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News and Dollar Exchange Rate Dynamics

Massimo TivegnaUniversity of Teramo

andLUISS Guido Carli, Rome

March 2000

ABSTRACT - The aim of this paper is to describe a news-based multivariate GARCH estimation andsimulation of the DM-$ and Yen-$ exchange rates jointly with two financial variables strictly connected with theirdynamics, i.e. long term yields and the Dow Jones Index. A twice daily frequency (Japanese-European andAmerican time zones) empirical approach and a new class of unscheduled news variables are proposed, usedtogether with the traditional scheduled macroeconomic news. The former are statements by policy makers, forexmarket reports and comments of various kind, economic, monetary and fiscal policy news and, in general, majorevents in the world financial markets. Some simultaneity bias in the single equation estimation of exchange ratesand of two of their main financial determinants - all depending on a common set of news - is detected. Themultivariate estimation of mean and volatility parameters and the simulation of the complete two-zone modelallow to asses the relative importance of different kind of news, both in mean and variance, and the dynamicimpulse response of exchange rates to different news-shocks. Some differences are detected in the conditionalbehavior of mean and volatility in the two trading zones.

INDEX1.Introduction 2.The news 2.1 A taxonomy of unscheduled news 2.2 News variables in this paper3. The impact of news 3.1 Estimation strategy 3.2 Econometrics 3.3 Estimation results 3.4 Simulation 3.5 News metrics4. Summary and conclusions

References

Keywords: News, Dollar exchange rate, Long-term yields, Dow Jones Industrial Index

Prof. Massimo TivegnaE-mail: MC1223@MCLINK.ITFax 39-06-8083097

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1) IntroductionIt is of common experience by both market practitioners and international economists that exchangerates – after 1973 - are determined by a set of theory-determined economic and financial variables -the so-called "fundamentals" - and by a miscellaneous of other factors which cause fluctuationsaround an equilibrium path resulting in a frequently-observed volatility of these rates. This lattereffect is produced by market dynamics (generally by "buy" and "sell" signals of technical filters)and by unexpected events, the so-called "news" - be they economic, political or market-determined- or by central bank intervention both classical - buying and selling currencies - and verbal -"talking one currency up or down" 1 .

The way fundamentals affect exchange rate dynamics has been dealt with by essentially threeapproaches: the flex and fix-price monetary model, the flow – or Mundell-Fleming – model and bythe portfolio balance model 2. The empirical results of this literature have been quite disappointingas the econometric estimates have proved to be unstable across periods and across different, butcomparable, specifications 3 and the overall tracking performance has only seldom been able to“beat the random walk” 4

This failure and the increased volatility of exchange rates over the last ten-to-fifteen years hasproduced a new class of models quite different from the equilibrium and substantially static natureof the above schemes 5 , much closer to the actual practice of forex trading. These models areinfluenced primarily from the asset pricing schemes, where the price of one currency is determined– as that of any other asset - by the expected values of its fundamental determinants. So theequilibrium path of exchange rates can still be explained by one of the three models mentionedabove, but their actual dynamics is dominated by the discrepancy between expected and actualvalues of the fundamentals and by any other piece of information related to them. This conclusion isreached by various mostly-theoretical papers – e.g. those by MUSSA(1979), DORNBUSCH(1980),FRENKEL(1981), ISARD(1983) – which lay down the foundations of the news approach toexchange rate determination.

The news specification of an exchange rate equation can therefore be represented in the followingform:

where St+1 is the exchange rate one period ahead, Zt+1 are the values of the m fundamentals at thesame time, Et is the expectation operator at time t and εt is a I.I.N.(0,σt) heteroskedastic error termfollowing – as frequently assumed – a GARCH scheme6. The news typically employed in thesquare-bracketed term of the above equation concerns the fundamentals in the main theories ofexchange rate determination, e.g. economic activity variables (industrial production and variousindicators connected with it, GDP growth, retail sales, business climate surveys, unemployment), 1 This is not a new strategy in the behavior of monetary authorities but it is getting used much more than old-fashioned Central Bankintervention. The specific terminology was originally devised by financial commentators to describe the initiatives of US TreasurySecretary Baker in the eighties.2 Some good textbook references to this huge literature are COPELAND(1994) Chapters 5-8, and HALLWOOD-MACDONALD(1994), Chapters 5, 8, 9, 10. Survey-articles on this argument were written by ISARD(1995), MACDONALD-TAYLOR(1995),TAYLOR(1995).3 For a synthesis of these results see BAILLIE-McMAHON(1989), Chapter 8.

4 Starting from the well known paper by MEESE-ROGOFF(1983).

5 HALLWOOD-MACDONALD(1994) pag. 179.6 The main references are BOLLERSLEV(1986, 1987, 1990). This literature is surveyed by BOLLERSLEV-ENGLE-NELSON(1994). Applications in finance are surveyed by BOLLERSLEV-CHOU-KRONER(1992).

ttjttj

m

jjttt ZEZSES εβα +−+=− ++

=++ ∑ )]([)( 1,1,

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inflation (CPI, PPI, wage increases, productivity), monetary aggregates, the balance of payments(trade balance and current account). Other news taken into consideration is represented by changesin official interest rates.

In the (probably) first empirical paper among those of interest in this study, HOFFMAN-SCHLAGENHAUF(1985) estimate various news-form equations of the traditional models ofexchange rates in the format of the equation reported above. This literature generally follows themonetary scheme of EDWARDS(1982, 1983, 1992). Under this approach, the error term of thereduced-form is a linear combination of the error terms of the various equations in the structuralmodel; their heteroskedastic fluctuations are attributed to the impact of news.

This original scheme is, in a way or another, behind most of the papers we will recall next.Edwards' model is very important in empirical analysis by offering some leeway in theinterpretation of seemingly wrong-signed coefficients. Many papers in the news literature do notdeclare explicitly whether they are making reference to monetary, or to Mundell-Fleming, or to theportfolio balance schemes of exchange rate determination 7. Others do not indicate whether they areestimating an equilibrium relationship or a policy reaction function.

Under Edwards' approach - and in other comparable approaches (see for instanceCOPELAND(1984), BOMHOFF-KORTEWEG(1983), BRANSON(1983), MACDONALD(1983a e b), FIORENTINI (1994), CIFARELLI(1986) - the news is considered responsible for the non-normal an frequently non-spherical properties of the error term produced by the econometricestimation of a reduced-form exchange rate model.

In other approaches (e.g., using daily and monthly frequencies, DERAVI-GREGOROWICZ-HEGJI(1988), HARDOUVELIS(1988), IRWIN(1989), HOGAN-MELVIN-ROBERTS(1991),DOUKAS-LIFELAND(1994), KARFAKIS-KIM(1995), HIN HOCK LEE(1995), EDISON(1996);or, using infra-daily frequencies, ITO-ROLEY(1988), EDERINGTON-LEE(1993,1995,1996),GOODHART-HALL-HENRY-PESARAN(1993), BAESTAENS-VAN DEN BERGH(1996),ALMEIDA-GOODHART-PAYNE(1998)), the impact of a news-event on the exchange rate isestimated directly, using sampled news variables collected by various research organizations (e.g.Money Market Services, MMS) which generally consist of market expectations on the mainmacroeconomic variables which determine the exchange rate in the most important models.

In sharp contrast with the abundance of this kind of literature, there is very little written scientificmaterial about the impact of political and market noise on financial asset prices 8 and particularlyon exchange rates 9. In recent times the label of "behavioral finance" has been attributed to a group 7 The coefficient of economic activity and interest rate variables should have different signs, on econometric estimation, according tothe theoretical reference model.

8 See AGMON-FINDLAY (1992), DIAMONTE-LIEW-STEVENS (1996), ERB-HARVEY-VISKANTA (1986), for therelationship between political risk and stock prices; ALLVINE-O'NEIL (1980), HOBBS-RILEY (1984) for the impact of USpresidential elections on the stock market.

9 TIVEGNA(1996a) follows the news-based approach in the study of the impact of unexpected events on the Lira exchange rateintegrating macroeconomic news with political news drawn from the Italian political and financial experience between March 1994and December 1995, during the Governments of Berlusconi and Dini. The paper is based on a detailed reconstruction andeconometric testing of daily political events and on the estimation of a daily Lira-DM exchange rate equation. The main thrust in theequation is exercised by the DM-$ rate which drove the Lira-DM rate during this period. What is not explained by this main drivingvariable is explained by political news and, to a lesser extent in the Italian political environment of our sample-period, by economicnews. An infra-daily blow-up of some turbulent episodes during the Dini Government is in TIVEGNA(1996b)

An extension of this line of research, is carried out in MONTICELLI, FORNARI, PERICOLI, TIVEGNA(1999). The main goal ofthis paper is to explain the fluctuations and the time-varying volatility of the Lira-DM exchange rate and of the 10-year Government

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of writings on the role of noise in financial analysis and trading. In the book containing a collectionof these papers ,THALER (1993), only one of them has some features comparable to those quotedin footnotes 8 and 9, namely CUTLER-POTERBA-SUMMERS (1989) which makes reference tothe US stock markets, like most of those dealing with politics and financial markets, footnote 8.

The aim of this paper is twofold: i) to investigate the determinants of the exchange rates of theDollar vis à vis the DM and the Japanese Yen between 1995 and 1997, in a period of considerableturbulence in the world financial markets, and, at the same time, ii) to enrich the literature ofbehavioral finance with a new contribution about the impact of political and market noise onexchange rates and – as a by-product – on other financial variables, i.e. bond yields and the DowJones Industrial Index.

The first goal of this paper is pursued through econometric estimation and joint simulation of twodaily exchange rate models of the DM-$ and Yen-$ rates by splitting the so-called forex globaltrading day into two time zones – as explained below – thus having twice-daily observations on thevariables of our models. These models include also equations for long term interest rates and theDow Jones Industrial Index, which exert an influence in the movements of the Dollar.

A strict adherence to a daily time frame does not allow to disentangle the impact of events occurredover various market zones: Asian, European and American. We therefore opted for splitting the so-called global trading day into two parts: what we called the European Time Zone goes from theprevious day's US closing at 16:00 Eastern Standard Time, EST, (corresponding to 22:00 CentralEuropean Time, CET) to an artificial European closing at 14:15 CET. At this time, severalEuropean Central Banks indicate some reliable exchange rate values prevailing in the Europeanmarket. That happens shortly before the announcement, almost every day, of key economicvariables by the US Authorities - at 8:30 EST or 14:30 CET - which normally causes a great dealof turbulence in the currency market. In the first trading-time zone - which we term as European asit ends before the US market steps in - we have Japanese trading and the morning of Europeantrading. It is therefore somehow artificial.

The second trading-time zone is more homogeneous as it refers mostly to US trading, except forpart of the morning where European afternoon trading is also present. It is called American TimeZone, it begins at 8:30 EST (corresponding to 14:30 CET) and covers the whole US forex tradingday, ending at 16:00 EST (22:00 CET).

The econometric estimation was carried out through maximum likelihood estimation of twosimultaneous equation models - one for each time zone - with a GARCH specification of the errorterm, as volatility is an essential feature of high-frequency asset prices, BOLLERLSEV-CHOU-KRONER (1992). The two exchange rates examined in this paper and two important variables –long term interest rates and the Dow Jones Industrial Index - explaining them are determined by thesame set of news variables. We estimated a three-variable GARCH for the European Time Zone,determining our two exchange rates and the German ten-year Bund rate, and a four-variableGARCH for the American Time Zone, where the endogenous variables are the ten-year USTreasury Bond, the DM-$, the Yen-$ and the Dow Jones Industrial Index. As to the estimation ofthe parameters of the error terms, we used a multivariate GARCH approach to their BEKKrepresentation, ENGLE-KRONER(1995).

bond yield and its futures traded at LIFFE. The sample spans a period of great political and financial turbulence in Italy: from theBerlusconi and the Dini Government (March 1994-December 1995) through to the re-entry of the Lira into the European exchangeRate Mechanism (ERM), at the end of November 1996. Another study in this line of research is written by KEIL (1993) who usespolitical news from German history - between February 1919 and October 1923 - collected by WEBB(1989) to measure the riskpremia on forward DM exchange rate. This paper uses some news categories comparable to ours, e.g. "domestic rebellion", "alliedsanctions", "government spending programs", etc., pag. 1297 of KEIL(1993).

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Besides, the two time zone models - European an American - have been simulated jointly using theiterative solution technique traditionally used for the large macroeconometric models, in order totake care of their simultaneous nature. The purpose of this exercise is to asses the overtime impactof some of the news studied in this paper.

As to the second goal of this paper we try here to lay out some criteria in order to create a taxonomyof the different kind of news affecting exchange rates and, at the same time, to build a metrics ofthe impact of each category of news on the exchange rates examined in this paper througheconometric estimation and simulation of the estimated models. The news we use are organized bytype of event in a data bank called NEWSMETRICS, see TIVEGNA-CHIOFI (2000).

The paper is organized as follows. Next section discusses the criteria we used to build the variouscategories of news used in this paper, namely the distinction between scheduled – or quantitative -and unscheduled – or qualitative - news as well as the methodology adopted for the identification ofthe relevant political and market-related events. The third section, on the econometrics of news,describes the multivariate GARCH methodology and presents estimation and simulation results. Inthe last part of this section a metrics of the static and dynamic impacts of news is proposed. Asummary of our results and some tentative conclusions are in the fourth.

2.) The newsIn their daily work, investors and traders are hit by a massive amount of information via severalchannels: news in general, macroeconomic releases, all sorts of rumors concerning political andeconomic events, various pieces of market analysis prepared by the major financial institutions. Allthis information can be grouped into two broad categories according to its timing: unscheduled(generally qualitative) and scheduled (generally quantitative) news.

Unscheduled news consists of an economic or institutional event, a declaration or a disclosure,which can be either totally unexpected or - even though expected to occur - has an unknown timing.This news therefore is not likely to be fully embodied into observed prices. The acquisition and theanalysis of the latest available information is the core of the daily activity in the trading of financialassets. Such a keen attention to the latest piece of available information stands in sharp contrast tostandard theories of asset pricing that hinge on the relationship between prices and fundamentalvariables. However, as the emerging approach of behavioral finance has brought to the fore, marketparticipants are more willing to follow the information coming from latest news rather thanforecasting the future developments in fundamental economic variables.

Scheduled news regards generally macroeconomic announcements by Governments or CentralBanks. Amongst the three countries we are following and during our sample period, 1995 – 1997,the United States established an annual calendar of statistical releases. Financial markets have longbeen following the 8.30 A.M. Eastern Standard Time (EST) ritual when the most importantmacroeconomic data are published, generating a cluster of volatility in every world financial marketopen at that time10. Japan also has been quite orderly. The scheduled macroeconomic news fromthis country are not used, as such, in this paper but just indirectly, when they are embodied intomarket commentaries or statements by Japanese Authorities. Germany used to set a flexible 10 See the papers which base their empirical analysis on infra-daily exchange rate and other financial data, e.g. EDERINGTON-LEE(1993, 1995, 1996) GOODHART-HALL-HENRY-PESARAN(1993), ALMEIDA-GOODHART-PAYNE(1998).

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calendar by the week and made occasional leaks - generally on unemployment data - one or twodays before data releases, when market or domestic political sensibilities were running high. Wecollected German "semi-scheduled" data (MMS source) on inflation, M3, unemployment and theIFO business climate index.

Most research work on the impact of news on exchange rate variations has been concentrated onmacroeconomic releases, especially from the United States 11. The US system of fixed-schedulereleases allows to create an ideal experimental set up, therefore it has been widely exploited.

The bulk of events used in the empirical analysis of this paper is made up by unscheduled news12.

2.1) A taxonomy of qualitative newsMost economic and political events can potentially affect financial markets. Our effort in theselection of what seems to be relevant to explain exchange rate fluctuations underwent severalstages 13. Our purpose here is to describe the qualitative news used in this paper through aclassification of events and some examples of the kind of news used in estimation.

Most market moving unscheduled qualitative events of the kind outlined above could be classifiedunder the following five main items.

A) Market information and opinion:A1) On market moving political (mostly fiscal and monetary policy) and economic or

financial events (e.g. referring to stability of financial institutions, general marketinstability, etc.);

A2) On current exchange rates values or oscillating ranges, e.g. due to technical situations or profit-taking situations or attitudes;A3) On market rumors and opinions, e.g. originating from respected market studies, from interviews to market leaders;A4) On rumors regarding policy making, opinions about policy makers (hawkish or

doveish philosophy), and, most of all, changes of opinion (e.g."Greenspan’s newdoveish attitude...").

