Persistent Impact of Macroeconomic Announcements in Financial

Market Data

Nicolas Boitouta, Radu Lupua,b,∗

aMarketScience France, 1 Rue des Hauts, 45380 La Chapelle Saint Mesmin, FrancebBucharest University of Economics, 6 Piata Romana, sector 1, Bucharest, Romania

Abstract

The impact of scheduled releases of macroeconomic variables on the dynamics of financialmarkets has always attracted a great deal of academic attention in efforts to quantify marketresponses in terms of volatility and jumps. We investigate whether the occurence of marketreaction due to macroeconomic annoucements has an impact on the probabiliy of a reactioncaused by the next release of the same macroeconomic value. We measure this impact bymeans of both post-event volatility changes and a proposed methodology for jump matching.Our findings show that previous market impact significantly changes the probability of animpact detected for the current release.

Keywords: event-study, jump detection, macroeconomic releases, volatility impact,jump-event matching

1. Introduction

Macroeconomic event releases represent fundamental sources for the process of infor-mation inclusion in price dynamics of financial instruments and have been widely used tocheck for market efficiency and policy implementation by means of event study analyses.The proliferation of volatility models and recent development of non-parametric jump de-tection mechanisms attracted the interest of many studies for which macroeconomic eventsrepresented important raw material.

We mention here the seminal work of [1] for public and private information impacton FX markets, [2] for the impact of data intervention and [3] for impact of scheduledmacroeconomic announcements on the bond market. The thread of research in this fieldcontinued, among others, with [4] for the analysis of the conditional mean jumps generatedby macroeconomic announcements, and [5] for the characterization of patterns evidenced inthe euro-dollar FX market as result of ECB intervention. More recently, the developmentof non-parametric jump detection methodology generated research in the field of matching

∗Corresponding author.Email addresses: nicolas.boitout@market-science.com (Nicolas Boitout),

radu.lupu@market-science.com (Radu Lupu)

Preprint submitted to 8th Financial Risks International Forum January 5, 2015

these discontinuities with macro events in [6] for futures and FX markets and [7] for equity,bonds and FX. The most recent work of [8], [9] and [10] reveal the impact of news on FXby measuring jump realization and volatility impact. Our paper focuses on the existenceof dependence of market reaction to macroeconomic scheduled announcements given thefact that a reaction was observed for the release of the same macroeconomic variable. As inprevious studies ([10], most recently) we measure this reaction by using on one hand a jump-detection mechanism that could account for an immediate consensus in market reaction andon the other hand the volatility impact that could be interpreted as an uncertain reaction.

2. Data and Methodology

We are using log-returns for series of approximately six months of five minute barsdownloaded from Bloomberg platform for three types of assets: currencies, bonds and stockindices, counting 164 instruments (51 currency pairs, 62 generic government bonds and 51equity indices) traded globally (36 countries).

During the same time period we are using the Bloomberg database containing releasetimes of all macroeconomic data for all the countries in which the financial instruments aretraded. Our sample comprises 664 different scheduled events with 40.008 recorded releases.Our purpose is to build an analysis of the impact that a release of an official statistical infor-mation will have on the market reaction for the next release, which requires that our releasesare issued regularly, according to a pre-scheduled framework. Therefore the repeatability ofthese releases is a feature that qualifies a release as being suited for our analysis. As a result,non-periodic scheduled events, like official speeches, are not integrated in the analysis dueto their lack of repeatability.

The achievement of this objective intrinsically relies on a formalized connection betweenassets on one hand and macroeconomic events on the other hand. In pursuit of such adependence structure we used an ad hoc approach according to which the two sets areconnected by the interim of countries. Under this paradigm, since macroeconomic datareleases refer to a certain country or region, which will help us to setlle their affiliation, forthe case of the financial assets we had to develop ourselves the system that will reveal theaffiliation of assets to countries.

