Soybean OLS Soybean Mean - The Effect of the Dispute between the EU and the US over Genetically

Corn OLS Corn Mean

Figure 6.8 CAARs of WTO Complaint

Table6.8 Testing the WTO Complaint

ConstantMean OLS Theil

1-day 3-day 1-day 3-day 1-day 3-day

SoybeanFutures

T-Test

Prob(CAAR>0) .198 .157 .181 .156 .198 .190

Prob(CAAR=0) .395 .315 .361 .311 .395 .380

RankTest .492 .481 .481 .484 .462 .489

Prob(CAAR=median)

SignTest .207 .207 .207 .051 .207 .207

Prob(CAAR=0)

EU CornFutures

T-Test

Prob(CAAR>0) .110 .135 .177 .132 .105 .177

Prob(CAAR=0) .220 .270 .356 .264 .210 .353

RankTest .464 .488 .479 .493 .471 .486

Prob(CAAR=median)

SignTest .5 .5 .207 .5 .5 .207

Prob(CAAR=0)

Note: Values in ells denote probability under null hypothesis, whih is

statedinitalis. Probabilitiesbelow0.5arein bold.

6.3 Time Series Regression

Thissetion presentsthe resultsof atimeseries regressionof North

Ameri-ansoybean futuresreturnsandEuropeanornfuturesreturnsrespetively.

Although the data struture suggests an event study approah the events

are very lose together in time, they `luster'. Therefore, it is diult to

nd a suitable estimation window and hypothesis tests with a time series

regression, inaddition,seem advisable.

6.3.1 Soybean Futures

I hoose a T-GARCH model over a GARCH or an EGARCH in view of

the Akaike Information Creterion (AIC). I speify for the soybean futures

returns the following model: As mentioned earlier there are ARCH eets

in the series so a GARCHmodelis mandated. I inlude two ARCH terms

and one GARCH term and the GARCH(2,1) model. A lagged-dependent

variable would be insigniant. I speify Student-T distributed errors

be-ause the model t is better in terms of kurtosis of the residuals. I donot

inlude a onstant beause ommodity futures `returns' do not represent

a real return on a investment in terms of real prodution but rather the

hedgingand speulating behaviourof investors. I inludetwo market

vari-ables namelythe DowJones Indexand the CRB CommodityIndex while I

determinethe lagstruture viewofthe lowerAIC, whihindiatesabetter

goodness-of-t 2

. The variane equation of the modelontains several

on-trolvariablesasdisussedinSetion5.5.3namelyadummyfortheplanting

season and adummy for the rollover.

The results for soybean futures returns are in line with my previous

ndings(see Table6.9). News onregulatorymeasures bythe EU thathave

tigthenedregulation(CONTRA-GMOEU)havedereasedpriesbyaround

half a perent on the event day eteris paribus (see Column II). Pivotal

deisions (VETOPOINT) do not bear an inuene (see Column III), but

the adaption dereases soybean futures pries by around one and a half

perent ontheeventday plus thetwofollowingdays(see ColumnIV). The

Note that if both dependent and independent variables are expressed in logs, the

interpretation is anelastiity, meaning that `a oneperent hange in the independent

variables ause all other variables held onstant aX perent hange in the dependent

variable'. Furthermore,thenullhypothesishangesto

H 0 : β = − 1

^. ^The^default^output

of statistial software is usually atest for

H 0 : β = 0

^and ^the ^unaware ^researh ^ould

anomalyof positivereturns aroundPro-GMOannounements (PRO-GMO

GOVERNMENT)isrepeatedinthe regression framework(see ColumnV).

The WTO omplaint does not have a signiant impat on returns (see

ColumnVI)

Therolloverdereasesthe varianeandduringtheplantingseasonthe

variane is signiantly higher but the oeients are tiny in omparison

with the movingaverage of the variane. Most of the variane isexplained

by the moving average. Shoks to the variane quikly die down. There

is an aysemtry in shoks. The negative sign indiatesthat positive shoks

havea larger impaton the variane thannegative shoks.

The testdiagnostisindiatethatthe residualsthat arefreeofARCH

eets told by Q-Statstis and an ARCH-LM test of up to 10 terms. The

residualsare free of serial autoorrelationtold by Q-Stastiis of severllag

lenghts. A Jarque-Bera test of the residuals is, nevertheless, strongly

re-jeted, whih is ommon for nanial time series. Non-normal residuals

violate an assumption of the GARCH model and the results ought to be

treatedwith due aution.

6.3.2 Corn Futures

The GARCH model for the orn futures series enompasses two ARCH

terms and one GARCH term and distinguishes between positive and

neg-ative shoks, a so alled TARCH model. Only then I annot detet any

more ARCH eets as told by ARCH-LM tests of various lag lengths. An

autoregressive element of orderone AR(1)is inluded toombat serial

au-toorrelation in the errors. Thus I speify an AR(1)-GARCH(2,1) model.