B) Qualitative economic or political event.

C) Qualitative description of a quantitative event, with or without any background information ormarket opinion and ultimate effect on the exchange rate.

11 Most empirical papers quoted in the first section use this source.

12 They are drawn from a large data bank of news, NEWSMETRICS, see TIVEGNA-CHIOFI (2000).

13 The steps of this pre-testing for each qualitative news were: 1) all the news of a particular kind were collected, 2) the events likelyto have a positive effect on the exchange rate (i.e. an appreciation effect) were separated from those likely to have a negative effect,3) binary (0,1) variables were built for both positive-effect and negative-effect dummies, 4) various regressions were run using thesedummies and letting the data determine the sign, significance and strength of the effect, 5) some careful analysis of the residuals ofthese regressions was carried out in order to spot the news having weaker positive or negative effects on the exchange rate of the day,6) based on the results of this inspection, some marginal changes of positive and negative effects and some rather painstakingreplicas of the steps from 3 to 5 were made.

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D) Policy statements by leading politicians, ministers or central bankers, both in the form of a freeand "candid" expression of opinion or in the form of a verbal intervention expressly aimed atmoving market prices or at influencing traders' expectations and opinions.

E) Official and well advertised interventions of Central Banks in the foreign exchange markets.

Let's now see some examples, in Italics, of the above qualitative news used - among many others -in this paper.

Some market moving news of the A1) kind above could be the following:

"The dollar rallied sharply as details emerged of a late agreement in Geneva between the US andJapan in their disputed car trade talks. The news surprised markets which had anticipated that thetalks would fail" - FT, 28.6.1995.

"News that Yeltsin had been admitted to hospital was sufficient to push the $ higher while putting adampener on the DM. Political uncertainty in Russia traditionally weighs on the DM" - FT,11.7.1995

"The dollar rallied sharply on intensified speculation of a cut in German interest rates when Bubacouncil meets on Thursday" - FT,27.11.1995.

Examples of information on the technical position of the market, under A2) could be:

"The DM lost ground across the board as investors took profits following the recent sharpappreciation of the currency" - FT, 1.3.1995.

"The dollar continued its recent consolidation on the foreign exchanges, but opinion remainsdivided about whether it has reached a bottom" - FT, 14.3.1995.

One example of news relative to rumors and opinions, under A3), could be:

"The first boost for the DM came from forecasts by German research institutes that gross domesticproduct growth this year would be 1.5%, twice as high as previously forecast" - FT, 29.10.1996.

An example of news under A4) could be:

"The dollar recovered with world asset markets as traders reassessed comments on Thursday nightby Mr Greenspan" - FT, 9.12.1995.

Examples of qualitative economic or political events, under B), can be of many kinds, for instance:

"Germany said that it was raising its unemployment forecast for 1997. This hurts the country'schances to meet Maastricht criteria" - FT 11.5.1997

"Weigel outlines proposals for revaluation of Buba gold reserves to support public finances" - FT15.5.1997

"France turns to the left in first round of elections and leaves the context wide open" - FT 26.5.1997

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In this paper we used quantitative news on twelve US macroeconomic variables and on fourGerman ones, using official data and market expectation data collected by MMS. In our empiricalanalysis we generally used these rather than the kind of qualitative information on quantitative dataunder C), above, except when the news items contained some extra information . Examples of thesekind of news are:

"The US payrolls report was stronger than expected but the expectation of higher US short terminterest rates proved supportive of the dollar" - FT,7.6.1996.

"$ surged versus DM after new figures confirmed the strength of the US economy. $ jumped afterUS stronger-than-expected US non-farm payrolls data for July, then briefly stumbled after anunexpected jump in the price component of July NAPM" -FT, 1.8.1997.

In this paper we tried to measure the effects of a new fashion of "talking one's currency up ordown" – directly or indirectly - established in the last few years by Central Bankers and FinanceMinisters. Examples of this kind of news are:

"Tietmeyer was reported as saying in a Japanese paper: "If all the information is going in the rightdirection I am not excluding some room for manoevre in reducing interest rates" - FT, 23.8.1995

"Rubin said that he "strongly believed" in a stronger dollar" - FT 6.10.1995

"Sakalibara told senior traders at a meeting on Monday that he would be persisting in his efforts toweaken the yen" - FT 12.9.1995

A last type of news, under D) above, is central bank interventions. Given the news context of ouranalysis, we use interventions which are either announced by Central Banks or are overt enough tobe recognized as such by forex traders. One example follows:

"Fed intervened actively on the foreign exch. in an attempt to curb the continued fall of the $.According to market sources the intervention was broad rather than substantial. It had a negligibleimpact with the $ continuing to trade at pre-intervention level" - FT, 3.4.1995.

2.2) News variables in this paperThe broad news categorization in section 2.1) from "A" to "E" was adapted to the actualconstruction of news variables to carry out the empirical analysis in section 3. As anticipated in theintroduction, we split the global trading day into two parts: the European Time Zone and theAmerican Time Zone. The scheduled and unscheduled news variables by exchange rate and timezone, as used in this paper, can be grouped together as follows.

DM- DOLLAR – EUROPEAN TIME ZONEScheduled News (generally in the morning of day of release)German CPI: preliminary release of the monthly consumer price index of Germany by the FederalStatistical Office, actual and expected values.

German IFO: business confidence index in Germany computed by IFO of Munich, actual valueonly.

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German M3: monthly growth rate of M3 in Germany, computed by Bundesbank, actual andexpected values.

German Unemployment: monthly variation of deseasonalized unemployment in Germany,computed by the Federal Labor Office, actual and expected values.

Unscheduled NewsStatements on DM-$: policy statements by Bundesbank council members, Germany's Finance andEconomics Ministers and by the Federal Chancellor concerning the exchange rate or monetary andfiscal policy variables affecting it; this group of news would go under "D" in section 2.1).

G3 Central Bank Interventions: foreign exchange market interventions by the Central Banks ofGermany and Japan, made public.

DM-$ News: a miscellaneous of political, monetary and fiscal policy and market eventsdetermining expectations on the exchange rate and on the future direction of interest rates; eventsbelonging to this group are classified under "A" and "B" in section 2.1) above.

German Economic News: a qualitative description of various German economic events which aredescribed by important headlines in the "Financial Times", the "International Herald Tribune" andthe "Wall Street Journal"; the episodes under this code make some good examples of newsclassified under "C" in section 2.1).

EMU News: events and policy statements concerning the convergence to EMU (actualconvergence and discussion on the Maastrich criteria) , the countries most likely to participate in itfrom the beginning and various other events relating to UME; all this kind of news affect only theDM-$ exchange rate 14 ; these episodes could be classified under "B" in 2.1.

German Official Rates: variations of official interest rates (discount, Lombard, repo) by theBundesbank.

DM- DOLLAR – AMERICAN TIME ZONEScheduled News (United States economic indicators, mostly released at 8:30 EST, by the USFederal Government, and during US trading, by the Federal Reserve and other US organizations ).We have here expected and actual values of the statistical indicator, the former being collected byMoney Market Services, a international consulting company of Standard and Poor. In theeconometric estimates in section 3 they are entered in difference form (actual less expected values).

US CPI: monthly consumer price index

US Durable Goods Orders: monthly

US Industrial Production: monthly industrial production index

US Leading Indicators: monthly index of leading economic indicators

14 As well known, many events and statements of this kind were aimed explicitly towards Italy, affecting mostly - but notexclusively - the Lira-DM exchange rate. This last group of event makes up a different category and were used in TIVEGNA(1996a)and FORNARI-MONTICELLI-PERICOLI-TIVEGNA(1999).

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US NAPM: survey index of the National Association of Purchasing Managers (NAPM), monthly

US Payrolls: non-farm employment (payroll), monthly

US PPI: monthly producer price index

US Retail Sales: monthly nominal value of retail sales

US Trade Balance: monthly nominal value

US Unemployment Rate: monthly unemployment rate, whole economy

US Wage Rate: monthly hourly earnings in the non-farm sector

Unscheduled NewsStatements on DM-$: policy statements, on the exchange rate and on monetary and fiscal policy, byleading US policy makers - mostly by Treasury Secretary Rubin, Treasury UndersecretarySummers, President Clinton - and, in the European afternoon, by Bundesbank council members,Germany's Finance and Economics Ministers and by Germany's Federal Chancellor; this newsgroup would be under "D" in section 2.1).

Statements by Greenspan: policy statements by the US Federal Reserve Governor, Alan Greenspan,generally – but not exclusively - on occasion of Congressional appearances; this news group wouldbe under "D" in section 2.1).

DM-$ News: a miscellaneous of political, monetary and fiscal policy and market eventsdetermining expectations on the exchange rate and on the future direction of interest rates; eventsbelonging to this group are classified under "A" and "B" in section 2.1).

US Stock Market News: major US financial markets upheavals, leading to sharp short termmovements of the Dollar exchange rate; the episodes under this code make good examples of newsclassified under "C" in section 2.1).

US Official rates: variations of official interest rates (discount, Federal Funds) by the Board ofGovernors of the Federal Reserve System.

YEN- DOLLAR – EUROPEAN TIME ZONEUnscheduled NewsStatements on Yen in European Time: policy statements made during the Japanese working day,which frequently extends to very late in the afternoon, on the exchange rate and on monetary andfiscal policy, by leading Japanese policy makers: mostly by one Director General of the Ministry ofFinance, "Mr Yen" Eisuke Sakakibara, by various Finance Ministers, by the Prime Minister -Murayama and Hashimoto, in our period - by the Governor of the Bank of Japan, by variousinfluential members of Japan's ruling Liberal Democratic Party (LDP), by various other topbureaucrats; this news group would be under "D" in section 2.1).

G3 Central Banks Interventions: foreign exchange market interventions by the Bank of Japan andby the Bundesbank, made public.

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Yen-related News in European Time: a miscellaneous of economic, political, monetary and fiscalpolicy and market events, of Japanese origin, determining expectations on the Yen component ofthe bilateral Yen-$ exchange rate and on future direction of Japanese interest rates; eventsbelonging to this group could be classified under "A" and "B" in section 2.1) above.

$-related News in European Time: a miscellaneous of economic, political, monetary and fiscalpolicy and market events, of Japanese origin or during Asian trading, determining expectations onthe Dollar component of the bilateral Yen-$ exchange rate and on future direction of Japaneseinterest rates; events belonging to this group could be classified under "A" and "B" in section 2.1)above.

BoJ Discount Rate: variations of official interest rates (discount, money market rate) by the Bank ofJapan.

YEN- DOLLAR – AMERICAN TIME ZONEScheduled News. We have here the same US economic indicators we described above for thedeterminants of the DM-$ during trading in the American Time Zone.

Unscheduled NewsStatements on Yen American Time: policy statements rendered during American trading by thesame Japanese policy-makers we saw above; this news group would be classified under "D" insection 2.1).

Statements on $ American Time: policy statements, referring explicitly to Japanese economic andtrade policy and on the Yen-$ exchange rate by leading US policy makers, mostly by TreasurySecretary Rubin, by Treasury Undersecretary Summers, by the various Trade Secretaries andspecial Trade Representatives, active in US politics during our sample period, and, last but not least,by President Clinton; this news group would be classified under "D" in section 2.1).

Yen-related News American Time: a miscellaneous of economic, political, monetary and fiscalpolicy and market events, of Japanese origin, generally occurred during the Japanese working daybut still having some impact during US trading over expectations on the Yen component of thebilateral Yen-$ exchange rate and on future direction of Japanese interest rates; events belonging tothis group could be classified under "A" and "B" in section 2.1) above.

Dollar-related News American Time: a miscellaneous of economic, political, monetary and fiscalpolicy and market events occurred during American trading which may affect - directly orindirectly - the Yen-$ exchange rate; all these events influence expectations on the Dollarcomponent of the bilateral Yen-$ exchange rate and on future direction of US and Japanese interestrates; events belonging to this group could be classified under "A" and "B" in section 2.1).

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3) The impact of newsIn the previous section, the news potentially relevant in explaining exchange rate movements weregrouped together into homogeneous categories in order to build explanatory variables to be used inthe estimation and simulation of daily equations.

In this section we set forth to building two small simultaneous econometric models – one for eachtime zone - using daily data between January 1995 and December 1997 in order to build a metricsof exchange rate response to scheduled and unscheduled news. This task is pursued first byestimating these model using multivariate GARCH techniques and looking at the estimatedcoefficients of news variables; these two models have then been coded together and simulatedjointly.

In the following part, 3.1, we describe the choices we had to make in order to deal with estimatingdaily models in a world of continuous data production like the international foreign exchangemarket. We then describe our econometric approach – 3.2 - and the estimation results – 3.3 -referred to the structural parameters of the mean part of the GARCH, in 3.3.1, and to the volatilitypart, in 3.3.2. The following sub-section 3.4 contains some simulation exercises aimed at assessingthe overtime impact of some news categories, through dynamic impulse response calculations, so asto establish a metrics of news, described in 3.5.

3.1) Estimation strategyThe issues relevant for estimation are:

(a) the specification of the daily equation,(b) how to cope with the 24-hours trading in the foreign exchange market,(c) how to measure the qualitative news,(d) how to compare the impact on exchange rates of different news-variables as they are measured using different scales(e) the econometrics.

(a) On a monthly - or lower - frequency basis, exchange rates equations have been traditionallyfollowing a monetary, Keynesian (Mundell-Fleming) or portfolio balance specification 15 . In themonetary model exchange rates are explained, broadly speaking, by money growth differentials,output differentials and, according to the interest rates hypothesis adopted, either by short or longterm interest rates differentials or by inflation differentials. The Keynesian specification explainsexchange rates as a function of output growth, inflation and interest rates differentials. Theportfolio balance theory uses interest rates differentials and the relative supply of financial assets inthe two countries connected by the exchange rate.

As well known, all the variables of potential interest to explain exchange rates are sampled on amonthly (or lower frequency) basis. Once you turn to explaining daily rates, you have only interestrates, stock prices and a few other financial market variables sampled at that frequency.

The scheduled news used in this paper refer to the economic variables appearing in the threedifferent families of exchange rate equations, the so-called "fundamentals": inflation (CPI or PPI),output growth (directly represented by industrial production and by surveys of industrial

15 See BAILLIE-McMAHON (1989), especially Chapters 3 and 8; HALLWOOD-MACDONALD (1994), Chapters 5, 8, 9, 10;COPPELAND (1994), Chapters 5, 6, 7, 8.

13

production, like those by NAPM and IFO, or indirectly represented by unemployment, or by retailsales, or by durable goods orders, or by the leading indicators of economic activity), trade balance(in the portfolio balance approach). In a daily time frame and in a news context, all these economicvariables do not work in terms of growth differentials - like in the classical monthly or quarterlyexchange rate regression equations 16 - but rather in terms of surprise variables, namely thedifference between their actual and expected values. Interest rates are sampled daily so they enterour equations as differentials, as in the classical exchange rate theories. We actually computeddifferentials of moving averages to smooth out volatility and anomalous one-day data.

Our daily equations therefore can be considered as a eclectic representation of the standardeconometric specifications stemming from the three theories mentioned above17. As to unschedulednews, in a daily frequency they play an important role whereas at a monthly frequency they areaveraged out over the period.

(b) The forex market works around the clock. It opens a new trading phase - or a new day - in theAsian market, it continues on in the European market, where trading begins roughly when it ends inTokio (but Japanese political and economic news keep on flowing after market close). The USmarket begins trading after the US Federal Government releases some key economic data at 8:30Eastern Standard Time (EST). The European market is active for another three hours or so but theflow of European political and market news goes on also after market close. The US market thenshuts off most forex operations around 4:00 PM EST, corresponding to 10:00 PM, CentralEuropean Time (CET). Trading is still active for another few hours but quotes do not deviatesubstantially from the closing ones in New York.

We have a three-equation model built on data sampled in the European Time Zone, which goesfrom the previous day's US closing at 4:00 PM EST (corresponding to 22:00 CET) to an artificialEuropean closing at 2:15 PM CET. We then built a four-equation model on data sampled in theAmerican Time Zone, which begins at 8:30 EST (corresponding to 14:30 CET) and ends at 16:00EST (22:00 CET).

