0.252 -1.378 36.482Kuwait 0.506 0.013 -1.209 2.126 39.475Latvia 0.506 0.020 -1.713 2.434 25.431Lithuania 0.506 0.017 -1.185 0.339 29.350Luxembourg 0.507 0.017 -0.934 0.297 29.277Malaysia 0.507 0.017 -2.683 8.680 29.648Malta 0.504 0.028 -1.119 -0.072 18.248Montenegro 0.505 0.033 -2.264 6.125 15.321Netherlands 0.507 0.017 -0.966 -0.308 30.340New Zealand 0.507 0.015 -1.291 0.794 33.972Norway 0.508 0.023 -2.237 5.170 21.625Oman 0.507 0.013 -1.375 2.646 37.753Poland 0.507 0.016 -1.093 0.449 30.770Portugal 0.510 0.012 -1.235 1.314 43.045Qatar 0.507 0.010 -0.075 -1.102 51.130Romania 0.507 0.017 -1.189 0.333 29.378Russia 0.507 0.016 -0.959 0.280 31.207Saudi Arabia 0.507 0.016 -1.608 3.120 32.360Singapore 0.507 0.008 0.491 0.214 61.183Slovakia 0.507 0.016 -1.096 -0.001 32.341Slovenia 0.507 0.017 -1.300 1.403 29.858South Korea 0.507 0.026 -3.395 12.265 19.478Spain 0.507 0.017 -1.059 0.887 30.203Sweden 0.506 0.015 -1.017 0.253 33.208Switzerland 0.507 0.015 -1.188 0.720 33.293Turkey 0.506 0.014 -0.759 0.214 35.367United Arab Emirates 0.507 0.012 -0.223 -0.665 43.329United Kingdom 0.506 0.016 -1.021 0.399 32.497United States 0.506 0.017 -1.107 0.152 29.962
Table 4: Main statistical indicators of A j ([ t , t ]) in Eq. (3) at country level.28 ank\Week ARE ARE LUX SGP MLT LUX ESP MLT CYP KOR ISL MLT ISL MNE ISL ISL GRC MNE GRC GRC MNE MNE MNE GRC JPN QAT HRV NOR ESP ESP AUT KAZ GRC GRC KOR GRC MYS MLT MYS GRC MLT GRC ISL ISL CYP GRC CYP ISL EST JPN BGR HRV NOR ITA LUX RUS MNE OMN GRC KOR CHN MYS ISR FIN ISL MLT LVA MLT GRC ISL GRC MLT EST DEU ESP ITA AUT KAZ GRC MLT MNE NOR MNE MNE ISR FIN ISR LVA ISL FIN LVA ISL MLT ISL KOR LTU SVN FRA HRV HRV HUN SAU KOR SAU SAU NOR KOR GRC CHN NLD MYS LVA MYS FIN MLT NOR MLT NOR SWE HKG PRT TUR NOR BEL NOR TUR RUS KWT LVA ISR FIN GRC MYS NLD MYS CYP NLD NOR LVA NOR LVA MYS OMN BGR LUX KAZ MLT PRT KAZ KWT BHR BHR PRT KOR PRT SWE SWE ISR NLD DNK LVA KOR KOR NLD ARE GBR AUT BEL NOR AUS RUS BHR OMN KWT BHR CZE CZE PRT FIN NLD CZE NOR DNK DNK LVA MYS NZL IRL POL TUR HRV ARE HRV MYS JPN MYS SAU ITA SVN DNK EST EST EST KOR SVK SVK FIN FIN CAN ITA FRA POL FRA TUR SAU ARE ARE SAU OMN CHN ITA CHN ISR SWE SVK CYP NLD NLD CHN DNK CHL BEL KAZ FRA POL CHL BHR CHN QAT OMN KWT HKG SGP CHE DNK CZE SWE SWE KOR DEU MYS CZE LTU POL SVN HUN IRL CAN OMN KAZ SGP CHN SGP CHE AUT CZE CZE FIN LTU ISR SWE CZE ARG DEU AUT SVN CZE RUS ISL GBR KWT CHL HKG QAT ARE PRT CHE HKG PRT PRT DNK MYS CHN SWE NLD SWE ESP ROU PRT CZE CZE USA NOR NOR LVA ARE QAT SGP HKG BGR KOR ROU SGP EST DEU USA DNK SVK NLD TUR BEL SVN PRT KOR BGR NZL CHN JPN LVA SVN SVK ROU ROU DNK ROU LTU LTU ARG CZE USA KOR SAU HUN PRT RUS MYS ROU LTU ISR ISR ITA AUT ESP LUX NZL LTU CHE SVN CZE MYS SVK BEL DNK RUS GBR IRL GBR ARG HUN JPN MNE DNK CYP DEU DEU CYP CAN CAN CAN DEU USA FIN DEU FRA JPN AUT IRL ARG SVN HUN LTU SGP MYS ISL DNK QAT POL AUT CHE KOR NZL USA NZL FRA SWE CHE EST LUX ROU BGR ROU CYP MYS TUR RUS EST NLD ESP FRA DEU USA SVK PRT SVK CHE LTU USA LTU IRL ARG BGR DEU DEU QAT ARG QAT EST PRT CHE ISL HUN ITA CHN SVN ARG ARG MYS NZL SGP IRL FRA SVK RUS USA TUR NZL CHL HRV LTU SWE DEU EST TUR BEL CHL USA SVN CHE ROU CHE FRA EST GBR HKG ARG KWT AUS OMN LVA HKG KAZ HKG CHL SVK ROU POL AUT NZL ISR CZE PRT CAN NZL ITA HUN CHL CHL SAU SAU