2.1. Jump Identification and Matching

For jump identification we are using the methodology developd by [11]. According totheir specification, we compute a jump statistic as:

Jt =|Rt|σ̂t

, (1)

where σ̂t is√

1n−1TVt, n is the sample size (we used 285 observations of five minute

returns, i.e. 24 hours of trading, used for all the assets in our analysis in order to keepconsitency in the estimation approach), t is the current moment when we compute the jumpstatistic, |Rt| is the absolute value of the log-return computed as log( LastPrice

F irstPrice) for each

2

5-minute time interval and the TVt represents the Truncated Variation, which we computedin two steps1: first we compute the squared root of the average of squared returns forthe sample interval of size n from which we eliminate the zero returns and the previously

detected jumps (σStep1 =

√∑nt=1(Rt)

21Rt 6=0,Rt 6=Jump

n); the second stage consists in the detection

of all Rt that are lower than twice the σStep1 in order to compute the Truncated Variance:

TVt =M∑i=1

(Rt)2 1|Rt|≤2σStep1

(2)

Following the lines of [11], we consider as jumps all returns that comply with the condition

Jt > G−1 (1− α)Sn + Cn (3)

where G−1 (1− α) is the 1 − α quantile function of the standard Gumbel distribution,

Cn = (2 log n)0.5 − log(π)−log(logn)2(2 logn)0.5

and Sn = 1(2 logn)0.5

.

One important issue is the procedure used for matching the macroeconomic data releasewith a jump occuring after this release. Previous studies do not specify a rigorous procedure.Our proposed procedure relies on a sequential algorithm that is triggered by the identificationof a 5-minute return jump.

The first step is therefore to identify all the jumps in the series of log-returns using thepreviously described methodology.

Given the time moment when a jump is identified, the next step consists in the sequentialemployment of the same algorithm for a series of returns computed for reduced frequencies(10-minute returns, 15-minute returns, 20-minute returns and so on). In order to keepconsistency of our statistical measures, for each frequency we use the same sample of 24hours (which means that the sample size is reduced as we move from one frequency toanother). The algorithm stops at the frequency at which a jump is no longer detected2.

According to this, if an event happened in the largest time interval for which a jumpwas identifed, then we consider a match between the event and the strong market reaction- a jump match is detected, i.e. there is high probability for the jump to be generated bythe macroeconomic data information released at the scheduled moment. Figure 1 provides agraphical description of this procedure: first we detect the jump at the 5 minute interval (thered candle), second we check if the 10-minute is a jump and if so we continute to check for

1The identification of a jump depends on the comparison between the size of the log-return at a certainmoment and the dimension of the volatility (as a measure of the usual size extracted from the previoushistory, which is considered relevant). We will therefore identify many jumps if the TVt is small (a casethat could be generated by leaving too many 0 returns in the sample used for its computation) or we willidentify very few jumps if the volatility is large (especially in the cases when we use the previous jumps inthis measurement). We therefore consider that we should eliminate the returns that are 0 and also eliminatethe returns that are too big, which have the probability to be generated by different volatility regimes.

2For instance, if a jump is identified for the time interval between 9:00 and 9:05, then we check if thereis a jump for the time interval 8:55 - 9:05 and in case a jump is identified, we continue with the algorithmfor the time interval 8:50 - 9:05 and so on as long as a jump is still identified.

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the 15-minute and so on. If the 20 minute return is still a jump (computed using historical20-minute time intervals) then the macro-event 1 is considered to be a match with the redcandle. If the 25-minute return is not a jump, then the macro-event 2 is not matched witha jump.

Figure 1: Event-Jump Matching with extending time frame

This procedure is fed on one hand by the intuition that both sudden movement and pricetrend could represent the market reaction to a release, in the sense that market reactioncould exhibit a smooth transition towards the jump, a trend in the same direction with thejump and on the other hand by the fact that there could be consecutive jumps that areidentified after the event and could be the result of updating market interpretation of theinformation provided by the release. Another important element of the matching proceduredeals with the overlapping time intervals for the sequential non-consecutive jumps. Thismeans that, in case a jump is identified (for instance) at 9:05 (we call it jump 1), anotherjump is identified at 9:15 (jump 2) and if the time frame of the jump 2 covers the perioduntil 9:05 (i.e. a jump was also identified for the time interval 9:05 - 9:15, a lower frequencyjump) then the time frame of jump 2 overlaps with the time frame of jump 1. We thereforeconsider that all releases that are observed in the time frame of jump 1 will be matchedto both jump 1 and jump 2. This procedure usually covers the situations when jump 1 andjump 2 have different signs. However, situations with sequential (consecutive or not) jumpsare relatively rare.