This by-partition of the global trading day allows a sharper allocation of the scheduled news to therelevant value of the exchange rate sampled at the right time. As to the unscheduled news somehypotheses had to be made and some further inquiry was necessary to allocate it in the right trading-time- zone 18 .

(c) The qualitative news - e.g. statements by Monetary Authorities, market information on eventscausing sharp exchange rate movements, qualitative description of extreme stock market news, etc.- are introduced in the empirical analysis as ternary variables. They are made up of a zero, when therelevant events does not occur, of a -1.0, when the news is theoretically linked to an appreciation ofDM and Yen, of a +1.0, when the news is linked to a devaluation.

It is therefore necessary in this analysis to fix the effect of an event a priori. This is less arbitrarythan it may appear and, in any case, it is absolutely mandatory in order to get a reliable quantitativemeasure of a qualitative event. First, it is not too arbitrary because an extensive statistical pre-

16 For a survey, see BAILLIE-McMAHON (1989), Chapter 8.

17 No monetary aggregates are considered in our equation as their importance has diminished considerably since the institutionalchanges in monetary management in the United States of the early eighties. See PERUGA(1996).18 Generally, it is not difficult to allocate unscheduled news to the right time zone. For a number of them, the correct allocationrequired the use of Reuters’ data bank of all news wired to the markets having the infra-daily time stamp. The bank, called “BusinessBriefing”, was kindly made available by the Italian branch of Reuters.

14

testing of the effect of these events has been carried out 19 and a comparably extensive discussion ofthe effects of the most popular kind of news was entertained with several market practitioners.Second, an a priori determination of the effect of a news is also necessary because each category ofnews can have positive or negative effects, therefore a standard (0,1) binary variable would mostlikely turn out to be statistically not significant, because the effects would cancel each other out. Itis left to future work the testing of the likely asymmetric effect of positive and negative news 20 .

(d) The variables in the exchange rate equation we are estimating are expressed in differentnumerical scales: from 0,1 dummies to rate of variations, to percentage rates, to nominal values. Inorder to make comparisons among the estimated parameters of our equations we standardized everyvariable so that the resulting values can be interpreted as beta coefficients.

3.2) EconometricsGiven the availability of a rich statistical base, described in section 2, the daily nature of our modelsand the recently-observed empirical influence of the US stock market on the Dollar movements, theexchange rate equation discussed in the first section has been modified for econometric estimationas follows (3.1)

where Fh contain the interest rate differentials and the Dow Jones Indexvariations; the bracketed terms contain the m actual and expected schedulednews on the fundamental determinants of exchange rates, the variables Zj ; Ui

contain the n unscheduled news and the lagged dependent variable, St-1,represents the time t exchange rate expected in period t-1 proxying Et(St+1) in theequation of the first section.

The news approach to asset price determination is not confined to exchange rates only. It ha beenused for share prices and stock indices, jointly with the traditional event studies approach 21 , forbond prices and for futures on various financial instruments 22. This literature indicates a ratherbroad influence of unexpected events on asset prices, including long term bond prices and the DowJones Index, which appear in our exchange rate equation. The equations for the latter two variablescould be therefore specified as (3.1) by simply substituting the dependent and lag-dependentvariable with them and adapting the Fh terms of the equation 23.

Bond yields and the Dow Jones Index can therefore be determined by the same set of newsvariables as the DM-$ and Yen-$ exchange rates. The estimates of the variance and covariancestatistics of these equations in both time zones could be therefore distorted and most likely

19 The procedure is described in footnote 13, second section of the paper.20 There exist an extensive literature on the asymmetric effect of good and bad news on volatility, mostly referred to stock prices.For an important reference see ENGLE-NG(1993).21 See CAMPBELL-LO-MACKINLEY(1997), chapter 4, for a survey.22 See BALDUZZI-ELTON-GREEN(1998), JOHNSON-SCHNEEWEIS(1994),McQUEEN-ROLEY(1993), MITCHELL-MULHERIN(1994), LI-HU(1998), EDERINGTON-LEE(1993, 1995, 1996).23 Some Augmented Dickey-Fuller stationarity tests performed on the exchange rates and interest rates used in this paper - and onthe Dow Jones Index - indicate one unit root in the level of these variables; the lagged dependent variable in equation (3.1) couldtherefore have a unit coefficient and make it advisable to estimate the equation in a log-difference format. The tests are not reportedin the paper but are available on request.

tt

n

iiitjttj

m

jjh

r

hht SUZEZFS εδγβϑα +++−++= −

=−

==∑∑∑ 1

1,1,

11)]([

15

inconsistent. Besides that, long term interest rates and the Dow Jones Index appear as right-hand-side variables in the exchange rates. All that makes it advisable to use a maximum likelihoodmultivariate estimation technique. This joint estimation of exchange rates, interest rates and theDow Jones Index can also yield some useful information on the covariance of the errors acrossequations 24 .

A simultaneous estimation procedure is therefore necessary which should also keep into account thevolatility of daily exchange rates and the of other financial variables. We opted for a multivariateGARCH procedure where the error term of the above specifications is modeled according to aBEKK representation 25 .

The mean part of our two systems can be represented as (3.2)

where the superscript i = EU, US refers to the European and American time zones (ETZ and ATZ,respectively); Yt

(i) is a vector of endogenous variables, 3x1 in the model of the ETZ and 4x1 in theATZ’s; the lagged dependent variable is bipartitioned into a first term, which includes German andAmerican long term interest rates, and a second term, with the underline TZ-1, representingexchange rates determined in the previous time zone; Xt

(i) is a vector of non-news exogenousvariables, 1x1 in ETZ and 2x1 in ATZ 26 ; S(i)

t and U(i)t are vectors of scheduled and unscheduled

news, the first corresponding to the bracketed terms in (3.1) and the second to the n Ui in the sameequation 27; Θt

(i) Θ(i) , K(i)t , Ds

(i) , D(i)

U are conformable matrices of parameters.

The error term vector in (3.2), given the information set ψ at time t-1 , is (3.3)

And the two (i = EU, US) conditional covariance matrices Ht(i) have the following BEKK

representation (3.4)

The above representation has the advantage of granting the positive-definiteness of Ht(i) matrices

but may contain some off-diagonal elements to estimate thus being possibly over-parametrized. Thescheduled and unscheduled news variables, St

(i) and Ut(i) in (3.4) are made up of ones when there is a

news and zero otherwise28 . The ΓS(i) and ΓU

(i) are diagonal matrices 29. The diagonality of the

24 This can be seen from the charts showing the conditional covariances of the GARCH terms, Fig. 3.125 See ENGLE-KRONER(1995).26 The only exogenous variables appearing in our estimates are the US bond yield in European time, as this variable is modeled inAmerican time, the German bond yield in American time, for the same reason, and the Japanese bond yield, which has not beenmodelled so far.27 The ETZ scheduled news is a 4x1 vector whose elements are reported in Tab. 3.1. The ETZ unscheduled news is a 10x1 vector,reported in the same table. In ATZ, the scheduled news is a 12x1 vector whose elements are reported in Tab. 3.3, whereas theunscheduled news is a 12x1 vector also reported in Tab. 3.3.28 Notice in fact that there is a hat on these matrices as they are different from those in (3.2) even though they represent the samescheduled news. In (3.2) they appear as difference between actual and expected values.

),0(| )()(1

)( it

it

it HN≈Ψ −ε

ˆˆ )()()()()()(1

)()()(1

)(1

)()()()()()()()()()()()( iW

it

it

iW

iit

iiit

it

iiU

it

it

iU

iS

it

it

iS

iiit WWBHBAAUUSSH ΓΓ+++ΓΓ+ΓΓ ′+ΩΩ=

′′′′′′′′′εεˆˆ

)()(

1

)()()(

1

)(

1

)()()()()()()()()()()()()()()()( ii

t

iii

t

i

t

ii

W

i

t

i

t

i

W

i

U

i

t

i

t

i

U

i

S

i

t

i

t

i

S

iii

t BHBAAWWUUSSH −′′

−−′′′′′′′

++ΓΓ+ΓΓ+ΓΓ ′+ΩΩ= εε

.ˆˆ )()(1

)()()(1

)(1

)()()()()()()()()()()()( iit

iiit

it

iiU

it

it

iU

iS

it

it

iS

iiit BHBAAUUSSH −

′′−−

′′′′′ ++ΓΓ+ΓΓ′+ΩΩ= εε

)()()()()()()()(1

)()(,

4

0

)()( it

it

iU

it

iS

it

iiTZ

iijtj

j

ij

it UDSDXKYYY ε++++Θ+Θ= −−

=∑

16

various matrix terms explaining the variance-covariance matrix Ht(i) , on the right-hand side of (3.4),

has been tested according to the suggestion of ENGLE-KRONER(1995), pagg. 139-40, and byinspection of the their statistical significance, which, except for a couple of coefficients, wasrejected. That allowed to impose restrictions on Ht

(i) .

In the estimation of our two BEKK models for Ht(i) in (3.4) it has been added a weekday effect,

which can be represented by the following term to be added to the above expression (3.4): (3.5)

The following restriction has been imposed in the off-diagonal elements of Ht(i) in both our systems

on estimation - implying constant conditional correlation coefficient - to allow for some boundedbehavior to volatility and conditional covariance 30 : (3.6)

where the left-hand terms are lagged covariances and the right-hand ones are lagged variances; inETZ, k,r=1,2,3; whereas in ATZ k,r=1,2,3,4.

3.3) Estimation ResultsEstimates of various specifications of the above (3.2) and (3.4) systems are reported in Tables 3.1 to3.4 , the first two report European time zone estimates, the second two American time zone ones.

3.3.1) The MeanTable 3.1 and 3.3 contain the complete models in the first two columns and therefore lendthemselves to a detailed discussion on the specification of the two time zones models.

The first equation of the European time zone model – Table 3.1 - explains the German ten-yearsinterest rates. Given the daily time frequency, the dependent variable is computed as a logdifference between the current value of the interest rate and a three-periods moving average of thesame variable 31 ; there is then a fourth-order autoregressive part of this term 32 plus news,scheduled and unscheduled. The first ones – from the sixth to the ninth term in Tab. 3.1 - are madeup by the differences between actual and expected values of four macroeconomic variables whichkeep traders very alert when they are to be announced shortly 33: they are the German M3, CPI, thePan-German deseasonalized number of unemployed people and the Ifo survey of industryconfidence. The unscheduled news – from the tenth to the fourteenth - refer to variations in Germanofficial rates (Discount, Lombard and Repo), to various other German macroeconomic news, to 29 They generate square coefficient on estimation which, when multiplied by the ones of the square scheduled and unscheduled newsvectors, guarantee positive definiteness of the variance-covariance matrix in equation (3.4).30 See also BOLLERSLEV (1990).31 It is maybe useful to remember here that time unit in this paper does not have the traditional frequency, i.e. daily, weekly, etc., butcovers two time zone in a twenty-four hours span non coinciding with the day in the Central European Time (CET) schedule. Alsothe same dependent variable in the American time model – the US ten-year Bond yield - is computed with the same movingaverage transformation.32 A lot of specification search has been performed before showing the results of this paper which are the final stages of a very longand complicated process. This autoregressive part is taken care of in equation 3.2.33 In the period covered by NEWSMETRICS they were the most praised market-moving macroeconomic news. In most recenttimes, several other short term indicators on the German economy have received quite a lot of attention by financial markets.

)()()()( iW

it

it

iW WW ΓΓ ′′

2/1)(1,

)(1,

)(1, )( i

tkki

trri

tkr hhh −−− = ρ

17

political, market and economic events mainly relevant for the DM-$ in European time, tostatements by Government and monetary authorities, generally – but not exclusively – referring tothe DM, and a to bunch of political and economic news relevant for construction of EMU and forthe choice of the countries participating to it.

The second equation in the same time zone (Table 3.1) explains the log variation of the exchangerate between the end of US trading, the previous day, and the 14:15 CET value of the current day34.This is a function of the previous time zone lag-dependent variable, the previous time zone’s logvariation of the Yen, the variation of the interest rate differential 35 between the ten-year Bund andthe US Bonds. There is then a series of news variables. Both the scheduled and unscheduled onesare the same as those in the above function plus G3 the central banks (those of the United States,Germany and Japan) announced interventions in the foreign exchange market 36

The non-news part of the Yen-$ equation - the third in the European time model, Table 3.1, whosedependent variable, as we indicated above, had the same log transformation of the exchange rateequation - is similar to the previous exchange rate equation, but for the interest rate differentialwhich is missing here, and does not have scheduled Japanese news. The unscheduled ones are thevariation in the discount rate by the Bank of Japan, the G3 interventions, mainly-Yen-related news,mainly-Dollar-related news and statements by Japanese Government and monetary authorities 37 onYen-related events and policy measures.

Table 3.3 contains the full, i.e. non-reduced model of the American time zone which is made-up byfour equation: the same three of the European time model plus an equation for the Dow JonesIndex, which in the last few years has shown a high degree of correlation with the exchange rate ofthe Dollar. This relationship is due to the need for the US economy to attract capital inflows(largely through the stock market) given the very large trade balance deficit of the country.

The first equation explains the US ten-year Bond. The structure of this equation is almost identicalto that of the German Bund: a fourth-degree autoregressive part, plus scheduled news, plusunscheduled news. The first type of US news is a much larger set than Germany’s , given thedifferent tradition of the two countries in communicating macroeconomic information to themarkets. We have here the famous triad of labor market news, payrolls, unemployment rate andwage rate, announced by the Department of Labor on the same occasion, the durable-good orders,the index of the manufacturing survey of the National Association of Purchasing Managers(NAPM), the Industrial production index; the index of leading indicators, the consumer price index,the producer price index, the retail sales and finally the trade balance. The only unscheduled newsof this equation consists of statements by Mr Greenspan.

The non-news part of the second equation, explaining the DM-$, contains the variation of the ten-year Bond-Bund differential, the log variation of the Dow Jones Index and the previous time zonelag-dependent variable. The scheduled news part is the same as that of the ten-year Bond equation,whereas the unscheduled news part has market and political events referring to DM-$ and astatement part divided up between those of Mr Greenspan and those of all the other policy makers(included also some members of the Federal Open Market Committee , FOMC).

34 This log transformation is carried out on every exchange rates equation in the two time zones and on the Dow Jones Index in theAmerican zone.35 That was necessary as the simple interest rate differential is integrated one.36 Of course, these would be unscheduled.37 Among the former, the loquacious “Mr Yen” Sakakibara is by far the most active.

18

The Yen-$ equation has one part which is similar to the DM-$’s: the non-news regressors, thescheduled news, the Greenspan statements, the Bank of Japan’s official rates changes and the G3central banks interventions. The unscheduled news relevant to this exchange rate are: the Yen-related political, economic and market news and those having the same nature for the Dollar, theYen-relevant statements of both Japanese and US authorities in American time and the non-Greenspan US authorities statements of interest for the Yen-$.

The Dow Jones Index equation is the last of this system in American time. It is a function ofcurrent and lagged ten-year US Bond interest rate, Greenspan statements, the same scheduledquantitative releases, as those of the other three equations, and of a dummy which captures thesharp movements of the index during the Asian crisis in the fall of 1997, a totally exogenous event.

The coefficients in Tables 3.1 and 3.3 compare the equation-by-equation GARCH (1,1) estimatesfor the two time zones with those obtained through a simultaneous procedure of the same kind. Inthese tables we have the complete models. There are some changes in the properties of theestimates and in the resulting GARCH volatilities but, overall, not dramatically differentcoefficients.

It is worth repeating that the above ETZ and ATZ are the full models. Many of the variablesindicated above turned out to be non-significant and have been deleted from the reduced models,which have been used for the simulation exercises. Let’s now describe the estimation results.

Going from single-equation to multivariate estimates the ETZ German long term interest rateequation – the upper one in Table 3.1 – shows some gain in statistical significance on somevariables. In the DM-$ equation, the second in the table, the coefficient of the variation in long-term interest rate – which is the most simultaneous coefficient of the system - is more than halvedand loses significance, moving to single-equation to multivariate estimation, whereas thecoefficient of the German M3 news becomes significant. In the Yen-$ three coefficients out of eightbecome insignificant and the volatility decreases considerably.