BEL CAN CYP DNK DEU SWE FRA IRL FRA LTU CHE USA BGR CAN EST BEL CAN SWE USA KWT ROU ITA SVK QAT AUS CYP NLD FRA GBR USA SVK LUX CHL HRV NZL BGR AUT CHE CYP ROU LTU USA CHL USA FRA POL ARG SWE ITA EST IRL CAN ESP BEL AUT KOR ROU AUT ROU JPN NZL AUS CHE CHE GBR ARG POL EST LVA PRT SGP LUX TUR GBR SGP SVK LUX AUT CAN SVN AUS AUT JPN RUS OMN SVK CHE BGR ESP ESP HUN ITA BGR BEL RUS BHR IRL HRV ARG AUS AUS AUS POL LTU AUS TUR KAZ SAU SVK NLD HKG PRT ISR FIN RUS ESP LUX NZL CHL HKG BGR JPN PRT EST SVN HUN AUT BEL HUN DEU NLD CHE HRV ARE GBR HRV FIN AUT CAN QAT TUR BGR IRL CHN HRV POL BEL ITA HUN QAT BHR AUS AUS ARE SGP AUS PRT IRL KAZ IRL CYP AUS GBR AUS AUS HKG IRL HUN CYP CAN POL KAZ AUS OMN LTU KWT IRL NZL FRA SVN AUT GBR POL LUX CAN SVN BEL BEL JPN ARG IRL SVN ESP ARG CZE LTU BHR CAN LUX SVK CAN POL CHE HKG ROU CHL NZL DEU POL BGR POL IRL ITA AUS LUX CHE CHN ARE EST SVK SVN HKG USA AUS CYP NOR NZL ARG HUN ITA HRV HUN AUT BEL HRV ESP ARG USA KWT NLD CAN CHL EST SGP BEL BGR SVK BGR ARE JPN USA POL NOR DEU HUN HRV KAZ IRL SVN ISR DEU CAN ARE OMN CZE GBR EST SVK POL FIN HUN BGR ARG NOR DEU MNE BEL FRA LUX LUX ROU FIN NZL BHR OMN SWE MNE AUT DEU NLD SVN ROU AUS OMN BHR IRL ITA POL SGP ISR ESP ROU GBR ITA QAT EST DNK EST LTU USA SVK DEU FRA CAN BEL KAZ AUS GBR CHN IRL HKG JPN JPN PRT SAU POL CAN HKG SWE LTU ROU SVN IRL TUR LUX ARG USA BEL KWT FRA JPN LUX FRA ITA PRT POL RUS KWT EST NZL QAT DNK DNK LUX BGR ESP BEL LTU JPN NLD SVN ARG FRA NOR LUX LUX SGP HRV CHL NOR NLD DNK KOR NZL BGR CZE CHE BEL LTU TUR KAZ CYP HRV SGP GBR ITA ITA RUS HUN ISR ISR CZE DNK QAT NZL LVA LVA BEL ITA NZL ESP NZL SWE KWT OMN BHR HUN FRA GBR KAZ BGR EST HRV CHN LVA LVA LVA QAT SWE FIN LUX ROU CZE MLT CHL ARE KAZ TUR HKG GBR CHN HKG ISR GBR KWT LVA FIN SWE JPN FIN ISR CHN NLD CHE IRL CZE ARG SWE MLT JPN SGP CHL RUS ESP CHN RUS CHN BHR SWE JPN FIN SGP AUT JPN ROU CAN CHL HRV NLD SAU QAT ESP TUR ESP KAZ GBR RUS KAZ PRT SAU ISR SGP HKG KOR KWT IRL SWE AUT CAN SVN BGR HRV ARE HUN ESP TUR KWT QAT GBR CHL OMN PRT JPN FIN ISR BHR DEU DNK POL GBR ROU USA BHR RUS SAU KWT BHR RUS CHL FIN SAU QAT ARE SVK ARE KOR CHN MYS BHR FRA ESP LUX HRV AUS HRV DNK RUS RUS KAZ KAZ TUR SAU CHL SAU BGR SGP KOR CHN SGP JPN CHN ISL DNK ARG USA KAZ DNK LTU LTU KAZ RUS QAT SAU CHL HKG HKG HKG MYS MLT ISL MYS ISR NLD ISR CZE HUN GBR POL LVA NOR NOR ARE CYP ARE MNE SGP KWT KWT KAZ ISL ISL ISR MLT HKG CHE DEU SVN USA TUR JPN KWT EST JPN OMN QAT OMN ESP OMN QAT OMN QAT MYS MYS ISL CHN FIN NLD AUT FRA HUN HUN OMN MLT EST SAU KWT KWT QAT ARE BHR BGR TUR JPN CHE FIN CZE ARG SVK LTU LVA MNE QAT ARE SAU ARE TUR OMN TUR MNE ITA ITA ISL CHL NZL RUS SAU KOR LVA CYP OMN BHR BHR KWT TUR ARE BHR ISL SWE MLT MLT AUS GRC NOR MNE KOR MNE SAU MLT OMN BHR ARE BHR SGP
Table 5: The ranked data week by week. Columns represent weeks, while rows are ranks. Specifi-cally, countries are sorted in descending order on the basis of the value of A j ([ t , t ]). The codesare taken from ISO 3166-1, alpha-3. 29 ountry Index max Ϛ max min Ϛ min µ σ Skew Kurt µ/σ