2.2. Measurement of Volatility Impact

For the measurement of volatility impact we eliminate the periodicity component thatis measured as in [12]. We use the Shortest Half estimator for which we had to organize

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the five minute log-returns on a matrix with a number of rows equal to the number oftime intervals inside a trading session, ranging from the day start to the day end, while thecolumns represent all the tradins sessions in our sample3.

The periodicity adjusted returns are used for the measurement of changes in volatilityin the period after each event. For our analysis we compute the mean squared error of aset of volatility models fitted4 on a sample of seven hundred realizations (i.e. 5-minute log-returns) before the release. We consider that the most important price action takes place ina time frame before the release, which was assumed to have a length of twenty-five periodsof 5-minute log-returns5.

This time interval before the realease will be used to measure the amount of fitting errorwith respect to the volatility model (by means of mean-squared errors - MSE), a gaugethat will be used as a reference for the identification of volatility changes in the post-releaseperiod. Therefore, the volatility model is used to provide a forecast for an interval after therelease.

We use a time frame of twenty intervals of 5-minute log-returns for the measurement ofthe volatility impact. The price reaction to this release could be immediate, in which casethere is a high probability for a jump to be identified, or it could have a longer range ofrealization, a phenomenon that would be captured by this volatility impact procedure. Giventhe fact that our objective is to measure the general market reaction to the macroeconomicreleases, we consider that the important element in our measuring procedure is to use afixed interval of market reaction for each event, so that the volatility impact measure thatwe propose may be comparable across all events.

In the proposed twenty intervals of 5-minute log-returns, we use the volatility model inorder to build forecasts and we compute the squared error at each post-release moment (ε2t ).According to this setup, we compare this squared error to the mean squared error recordedin the pre-release period (the twenty-five periods of 5-minute intervals). We consider thatan impact is identified if ε2t > 2MSE for at least one t after the event6.

We construct in this way a binary variable that takes the value of 0 if no impact wasdetected and the value of 1 if at least one ε2t exceeds the threshold. All previously detectedjump returns are not taken into account for the verification of this condition.

We therefore end up with two variables with a number of occurences that is equal to

3We refer to [12] for a presentation of the whole procedure.4We have used various volatility models for the periodicity-adjusted returns (GARCH, EGARCH, GJR-

GARCH, ZARCH, APARCH, NAGARCH, IGARCH and FIGARCH) and acknowledged same results irre-spective of the volatility model. In the results presented here we use the standard RiskMetrics volatilitymodel with a λ of 0.94, estimated on a sample of seven hundred 5-minute returns before the release. How-ever, as mentioned in a following paragraph, the proposed procedure used for the measurement of volatilityimpact is constructed so that there is no dependence on the selected model.

5A simple analysis of the price dynamics in the period before the release of important macroeocmomicstatistics revealed the fact that the volatility is relatively reduced (with respect to the usual volatilities ofreturns occuring at the same time inside the trading session in previous days, i.e. lower than the usualperiodical levels), which could be interpreted as a period in which the markets are in a waiting mode.

6The proposed identification of the volatility impact is not model dependent, we are using the volatilityfit only as reference.

5

the number of releases: a binary jump realization variable and a binary volatility impactvariable.

3. Results

Figure 2 exhibits a histogram for the jumps matched with macroeconomic events foreach class of assets. One can notice that the most numerous jumps that are matched withmacroeconomic events are detected on currencies. For this analysis we standardized theabsolute value of returns detected as jumps with the truncated volatility TVt. We considerthis as a measure for the jump power or jump dimension. We observe that the three types ofassets display similar structures, with almost uniform and small frequencies for large powersand large frequencies for small powers on one hand and they share the same upper bound(jump returns are not bigger than approximately 35 times than their truncated volatilities),on the other hand.