In the US time zone we observe a remarkable improvement of statistical significance in most of theparameters of the US interest rate equation – first equation in Table 3.3 – and an improvement ofsignificance in the parameter of interest rate differentials in the DM-$ equation, which is asimultaneous parameter. The other simultaneous parameter of this equation – the one of the DowJones Index - loses significance going from single equation to system estimation. Some statisticalsignificance changes occur also in the two other equation of the US time zone model.

Having shown some advantages of multivariate estimation, we now describe the process ofparameter reduction in our systems, in Tables 3.1 and 3.3, and of news introduction in the volatilityof our equations, Tables 3.2 and 3.4.

The statistically insignificant parameters of the complete models in the second columns of Tables3.1 and 3.3 have been deleted and the resulting, more parsimonious systems are in the third columnsof these two tables. The coefficients remain generally the same up to the second decimal digit,more so maybe in the US time zone model; the volatility and conditional correlation parameters arealso very similar. The coefficients of the reduced models for both time zones all have the right signand are generally significant.

19

Only the M3 growth news in European time remains significant with the correct positive sign 38

after reduction in the interest rate equation, Tab. 3.1. This results is not surprising, given theimportance of money growth in guiding monetary policy in Germany. The strategy adopted by theGerman authorities in releasing information to the markets could also explain this result.

As we mentioned in section 2, Germany publishes just a weekly calendar of statistical releaseswithout any indication of the day of release. The market reaction to German releases is statisticallydifferent from the same reaction to US data releases as they are scheduled far in advance, indicatingday and hour. This result is consistent with those of ALMEIDA-GOODHART-PAYNE (1998),who work, though, with much higher frequency data than us 39.

Money growth is also the only significant scheduled news – with a correct negative sign – in theDM-$ equation; the unscheduled news, from the ninth, to the fourteenth, are all significant andsome of them have pretty big coefficients.

In the Yen-$ equation, everything seems to be explained by the last three unscheduled news, inTable 3.1.

Contrary to the German experience, the US scheduled macroeconomic releases have an influence inthe variations of the US ten-year Bond yield, except for the trade balance releases, Tab. 3.3.Looking at the relative weight of the coefficients 40, the payroll and the NAPM releases are – notsurprising for market practitioners – the most powerful news.

The US scheduled news seem to be far less important in explaining the two exchange rates inAmerican time: only payroll and wage rate matter. The unscheduled news are far more importantfor both exchange rates.

The scheduled news explaining the Dow Jones Index are only those referring to durable goods andretail sales, both with the negative sign as producing their effect via expected interest rate increases.They have in fact a positive coefficient in the equation for US long term interest rates, which have anegative sign in the Dow Jones equation.

3.3.2) The Variance-covarianceThe volatility part of the estimated models is described by equation (3.4) and the relevant estimatesare in the third part of Tables 3.1 to 3.4. As we said earlier, we estimated our models both equationby equation and simultaneously. The volatility part of our multivariate models has threespecifications: the standard one in the GARCH literature, BERA-HIGGINS (1993), first, fourth andfifth terms of the right-hand-side of equation (3.4), Tables 3.1 and 3.3; a news-augmented one,adding to the above the second and third term in (3.4), Tables 3.2, 3.4; and a third one adding to allthe above terms also weekday dummies, the term in (3.5), Tables 3.2 and 3.4.

38 The positive sign is correct incorporating expectation of future tightening.39They write in the "Conclusions and Proposal for Further Work": "First, the news from German announcements tend to beincorporated in the exchange rate more slowly than the news emanating from the US, due to difference in timing arrangements (i.e.the substantially unscheduled nature of German releases, our clue). Second, the impact on the exchange rates is, on average,quantitatively smaller for the German announcements"

40 This is possible as our estimates have been run on standardized variables.

20

With one exception, in Tab. 3.4 (referring to the Dow Jones Index), the coefficient estimates of thevolatility part of the models – terms in the A(i) and B(i) matrices in (3.4) - indicate stationarity of theGARCH process, ENGLE –KRONER(1995) pag. 133.

All the standard volatility terms – i.e. those referring to the lagged square error term and to laggedvolatility - are very significant. They remain so also when we introduce the news terms in variance,but with some slight coefficient changes, more so in American time. The introduction of weekdayeffect on volatility (Tables 3.2 and 3.4) 41 produces some effects both on the value of thecoefficients and on their significance. As to the significance of the various days of the week, itseems to be stronger in American time and also, quite curiously, there is always a Monday effect inEuropean time and a Thursday effect in American time. There is a Friday effect only for the Bondyield in European time and for the Yen-$ in American time.

The volatility and conditional covariance of the error terms of our equations, the diagonal andlower-triangular elements of Ht

(i) in (3.4), are reported in Figure 3.142.

Making reference to Fig. 3.1, we observe that the volatilities of DM-$ and Yen-$ are higher inEuropean time than American time and their time profiles are somewhat different in the two timezones. The volatility of the German ten-year Bond yield is higher than its US counterpart, maybebecause the US market is larger.

Turning to the conditional covariances of the errors and making still reference to Fig. 3.1, weobserve that there is always a negative relationship between the errors the two exchange rates andthe long term interest rates in both time zones, as to be expected 43 . The covariance between theerrors of the two exchange rates and those of the Dow Jones Index in American time is positive.This is consistent with the relevant coefficient in the mean equation and also with recent evidencewhereby there is a strong positive correlation between Dollar appreciation US stock marketstrength. The covariance between the two exchange rates is obviously positive and that between thelong term interest rate and the Dow Jones is obviously negative. These results confirm the signs ofall the conditional correlations in Tables 3.1-3.4; some of them are not significant.

It can be of some interest trying to make some sense of the volatility peaks in our charts. To keepthe paper within manageable limits, we will describe just a few events in Figure 3.1. The secondpeak in the German ten-year bond – first chart, a little after the 477th observation – happens aroundFebruary 20, 1996 and occurs after a long series of repo rate cuts by the Bundesbank, after areduction in the official interest rates by the Fed and by the Banque de France, when GovernorTietmeyer makes it clear that there will not official rate cuts (Discount and Lombard) by theGerman Central Bank.

The biggest peaks of DM and Yen volatility in European time – second and third charts in Fig. 3.1 -occur in mid-August 1995 when the Federal Reserve, the Bundesbank and the Bank of Japan makea joint intervention to steer the Dollar towards revaluation. Interestingly, the corresponding peak in

41 A frequent practice in the literature, e.g. JONES-LAMONT-LUMSDAINE(1996), MONTICELLI-FORNARI-PERICOLI-TIVEGNA(1999).42 We report only one chart of volatility and conditional covariance (reduced form equations in Tabb. 3.1 and 3.3, third columns) asmost time profiles of the other estimated equations are similar. The volatility and conditional covariance have broadly similar shapesacross the different estimation methods and specifications but not across the two time zones. This is visible in Fig. 3.1. We have onlysome marginal differences in the levels. When we move from the single-equation estimate of the Yen-$ volatility in American timeto its multivariate estimate we have a big reduction. This might indicate some possible bias in single equation estimation of ourmodels.43 Notice that the interest rate differentials are reversed in the two time zones.

21

American time is much smaller, maybe indicating that the unexpected part of the interventionoccurred in European time.

The first peak of the Dow Jones in American time occurs after a payroll release of 705.000 newjobs on March 8, 1996; the second peak in a period of stock market turbulence after a Dow crashproduced by a very negative earning announcement by IBM (a big component of the index). Thelast two peaks of this chart occur at the end of October 1997, at the climax of the Asian crisis, afterthe collapse of the Hong Kong stock market, when the Dow Jones suffers the biggest one-daypoints loss of 554.26, swiftly followed by a 337.17 rebound (third largest on record on a pointbasis) 44 .

3.4) SimulationIt is useful at this point to illustrate the properties of the econometric system resulting from the jointcoding of the two time-zone models, estimated in the previous section, in order to get some insighton the dynamic impact of news on exchange rates. This goal is pursued through impulse responseanalysis of the model to some shocks imposed on some selected news-variables, described in 3.4.2.Part 3.4.1 describes the model with the help of a flow-chart; some within-the-sample simulationshave been performed – results discussed in 3.4.3 - to evaluate the tracking performance of theglobal-trading-day system. The impulse response results are described in 3.4.4 and they are used –jointly with the estimation results examined in 3.3 – to make some statements on the metrics ofnews impact in 3.5.

The European time zone (ETZ) and American time zone (ATZ) model specifications used in oursimulation exercises are those containing news in the mean – equation (3.2) - and in the variance -equation (3.4). Their coefficient estimates are in the first columns of Tabb. 3.2 and 3.4. Thepresence of news both in mean and variance allows to measure the effects of some news-variablesimpulse on variations of exchange rates and of their volatility.

3.4.1) System descriptionThe two models estimated in 3.2 have been coded together in one single simulation model of theperformance of the two rates over the global trading day. Figure 3.2 contains a flow-diagramdescription of the joint model.

The upper part describes how DM-$ and Yen-$ are determined in ETZ. Forex trading occurs incontinuous time, so three important determinants of these exchange rates (their lagged values andUS long term interest rate) originate in the previous day’s ATZ. The DM-$ rate is influenced bylong term interest rate differentials between the ten-year German Bund and the equivalent USinstrument 45. The German ten-year rate is determined by one equation in ETZ. German M3releases – the only scheduled variable found to be significant in ETZ – and unscheduled Germannews 46 influence the DM-$. The Yen-$ exchange rate is also influenced by unscheduled Yen-relevant and Dollar-relevant news. No scheduled Japanese news has been used at this stage of theresearch.

44 All this happens even though an attempt has been made to dummy these effects out.45 At this stage of the modelling work, this latter rate is determined in the US market in the previous day, even though trading onthis instrument occurs also in European time. We assume that the main part of an interest rate variation over the twenty-four hourspan – on German, US and Japanese debt instruments - occurs during the trading period of their domestic markets. So it is quitelikely that the highest daily variation of US rates occurs in ATZ.46 Which include policy statements, also for the Yen-$.

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The ETZ values of these exchange rates become an important determinant of the same rates inATZ: this is shown in Fig. 3.2 moving from the upper to the lower part of the flow-chart. The samelong term yield differential seen in ETZ is also a determinant of both DM-$ and Yen-$. TheJapanese long term rate is exogenous in our model; there is an equation for the US long terminterest rate. An important influence over Dollar variation is represented by the behavior of thestock market. An equation has been estimated for the Dow Jones Industrial Index, which enters bothour two exchange rates. The other determinants of the exchange rates are scheduled andunscheduled news, as shown in Fig. 3.2.

The global-day dynamics of exchange rates is therefore determined by their trading zone laggedvalues, by asset market behavior in ETZ and ATZ - as represented by interest rate differentials andby the Dow Jones Industrial Index - and by news.

3.4.2) Impulse response outlineThe global model has been used to carry out some impulse response exercises by administeringone-day shocks – in mean and variance - to some scheduled and unscheduled news and a singlethree-day shock – in mean and variance - to one typology of unscheduled news. To keep this paperwithin manageable dimensions, we did not shock any news-variable in the system but just thoseconveying the highest amount of information on its dynamics. Similarly, the typology ofexperiments performed with our global model has been limited to one-day shocks – as theytypically occur in practice – except in one simulation exercise, where the shocks on oneunscheduled news has been maintained for three days as this duration is not unusual in the period1995-1997 used in this paper.

For these simulations we used the ETX and ATZ equations in Tabb. 3.2 and 3.4, first columns,showing a news-augmented volatility term.

Before presenting our results, let’s discuss how the various simulations have been performed. Theglobal-day model used for the simulation has been obtained by stacking the ETZ model equationsover the ATZ’s according to the direction of daylight timing. This model is functionally equivalentto that represented in equations (3.2) and (3.4) with the restrictions on the off-diagonal elements in(3.6). The vectors Ytj, Xt, St, Ut and εt have now different dimensions as they contain variables fortwo time zones. The simulation model is completed by so-called normalization equationsexpressing the levels of the exchange and interest rates and of the Dow Jones Index in their naturalorder of magnitude. These are non-linear equations which do not alter the basic linear structure ofthe system. These expressions contain the standard error of the endogenous variables as the modelhas been estimated on standardized variables.

We recall that the scheduled news-variables enter as difference between the actual and the expectedvalues 47; the unscheduled variables enter as +1, -1 or zero if the news is Dollar-positive, Dollar-negative or not occurring. The shocks on the scheduled news consist of a one-day increase in thevalues appearing in the baseline simulation 48 by the mean of the positive difference – when itoccurs - between actual and expected values, over the sample period 1995-1997. The unschedulednews have been shocked in two ways, as mentioned above: by a one-day only shock increasing thebaseline by one and by keeping the unit shock for three days49. 47 This latter one is sampled by Money Market Services.48 In the jargon of macroeconometric model simulation - KLEIN(1983), Chapter 2 - this is the reference simulation computing thevalues of the endogenous variables of the model as a function of the observed historical values of the exogenous and news variables.49 This is the maximum historical repetition of the shocked unscheduled event used in simulation.

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As the sample used for estimation is quite large (914 data points), the comparison between shockedsimulation values and baseline values can yield somewhat different results according to the initialpoint of the shock. Even though our results were not dramatically affected by this occurrence, weshocked the baseline in four different days and observed the behavior over sixty data points, a littleless than three months.

The results are presented from Figure 3 .3 to Figure 3.9 and are summarized in Table 3.5. Each ofthese charts has four lines corresponding to the four samples used in the exercise. The numbers onthe vertical axis of them represent the difference between shocked and baseline values expressed inpfennings, for the DM-$, and in cents, for the Yen-$.

A first eye-bird impression of the simulation results suggests that generally the news do notsingularly cause any major change in the overall dynamics of the exchange rates but may, in fact,cause some sensible trend changes when they occur in clusters. This is clear by just simplymultiplying the results on the vertical axis of the charts by multiples of one and from a three-dayshock simulations which we will describe later in 3.4.4.

3.4.3) Within-the-sample simulationsThe tracking performance of the model seems to be sufficiently accurate in relation to the basicgoals of this paper, namely the static and dynamic analysis of the relationship between news andexchange rates.

We computed some simple mean average errors for simulations up to ten periods in the future,reported in Table 3.6, and drew two sets of charts of static (i.e. one-step ahead) and dynamicsimulations in Figg. 3.10 and 3.11 . We have limited the chart representation of these simulations tothe second sample used in the impulse response analysis as the others are not substantially different:the summary performance of all four simulations is in Tab. 3.6 anyway.

The static simulations – Fig. 3.10 and first step in Tab. 3.6 – are quite good , apart from one-periodmiss around some turning points, as typical with high-frequency models with lagged variables. Thedynamic simulation – Tab. 3.6 and Fig. 3.11 - are still quite good when the forecasting horizon doesnot go beyond one week, as natural in a daily model. For longer time horizons, the performance isreasonable – quite surprisingly – even though the model tends to systematically over-or-under-predict the actual values of the exchange rates 50. This is due to the high autocorrelation of financialvariables and probably to the fixed-coefficient constraints of the dynamic structure of the model 51 .The above simulation performance of the global model can, at any rate, allow its use for ourpurposes: to build a static and dynamic metrics of the news.

3.4.4) Impulse response resultsThe types of impulse response simulations using the global model are described in Tab. 3.5; theirsummary results are also presented in the same table.

50 No error correction specifications have been tried at this first stage of the modelling work as our interest has focused mainly onvolatility and impact of news on mean and volatility. In future work this approach will be followed, possibly with a time-varyingerror correction scheme.51Let’s make two points: (1) the autocorrelation of the forecast error can be actually useful to significantly enhance the trackingperformance of the model by constant-adjusting the intercepts, at the cost of course of missing some turning points; (2) adding thelagged forecast error to the intercept of the equation improves the sample forecasting performance quite a lot and the same could beachieved by adding to the same term an appropriate transformation of the diagonal elements of the covariance matrix in (3.4). Butthe purpose of this paper is just assessing the impact of news.

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We notice, first, that the DM-$ and Yen-$ variations stemming from all the news-shocks of thetable are either slightly smaller or slightly higher than the average daily variations of the of our twoexchange rates, which include of course the effect of the shocks. So the average historical dailyvariation – net of the effect of news – seems to be quite small. This circumstance indicates,therefore, that the effect of news is quite large in the daily variations of exchange rates, actuallygoverning their short term behavior, when it occur.

The time-shape of the response of DM-$ and Yen-$ to the shocks is drawn from Fig. 3.3 to Fig. 3.9.The right-hand side charts contain their effects on the mean values of the exchange rates, the left-hand charts show the effects on volatility. We will make just some general comments on theseexercises.