Argentina S&P MERVAL INDEX 0.514 9 0.465 1 0.500 0.008 -2.744 9.300 61.631S&P/ASX 200 0.509 5 0.440 0 0.499 0.009 -6.300 42.788 56.705S&P/ASX 300 0.509 5 0.440 0 0.499 0.009 -6.293 42.719 56.645Austria ATX - AUSTRIAN TRADED INDEX 0.507 9 0.477 1 0.501 0.005 -3.228 13.247 100.479MSCI BAHRAIN 0.551 0 0.498 15 16 18 0.503 0.009 4.182 19.640 57.820MSCI BAHRAIN $ 0.567 0 0.498 15 16 18 0.503 0.011 4.810 26.024 47.043Belgium BEL 20 0.521 3 0.440 0 0.501 0.011 -4.304 22.483 46.232Bulgaria BULGARIA SE SOFIX 0.516 4 0.489 1 0.502 0.005 0.652 1.995 105.606S&P/TSX 60 INDEX 0.507 3 8 9 0.438 0 0.501 0.009 -6.381 42.982 53.798S&P/TSX COMPOSITE INDEX 0.507 7 8 9 0.415 0 0.501 0.013 -6.740 46.717 40.372Chile S&P/CLX IGPA CLP INDEX 0.509 3 4 0.452 0 0.501 0.008 -5.474 34.981 66.096SHANGHAI SE A SHARE 0.502 38 39 44 45 0.470 1 0.491 0.008 -0.374 -0.190 65.452SHENZHEN SE B SHARE 0.502 38 39 44 45 0.481 4 5 19 0.492 0.007 0.006 -1.320 74.779Croatia CROATIA CROBEX 0.509 13 17 18 0.484 0 0.500 0.005 -0.822 2.309 102.002Cyprus CYPRUS GENERAL 0.527 1 0.485 37 0.502 0.010 -0.134 -1.083 49.747Czech Republic PRAGUE SE PX 0.505 13 0.465 0 0.499 0.007 -3.918 17.128 76.321OMX COPENHAGEN (OMXC) 0.511 11 12 [19-22] 0.459 0 0.504 0.010 -4.029 17.348 52.754OMX COPENHAGEN (OMXC20) 0.511 11 12 [19-22] 0.450 0 0.504 0.011 -4.241 18.684 45.541Estonia OMX TALLINN (OMXT) 0.515 3 4 0.453 0 0.503 0.008 -4.721 28.766 61.084Finland OMX HELSINKI (OMXH) 0.507 3 0.484 1 0.498 0.005 -0.791 -0.380 91.540FRANCE CAC 40 0.516 3 0.454 0 0.500 0.008 -4.757 29.627 66.787SBF 120 0.519 3 0.449 0 0.500 0.008 -4.750 30.238 61.046DAX 30 PERFORMANCE 0.509 8 0.445 0 0.498 0.009 -5.020 30.841 58.365MDAX FRANKFURT 0.509 3 8 0.440 0 0.498 0.010 -4.583 24.677 50.543PRIME ALL SHARE (XETRA) 0.509 3 8 0.445 0 0.498 0.009 -4.756 28.379 57.124ATHEX COMPOSITE 0.518 5 0.473 0 0.501 0.005 -2.020 17.092 93.853FTSE/ATHEX LARGE CAP 0.515 5 0.491 0 0.501 0.004 1.814 4.978 126.988HANG SENG 0.507 32 35 36 37 0.475 8 9 0.494 0.009 -0.587 -0.451 55.998HANG SENG CHINA AFFILIATED CORP 0.507 32 35 36 37 0.475 8 9 0.494 0.009 -0.691 -0.116 58.235HANG SENG CHINA ENTERPRISES 0.507 32 35 36 37 0.454 0 0.493 0.010 -1.390 3.030 48.525Hungary BUDAPEST (BUX) 0.514 3 0.438 0 0.500 0.010 -5.290 32.563 50.278Iceland OMX ICELAND ALL SHARE 0.512 3 0.490 1 0.502 0.003 -0.849 10.345 190.485Ireland ISEQ ALL SHARE INDEX 0.512 5 0.468 0 0.501 0.006 -3.413 17.699 81.892Israel ISRAEL TA 125 0.516 3 0.493 19 40 41 42 0.500 0.005 1.244 1.194 94.872Italy FTSE MIB INDEX 0.505 17 18 19 0.400 0 0.496 0.017 -4.751 24.584 29.866NIKKEI 225 STOCK AVERAGE 0.524 3 0.498 7 [14-23] [30-34] 0.501 0.005 3.447 12.867 99.641TOPIX 0.522 0 3 0.498 7 [14-23] [30-34] 0.501 0.005 2.774 7.487 92.170TSE SECOND SECTION 0.526 3 0.491 0 0.501 0.006 2.838 8.749 84.948MSCI KAZAKHSTAN 0.512 3 0.449 1 0.499 0.009 -4.280 22.439 57.636MSCI KAZAKHSTAN U$ 0.512 3 0.449 1 0.499 0.009 -4.280 22.439 57.636Kuwait DJ Islamic Market Kuwait 0.512 3 6 7 0.479 0 0.501 0.005 -1.317 10.495 109.350Latvia OMX RIGA (OMXR) 0.514 1 0.484 0 0.506 0.004 -4.249 27.331 139.940Lithuania OMX VILNIUS (OMXV) 0.511 8 0.428 0 0.502 0.012 -5.380 32.209 42.485Luxembourg LUXEMBOURG SE GENERAL 0.507 11 0.461 0 0.500 0.007 -4.539 21.268 68.256Malaysia FTSE BURSA MALAYSIA KLCI 0.502 3 4 0.493 1 5 0.499 0.002 -1.188 0.897 243.889Malta MALTA SE MSE 0.514 1 0.493 44 0.498 0.003 3.985 18.747 158.192Montenegro MONTENEGRO SE MONEX 0.512 1 49 50 0.466 0 0.505 0.006 -5.740 38.116 85.054AEX ALL SHARE 