BondsIndices

Currencies510

1520

2530

35

0

100

200

300

400

500

600

Jump Power

Fre

quency

Figure 2: Histogram of matched jumps according to their power

Our objective is to analyze the dependence of the impact of a release based on the oc-curence of an impact in the previous release. We produce here results of logistic regressions7

using our binary variables for the whole sample and for each asset category.Table 1 shows these results for all the assets, revealing the dependence of the probability

of a jump matching for a release on information concerning the impact produced in the pre-vious release, i.e. the probability of a jump realization and the probability for the occurenceof a volatility impact. We notice that the regression provides evidence for the significantimpact of a previous jump occurence and less significant impact of the probability for aprevious occurence of a volatility impact on the realization of a jump matching phenomenonin the current release.

7Probit regressions provided similar results, which are available upon request.

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Table 1: Probability of jump realization in Logistic regression for the whole sample

Coefficients Standard Errors Z-Score p-value

Intercept -3.4036 0.0322 -105.6464 0.0000Jump realization (-1) 2.8176 0.0678 41.5850 0.0000

Volatility Impact realization (-1) -0.0852 0.0618 -1.3780 0.0841

Table 2: Probability of Volatility Impact in Logistic regression for the whole sample

Coefficients Standard Errors Z-Score p-value

Intercept -1.3480 0.0143 -94.1075 0.0000Volatility Impact realization (-1) 2.1321 0.0261 81.6289 0.0000

Jump realization (-1) -0.2032 0.0722 -2.8155 0.0024

A similar analysis is presented in table 2, where we notice that the probability for theoccurence of a volatility impact for a release is significantly dependent on both the previousvolatility impact realization and the previous jump matching realization. Corroborating theinformation provided in table 1 with the negative significant impact for the jump realizationhelps us to conclude that the occurence of a jump in a previous release has a positive impactin the realization of a jump matched with the current release in absence of volatility impact.The lack of a jump in a previous release and the realization of a volatility impact providesevidence for the occurence of volatility impact in the current release.

These results generally hold for each asset class as can be seen in table 3. The mostsignificant dependence is noticed on one hand for the cases of an increase in the probabilityof observing a jump matched with the current release based on such an event occuring in theprevious release (for all the three asset classes) and on the other hand for the realization of avolatility impact in the time interval following the current release conditional on a volatilityimpact detected in the previous release.

The least significant impact is the one that refers to the manner in which the previousrealization of a volatility impact might trigger a jump matched with the current release. Theonly case where this approaches a reasonable level of significance could be considered thesituation for the bond class, in which case the coefficient is negative signaling the fact thatwhen a volatility impact is observed previously, the probability of a jump occuring currentlyis reduced.

A larger level of significance is observed for the current occurence of a volatility impactconditional on a jump previously materializing for the case of currencies. The sign is alsonegative, suggesting the same rationing according to which a previous realization of a jumpmatch reduces the probability of a current volatilty impact. This coefficient is not signif-icant for the case of bonds and tends to be significant in the case of indices. However, inthis last situation we notice that the coefficient is positive, which induces the fact that onequity markets a jump matching or a volatility impact in the previous release increases theprobability of a volatility impact in the current release.

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Table 3: Logistic regressions results for each asset class

Currencies - Dependent variable: Current Jump RealizationCoefficients Standard Errors Z-Score p-value

Intercept -3.3317 0.0415 -80.3759 0.0000Jump realization (-1) 2.5271 0.0886 28.5090 0.0000

Volatility Impact realization (-1) -0.0177 0.0732 -0.2412 0.4047

Currencies - Dependent variable: Current Volatility Impact RealizationCoefficients Standard Errors Z-Score p-value

Intercept -1.0517 0.0176 -59.6518 0.0000Volatility Impact realization (-1) 1.8749 0.0318 59.0179 0.0000

Jump realization (-1) -0.1738 0.0865 -2.0081 0.0223

Bonds - Dependent variable: Current Jump RealizationCoefficients Standard Errors Z-Score p-value

Intercept -3.4215 0.0851 -40.2062 0.0000Jump realization (-1) 2.6911 0.1884 14.2823 0.0000

Volatility Impact realization (-1) -0.2859 0.1937 -1.4762 0.0699

Bonds - Dependent variable: Current Volatility Impact RealizationCoefficients Standard Errors Z-Score p-value