The response of volatility to the shocks – operating through the lagged error term of the GARCHscheme and through news variables included in the volatility equations, (3.4) – is different in theEuropean and American time zones. The ATZ volatilities (Figg. 3.5 to 3.8) have a smoother returnto baseline values then the ETZ’s and show a more homogeneous behavior. The large swings of thefirst sample in ETZ can be attributed to the very high volatility of the Dollar in that period,approximately corresponding to March, April and May 1995.

As to the size of volatility response, we observe that the maximum positive difference betweenshocked and baseline values in ETZ has roughly the same magnitude as the highest peak inestimated GARCH volatility, for the DM-$, Fig. 3.3, and about half, by making the samecomparison, for the Yen-$, Fig. 3.4. In ATZ, these positive volatility differences are much smallerthan the GARCH-estimated ones: never above ten percent, Figg. 3.5 – 3.8. Besides, theconvergence of these values to zero is much faster in ATZ then in ETZ, between ten and twenty-five days in the former time zone.

The observed differences of the mean responses to scheduled and unscheduled news in ATZ is aquite interesting result of these impulse response exercises. The response to unscheduled news isgenerally flat – or slightly increasing - overtime, after an initial positive peak for a couple of days,Figg. 3.7 – 3.8; the response to scheduled news increases gradually as time elapses, Figg. 3.5 – 3.6.That happens because some scheduled news-variables appear in all the equations of the ATZ model.When we impose a positive shock to US payrolls (meaning that the announced number of news jobscreated in the month is higher than its expected value), the appreciation of the Dollar increasesovertime because US interest rates keep on increasing 52 .

The simulation experiments described so far consist of a one-day shock, as happening morefrequently in practice. It happens sometimes that we a sequence of one-day shocks. The maximumobserved length in ATZ of this phenomenon - in our sample data – is three days. We made thisimpulse on the variable “US DM-$ Market Events” and observed a sharp impact responseremaining at that level for the rest of the sixty days period, Fig. 3.9, upper panels.

It happened in a few cases, during our sample period, that the announced number of new jobs in theUS economy was much larger that the expected one causing important episodes of stock marketturbulence and sharp swings of the Dollar 53 . We then performed a one-day impulse simulation of

52 It must be noticed that this variable does not appear in the Dow Jones equation. If it were there the response of the Dollar could bedifferent, as observed in most recent times, after 1997.53 We want to stress again that in our sample period, 1995-1997, Dollar fluctuations, caused by stock market turbulence, occurred injust a limited number of cases. The most recent experience of 1998-1999 is quite different.

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the effects of a twice-larger shock on the number of new jobs54, accompanied by a Dollar-negativeshock to the variable representing Dollar-moving episodes of stock market turbulence. The result inFig. 3.9, lower panels, shows a weakening Dollar with respect to DM, but by a very small amount,getting smaller overtime, and a similar behavior of the Yen-$ which starts strengthening – byminimal amounts - after about a month. This is due to higher US interest rates, caused by the tighterlabor market. Interest rates and stock market prices have an offsetting impact on the Dollar.

3.5) News metricsWe are now in a position to draw some conclusions on the impact of news. The various pieces ofevidence are scattered amongst the tables and charts of the paper. The average impact over thesample period is represented by the standardized coefficients of our multivariate GARCHequations, Tabb. 3.1 – 3.4. The one-period, or static, impact of some news is the initial value of theimpulse response simulations shown in Figg. 3.3 through 3.8 and on Tab. 3.5. The multi-perioddynamic impact of the same news in shown in the same charts.

Table 3.7 contains the ranking of the average impact of news as represented by one set ofmultivariate GARCH estimates, those produced by the reduced ETZ and ATZ models with news invariance55. For ease of comparison, we report in the same Table 3.7 the percentage values of theimpulse response simulations described before. We thus observe that in ETZ the highest averageimpact is exerted by the Germany-US interest rate differential (-0.6434), followed by the “DM-$Market News” (0.4129), the “Statements on DM-$” (0.2350), etc.. The “DM-$ Market News” hasan impulse response impact of 0.43% and a longer term impact of 0.41%.

From Tab. 3.7 we generally conclude that the average impact is stronger for the unscheduled newsvis à vis the scheduled ones. The same conclusion is reached by looking at the shocked simulationsresults in the right-hand side of the table. When we move to the dynamics of these results, the aboveconclusions remain valid but we observe from Figg. 3.5 and 3.6 that the impact of unschedulednews gets bigger, in ATZ, as time goes on because the shocked US payrolls news enters exchangerates and interest rates as well.

4) Summary and ConclusionsTo the best of our knowledge – this paper presents the first direct measures of the static anddynamic impact of various categories of news on exchange rates and interest rates 56. The largeliterature recalled in the first section of the paper just assesses the influence of news on asset-pricedynamics. These conclusions are reached using almost exclusively ordinary least squares,frequently on monthly observations sometimes spanning very long periods, during which it is quitelikely that some regime shifts have occurred. Besides, most of these papers take into considerationonly US macroeconomic scheduled news.

This paper innovates in several respects. First of all we use a multivariate approach which takes intoconsideration the joint determination of asset-price values and the pervasive influence of scheduledevents on a wide range of assets. These events are important determinants not only of exchange 54 With respect to the mean forecasting error on this variable, used in the other one-day shock simulations.55 They are contained in Tabb. 3.2 and 3.4, first columns.56 FORNARI-MONTICELLI-PERICOLI-TIVEGNA(1999) measure the volatility impact of a one-day shock on the news-variablesappearing in the GARCH term of their equations.

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rates but also of long term bond yields and share prices. Since market experience and financialliterature indicate that these latter variables influence exchange rate dynamics, the multivariateestimation approach becomes a necessary choice. The above asset-prices are simultaneous withrespect to the dollar exchange rates and all these variables share news as a common set ofexplanatory variables. All suggests that single equation estimation of the relationship betweenexchange rates and news is affected by simultaneity bias; we detected some in Tabb. 3.1 and 3.3.Besides, we used a GARCH scheme to model the likely heteroskedastic behavior of the error termsof these financial variables, described in part 3.2.

A second innovative element is the use of daily data over a time span of three years - likely to behomogeneous in term of trading practices and institutional set up – and the separation of the globaltrading day into two time zones. We split it into an Asian-European zone and an American Zone.We made some efforts to line-up events in the right time zones, also with the help of the timestamp of the Reuters historical archive, called “Business Briefing”. This strategy allowed to makea much more focused investigation – within the daily frequency - of the relationships betweenevents and exchange rate fluctuations.

The results indicate that, generally speaking, the quality of our estimates is better in American time.This is probably due to the much larger and much more orderly availability of information on theeconomy and on the markets and maybe to a closer adherence to news trading in the US, at least inour time period, 1995 - 1997. Our equation estimates in Tabb. 3.1 – 3.4 and the behavior of bothestimated volatility, Fig. 3.1, and simulated volatility, Figg. 3.3 – 3.8 seem to indicate a substantialnon–homogeneity of foreign exchange dynamics in the two time zones.

A third new feature of this paper is the construction of an integrated econometric model of DM-$,Yen-$ and of the asset price, jointly determined with them, in the two time zones. This instrumentallows to make inferences on the static (one-day) and dynamic (thirty days) impact of the differenttypologies of news proposed in the paper. This result is attained by impulse response simulations ofsome classes of scheduled and unscheduled events, in part 3.4.

This exercise has been carried out for and is confined to the overtime evaluation of news impact onexchange rates. But our econometric model approach could also be used, for instance, to figure outthe amount of “verbal support” – “talking one currency up or down” - needed to achieve a desireddevaluation or revaluation 57. If we had the Dollar amount of daily foreign exchange interventionsby central banks among our regressors, we could compute the required amount and overtimepersistence of official Dollar sales and purchases to achieve a certain depreciation or appreciationtarget. Needless to say a correct sample choice for estimation and a wise and experienced use ofthe “lining-up” procedures for the model 58 would be necessary.

So, outspoken policy-makers could have some measure of the medium term – say between twoweeks and one month – effects of their “naive” or “target-focused” loquacity.

A final novelty is represented by the use of unscheduled news together with the more traditionalscheduled news used in the literature.

57 In our sample period this procedure has been frequently used the US Secretary of the Treasury, Rubin, by one top official of theJapanese Ministry of Finance, Mr “Yen” E. Sakakibara and sometimes by Hans Tietmeyer, Governor of the Bundesbank untilrecently, and by many Bundesbankers”. See TIVEGNA-CHIOFI(2000), Chapter 6.58 This is a widely used forecasting practice by macroeconometric model builders and managers. See, for instance, KLEIN-YOUNG(1980). More recently, this methodology has been positively surveyed and enriched by CLEMENTS-HENDRY(1998),especially Chapter 8.

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We proposed in section 2 of the paper a taxonomy of news drawn from a sample of the massiveflow of information that hits traders during the global trading day, from early Tokio to mid-afternoon New York.

A lot of events under A) in section 2.1 - market information and opinion - belong to what FischerBlack (1993) called, in his famed Presidential Address to the American Finance Association,"noise". But it seems to matter a lot: the empirical results of this paper suggest that German,Japanese and American events - what we called DM-$ and Yen-$ market news - have the highestimpact on DM and Yen movements.

One important market mover seems to be the huge flow of statements by policy makers, observed inour sample period. Our analysis tends to suggest - but just suggest, as we did not use officialintervention data - that "talking one currency up or down" can be an effective and cheap way ofintervening in the foreign exchange market. The coefficient size of our political and market newsis much larger than those on the most significant US macroeconomic releases.

A word of caution seems to be appropriate here. Even though the selection of unscheduled newsand the allocation of them to the right time zone underwent a careful and painstaking process (seefootnotes 13 and 18), some subjective options remained over the whole procedure as based on a“post hoc ergo propter hoc” approach. Some very simple sensitivity analysis was carried out byeliminating some episodes in the unscheduled news vectors: no significant change of coefficientsand definitely no change of the ranking of news in Tab. 3.7 occurred. Some more sophisticatedstatistical analysis (principal components techniques, also applied to factor analysis) is scheduledfor the near future.

Our results indicate that unscheduled news exert a higher impact on exchange rates than schedulednews in American time. As time goes on, the impact of the latter increases because many of themhave an influence also over US interest rates and the Dow Jones and therefore their higher levelsremain locked in the system causing a longer term appreciation or depreciation of the Dollar.

The impact of German macroeconomic news on financial variables is nil, with the exception of M3announcements, given its importance in influencing interest rate setting by the Bundesbank. Thiscould be due to the relative unpredictability of German releases, scheduled by the week. A similarconclusion is reached by ALMEIDA-GOODHART-PAYNE (1998).

Generally speaking, news exerts a considerable impact on exchange rates dynamics. Even thoughthe magnitudes involved in the impulse response exercises are small, it must be stressed that theyrepresent the response to just one-day impulse. Besides that, Tab. 3.5 shows that both the maximumand minimum values of these responses are frequently larger than the average one-day variation ofthese exchange rates. Therefore, since the impulse response should be added to their normalvariation and the amount of the latter contains the effects of macroeconomic and market events, a“quick and dirty rule” could suggest that news, when they occur, more than double the normal dailyvariation of exchange rates.

As to volatility, the effects of impulse response shocks appear to be higher and more persistent inEuropean time than in American time. The peak differences of these volatility responses are quitehigh in Europe, when compared to the estimated values of volatility; in the US is considerablysmaller and returns to equilibrium much earlier than in Europe.

Finally, we believe that our econometric model approach can also have a couple of helpful tradinguses: i) to test the invariance of market response to news and to build a boundary range for market

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reactions to scheduled and unscheduled (but expected to happen) news, as a tool for an earlyassessment of regime shifts; ii) to estimate ex-ante some impulse response functions for a range ofpossible outcomes of scheduled news, and to build a series of post-event profiles of the exchangerate for some reasonable and somewhat predictable unscheduled news.

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Tivegna M., "Analisi infragiornaliera del cambio Lira-Marco in periodi di particolareturbolenza: Gennaio 1995-Gennaio 1996", (Infra-daily analysis of the Lira-Mark exchange rate inparticularly turbulent periods between January 1995 and February 1996), University of Rome "TorVergata", mimeo (1996a).

Tivegna M., G. Chiofi, "News e dinamica dei tassi di cambio. NEWSMETRICS: una banca dati perlo studio delle relazioni tra eventi economico-politici, clima dei mercati finanziari e tassi dicambio", (News and exchange rate dynamics. NEWSMETRICS: a data bank for the analysis of therelationship between economic and political events, market climate and exchange rates", to bepublished by Il Mulino, Bologna (2000).

Webb S., "Hyperinflation and stabilization in Weimar Germany", Oxford University Press, NewYork (1989).

33

TABLE 3.1 – EUROPEAN TIME ZONE. GERMAN LONG TERM INTEREST RATES, DM-$, YEN-$SINGLE-EQUATION AND MULTIVARIATE GARCH(1,1) ESTIMATES (*)

GERMAN LONG TERMINTEREST RATE EQUATION

Single EquationEstimates

Multivariate GARCHEstimates

Reduced Multivar.GARCH Estimates

Intercept -0.0761 -2.68 -0.0727 -2.77 -0.0757 -2.732 Dep.Variable Lagged 1 0.6612 15.38 0.6653 23.1 0.6634 20.263 Dep.Variable Lagged 2 -0.1133 -2.57 -0.1003 -2.75 -0.0920 -2.804 Dep.Variable Lagged 3 -0.1421 -3.12 -0.1800 -4.62 -0.1942 -6.145 Dep.Variable Lagged 4 0.0717 1.89 0.1000 3.45 0.1116 3.786 German M3 0.0732 1.49 0.0758 3.91 0.0757 4.037 German CPI 0.0095 0.24 -0.0164 -0.588 German Unemployment -0.0052 -0.12 -0.0084 -0.519 German IFO 0.0186 0.53 0.0150 0.56

10 German Official Rates -0.0261 -1.13 -0.0154 -0.5111 German Economic News -0.0306 -0.91 -0.0224 -0.8012 Statements on DM-$ -0.0237 -0.89 -0.0234 -0.8313 DM-$ Market News -0.0273 -1.16 -0.0176 -0.6414 EMU News 0.0409 1.57 0.0405 1.47

DM - $ EQUATION

1 Intercept 0.0179 0.76 0.0416 2.11 0.0403 2.142 Long-Term Int.Differ.GE-US -1.3022 -3.32 -0.5047 -1.62 -0.4912 -1.193 US Zone Yen-$,Previous Day -0.0469 -3.16 -0.0531 -1.13 -0.0545 -1.284 US Zone DM-$,Previous Day -0.0683 -2.33 -0.0583 -2.45 -0.0606 -2.495 German CPI 0.0114 0.54 0.0168 0.786 German Unemployment -0.0211 -0.69 -0.0053 -0.457 German M3 -0.0515 -0.82 -0.0423 -3.92 -0.0387 -3.758 German IFO -0.0227 -1.00 -0.0204 -0.909 German Official Rates 0.0615 4.02 0.0428 1.98 0.0422 2.76

10 G3 Central Banks Intervent. 0.0024 0.15 0.0583 0.96 0.0664 2.0011 German Economic News 0.129 4.68 0.1176 6.20 0.1149 5.6812 Statements on DM-$ 0.2105 12.74 0.2137 12.73 0.211 9.2113 DM-$ Market News 0.497 24.23 0.4245 19.56 0.4244 18.8914 EMU News 0.2257 9.97 0.191 9.91 0.1928 9.36

YEN - $ EQUATION

1 Intercept 0.026 1.13 0.0133 0.56 0.0115 0.51

34

2 US Zone DM-$,Previous Day 0.0676 2.31 0.0137 0.46 0.0151 0.523 US Zone Yen-$, Previous Day -0.1743 -6.71 -0.0905 -1.56 -0.0921 -1.654 BoJ Discount Rate -0.1216 -0.12 0.0041 0.325 G3 Central Banks Intervent. 0.3524 3.15 -0.0083 -0.206 Statements on Yen-$ 0.2409 10.99 0.2414 10.27 0.2405 10.787 Yen-related Market News 0.5172 34.21 0.4228 25.33 0.4240 22.858 $-related Market News 0.289 12.25 0.2591 9.99 0.2588 10.61

TABLE 3.1 (CONTINUE)

MULTIVARIATE GARCH(1,1) VOLATILITIES AND CORRELATIONS

German Long Term Int.RateIntercept 0.0260 8.50 0.0455 8.11 0.0449 7.10e(t-1) 0.0467 7.91 0.0558 8.68 0.0561 8.31h(t-1) 0.8988 90.85 0.8851 69.57 0.8861 66.3

DM-$ Exchange RateIntercept 0.0238 13.44 0.0382 5.36 0.0377 5.74e(t-1) 0.0961 13.09 0.1328 4.42 0.1275 4.96h(t-1) 0.8574 104.19 0.8317 31.93 0.8359 36.41

Yen-$ Exchange RateIntercept 0.0322 14.79 0.0358 5.89 0.0364 5.66e(t-1) 0.2020 20.74 0.0851 3.91 0.0863 4.04h(t-1) 0.7802 91.69 0.8775 48.17 0.8757 46.39

Correlation LT Int.Rate DM-$ -0.0931 -2.66 -0.0987 -3.53Correlation DM-$ Yen-$ 0.4897 11.78 0.4906 13.20Correlation LT Int.Rate Yen-$ -0.0717 -2.05 -0.0783 -2.34

Iterations 56 Iterations 46 ML Val. -302.7 ML Val. -305.7

(*) The first, third and fifth columns contain parameter values. The second, fourth and sixth contain “t” values.(**) The intercept, lagged error – e(t-1) – and lagged volatility – h(t-1) - make reference to the standard GARCH model(BOLLERLSEV-ENGLE-NELSON, 1994).