0.511 3 0.445 0 0.501 0.011 -4.483 20.758 47.356AEX INDEX (AEX) 0.509 3 0.440 0 0.501 0.011 -4.687 22.875 46.212New Zealand S&P/NZX 50 0.507 4 [6-14] 0.482 0 0.501 0.004 -2.059 11.283 126.313Norway OSLO EXCHANGE ALL SHARE 0.507 3 6 0.472 0 0.502 0.005 -5.071 27.999 101.428Oman OMAN MUSCAT SECURITIES MKT. 0.507 0 3 8 9 10 0.495 1 0.502 0.002 0.690 1.077 212.642Poland WARSAW GENERAL INDEX 0.509 6 8 9 0.472 0 0.501 0.006 -2.889 14.892 90.044PORTUGAL PSI ALL-SHARE 0.514 3 0.465 0 0.497 0.006 -3.514 22.349 86.154PORTUGAL PSI-20 0.517 3 0.469 0 0.498 0.005 -2.735 25.398 100.432MSCI QATAR 0.513 1 0.463 0 0.497 0.006 -3.840 25.296 85.521MSCI QATAR $ 0.513 1 0.463 0 0.497 0.006 -3.840 25.296 85.521Romania ROMANIA BET (L) 0.512 10 11 0.438 0 0.501 0.011 -4.731 26.581 47.579MOEX RUSSIA INDEX 0.519 3 0.477 0 0.501 0.005 -0.749 13.386 100.234RUSSIA RTS INDEX 0.519 3 4 0.477 0 0.501 0.006 0.104 7.538 83.881MSCI SAUDI ARABIA 0.509 6 0.467 0 0.501 0.006 -4.384 25.394 88.382MSCI SAUDI ARABIA $ 0.509 6 0.476 0 0.501 0.005 -3.191 15.193 105.779Singapore STRAITS TIMES INDEX L 0.512 [22-25] 0.481 8 9 0.500 0.009 -0.489 -1.109 53.898Slovakia SLOVAKIA SAX 16 0.507 12 0.491 3 0.501 0.002 -0.924 5.984 203.538Slovenia SLOVENIAN BLUE CHIP (SBI TOP) 0.505 3 10 11 12 [15-20] 0.454 0 0.500 0.007 -6.068 40.443 71.938KOREA SE COMPOSITE (KOSPI) 0.509 3 0.452 0 0.499 0.007 -5.542 36.007 68.519KOREA SE KOSPI 200 0.507 1 3 0.447 0 0.499 0.008 -5.715 37.381 63.011IBEX 35 0.507 8 0.438 0 0.500 0.009 -6.340 42.924 53.920MADRID SE GENERAL (IGBM) 0.507 8 0.433 0 0.500 0.010 -6.507 44.612 50.699OMX STOCKHOLM (OMXS) 0.530 3 0.475 0 0.504 0.008 0.108 4.526 63.761OMX STOCKHOLM 30 (OMXS30) 0.527 3 0.480 0 0.505 0.007 0.349 3.149 68.270Switzerland SWISS MARKET (SMI) 0.512 7 0.438 0 0.500 0.011 -4.758 26.526 48.007Turkey BIST NATIONAL 100 0.509 [9-14] 0.484 0 0.502 0.005 -1.214 3.323 101.176MSCI UAE 0.528 1 0.494 4 0.500 0.005 4.768 28.447 109.837MSCI UAE $ 0.528 1 0.494 4 0.500 0.005 4.602 26.559 108.167FTSE 100 0.516 3 4 0.433 0 0.501 0.011 -5.370 35.587 47.697FTSE 250 0.519 4 0.442 0 0.501 0.009 -4.928 32.381 53.838FTSE ALL SHARE 0.521 3 0.438 0 0.501 0.010 -4.884 32.159 49.932FTSE TECHMARK FOCUS (£) 0.516 4 0.433 0 0.500 0.010 -5.748 38.462 48.849DOW JONES INDUSTRIALS 0.514 3 0.468 0 0.502 0.006 -3.841 24.215 85.246NASDAQ 100 0.509 5 6 0.403 0 0.500 0.014 -6.601 45.426 35.075NASDAQ COMPOSITE 0.512 6 0.403 0 0.500 0.015 -6.406 43.439 34.691NYSE COMPOSITE 0.512 [4-7] 0.468 0 0.502 0.006 -4.019 25.496 86.318RUSSELL 2000 0.519 6 0.449 0 0.502 0.009 -4.863 31.991 59.408S&P 500 COMPOSITE 0.514 3 0.444 0 0.502 0.009 -5.687 37.627 57.465United KingdomUnited StatesSaudi ArabiaSouth KoreaSpainSwedenUnited Arab EmiratesKazakhstanNetherlandsPortugalQatarRussiaFranceGermanyGreeceHong KongJapanAustraliaBahrainCanadaChinaDenmark
Table 6: Main statistical indicators of H ( ζ ) j ( k ) in Eq. (6), at stock index level. The values of thereference thresholds ζ s are also shown. 30 ountry max Ϛ max min Ϛ min µ σ Skew Kurt µ/σ