Intercept -1.6039 0.0404 -39.7321 0.0000Volatility Impact realization (-1) 2.0905 0.0734 28.4630 0.0000

Jump realization (-1) -0.1677 0.2072 -0.8093 0.2092

Indices - Dependent variable: Current Jump RealizationCoefficients Standard Errors Z-Score p-value

Intercept -3.0569 0.0765 -39.9701 0.0000Jump realization (-1) 2.8363 0.1676 16.9208 0.0000

Volatility Impact realization (-1) -10.3121 221.8628 -0.0465 0.4815

Indices - Dependent variable: Current Volatility Impact RealizationCoefficients Standard Errors Z-Score p-value

Intercept -5.7221 0.2778 -20.5975 0.0000Volatility Impact realization (-1) 5.8762 0.6219 9.4496 0.0000

Jump realization (-1) 1.2232 0.7631 1.6030 0.0545

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4. Concluding remarks

This paper analyzes the impact that macroeconomic data releases produce on the dy-namics of financial instruments at the intra-day level. The investigation uses a set of 164traded assets and 664 macroeconomic events to measure the expected impact that a certainrelease could have on trading activity. We measure the impact in terms of volatility changesby fitting a standard volatility model on periodicity adjusted five minute log-returns and bydeveloping a jump matching procedure to account for the probabiliy of jump occurence inthe forthcoming macroeconomic event. Fitting logit regressions for each class of assets wefound evidence that the probability of an impact produced by the current release is depen-dent on the materialization of impact for the previous release. This conditionality varieswhen we refer to volatility impact as opposed to jumps and has different ways to materializewhen we look at different asset classes.

Appendix A. Appendix 1 - Structure of connections between assets and coun-tries

Figure A.3: Structure of connections among assets and countries or regions

Appendix B. References

[1] R. P. DeGennaro, R. E. Shrieves, Public information releases, private information arrival and volatilityin the foreign exchange market, Journal of Empirical Finance 4 (4) (1997) 295–315. doi:10.1016/S0927-5398(97)00012-1.URL http://www.sciencedirect.com/science/article/pii/S0927539897000121

[2] C. J. Neely, The temporal pattern of trading rule returns and exchange rate intervention: interventiondoes not generate technical trading profits, Journal of International Economics 58 (1) (2002) 211–232.doi:10.1016/S0022-1996(01)00163-5.URL http://www.sciencedirect.com/science/article/pii/S0022199601001635

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Table A.4: Bonds and their identification numbers in figure A.3

Asset Ticker ID in chart Asset Ticker ID in chart

’GACGB10 Index’ 1 ’GSWISS10 Index’ 32’GACGB2 Index’ 2 ’GUKG10 Index’ 33’GACGB3 Index’ 3 ’GUKG2 Index’ 34

’GCAN10YR Index’ 4 ’GUKG5 Index’ 35’GCAN5YR Index’ 5 ’USGG10YR Index’ 36

’GDGB10YR Index’ 6 ’USGG2YR Index’ 37’GDGB5YR Index’ 7 ’USGG30YR Index’ 38

’GAGB10YR Index’ 8 ’USGG5YR Index’ 39’GBGB10YR Index’ 9 ’GIND10YR Index’ 40’GFIN10YR Index’ 10 ’GIND7YR Index’ 41

’GFRN10 Index’ 11 ’GMXN05YR Index’ 42’GFRN5 Index’ 12 ’GMXN10YR Index’ 43

’GDBR10 Index’ 13 ’GSAB10YR Index’ 44’GDBR5 Index’ 14 ’GSAB5YR Index’ 45’GDBR7 Index’ 15 ’SFSW10 Curncy’ 46

’GIGB10YR Index’ 16 ’ADSW10 Curncy’ 47’GBTPGR10 Index’ 17 ’CDSW10 Curncy’ 48’GBTPGR5 Index’ 18 ’CDSW5 Curncy’ 49

’GNTH10YR Index’ 19 ’EUSA10 Curncy’ 50’GSPT10YR Index’ 20 ’EUSA5 Curncy’ 51’GSPG10YR Index’ 21 ’JYSW10 Curncy’ 52’GSPG5YR Index’ 22 ’NKSW10 Curncy’ 53