35

TABLE 3.2 – EUROPEAN TIME ZONE. GERMAN LONG TERM INTEREST RATE S, DM-$, YEN-$MULTIVARIATE GARCH(1,1) ESTIMATES. REDUCED MODEL. NEWS IN VARIANCE (FIRSTTWO COLUMNS) NEWS IN VARIANCE PLUS WEEKDAY EFECT( SECOND TWO COLUMNS) (*)

GERMAN LONG TERM INTERESTRATE EQUATION

1 Intercept -0.0762 -2.88 -0.0795 -2.962 Dep.Variable Lagged 1 0.6636 19.75 0.6541 20.163 Dep.Variable Lagged 2 -0.0922 -2.31 -0.0914 -2.304 Dep.Variable Lagged 3 -0.1932 -4.76 -0.195 -4.785 Dep.Variable Lagged 4 0.1109 3.33 0.1223 3.296 German M3 0.0755 3.86 0.0801 4.227 German CPI8 German Unemployment9 German IFO

10 German Official Rates11 German Econ. News12 Statements on DM-$13 DM-$ Market News14 EMU News

DM - $ EQUATION

1 Intercept 0.0436 2.40 0.0429 2.312 Long-Term Int.Differ.GE-US -0.6434 -1.70 -0.4890 -1.353 US Zone Yen-$,Previous Day -0.074 -2.73 -0.0687 -2.934 US Zone DM-$,Previous Day -0.053 -2.23 -0.0516 -2.145 German CPI6 German Unemployment7 German M3 -0.0392 -3.77 -0.0415 -3.848 German IFO9 German Official Rates 0.0482 2.07 0.0464 1.62

10 G3 Central Banks Intervent. 0.0863 3.02 0.0797 2.4411 German Econ. News 0.1128 6.01 0.1156 6.0312 Statements on DM-$ 0.235 13.37 0.2425 13.7513 DM-$ Market News 0.4129 18.94 0.4113 19.2814 EMU News 0.1915 9.11 0.1870 8.92

YEN - $ EQUATION

1 Intercept 0.0165 0.78 0.0152 0.762 US Zone DM-$,Previous Day 0.0196 0.76 0.0216 0.983 US Zone Yen-$,Previous Day -0.1006 -2.04 -0.0992 -3.694 BoJ Discount Rate5 G3 Central Banks Intervent.6 Statements on Yen-$ 0.2436 10.11 0.2423 9.877 Yen-related Market News 0.4017 17.19 0.4002 15.988 $-related Market News 0.2642 10.1 0.2668 9.95

36

TABLE 3.2 (CONTINUE)

MULTIVARIATE GARCH(1,1) VOLATILITIES AND CORRELATIONS (**)

German Long Term Int.RateIntercept 0.0451 13.52 0.0001 0.00e(t-1) 0.0562 7.44 0.0643 15.01h(t-1) 0.8858 197.66 0.8484 159.42Skeduled news 0.0001 0.00 0.0001 0.00Monday 0.0403 7.34Tuesday 0.0001 0.00Thursday 0.0001 0.00Friday 0.0777 17.31

DM-$ Exchange RateIntercept 0.0100 1.63 0.0001 0.00e(t-1) 0.1231 5.14 0.1176 7.32h(t-1) 0.8151 28.44 0.8264 55.88Skeduled news 0.0150 0.85 0.0001 0.00Unskeduled news 0.0623 6.67 0.0321 7.22G3 Interv.Ger.Off.Rates 0.2780 3.82 0.2618 5.39Monday 0.0363 8.97Tuesday 0.0001 0.00Thursday 0.0001 0.00Friday 0.0001 0.00

Yen-$ Exchange RateIntercept 0.0080 2.18 0.0001 0.00e(t-1) 0.0809 4.90 0.0885 4.55h(t-1) 0.8710 44.55 0.8530 28.06Unskeduled news 0.0868 5.70 0.0917 3.92Monday 0.0170 2.18Tuesday 0.0001 0.00Thursday 0.0001 0.00Friday 0.0069 1.11

Corelations LT Int.Rate DM-$ -0.0974 -3.17 -0.1031 -3.35Correlations DM-$ Yen-$ 0.5014 20.2 0.5078 24.2Correlations LT Int.Rate Yen-$ -0.0913 -2.87 -0.0826 -2.66

Iterations 47 Iterations 75 ML Val. -266.0 ML Val. -251.5

(*) The first and third columns contain parameter values. The second and fourth contain “t” values.(**) The intercept, lagged error – e(t-1) – and lagged volatility – h(t-1) - make reference to the standard GARCHmodel (BOLLERSLEV-ENGLE-NELSON,1994).

37

TABLE 3.3 – AMERICAN TIME ZONE. US LONG TERM INTEREST RATES, DM-$, YEN-$, DOW JONESSINGLE-EQUATION AND MULTIVARIATE GARCH(1,1) ESTIMATES (*)

US LONG TERM INTER.RATE EQUATION

Single EquationEstimates

Multivariate GARCHEstimates

Reduced Multivar.GARCH Estimates

1 Intercept -0.0364 -1.68 -0.0417 -1.85 -0.0407 -1.802 Dep.Variable Lagged 1 0.7376 31.83 0.7509 31.79 0.7542 33.643 Dep.Variable Lagged 2 -0.1814 -5.04 -0.1687 -8.49 -0.1712 -9.124 Dep.Variable Lagged 3 -0.1540 -4.69 -0.1786 -9.56 -0.1767 -9.325 Dep.Variable Lagged 4 0.0704 2.95 0.0517 2.45 0.0505 2.226 US Payroll 0.2558 1.89 0.1693 5.25 0.1621 6.737 US Unemployment Rate -0.0550 -0.71 -0.0590 -2.18 -0.0603 -3.118 US Wage Rate 0.0375 0.39 0.0463 2.24 0.0564 3.209 US Durable Goods Ord. 0.0953 4.14 0.0719 3.43 0.0693 3.21

10 US NAPM 0.1175 5.68 0.1335 5.77 0.1259 5.4211 US Industrial Prod. 0.0502 2.48 0.0398 2.46 0.0328 1.9912 US Leading Indicators 0.0579 1.78 0.0490 2.69 0.0407 1.6013 US CPI 0.0827 4.67 0.0764 4.70 0.0811 5.2614 US PPI 0.0052 0.21 0.0503 2.28 0.0519 2.1415 US Retail Sales 0.0670 3.64 0.0919 4.65 0.0926 4.3216 US Trade Balance -0.0179 -0.69 -0.0035 -0.1417 US Official Rates 0.0072 0.29 0.0255 0.9718 Statements by Greenspan -0.0137 -0.36 -0.0017 -0.12

DM - $ EQUATION

1 Intercept -0.0343 -1.34 -0.0537 -2.63 -0.0606 -2.422 Dow Jones Index 0.0686 3.51 0.0636 1.33 0.0464 0.893 Long-Term Int.Differ.US-GE 0.0657 1.35 0.0984 2.60 0.1027 2.444 EU Zome DM-$ -0.1076 -4.19 -0.0913 -4.12 -0.0917 -4.165 US Payroll 0.0398 1.27 0.0798 4.55 0.0771 4.026 US Unemployment Rate 0.0693 1.21 0.0230 1.137 US Wage Rate 0.0846 1.54 0.0815 5.07 0.0843 3.968 US Durable Goods Ord. -0.0706 -2.55 -0.0382 -2.08 -0.0264 -1.299 US NAPM -0.0254 -1.37 0.0117 0.56

10 US Industrial Prod. -0.0094 -0.16 0.0005 0.0211 US Leading Indicators 0.0285 1.17 0.0044 0.2312 US CPI 0.0580 2.66 0.0311 1.6813 US PPI -0.0398 -0.86 -0.0236 -1.1514 US Retail Sales -0.0193 -0.51 0.0241 0.8215 US Trade Balance -0.0013 -0.06 0.0135 0.6916 German Official Rates -0.0669 -0.69 -0.0060 -0.2517 US Official Rates 0.0437 2.75 0.0232 1.24 0.0237 3.3318 G3 Central Banks Intervent. 0.0333 0.70 0.0126 0.8119 Statements on DM-$ 0.2009 9.05 0.1930 9.84 0.1924 9.7120 Statements by Greenspan 0.1702 6.56 0.1752 8.24 0.1778 9.0521 DM-$ Market News 0.5066 24.01 0.4081 21.77 0.4059 21.1322 US Stock Exchange News 0.2732 14.05 0.2842 15.95 0.2877 13.84

38

TABLE 3.3 (CONTINUE)

YEN - $ EQUATION

1 Intercept -0.2610 -1.30 -0.3642 -1.84 -0.3461 -1.752 US Stock Exchange News 0.1337 4.87 0.1528 7.73 0.1522 6.443 Long-Term Int.Differ.US-JA 0.0382 1.71 0.0517 2.26 0.0496 2.194 EU Zone Yen-$ -0.0638 -1.35 -0.0992 -4.24 -0.1014 -4.445 US Payroll 0.0571 1.45 0.0705 3.20 0.0671 3.256 US Unemployment Rate 0.0080 0.21 0.0212 0.827 US Wage Rate 0.1077 2.68 0.0964 4.31 0.0985 3.858 US CPI -0.0265 -1.07 -0.0095 -0.459 US Durable Goods Ord. 0.0862 2.67 -0.0258 -1.13

10 US NAPM 0.0957 1.70 0.0289 1.2911 US Industrial Prod. 0.0244 0.61 0.0247 1.4612 US Leading Indicators -0.0012 -0.04 -0.0286 -1.2413 US PPI 0.0529 1.70 0.0237 0.9514 US Retail Sales -0.1300 -1.78 0.0122 0.3915 US Trade Balance 0.0005 0.02 -0.0028 -0.1316 BoJ Discount Rate 0.0022 0.08 -0.0037 -0.2217 US Official Rates 0.0257 1.92 0.0030 0.1118 G3 Central Banks Intervent. 0.0202 0.36 0.0161 0.9319 Statements on Yen 0.1326 3.41 0.1038 3.97 0.1023 3.9620 Statements on Dollar 0.1417 7.23 0.1159 4.32 0.1220 4.3821 Statements by Greenspan 0.0901 2.22 0.1017 4.17 0.1008 3.0622 Yen-related Market News 0.3202 10.93 0.3181 11.33 0.3161 12.4523 $-related Market News 0.3185 13.74 0.2707 10.75 0.2737 10.90

DOW JONES INDEX EQUATION

1 Intercept 0.0770 2.77 0.1009 4.01 0.1012 3.952 US Long Term Int.Rate -0.2579 -6.64 -0.0944 -2.28 -0.1054 -2.893 US LT Int.Rate Lagged 1 -0.0783 -2.13 -0.0176 -0.594 US Payroll 0.0813 1.98 -0.0162 -0.405 US Unemployment Rate -0.0171 -0.28 -0.0108 -0.356 US Wage Rate 0.1399 2.65 0.0351 1.527 US Durable Goods Ord. -0.0424 -1.66 -0.0534 -2.07 -0.0504 -1.918 US NAPM 0.0150 0.55 -0.0225 -0.839 US Industrial Prod. -0.0129 -0.36 -0.0285 -1.25

10 US Leading Indicators -0.0400 -1.32 -0.0261 -1.2711 US CPI 0.0488 1.58 0.0163 0.8312 US PPI 0.0012 0.03 -0.0012 -0.0313 US Retail Sales -0.0150 -0.66 -0.0626 -3.59 -0.0629 -2.5714 US Trade Balance 0.0180 0.75 0.0194 0.9215 Statements by Greenspan 0.0133 0.55 0.0115 0.6516 Extreme Movements of DJ -5.5596 -8.15 -5.0935 -20.85 -5.1027 -7.31

39

TABLE 3.3 (CONTINUE)

MULTIVARIATE GARCH(1,1) VOLATILITIES AND CORRELATIONS (**)

US Long Term Int.RateIntercept 0.0127 9.34 0.0241 7.81 0.0236 8.20e(t-1) 0.0233 3.36 0.0296 5.47 0.0291 5.86h(t-1) 0.9405 134.06 0.9289 138.87 0.9303 146.28

DM-$ Exchange RateIntercept 0.0271 4.86 0.0542 7.30 0.0593 8.78e(t-1) 0.2475 7.23 0.1360 8.14 0.1326 9.67h(t-1) 0.7131 23.25 0.7909 37.19 0.7832 44.85

Yen-$ Exchange RateIntercept 0.2166 10.13 0.1564 1.67 0.1501 1.90e(t-1) 0.5904 3.76 0.1200 3.07 0.1115 3.42h(t-1) 0.0895 2.02 0.6600 3.65 0.6772 4.46

Dow Jones IndexIntercept 0.0217 3.84 0.0309 6.50 0.0315 6.87e(t-1) 0.1033 6.42 0.1120 7.54 0.1086 7.67h(t-1) 0.8637 37.19 0.8773 58.24 0.8799 64.09

Correlation LT Int.Rate DM-$ -0.0206 -0.56 -0.0317 -0.81Correlation DM-$ Yen-$ 0.5176 16.51 0.5182 16.95Correlation LT Int.Rate Yen-$ -0.0592 -2.33 -0.0601 -2.31Correlation DJ LT Int.Rate -0.3866 -10.30 -0.3863 -11.34Correlation Dow Jones DM-$ 0.0289 0.40 0.0536 0.70Correlation Dow Jones Yen-$ 0.0264 0.99 0.0263 0.87

Iterations70 Iter. 66 ML Val. -417.4 ML

Val.–429.9

(*) The first, third and fifth columns contain parameter values. The second, fourth and sixth contain “t” values.(**) The intercept, lagged error – e(t-1) – and lagged volatility – h(t-1) - make reference to the standard GARCH model(BOLERLEV-ENGLE-NELSON,1994).