Boththe varianeand the meanequation inludeaontrolvariableforthe

rollover; the variable inthe meanfuntion is a dummy and in the variane

funtionaindexountingtomaturity. Notethelargeandpositiveoeient

of the lag dependent variable in the mean funtion indiates that returns

bear inertia. The variane funtion inludes as well a dummy variable for

the planting season.

Table6.11presentstheresultsofGARCHregressionofEuropeanorn

futures returns. The regression indiates that around half a perentage

point of the positive returns is attributable to news that the EU is losing

its market to GMOs (see Column II CONTRA EU). None of the other

politial events does bear aninuene on average onreturns (see Columns

Table6.9 GARCH of Soybean Futures Returns

DOWJONES

t − 2

^0.046*** ^0.047*** ^0.047***

(0.016) (0.016) (0.016)

COMMODITY INDEX 0.033 0.032 0.033

(0.033) (0.032) (0.033)

Log likelihood 12887 12893 12891

Akaikeinfo riterion -5.957 -5.958 -5.957

Shwarzriterion -5.943 -5.940 -5.941

LB(2) .003 .001 .002

Note: *,**,***denotesignianeat the10-,5-and1-perentlevel.

Parenthesisbehindvariablesindiatetheeventwindowsize

(b, a)

Table 6.9 GARCH of Soybean Futures Returns (ontinued)

DOWJONES

t − 2

^0.046** ^0.047*** ^0.047***

(0.016) (0.016) (0.016)

COMMODITY INDEX 0.033 0.033 0.032

(0.032) (0.033) (0.033)

Log likelihood 12895 12893 12891

Akaikeinfo riterion -5.959 -5.958 -5.958

Shwarzriterion -5.943 -5.940 -5.941

LB(2) .003 .002 .002

Note: *,**,***denotesignianeat the10-,5-and1-perentlevel.

Parenthesisbehindvariablesindiatetheeventwindowsize

(b, a)

Table 6.11 GARCH of Corn Futures Returns

COMMODITY INDEX 0.047** 0.05** 0.046**

(0.022) (0.022) (0.023)

EUROSTOXX

t − 1

^0.009 ^0.011* ^0.010

(0.006) (0.006) (0.007)

β t − 1

0.000005*** 0.00005*** 0.000004***

(0.0000006) (0.0000006) (0.0000006)

Log likelihood 6707357 6709558 6707191

Akaikeinfo riterion -7.371 -7.371 -7.370

Shwarzriterion -7.338 -7.332 -7.333

LB(2) .022 -.001 .020

Note: *,**,***denotesignianeat the10-,5-and1-perentlevel.

Parenthesisbehindvariablesindiatetheeventwindowsize

(b, a)

6.11 GARCH of Corn Futures Returns (ontinued)

COMMODITY INDEX 0.047** 0.048** 0.046**

(0.023) (0.022) (0.023)

EUROSTOXX

t − 1

^0.010 ^0.009 ^0.011

(0.006) (0.006) (0.006)

β t − 1

^0.00005*** 0.000005*** 0.000005***

(0.0000006) (0.0000006) (0.0000006)

Log likelihood 6707020 6707670 6706291

Akaikeinfo riterion -7.369 -7.369 -7.369

Shwarzriterion -7.333 -7.330 -7.332

LB(2) .002 .009 .004

Note: *,**,***denotesignianeat the10-,5-and1-perentlevel.

Parenthesisbehindvariablesindiatetheeventwindowsize

(b, a)

Theontrolvariablesinthevarianefuntionaresigniantbutsmall

in magnitude. Note that the predition error of two days ago is the best

preditorof the today'sreturndeviation. This meansthat volatilityisvery

spiky and doeshardly ontain any long-termomponent. Corn futures

re-turnsreatdierentlytopositiveandnegativeshoksasdosoybeanfutures,

but orn futures reat more strongly tonegativeshoks.

The diagnostis of the model are mixed. ARCH and serial

autoor-relation are taken are of by the ontrol variables, still the residuals are

not normally distributed. Mean and standard deviation are as expeted,

but the kurtosis of the residuals is high an exess kurtosis of around 7

indiating that the variane funtion annot predit spikes in returns.

Therefore, the Jarque-Bera test of normality is rejeted, asting doubt on

the goodness-of-tof the model.

Volatility Analysis As stated in Hypothesis 3 and 4 that I expet the

events toinuene the variane as well. However, I refrain from analysing

thevarianebeauseIdonotonsiderthevarianeequationsuientlywell

speiedespeiallyfor orn futures returns. The orn futures'volatility

re-ats mostly to short term inuenes while the volatilityof soybean futures

is mostly determined by the moving average. Any other regressor in the

variane equation is dwarfed in omparison with the ARCH and GARCH

terms. Bothtimeseriessuer fromhighkurtosisofthe residuals,whih

vio-lates the assumptionof normallydistributed errorsfor the GARCH model.

Surges in the variane are hardly preditable by the regressors. Speifying

Student-Tdistributederrorshasnotremediedtheproblem. Theoeients

Im Dokument The Effect of the Dispute between the EU and the US over Genetically Modified Organisms in Food and Feed on Financial Markets (Seite 92-99)