’GJGB10 Index’ 23 ’PZSW10 Curncy’ 54’GNZGB10 Index’ 24 ’SKSW10 Curncy’ 55’GNZGB5 Index’ 25 ’BPSW10 Curncy’ 56

’GNOR10YR Index’ 26 ’USSW10 Curncy’ 57’POGB10YR Index’ 27 ’USSW2 Curncy’ 58’POGB7YR Index’ 28 ’USSW30 Curncy’ 59

’GSGB10YR Index’ 29 ’USSW5 Curncy’ 60’GSGB5YR Index’ 30 ’MPSW10 Curncy’ 61’GSWISS05 Index’ 31 ’SASW5 Curncy’ 62

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Table A.5: Currencies and their identification numbers in figure A.3

Asset Ticker ID in chart Asset Ticker ID in chart

’AUDUSD Curncy’ 1 ’EURTRY Curncy’ 27’EURAUD Curncy’ 2 ’EURUSD Curncy’ 28’EURCNH Curncy’ 3 ’EURZAR Curncy’ 29’EURCNY Curncy’ 4 ’BBDXY Curncy’ 30’EURJPY Curncy’ 5 ’DX1 Curncy’ 31’EURNZD Curncy’ 6 ’DXA Curncy’ 32’KRWJPY Curncy’ 7 ’DXY Curncy’ 33’NZDUSD Curncy’ 8 ’GBPUSD Curncy’ 34’SGDJPY Curncy’ 9 ’USDBRL Curncy’ 35’USDCNH Curncy’ 10 ’USDCAD Curncy’ 36’USDCNY Curncy’ 11 ’USDCHF Curncy’ 37’USDJPY Curncy’ 12 ’USDHUF Curncy’ 38

’USDKRW Curncy’ 13 ’USDILS Curncy’ 39’USDSGD Curncy’ 14 ’USDINR Curncy’ 40’EURBRL Curncy’ 15 ’USDMXN Curncy’ 41’EURCAD Curncy’ 16 ’USDNOK Curncy’ 42’EURCHF Curncy’ 17 ’USDPLN Curncy’ 43’EURGBP Curncy’ 18 ’USDRUB Curncy’ 44’EURHUF Curncy’ 19 ’USDSEK Curncy’ 45

’EURILS Curncy’ 20 ’USDTRY Curncy’ 46’EURINR Curncy’ 21 ’USDZAR Curncy’ 47

’EURMXN Curncy’ 22 ’INT1 Curncy’ 48’EURNOK Curncy’ 23 ’INTA Curncy’ 49’EURPLN Curncy’ 24 ’KU1 Curncy’ 50’EURRUB Curncy’ 25 ’KUA Curncy’ 51’EURSEK Curncy’ 26

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Table A.6: Indices and their identification numbers in figure A.3

Asset Ticker ID in chart Asset Ticker ID in chart

’NZSE50FG Index’ 1 ’IBEX Index’ 27’NZSE50FF Index’ 2 ’OBX Index’ 28

’AS51 Index’ 3 ’OSEAX Index’ 29’AS30 Index’ 4 ’KFX Index’ 30’NKY Index’ 5 ’KAX Index’ 31’TPX Index’ 6 ’OMX Index’ 32’HSI Index’ 7 ’SAX Index’ 33

’HSCEI Index’ 8 ’SMI Index’ 34’FSSTI Index’ 9 ’NMX Index’ 35’SXXP Index’ 10 ’UKX Index’ 36’SX5E Index’ 11 ’WIG20 Index’ 37’ATX Index’ 12 ’WIG30 Index’ 38

’CEESEGE Index’ 13 ’SPTSX Index’ 39’BEL20 Index’ 14 ’SPTSX60 Index’ 40

’HEX Index’ 15 ’CCMP Index’ 41’HEX25 Index’ 16 ’NDX Index’ 42

’CAC Index’ 17 ’SPX Index’ 43’SBF120 Index’ 18 ’RAY Index’ 44

’DAX Index’ 19 ’RTY Index’ 45’MDAX Index’ 20 ’INDU Index’ 46

’ASE Index’ 21 ’TA-25 Index’ 47’FTASE Index’ 22 ’TA-100 Index’ 48

’ISEQ Index’ 23 ’PX Index’ 49’FTSEMIB Index’ 24 ’BUX Index’ 50

’AEX Index’ 25 ’KOSPI Index’ 51’PSI20 Index’ 26

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Table A.7: Countries and their identification numbers in figure A.3