40

TABLE 3.4 – AMERICAN TIME ZONE. US LONG TERM INTEREST RATE, DM-$, YEN-$, DOWJONES MULTIVARIATE GARCH(1,1) ESTIMATES. REDUCED MODEL. NEWS IN VARIANCE(FIRST TWO COLUMNS). NEWS IN VARIANCE PLUS WEEKDAY EFFECT (SECOND TWOCOLUMNS) (*)

US LONG TERM INTEREST RATE EQUATION

1 Intercept -0.0403 -1.73 -0.0375 -1.582 Dep.Variable Lagged 1 0.7523 26.67 0.7514 32.193 Dep.Variable Lagged 2 -0.1655 -5.41 -0.1665 -8.934 Dep.Variable Lagged 3 -0.1782 -6.15 -0.1777 -9.785 Dep.Variable Lagged 4 0.0508 1.80 0.0539 2.186 US Payroll 0.1614 7.12 0.1620 8.517 US Unemployment Rate -0.0588 -2.90 -0.0604 -3.128 US Wage Rate 0.0560 2.79 0.0597 2.839 US Durable Goods Ord. 0.0702 3.14 0.0680 2.67

10 US NAPM 0.1264 5.33 0.1259 5.0711 US Industrial Prod. 0.0319 1.70 0.0320 1.6612 US Leading Indicators 0.0397 1.91 0.0410 1.4413 US CPI 0.0725 3.73 0.0573 2.1114 US PPI 0.0509 2.01 0.0507 1.9815 US Retail Sales 0.0883 3.32 0.0857 3.2316 US Trade Balance17 US Official Rates18 Statements by Greenspan

DM - $ EQUATION

1 Intercept -0.0617 -3.24 -0.0555 -2.532 Dow Jones Index 0.0497 1.08 0.0569 1.163 Long-Term Int.Diff.US-GE 0.1013 2.70 0.0927 2.364 EU Zone DM-$ -0.0925 -4.21 -0.0905 -4.045 US Payroll 0.0772 4.07 0.0760 3.786 US Unemployment Rate7 US Wage Rate 0.0852 3.79 0.0804 3.868 US Durable Goods Ord. -0.0244 -1.19 -0.0197 -1.029 US NAPM

10 US Industrial Prod.11 US Leading Indicators12 US CPI13 US PPI14 US Retail Sales15 US Trade Balance16 German Official Rates17 US Official Rates 0.0210 2.79 0.0198 2.5918 G3 Central Banks Intervent.19 Statements on DM-$ 0.1921 9.65 0.1913 10.0020 Statements by Greenspan 0.1774 8.93 0.1778 8.9221 DM-$ Market News 0.4033 20.69 0.4090 21.4922 US Stock Exchange News 0.2859 14.57 0.2869 13.90

41

TABLE 3.4 (CONTINUE)

YEN - $ EQUATION

1 Intercept -0.3607 -2.13 -0.3463 -2.892 US Stock Exchange News 0.1525 7.56 0.1529 6.293 Long-Term Int.Differ.US-JA 0.0511 2.68 0.0491 3.594 EU Zone Yen-$ -0.1012 -4.36 -0.1059 -4.775 US Payroll 0.0655 2.89 0.0627 2.326 US Unemployment Rate7 US Wage Rate 0.1000 3.38 0.0927 3.398 US Durable Goods Ord.9 US NAPM

10 US Industrial Prod.11 US Leading Indicators12 US CPI13 US PPI14 US Retail Sales15 US Trade Balance16 BoJ Discount Rate17 US Official Rates18 G3 Central Banks Intervent.19 Statements on Yen 0.1015 4.13 0.1066 4.1320 Statements on Dollar 0.1217 4.14 0.1320 6.2621 Statements by Greenspan 0.0988 6.65 0.0839 2.7122 Yen-related Market News 0.3118 11.97 0.3108 11.5523 $-related Market News 0.2743 11.48 0.2801 12.65

DOW JONES INDEX EQUATION

1 Intercept 0.1004 3.80 0.1082 4.052 US Long Term Int.Rate -0.1056 -2.63 -0.1201 -4.393 US LT Int.Rate Lagged 14 US Payroll5 US Unemployment Rate6 US Wage Rate7 US Durable Goods Ord. -0.0505 -1.92 -0.0486 -1.938 US NAPM9 US Industrial Prod.

10 US Leading Indicators11 US CPI12 US PPI13 US Retail Sales -0.0578 -1.90 -0.0498 -2.6414 US Trade Balance15 Statements by Greenspan16 Extreme Movements of DJ -5.1060 -6.24 -5.1843 -5.68

42

TABLE 3.4 (CONTINUE)MULTIVARIATE GARCH(1,1) VOLATILITIES, CORRELATIONS (**)

US Long Term Int.RateIntercept 0.0143 3.93 0.0001 0.00e(t-1) 0.0260 5.50 0.0240 3.62h(t-1) 0.9353 180.56 0.9380 99.56Skeduled news 0.0287 2.29 0.0001 0.07Monday -0.0001 0.00Tuesday 0.0010 0.20Thursday 0.0296 3.09Friday 0.0084 0.95

DM-$ Exchange RateIntercept 0.0574 8.56 0.0200 1.17e(t-1) 0.1323 10.62 0.1201 8.00h(t-1) 0.7820 42.42 0.8068 35.62Skeduled news 0.0000 0.00 0.0001 0.00Unskeduled news 0.0097 0.94 0.0148 1.16Monday 0.0001 0.00Tuesday 0.0086 0.82Thursday 0.0331 2.45Friday 0.0056 0.59

Yen-$ Exchange RateIntercept 0.1089 2.73 0.0768 1.69e(t-1) 0.1165 7.00 0.1456 5.68h(t-1) 0.7159 9.37 0.5617 6.96Skeduled news 0.1813 4.56 0.0200 0.31Unskeduled news 0.0463 2.67 0.0342 1.67Monday 0.0444 3.55Tuesday 0.0001 0.00Thursday 0.0754 5.34Friday 0.0963 5.28

Dow Jones IndexIntercept 0.0259 4.53 0.0001 0.01e(t-1) 0.1063 7.25 0.0907 5.62h(t-1) 0.8842 64.74 0.9102 59.55Skeduled news 0.0395 1.21 0.0725 2.13Monday 0.0001 0.00Tuesday 0.0001 0.00Thursday 0.0206 1.88Friday 0.0001 0.00

Correlations LT Int.Rate DM-$ -0.0355 -0.93 -0.0277 -0.72Correlations DM-$ Yen-$ 0.5292 23.39 0.4970 16.11Correlations LT Int.Rate Yen-$ -0.0701 -2.37 -0.0425 -1.22Correlations DJ LT Int.Rate -0.3859 -10.27 -0.3727 -13.34Correlations Dow Jones DM-$ 0.0510 0.75 0.0313 0.41Correlations Dow Jones Yen-$ 0.0286 0.97 0.0231 0.72

Iterations 67 Iterations 130 ML Val. -426.50 ML Val. -412.20

(*) The first and third columns contain parameter values. The second and fourth contain “t” values. (**) As Tab. 3.3.

43

TABLE 3.5 - IMPULSE RESPONSE VALUES AND HISTORICAL VALUES OF DOLLAR (A)

Var. w.r.t. Base History Average Percentages NewsSimulations Max Min Pos.Var. Ex. R. (1):(4) (2):(4) (3):(4) Coeffic.

(1) (2) (3) (4) (5) (6) (7) (8)EUROPEAN TIME ZONEGerman Policy Statement, DM-$German Market News, DM-$German Official Rates, DM-$Yen-related Market News, Yen-$Japanese Policy Statements, Yen-$

AMERICAN TIME ZONEUS Payrolls, DM-$US Hourly Wages, DM-$US Payrolls, Yen-$US Hourly Wages, Yen-$US Policy Statements, DM-$US Market News, DM-$Greenspan Statements, DM-$Yen-related Market News, Yen-$Japanese Policy Statements, Yen-$

Three-day Shocks from US News, DM-$Shocks from US Labor Markets, DM-$

0.00580.00770.0029

0.660.68

0.00320.0033

0.210.26

0.00570.00800.0099

0.520.34

0.02200.0109

0.00550.00730.0027

0.590.61

0.00210.0030

0.120.24

0.00520.00720.0087

0.440.29

0.00610.0093

0.00460.00460.0046

0.400.40

0.00370.0037

0.400.40

0.00370.00370.0037

0.400.40

0.00370.0037

1.76341.76341.7634121.45121.45

1.76191.7619121.64121.641.76191.76191.7619121.64121.64

1.76191.7619

0.320.430.160.540.56

0.180.180.170.210.320.450.560.420.27

1.240.61

0.310.410.150.480.50

0.120.170.090.190.290.400.490.360.24

0.340.52

0.260.260.260.320.32

0.210.210.330.330.210.210.210.330.33

0.210.21

0.23440.41260.04810.40190.2436

0.07720.08520.06490.09930.19920.40340.17740.31220.1018

0.4034-

(A) The table reports the maximum and minimum values of the four sample simulations indicated in the first column.(1) Maximum shocked simulation values in the fourth sample.(2) Minimum shocked simulation values in the fourth sample.(3) This column contains the fourth sample historical average positive errors of DM-$ and Yen-$ in their time zones.(4) This column reports the fourth sample average values of DM-$ and Yen-$ in their time zones.(8) Multivariate GARCH coefficients of the news variables reported in the first column. Tabb. 3.2 and 3.4.

44

TABLE 3.6 – MEAN AVERAGE ERROR OF STATIC AND DYNAMIC SIMULATIONS IN EUROPEAN ANDAMERICAN TIME ZONES

FIRST SAMPLE SECOND SAMPLE

European Time American Time European Time American Time

DM-$ YEN-$ DM-$ YEN-$ DM-$ YEN-$ DM-$ YEN-$

1 -0.0002 0.0274 -0.0030 -0.1263 0.0019 0.0695 0.0034 0.45762 0.0008 0.0458 0.0009 -0.0334 0.0064 0.9060 0.0076 0.46313 0.0034 -0.0751 0.0033 -0.0623 0.0064 0.1568 0.0086 -0.03674 0.0057 0.2007 0.0044 0.1683 0.0088 0.1449 0.0157 0.51775 0.0041 -0.1002 0.0120 0.2460 0.0170 0.4688 0.0173 0.28616 0.0106 -0.0080 0.0090 -0.1548 0.0143 0.4185 0.0179 0.83877 0.0086 -0.0874 0.0050 -0.0954 0.0167 1.1429 0.0216 1.28468 -0.0002 0.0167 -0.0182 -1.2379 0.0202 1.2802 0.0197 1.18089 -0.0150 -1.5104 -0.0368 -2.3351 0.0211 0.8207 0.0206 1.1709

10 -0.0594 -3.1172 -0.0554 -0.9802 0.0242 1.6598 0.0253 1.7861

THIRD SAMPLE FOURTH SAMPLE

European Time American Time European Time American Time

DM-$ YEN-$ DM-$ YEN-$ DM-$ YEN-$ DM-$ YEN-$

1 0.0014 -0.1665 -0.0036 -0.1490 -0.0084 -1.1846 -0.0098 -1.16732 -0.0023 0.2646 -0.0051 0.3382 -0.0057 -0.8143 -0.0082 -1.14323 -0.0031 0.7229 -0.0064 0.5520 -0.0043 -1.2777 -0.0054 -1.00484 -0.0122 -0.6081 -0.0121 -0.2774 -0.0031 -1.2690 0.0094 -0.53435 -0.0088 -0.1440 -0.0059 -0.4931 0.0147 -0.1980 0.0277 -0.29886 -0.0049 -0.9845 0.0002 -0.6436 0.0246 -0.1199 0.0225 -0.14497 -0.0047 -1.5438 -0.0073 -0.9570 0.0245 0.2186 0.0174 0.55188 -0.0075 -0.7866 -0.0088 -0.8452 0.0173 0.6137 0.0150 0.74659 -0.0098 -1.1189 -0.0102 -1.1441 0.0154 0.7625 0.0095 0.6624

10 -0.0145 -2.8210 -0.0094 -2.5881 0.0119 -1.0314 0.0128 -0.9702

45

TABLE 3.7 – RANKING OF NEWS BY MULTIVARIATE GARCH ESTIMATES ANDDYNAMIC SIMULATIONS VALUES IN EUROPEAN AND AMERICAN TIMEVariable Description Equation

CoefficientsImpulseResponsePercentage Values

(1) (2)EUROPEAN TIME ZONE

GERMAN LONG TERMINTEREST RATE EQUATION

German M3 0.0755

DM - $ EQUATION

Long-Term Int.Differ.GE-US -0.6434DM-$ Market News 0.4129 0.43 0.41Statements on DM-$ 0.2350 0.32 0.31EMU News 0.1915German Econ. News 0.1128G3 Central Banks Intervent. 0.0863German Official Rates 0.0482 0.16 0.15German M3 -0.0392

YEN - $ EQUATION

Yen-related Market News 0.4240 0.54 0.48$-related Market News 0.2588Statements on Yen-$ 0.2405 0.56 0.5

AMERICAN TIME ZONEUS LONG TERM INTERESTRATE EQUATION

US Payroll 0.1614US NAPM 0.1264US Retail Sales 0.0883US CPI 0.0725US Durable Goods Ord. 0.0702US Unemployment Rate -0.0588US Wage Rate 0.0560US PPI 0.0509US Leading Indicators 0.0397US Industrial Prod. 0.0319

DM - $ EQUATION

DM-$ Market News 0.4033 0.45 0.4US Stock Exchange News 0.2859Statements on DM-$ 0.1921 0.32 0.29Statements by Greenspan 0.1774 0.56 0.49Long-Term Int.Differ.US-GE 0.1013US Wage Rate 0.0852 0.18 0.17US Payroll 0.0772 0.18 0.12Dow Jones Index 0.0497US Official Rates 0.0210

46

TABLE 3.7 (CONT.)

YEN - $ EQUATION

Yen-related Market News 0.3118 0.42 0.36$-related Market News 0.2743 0.27 0.24US Stock Exchange News 0.1525Statements on $ 0.1217Statements on Yen 0.1015US Wage Rate 0.1000 0.21 0.19Statements by Greenspan 0.0988US Payroll 0.0655 0.17 0.09Long-Term Int.Diff.US-JA 0.0511

DOW JONES INDEX EQUATION

US Long Term Int.Rate -0.1056US Retail Sales -0.0578US Durable Goods Ord. -0.0505

(1)European time:Tab.3.2, reduced equation with news in volatility. American Time: Tab.3.4, same equation.(2) From Tab. 3.5.

47

FIGURES

FIGURE 3.1: VOLATILITIES AND CONDITIONAL COVARIANCES. EUROPEAN AND AMERICAN TIMEZONES (LAST PANNEL MISSING)

FIGURE 3.2: FLOW-CHART OF GLOBAL TRADING DAY MODEL (IN A SEPARATE FILE)

FIGURE 3.3: DM-DOLLAR MEAN AND VOLATILITY IMPULSE RESPONSE. EUROPEAN TIME ZONE.UNSCHEDULED NEWS

FIGURE 3.4: YEN-DOLLAR MEAN AND VOLATILITY IMPULSE RESPONSE. EUROPEAN TIME ZONE.UNSCHEDULED NEWS

FIGURE 3.5: DM-DOLLAR MEAN AND VOLATILITY IMPULSE RESPONSE. AMERICAN TIME ZONE.SCHEDULED NEWS

FIGURE 3.6: YEN-DOLLAR MEAN AND VOLATILITY IMPULSE RESPONSE. AMERICAN TIME ZONE.SCHEDULED NEWS

FIGURE 3.7: DM-DOLLAR MEAN AND VOLATILITY IMPULSE RESPONSE.AMERICAN TIME ZONE.UNSCHEDULED NEWS

FIGURE 3.8: YEN-DOLLAR MEAN AND VOLATILITY IMPULSE RESPONSE. AMERICAN TIME ZONE.UNSCHEDULED NEWS

FIGURE 3.9: DM-DOLLAR MEAN AND VOLATILITY IMPULSE RESPONSE. AMERICAN TIME ZONE.SPECIAL SHOCKS (A EMPY PAGE AFTER 3.9).

FIGURE 3.10: DM-$ AND YEN-$ ONE-PERIOD SIMULATIONS.

FIGURE 3.11: DM-$ AND YEN-$ SIXTY-PERIOD DYNAMIC SIMULATIONS.

48

FIGURE 3.1

Vo lat ility o f Ge rm a n 10 Y Bo nd - E .Zo ne

2 01 2 93 3 85 4 77 5 69 6 61 7 53 8 450.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00G E 1 0Y

Vo lat ility o f D M -$ - E .Zo ne

2 00 2 90 3 80 4 70 5 60 6 50 7 40 8 300 .0

0 .5

1 .0

1 .5

2 .0

2 .5

3 .0

3 .5

4 .0DM -$

V ola ti lity of Y en -$ - E .Zo ne

1 98 2 85 3 72 4 59 5 46 6 33 7 20 8 07 8 940

1

2

3

4

5 Yen -$

49

FIGURE 3.1 (CONT.)