Argentina 0.514 9 0.465 1 0.500 0.008 -2.744 9.300 61.631Australia 0.509 5 0.440 0 0.499 0.009 -6.300 42.784 56.684Austria 0.507 9 0.477 1 0.501 0.005 -3.228 13.247 100.479Bahrain 0.559 0 0.498 15 16 18 0.503 0.010 4.523 23.068 51.964Belgium 0.521 3 0.440 0 0.501 0.011 -4.304 22.483 46.232Bulgaria 0.516 4 0.489 1 0.502 0.005 0.652 1.995 105.606Canada 0.507 8 9 0.427 0 0.501 0.011 -6.609 45.353 46.197Chile 0.509 3 4 0.452 0 0.501 0.008 -5.474 34.981 66.096China 0.502 38 39 44 45 0.480 1 0.492 0.007 0.074 -1.293 73.282Croatia 0.509 13 17 18 0.484 0 0.500 0.005 -0.822 2.309 102.002Cyprus 0.527 1 0.485 37 0.502 0.010 -0.134 -1.083 49.747Czech Republic 0.505 13 0.465 0 0.499 0.007 -3.918 17.128 76.321Denmark 0.511 11 12 [19-22] 0.454 0 0.504 0.010 -4.145 18.072 48.899Estonia 0.515 3 4 0.453 0 0.503 0.008 -4.721 28.766 61.084Finland 0.507 3 0.484 1 0.498 0.005 -0.791 -0.380 91.540France 0.517 3 0.451 0 0.500 0.008 -4.761 30.006 63.821Germany 0.509 8 0.443 0 0.498 0.009 -4.787 27.922 55.327Greece 0.517 5 0.482 0 0.501 0.005 -0.106 9.170 111.827Hong Kong 0.507 32 35 36 37 0.472 0 0.494 0.009 -0.702 -0.172 55.112Hungary 0.514 3 0.438 0 0.500 0.010 -5.290 32.563 50.278Iceland 0.512 3 0.490 1 0.502 0.003 -0.849 10.345 190.485Ireland 0.512 5 0.468 0 0.501 0.006 -3.413 17.699 81.892Israel 0.516 3 0.493 19 40 41 42 0.500 0.005 1.244 1.194 94.872Italy 0.505 17 18 19 0.400 0 0.496 0.017 -4.751 24.584 29.866Japan 0.524 3 0.498 7 [14-23] [30-34] 0.501 0.005 3.091 10.265 97.770Kazakhstan 0.512 3 0.449 1 0.499 0.009 -4.280 22.439 57.636Kuwait 0.512 3 6 7 0.479 0 0.501 0.005 -1.317 10.495 109.350Latvia 0.514 1 0.484 0 0.506 0.004 -4.249 27.331 139.940Lithuania 0.511 8 0.428 0 0.502 0.012 -5.380 32.209 42.485Luxembourg 0.507 11 0.461 0 0.500 0.007 -4.539 21.268 68.256Malaysia 0.502 3 4 0.493 1 5 0.499 0.002 -1.188 0.897 243.889Malta 0.514 1 0.493 44 0.498 0.003 3.985 18.747 158.192Montenegro 0.512 1 49 50 0.466 0 0.505 0.006 -5.740 38.116 85.054Netherlands 0.510 3 0.443 0 0.501 0.011 -4.583 21.763 46.811New Zealand 0.507 4 [6-14] 0.482 0 0.501 0.004 -2.059 11.283 126.313Norway 0.507 3 6 0.472 0 0.502 0.005 -5.071 27.999 101.428Oman 0.507 0 3 8 9 10 0.495 1 0.502 0.002 0.690 1.077 212.642Poland 0.509 6 8 9 0.472 0 0.501 0.006 -2.889 14.892 90.044Portugal 0.515 3 0.467 0 0.498 0.005 -3.255 25.052 94.406Qatar 0.513 1 0.463 0 0.497 0.006 -3.840 25.296 85.521Romania 0.512 10 11 0.438 0 0.501 0.011 -4.731 26.581 47.579Russia 0.519 3 0.477 0 0.501 0.005 -0.407 10.179 92.818Saudi Arabia 0.509 6 0.472 0 0.501 0.005 -3.806 20.311 96.753Singapore 0.512 [22-25] 0.481 8 9 0.500 0.009 -0.489 -1.109 53.898Slovakia 0.507 12 0.491 3 0.501 0.002 -0.924 5.984 203.538Slovenia 0.505 3 10 11 12 [15-20] 0.454 0 0.500 0.007 -6.068 40.443 71.938South Korea 0.508 3 0.450 0 0.499 0.008 -5.714 37.367 65.922Spain 0.507 8 0.435 0 0.500 0.010 -6.430 43.834 52.273Sweden 0.528 3 0.477 0 0.505 0.008 0.246 4.104 66.680Switzerland 0.512 7 0.438 0 0.500 0.011 -4.758 26.526 48.007Turkey 0.509 [9-14] 0.484 0 0.502 0.005 -1.214 3.323 101.176United Arab Emirates 0.528 1 0.494 4 0.500 0.005 4.686 27.527 109.050United Kingdom 0.516 4 0.436 0 0.501 0.010 -5.343 35.349 50.233United States 0.512 6 0.439 0 0.502 0.010 -5.949 39.841 53.236
Table 7: Main statistical indicators of H ( ζ ) j in Eq. (7), at country level. Also in this case, thevalues of the reference thresholds ζ s are illustrated.31 ountry Index max Ϛ max min Ϛ min µ σ Skew Kurt µ/σ