Country ID in chart Country ID in chart

’US’ 1 ’GR’ 19’JN’ 2 ’FI’ 20’UK’ 3 ’DE’ 21’CH’ 4 ’SW’ 22’AU’ 5 ’NO’ 23’NZ’ 6 ’PD’ 24’CA’ 7 ’CZ’ 25’SZ’ 8 ’HU’ 26’EC’ 9 ’TU’ 27’FR’ 10 ’IS’ 28’GE’ 11 ’RU’ 29’IT’ 12 ’SA’ 30’SP’ 13 ’MX’ 31’NE’ 14 ’BZ’ 32’BE’ 15 ’SK’ 33’AS’ 16 ’HK’ 34’IR’ 17 ’SI’ 35

’PO’ 18 ’IN’ 36

[3] P. Balduzzi, E. J. Elton, T. C. Green, Economic news and bond prices: Evidence from theu.s. treasury market, The Journal of Financial and Quantitative Analysis 36 (4) (2001) 523–543.doi:10.2307/2676223.URL http://www.jstor.org/stable/2676223

[4] T. G. Andersen, T. Bollerslev, F. X. Diebold, C. Vega, Micro effects of macro announcements: Real-time price discovery in foreign exchange, Working Paper 8959, National Bureau of Economic Research(May 2002).URL http://www.nber.org/papers/w8959

[5] M. J. Sager, M. P. Taylor, The impact of european central bank governing council announcements onthe foreign exchange market: a microstructural analysis, Journal of International Money and Finance23 (78) (2004) 1043–1051. doi:10.1016/j.jimonfin.2004.08.002.URL http://www.sciencedirect.com/science/article/pii/S0261560604000750

[6] J. Lahaye, S. Laurent, C. J. Neely, Jumps, cojumps and macro announcements, Journal of AppliedEconometrics 26 (6) (2011) 893–921. doi:10.1002/jae.1149.URL http://onlinelibrary.wiley.com/doi/10.1002/jae.1149/abstract

[7] G. Tauchen, H. Zhou, Realized jumps on financial markets and predicting credit spreads, Journal ofEconometrics 160 (1) (2011) 102–118. doi:10.1016/j.jeconom.2010.03.023.URL http://www.sciencedirect.com/science/article/pii/S0304407610000655

[8] A. Chatrath, H. Miao, S. Ramchander, S. Villupuram, Currency jumps, cojumps and the role of macronews, Journal of International Money and Finance 40 (2014) 42–62. doi:10.1016/j.jimonfin.2013.08.018.URL http://www.sciencedirect.com/science/article/pii/S0261560613001204

[9] I. E. Ouadghiri, V. Mignon, N. Boitout, On the impact of macroeconomic news surprises on treasury-bond yields, EconomiX Working Paper 2014-20, University of Paris West - Nanterre la Dfense,EconomiX (2014).URL https://ideas.repec.org/p/drm/wpaper/2014-20.html

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[10] H. Dewachter, D. Erdemlioglu, J.-Y. Gnabo, C. Lecourt, The intra-day impact of communicationon euro-dollar volatility and jumps, Journal of International Money and Finance 43 (2014) 131–154.doi:10.1016/j.jimonfin.2014.01.003.URL http://www.sciencedirect.com/science/article/pii/S0261560614000072

[11] S. S. Lee, J. Hannig, Detecting jumps from lvy jump diffusion processes, Journal of Financial Economics96 (2) (2010) 271–290. doi:10.1016/j.jfineco.2009.12.009.URL http://www.sciencedirect.com/science/article/pii/S0304405X09002578

[12] K. Boudt, C. Croux, S. Laurent, Robust estimation of intraweek periodicity in volatility and jumpdetection, Journal of Empirical Finance 18 (2) (2011) 353–367. doi:10.1016/j.jempfin.2010.11.005.URL http://www.sciencedirect.com/science/article/pii/S0927539810000836

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