C o nd ition al C ova r ian ce of Ge rm an 1 0Y B on d an d D M -$ - E .Zo ne

2 02 2 96 3 90 4 84 5 78 6 72 7 66 8 60-0.16

-0.14

-0.12

-0.10

-0.08

-0.06

-0.04

-0.02

0.00

GE 10Y - DM - $

C o n ditio na l C ov arian ce o f G erm a n 1 0 Y B o nd a nd Y en -$ - E .Zo ne

2 02 2 96 3 90 4 84 5 78 6 72 7 66 8 60-0.14

-0.12

-0.10

-0.08

-0.06

-0.04

-0.02

0.00 G E 10 Y - Y e n- $

C on ditio na l C o va ria nce o f D M-$ a nd Y en -$ - E .Zo ne

2 01 2 93 3 85 4 77 5 69 6 61 7 53 8 450.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

2.25 DM - $ - Yen -$

50

FIGURE 3.1 (CONT.)

Vo la til i ty of DM -$ - A .Z on e

20 0 29 0 38 0 47 0 56 0 65 0 74 0 83 00.00.51.01.52.02.53.03.54.0 D M- $

Volati li ty o f Yen -$ - A.Z on e

20 0 29 0 38 0 47 0 56 0 65 0 74 0 83 00.00.51.01.52.02.53.03.54.0 Y en- $

Volat il it y of U S 1 0Y B ond - A.Z on e

20 0 29 0 38 0 47 0 56 0 65 0 74 0 83 00.00.10.20.30.40.50.60.70.80.9 U S 10Y

V olatili ty of D ow J one s - A .Z on e

20 0 29 0 38 0 47 0 56 0 65 0 74 0 83 00.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5 DJ

51

FIGURE 3.1 (CONT.)

C o nd ition a l C ov arian ce of U S 1 0Y B on d an d D M -$ - A .Zo ne

2 03 3 00 3 97 4 94 5 91 6 88 7 85 8 82-0 .0 30

-0 .0 25

-0 .0 20

-0 .0 15

-0 .0 10

-0 .0 05

0 .0 00 US 1 0Y -DM - $

C o n ditio na l C ov ar ia nce o f US 1 0 Y B o nd a nd Y en -$ - A .Zo ne

2 03 3 00 3 97 4 94 5 91 6 88 7 85 8 82-0 .1 00

-0 .0 75

-0 .0 50

-0 .0 25

-0 .0 00 US 1 0Y -Y e n- $

C on ditio na l C o va ria nce o f D M-$ a nd Y en -$ - A .Zo ne

2 01 2 93 3 85 4 77 5 69 6 61 7 53 8 450.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75 DM - $ - Y en -$

52

FIGURE 3.2

EUROPEAN TIME ZONE

FROM AMERICAN TIME ZONE AT T-1

[US 10Y BOND] [DM-$ ] [YEN-$]

GERMAN YEN-$ 10 Y BUND UNSCHEDULED NEWS AND GERMAN POLICY M3 STATEMENTS

DM-$UNSCHEDULED NEWS AND POLICY STATEMENTS

AMERICAN TIME ZONE

US 10Y JAPANESE BOND 10Y BOND

DM-$ YEN-$ UNSCHEDULED UNSCHEDULED NEWS AND NEWS AND POLICY POLICY STATEMENTS STATEMENTS

US SCHEDULED NEWS

DOW JONES INDUSTRIAL INDEX

EUTDM $)( − EU

TYEN $)( −

USTDM $)( − US

TYEN $)( −

53

FIGURE 3.3

G er ma n P oli cy S ta teme n ts

1 8 15 22 29 36 43 50 570 .0042

0 .0044

0 .0046

0 .0048

0 .0050

0 .0052

0 .0054

0 .0056

0 .0058F i r st S a mpl eSe co nd S a mpl eTh ir d S a mpl eF ou rt h S a mpl e

G e rma n O f fic ia l Ra t es

1 8 15 22 29 36 43 50 570. 00216

0. 00228

0. 00240

0. 00252

0. 00264

0. 00276

0. 00288

0. 00300F i r st S a mpl eSe co nd S a mpl eTh ir d S a mpl eF ou rt h S a mpl e

G er ma n M ar k et E ve n ts

1 8 15 22 29 36 43 50 570. 00575

0. 00600

0. 00625

0. 00650

0. 00675

0. 00700

0. 00725

0. 00750

0. 00775F i r st S a mpl eSe co nd S a mpl eTh ir d S a mpl eF ou rt h S a mpl e

G e rm an Po lic y St at e ment s - V o lat ilit y

1 8 15 22 29 36 43 50 57- 1.5

- 1.0

- 0.5

0.0

0.5

1.0

1.5

2.0

2.5F i r st S a mpl eSe co nd S a mpl eTh ir d S a mpl eF ou rt h S a mpl e

G erm an O ff ic ia l R ates - V o lat ilit y

1 8 15 22 29 36 43 50 57- 1.5

- 1.0

- 0.5

0.0

0.5

1.0

1.5

2.0

2.5F i r st S a mpl eSe co nd S a mpl eTh ir d S a mpl eF ou rt h S a mpl e

G e rma n Ma rke t Ev ent s - V o lat ilit y

1 8 15 22 29 36 43 50 57- 1.5

- 1.0

- 0.5

0.0

0.5

1.0

1.5

2.0

2.5F i r st S a mpl eSe co nd S a mpl eTh ir d S a mpl eF ou rt h S a mpl e

54

FIGURE 3.4

Ye n-R el at e d M a rket Eve nts

1 8 15 22 29 36 43 50 570 .4 50

0 .4 75

0 .5 00

0 .5 25

0 .5 50

0 .5 75

0 .6 00

0 .6 25

0 .6 50

0 .6 75F i rs t S am p le

S e co n d S am p le

T h i rd S am p le

Fo u rth S am p le

J ap an ese Pol icy S ta te m e nts

1 8 15 22 29 36 43 50 570 .4 75

0 .5 00

0 .5 25

0 .5 50

0 .5 75

0 .6 00

0 .6 25

0 .6 50

0 .6 75

0 .7 00F i rs t S am p le

S e co n d S am p le

T h i rd S am p le

Fo u rth S am p le

Ye n-R el ated M a rket Eve nt s - V ol at il it y

1 8 15 22 29 36 43 50 57- 3.2

- 2.4

- 1.6

- 0.8

- 0.0

0.8

1.6

F irs t Sa m ple

Sec on d Sa m ple

T hi rd Sa m ple

F ou rth Sa m ple

J ap an ese P ol icy S tate m e nts - V ol at il ity

1 8 15 22 29 36 43 50 57- 3.2

- 2.4

- 1.6

- 0.8

- 0.0

0.8

1.6

F irs t Sa m ple

Sec on d Sa m ple

T hi rd Sa m ple

F ou rth Sa m ple

55

FIGURE 3.5

U S Payr ol ls

1 8 15 22 29 36 43 50 570 .0 01 25

0 .0 01 50

0 .0 01 75

0 .0 02 00

0 .0 02 25

0 .0 02 50

0 .0 02 75

0 .0 03 00

0 .0 03 25

0 .0 03 50F i rs t S am p le

S e co n d S am p le

T h i rd S am p le

Fo u rth S am p le

US H .W a ge Ra te s

1 8 15 22 29 36 43 50 570 .0 01 92

0 .0 02 08

0 .0 02 24

0 .0 02 40

0 .0 02 56

0 .0 02 72

0 .0 02 88

0 .0 03 04

0 .0 03 20

0 .0 03 36F i rs t S am p le

S e co n d S am p le

T h i rd S am p le

Fo u rth S am p le

U S P ayro ll s - V ol at il ity

1 8 15 22 29 36 43 50 57- 0 .01

0 .00

0 .01

0 .02

0 .03

0 .04F i rs t S am p le

S e co n d S am p le

T h i rd S am p le

Fo u rth S am p le

US H. W a ge Ra te s - V ol at il ity

1 8 15 22 29 36 43 50 57-0 .0 08

0 .0 00

0 .0 08

0 .0 16

0 .0 24

0 .0 32

0 .0 40

0 .0 48

0 .0 56

0 .0 64F i rs t S am p le

S e co n d S am p le

T h i rd S am p le

Fo u rth S am p le

56

FIGURE 3.6

U S Payr ol ls

1 8 15 22 29 36 43 50 570 .0 75

0 .1 00

0 .1 25

0 .1 50

0 .1 75

0 .2 00

0 .2 25F i rs t S am p le

S e co n d S am p le

T h i rd S am p le

Fo u rth S am p le

US H .W a ge Ra te s

1 8 15 22 29 36 43 50 570 .1 50

0 .1 75

0 .2 00

0 .2 25

0 .2 50

0 .2 75F i rs t S am p le

S e co n d S am p le

T h i rd S am p le

Fo u rth S am p le

U S Payro ll s - V ol at il ity

1 8 15 22 29 36 43 50 57-0 .0 25

0 .0 00

0 .0 25

0 .0 50

0 .0 75

0 .1 00

0 .1 25

0 .1 50

0 .1 75

0 .2 00F i rs t S am p le

S e co n d S am p le

T h i rd S am p le

Fo u rth S am p le

US H. W a ge Ra te s - V ol at il ity

1 8 15 22 29 36 43 50 57-0 .0 25

0 .0 00

0 .0 25

0 .0 50

0 .0 75

0 .1 00

0 .1 25

0 .1 50

0 .1 75

0 .2 00F i rs t S am p le

S e co n d S am p le

T h i rd S am p le

Fo u rth S am p le

57

FIGURE 3.7

U S P oli cy S ta temen ts

1 8 15 22 29 36 43 50 570. 00400

0. 00425

0. 00450

0. 00475

0. 00500

0. 00525

0. 00550

0. 00575F i rst S a mpl eSe co nd S a mpl eTh ird S a mpl eF ou rt h S a mpl e

G re e ns p an S tateme n ts

1 8 15 22 29 36 43 50 570 .0065

0 .0070

0 .0075

0 .0080

0 .0085

0 .0090

0 .0095

0 .0100F i rst S a mpl eSe co nd S a mpl eTh ird S a mpl eF ou rt h S a mpl e

US M ar k et E ve n ts

1 8 15 22 29 36 43 50 570 .0055

0 .0060

0 .0065

0 .0070

0 .0075

0 .0080F i rst S a mpl eSe co nd S a mpl eTh ird S a mpl eF ou rt h S a mpl e

US P olic y S tat em en t s - Vo la ti tit y

1 8 15 22 29 36 43 50 570.00

0 .02

0 .04

0 .06

0 .08

0 .10

0 .12

0 .14

0 .16F i rst S a mpl eSe co nd S a mpl eTh ird S a mpl eF ou rt h S a mpl e

G ree ns p an St at e ment s - V o lat ilit y

1 8 15 22 29 36 43 50 570.00

0 .05

0 .10

0 .15

0 .20

0 .25

0 .30

0 .35F i rst S a mpl eSe co nd S a mpl eTh ird S a mpl eF ou rt h S a mpl e

U S Ma rket Ev ent s - V o lat ilit y

1 8 15 22 29 36 43 50 570.00

0 .05

0 .10

0 .15

0 .20

0 .25

0 .30F i rst S a mpl eSe co nd S a mpl eTh ird S a mpl eF ou rt h S a mpl e

58

FIGURE 3.8

Ye n-R el ate d Ma rket Eve nts

1 8 15 22 29 36 43 50 570 .3 00

0 .3 25

0 .3 50

0 .3 75

0 .4 00

0 .4 25

0 .4 50

0 .4 75

0 .5 00

0 .5 25F irs t S am p le

Se co n d S am p le

T h i rd S am p le

Fo u rt h S am p le

J ap an ese Pol icy Sta te me nts

1 8 15 22 29 36 43 50 570 .20

0 .22

0 .24

0 .26

0 .28

0 .30

0 .32

0 .34F irs t Sa m pl e

Se c on d Sa m pl e

T hi rd Sa m pl e

F o u rth Sa m ple

Ye n-R el ated Ma rket Eve nts - V ol at il ity

1 8 15 22 29 36 43 50 57- 0 .05

0 .00

0 .05

0 .10

0 .15

0 .20

0 .25F irs t S am p le

Se co n d S am p le

T h i rd S am p le

Fo u rt h S am p le

J ap an ese P ol icy State m e nts - V ol at il ity

1 8 15 22 29 36 43 50 57- 0 .05

0 .00

0 .05

0 .10

0 .15F irs t S am p le

Se co n d S am p le

T h i rd S am p le

Fo u rt h S am p le

59

FIGURE 3.9

Three-D ay S hock s f rom U S Ev en t s, D M-$

1 8 15 22 29 36 43 50 570.006

0.008

0.010

0.012

0.014

0.016

0.018

0.020

0.022

0.024

F i rst S a mpl eSe co nd S a mpl eTh ird S a mpl eF ou rth S a mpl e

U S Labo r Marke t s, D M-$

1 8 15 22 29 36 43 50 57-0 .0045

-0 .0040

-0 .0035

-0 .0030

-0 .0025

-0 .0020

-0 .0015

-0 .0010

-0 .0005

0 .0000

F i rst S a mpl eSe co nd S a mpl eTh ird S a mpl eF ou rth S a mpl e

U S Labor Mark ets , YE N-$

1 8 15 22 29 36 43 50 57- 0.080

- 0.040

0.000

0.040

0.080

0.120

0.160

F i rst S a mpl eSe co nd S a mpl eTh ird S a mpl eF ou rth S a mpl e

Three-D ay S hoc k s from US E vents , D M-$ V ol .

1 8 15 22 29 36 43 50 570.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45F i rst S a mpl eSe co nd S a mpl eTh ird S a mpl eF ou rth S a mpl e

US Labo r Markets , D M-$ V ol .

1 8 15 22 29 36 43 50 57- 0.016

0.000

0.016

0.032

0.048

0.064

0.080

0.096F i rst S a mpl eSe co nd S a mpl eTh ird S a mpl eF ou rth S a mpl e

U S Labor M ark et s , Y EN -$ V ol .

1 8 15 22 29 36 43 50 57- 0.050

0.000

0.050

0.100

0.150

0.200

0.250F i rst S a mpl eSe co nd S a mpl eTh ird S a mpl eF ou rth S a mpl e

60

FIGURE 3.10

Si mu la ti on s of DM -$ in E .Zo ne

4 31 4 39 4 47 4 55 4 63 4 71 4 79 4 871 .4 32

1 .4 40

1 .4 48

1 .4 56

1 .4 64

1 .4 72

1 .4 80

1 .4 88

1 .4 96Fo re c as t

Ac tu al

S imu la ti on s of YEN -$ in E .Zo ne

4 31 4 39 4 47 4 55 4 63 4 71 4 79 4 871 0 3.5

1 0 4.0

1 0 4.5

1 0 5.0

1 0 5.5

1 0 6.0

1 0 6.5

1 0 7.0

1 0 7.5Fo re c as t

Ac tu al

S i mu la ti on s of DM -$ in A .Zo ne

4 31 4 39 4 47 4 55 4 63 4 71 4 79 4 871 .43

1 .44

1 .45

1 .46

1 .47

1 .48

1 .49

1 .50F o re ca s t

Ac tua l

S imu la ti on s of YEN -$ in A .Zo ne

4 31 4 39 4 47 4 55 4 63 4 71 4 79 4 871 0 4.0

1 0 4.5

1 0 5.0

1 0 5.5

1 0 6.0

1 0 6.5

1 0 7.0

1 0 7.5Fo re c as t

Ac tu al

61

FIGURE 3.11

Si mu la ti on s of DM -$ in E.Zo ne

4 31 4 39 4 47 4 55 4 63 4 71 4 79 4 871 .4 16

1 .4 28

1 .4 40

1 .4 52

1 .4 64

1 .4 76

1 .4 88

1 .5 00Fo re c as t

Ac tu al

S imu la ti on s of YEN -$ in E.Zo ne

4 31 4 39 4 47 4 55 4 63 4 71 4 79 4 871 0 3.5

1 0 4.0

1 0 4.5

1 0 5.0

1 0 5.5

1 0 6.0

1 0 6.5

1 0 7.0

1 0 7.5Fo re c as t

Ac tu al

S i mu la ti on s of DM -$ in A.Zo ne

4 31 4 39 4 47 4 55 4 63 4 71 4 79 4 871 .4 16

1 .4 28

1 .4 40

1 .4 52

1 .4 64

1 .4 76

1 .4 88

1 .5 00Fo re c as t

Ac tu al

S imu la ti on s of YEN -$ in A.Zo ne

4 31 4 39 4 47 4 55 4 63 4 71 4 79 4 871 0 3.5

1 0 4.0

1 0 4.5

1 0 5.0

1 0 5.5

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