Argentina S&P MERVAL INDEX 0.505 6 0.468 0 1 0.498 0.007 -4.252 18.219 76.251S&P/ASX 200 0.514 2 0.454 0 0.499 0.007 -5.632 38.317 72.133S&P/ASX 300 0.514 2 0.454 0 0.499 0.007 -5.357 35.016 70.676Austria ATX - AUSTRIAN TRADED INDEX 0.505 1 3 4 5 0.472 0 0.500 0.004 -5.806 39.154 120.645MSCI BAHRAIN 0.537 0 0.500 [2-50] 0.501 0.006 5.295 28.336 83.518MSCI BAHRAIN $ 0.565 0 0.500 [2-50] 0.502 0.010 5.906 36.395 51.702Belgium BEL 20 0.523 2 0.463 0 0.501 0.007 -2.665 22.162 75.245Bulgaria BULGARIA SE SOFIX 0.505 [3-6] 0.477 0 0.500 0.003 -5.628 38.402 144.571S&P/TSX 60 INDEX 0.509 2 3 0.450 0 0.500 0.008 -5.856 39.296 66.291S&P/TSX COMPOSITE INDEX 0.518 2 0.436 0 0.500 0.010 -5.708 38.948 51.904Chile S&P/CLX IGPA CLP INDEX 0.509 4 5 0.454 0 0.500 0.008 -4.796 26.899 66.081SHANGHAI SE A SHARE 0.500 [3-50] 0.481 0 1 0.499 0.004 -4.284 17.705 130.729SHENZHEN SE B SHARE 0.514 0 0.500 [3-50] 0.501 0.002 4.741 23.030 209.801Croatia CROATIA CROBEX 0.518 2 3 0.486 0 0.501 0.005 1.844 7.965 105.528Cyprus CYPRUS GENERAL 0.524 1 0.494 0 0.501 0.005 3.362 13.220 109.736Czech Republic PRAGUE SE PX 0.505 [3-6] 0.453 0 0.499 0.007 -6.031 39.432 72.275OMX COPENHAGEN (OMXC) 0.514 2 0.463 0 0.499 0.006 -4.141 24.040 80.504OMX COPENHAGEN (OMXC20) 0.514 2 0.454 0 0.499 0.008 -4.642 26.610 66.297Estonia OMX TALLINN (OMXT) 0.509 2 3 0.453 0 0.499 0.007 -5.644 36.742 69.621Finland OMX HELSINKI (OMXH) 0.518 1 0.500 0 [8-50] 0.501 0.003 4.229 20.731 163.965FRANCE CAC 40 0.505 [1-9] 0.458 0 0.500 0.006 -6.206 42.539 81.298SBF 120 0.505 [1-9] 0.454 0 0.500 0.007 -6.369 43.954 73.868DAX 30 PERFORMANCE 0.514 2 0.431 0 0.499 0.010 -6.182 42.386 49.564MDAX FRANKFURT 0.509 3 0.422 0 0.499 0.011 -6.679 46.596 44.906PRIME ALL SHARE (XETRA) 0.518 2 0.427 0 0.499 0.011 -5.955 40.462 46.002ATHEX COMPOSITE 0.500 [6-50] 0.488 0 1 2 0.499 0.003 -2.940 7.654 159.558FTSE/ATHEX LARGE CAP 0.500 [6-50] 0.488 1 2 0.499 0.003 -3.081 9.081 178.302HANG SENG 0.509 2 0.468 0 0.499 0.006 -4.514 22.529 88.329HANG SENG CHINA AFFILIATED CORP 0.505 2 0.477 0 0.499 0.004 -5.072 27.369 133.253HANG SENG CHINA ENTERPRISES 0.509 2 0.445 0 0.499 0.009 -5.428 31.527 58.017Hungary BUDAPEST (BUX) 0.519 2 3 0.458 0 0.501 0.007 -3.121 22.725 68.434Iceland OMX ICELAND ALL SHARE 0.514 1 2 0.500 [7-50] 0.501 0.003 3.546 13.001 165.438Ireland ISEQ ALL SHARE INDEX 0.514 2 3 4 0.463 0 0.500 0.006 -3.489 24.217 78.581Israel ISRAEL TA 125 0.523 0 0.500 [6-50] 0.501 0.004 3.920 17.515 126.334Italy FTSE MIB INDEX 0.505 5 0.421 0 0.498 0.012 -5.911 36.715 41.962NIKKEI 225 STOCK AVERAGE 0.517 1 0.500 [4-50] 0.501 0.004 3.545 11.735 139.959TOPIX 0.526 0 0.500 [4-50] 0.501 0.004 4.623 22.160 113.310TSE SECOND SECTION 0.526 1 0.500 [3-50] 0.501 0.005 4.274 17.791 102.089MSCI KAZAKHSTAN 0.509 2 3 0.491 1 0.500 0.003 0.822 9.432 200.434MSCI KAZAKHSTAN U$ 0.509 2 3 0.491 1 0.500 0.003 0.822 9.432 200.434Kuwait DJ Islamic Market Kuwait 0.505 1 2 3 0.472 0 0.500 0.004 -6.271 43.554 123.714Latvia OMX RIGA (OMXR) 0.505 [10-13] 0.463 0 0.499 0.006 -6.000 39.837 90.399Lithuania OMX VILNIUS (OMXV) 0.504 2 [4-8] 0.439 0 0.499 0.009 -6.777 47.575 57.276Luxembourg LUXEMBOURG SE GENERAL 0.509 [4-7] 0.459 0 0.499 0.007 -4.185 22.310 70.029Malaysia FTSE BURSA MALAYSIA KLCI 0.500 [6-50] 0.486 1 0.499 0.002 -4.174 20.378 219.740Malta MALTA SE MSE 0.510 1 0.500 [2-50] 0.500 0.001 5.654 33.118 338.369Montenegro MONTENEGRO SE MONEX 0.506 1 2 0.482 0 0.500 0.003 -4.926 34.715 176.675AEX ALL SHARE 0.514 3 0.450 0 0.499 0.008 -4.650 25.601 59.359AEX INDEX (AEX) 0.514 3 0.445 0 0.499 0.009 -4.846 26.221 54.684New Zealand S&P/NZX 50 0.500 4 [6-50] 0.472 0 0.499 0.005 -4.201 18.213 101.422Norway OSLO EXCHANGE ALL SHARE 0.505 2 3 0.467 0 0.499 0.006 -4.876 24.372 88.643Oman OMAN MUSCAT SECURITIES MKT. 0.509 0 0.500 [2-50] 0.500 0.001 5.654 33.120 354.632Poland WARSAW GENERAL INDEX 0.519 3 0.463 0 0.501 0.007 -2.761 19.523 72.977PORTUGAL PSI ALL-SHARE 0.505 2 3 0.467 0 0.499 0.005 -6.051 39.932 102.842PORTUGAL PSI-20 0.505 2 3 0.458 0 0.499 0.006 -6.429 43.769 81.772MSCI QATAR 0.509 3 0.469 0 0.500 0.005 -4.997 32.597 102.766MSCI QATAR $ 0.509 3 0.469 0 0.500 0.005 -4.997 32.597 102.766Romania ROMANIA BET (L) 0.505 5 6 0.431 0 0.499 0.010 -6.669 46.032 50.601MOEX RUSSIA INDEX 0.505 2 3 4 0.440 0 0.499 0.009 -6.714 46.751 58.481RUSSIA RTS INDEX 0.505 1 3 4 0.444 0 0.499 0.008 -6.646 46.072 63.137MSCI SAUDI ARABIA 0.500 2 3 [5-50] 0.448 0 0.499 0.007 -6.750 46.843 68.313MSCI SAUDI ARABIA $ 0.500 2 3 [5-50] 0.467 0 0.499 0.005 -6.293 41.657 104.742Singapore STRAITS TIMES INDEX L 0.510 [0-5] 0.500 [6-50] 0.501 0.003 2.446 4.144 163.309Slovakia SLOVAKIA SAX 16 0.505 1 0.500 0 [2-50] 0.500 0.001 7.141 51.000 764.662Slovenia SLOVENIAN BLUE CHIP (SBI TOP) 0.514 2 3 0.477 0 0.501 0.005 -1.308 13.235 105.358KOREA SE COMPOSITE (KOSPI) 0.500 1 [3-50] 0.459 0 0.499 0.006 -7.015 49.685 86.853KOREA SE KOSPI 200 0.505 1 0.454 0 0.499 0.006 -6.920 48.869 77.686IBEX 35 0.514 2 0.449 0 0.500 0.008 -4.789 30.210 61.181MADRID SE GENERAL (IGBM) 0.514 2 0.449 0 0.500 0.008 -4.789 30.210 61.181OMX STOCKHOLM (OMXS) 0.514 3 0.482 1 0.500 0.004 -1.544 11.137 121.373OMX STOCKHOLM 30 (OMXS30) 0.514 3 0.486 0 0.500 0.003 0.170 11.270 152.713Switzerland SWISS MARKET (SMI) 0.505 2 3 5 6 7 0.440 0 0.499 0.010 -5.331 29.966 52.025Turkey BIST NATIONAL 100 0.505 5 6 0.486 0 0.500 0.003 -3.281 13.267 179.107MSCI UAE 0.513 1 0.487 0 0.500 0.003 -0.335 16.006 174.802MSCI UAE $ 0.513 1 0.491 4 0.500 0.002 2.270 22.026 215.834FTSE 100 0.509 1 2 6 0.444 0 0.500 0.008 -6.088 41.749 60.777FTSE 250 0.509 1 4 5 0.463 0 0.500 0.006 -4.808 31.130 85.183FTSE ALL SHARE 0.509 2 3 6 0.449 0 0.500 0.008 -5.911 40.311 65.655FTSE TECHMARK FOCUS (£) 0.509 4 0.463 0 0.500 0.006 -5.931 40.824 90.912DOW JONES INDUSTRIALS 0.514 1 2 0.477 0 0.500 0.004 -1.873 18.181 113.195NASDAQ 100 0.509 2 0.426 0 0.499 0.010 -6.914 48.941 48.018NASDAQ COMPOSITE 0.505 2 6 7 0.431 0 0.499 0.010 -6.896 48.649 51.156NYSE COMPOSITE 0.509 1 2 6 7 0.481 0 0.500 0.004 -1.613 14.685 135.393RUSSELL 2000 0.519 1 0.463 0 0.500 0.006 -3.505 25.055 79.248S&P 500 COMPOSITE 0.509 1 2 0.458 0 0.500 0.006 -5.839 40.084 80.415United KingdomUnited StatesSaudi ArabiaSouth KoreaSpainSwedenUnited Arab EmiratesKazakhstanNetherlandsPortugalQatarRussiaFranceGermanyGreeceHong KongJapanAustraliaBahrainCanadaChinaDenmark
Table 8: Main statistical indicators of R ( ζ ) j ( k ) in Eq. (8), at stock index level, along with themeaningful thresholds ζ s. 32 ountry max Ϛ max min Ϛ min µ σ Skew Kurt µ/σ
Argentina 0.505 6 0.468 0 1 0.498 0.007 -4.252 18.219 76.251Australia 0.514 2 0.454 0 0.499 0.007 -5.499 36.750 71.468Austria 0.505 1 3 4 5 0.472 0 0.500 0.004 -5.806 39.154 120.645Bahrain 0.551 0 0.500 [2-50] 0.501 0.008 5.654 33.120 64.042Belgium 0.523 2 0.463 0 0.501 0.007 -2.665 22.162 75.245Bulgaria 0.505 [3-6] 0.477 0 0.500 0.003 -5.628 38.402 144.571Canada 0.514 2 0.443 0 0.500 0.009 -5.815 39.342 58.322Chile 0.509 4 5 0.454 0 0.500 0.008 -4.796 26.899 66.081China 0.500 [2-50] 0.493 1 0.500 0.001 -6.273 41.026 490.895Croatia 0.518 2 3 0.486 0 0.501 0.005 1.844 7.965 105.528Cyprus 0.524 1 0.494 0 0.501 0.005 3.362 13.220 109.736Czech Republic 0.505 [3-6] 0.453 0 0.499 0.007 -6.031 39.432 72.275Denmark 0.514 2 0.459 0 0.499 0.007 -4.427 25.467 72.761Estonia 0.509 2 3 0.453 0 0.499 0.007 -5.644 36.742 69.621Finland 0.518 1 0.500 0 [8-50] 0.501 0.003 4.229 20.731 163.965France 0.505 [1-9] 0.456 0 0.500 0.007 -6.293 43.293 77.413Germany 0.512 2 0.427 0 0.499 0.011 -6.375 43.869 46.941Greece 0.500 [6-50] 0.488 1 2 0.499 0.003 -2.933 7.776 169.887Hong Kong 0.508 2 0.463 0 0.499 0.006 -5.076 27.686 83.566Hungary 0.519 2 3 0.458 0 0.501 0.007 -3.121 22.725 68.434Iceland 0.514 1 2 0.500 [7-50] 0.501 0.003 3.546 13.001 165.438Ireland 0.514 2 3 4 0.463 0 0.500 0.006 -3.489 24.217 78.581Israel 0.523 0 0.500 [6-50] 0.501 0.004 3.920 17.515 126.334Italy 0.505 5 0.421 0 0.498 0.012 -5.911 36.715 41.962Japan 0.520 0 1 0.500 [4-50] 0.501 0.004 4.058 15.910 119.395Kazakhstan 0.509 2 3 0.491 1 0.500 0.003 0.822 9.432 200.434Kuwait 0.505 1 2 3 0.472 0 0.500 0.004 -6.271 43.554 123.714Latvia 0.505 [10-13] 0.463 0 0.499 0.006 -6.000 39.837 90.399Lithuania 0.504 2 [4-8] 0.439 0 0.499 0.009 -6.777 47.575 57.276Luxembourg 0.509 [4-7] 0.459 0 0.499 0.007 -4.185 22.310 70.029Malaysia 0.500 [6-50] 0.486 1 0.499 0.002 -4.174 20.378 219.740Malta 0.510 1 0.500 [2-50] 0.500 0.001 5.654 33.118 338.369Montenegro 0.506 1 2 0.482 0 0.500 0.003 -4.926 34.715 176.675Netherlands 0.514 3 0.447 0 0.499 0.009 -4.779 26.089 57.020New Zealand 0.500 4 [6-50] 0.472 0 0.499 0.005 -4.201 18.213 101.422Norway 0.505 2 3 0.467 0 0.499 0.006 -4.876 24.372 88.643Oman 0.509 0 0.500 [2-50] 0.500 0.001 5.654 33.120 354.632Poland 0.519 3 0.463 0 0.501 0.007 -2.761 19.523 72.977Portugal 0.505 2 3 0.462 0 0.499 0.006 -6.268 42.134 91.164Qatar 0.509 3 0.469 0 0.500 0.005 -4.997 32.597 102.766Romania 0.505 5 6 0.431 0 0.499 0.010 -6.669 46.032 50.601Russia 0.505 3 4 0.442 0 0.499 0.008 -6.959 49.302 61.592Saudi Arabia 0.500 2 3 [5-50] 0.458 0 0.499 0.006 -6.584 44.984 82.818Singapore 0.510 [0-5] 0.500 [6-50] 0.501 0.003 2.446 4.144 163.309Slovakia 0.505 1 0.500 0 [2-50] 0.500 0.001 7.141 51.000 764.662Slovenia 0.514 2 3 0.477 0 0.501 0.005 -1.308 13.235 105.358South Korea 0.502 1 0.456 0 0.499 0.006 -6.994 49.508 82.115Spain 0.514 2 0.449 0 0.500 0.008 -4.789 30.210 61.181Sweden 0.514 3 0.486 0 0.500 0.004 -0.416 9.080 138.936Switzerland 0.505 2 3 5 6 7 0.440 0 0.499 0.010 -5.331 29.966 52.025Turkey 0.505 5 6 0.486 0 0.500 0.003 -3.281 13.267 179.107United Arab Emirates 0.513 1 0.491 0 4 0.500 0.003 1.175 17.126 197.300United Kingdom 0.507 4 6 0.455 0 0.500 0.007 -6.185 42.517 75.195United States 0.509 2 0.456 0 0.500 0.007 -6.095 42.091 77.219
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