Content uploaded by Jonathan Benchimol
Author content
All content in this area was uploaded by Jonathan Benchimol on Nov 08, 2022
Content may be subject to copyright.
Forecast Performance in Times of Terrorism
34th CIRET Conference
Jonathan Benchimol1and Makram El-Shagi2
This pre senta tion do es not n ecess arily re‡ect the v iews of t he Bank of Israe l
September 2018
1 / 38
What do we know ?
IAssessing the future paths of in‡ation and exchange rate :
essential for decision makers and market players in a small
open economy.
IExpert and market-based forecasts : main tools used by these
decision makers.
IFinancial instabilities : change expectations.
ITerrorism : change expectations too.
2 / 38
Why is it important ?
IThe impact of terrorism on forecasting has not been
analyzed in the literature.
IHow reliable are forecasts made in times of terrorism ?
Financial instability ?
IAs a policymaker in times of terrorism or crisis, is it better to
believe expert or market-based forecasts ?
ITerrorism is now important for Europe and UK.
3 / 38
What do we do ?
IWe test expert and market-based forecast accuracy,
conditioned on …nancial instability and terrorism periods.
4 / 38
What do we …nd ?
IExpert forecasts are better than market-based forecasts,
especially during instability.
ITerrorism variables have a strong explanatory power of both
forecasts’predictive ability.
5 / 38
Literature
IGiacomini and White (2006) develop test of conditional
predictive ability and compare to unconditional ones
(Diebold and Mariano, 1996).
IGiacomini and Rossi (2010) present relative local
forecasting performance tests of two models (Fluctuation
and One-Time Reversal tests). This forecast comparison in
unstable environments essentially uses rolling window
Giacomini and White (2006) tests.
IRossi and Sekhposyan (2016) implement regression-based
tests of predictive ability in unstable environments, and apply
these unbiasedness and e¢ ciency tests (forecast rationality
tests) to survey forecasts.
6 / 38
In a nutshell
Unstable environment : rolling window tests.
IUnbiasedness test : is there at least one bias ?
IFluctuation test : is there a signi…cant di¤erence in predictive
ability ?
IOne-time reversal test : is there joint (full and sub-sample)
equal performance at any point in time ? Break in predictive
ability ?
IConditional relative performance test : to which indicator the
forecast performance is most related ?
ISwitch to the results.
7 / 38
Are the considered forecasts unbiased ?
IThe underlying test statistic is based on a standard regression:
yt+h=α+βˆyt+h,t+ηt,h(1)
where
Iyt+his the variable of interest at t+h;
Iˆyt+h,tis the corresponding forecast made at time t;
Iηt,his the residual of the test regression (test the joint
hypothesis that α=0 and β=1).
INull hypothesis : the forecast under consideration is rational
at any point in time during the sample.
IRejection : a forecast was biased at least once (6=
permanently) during the sample period.
IRossi and Sekhposyan (2016) suggested using the maximum
of rolling-window unbiasedness tests as the test statistic.
8 / 38
Fluctuation test (1)
ITest for relative forecast performance in unstable
environments : the maximum of traditional (unconditional)
relative forecast performance tests over a rolling window.
INull hypothesis : the forecasts under consideration perform
equally well at any point in time.
9 / 38
Fluctuation test (2)
IExceeding the critical value :
Idoes not imply that one model constantly outperforms the
other.
Iimplies that there is a meaningful di¤erence in predictive
ability for some subsample.
IThe test statistic is the maximum of local Diebold and
Mariano (1995) test statistics, in which the variance estimator
is based on the full sample of forecasts, rather than the
individual window for which the mean di¤erence in predictive
ability is computed.
10 / 38
Fluctuation test (3)
ILoss function for the two forecasts under consideration at
time t:L1,t,hand L2,t,h.
ICorresponding loss di¤erence : ∆Lt,h=L1,t,hL2,t,h.
ITest statistic is
max
j2fm,...,Pgˆ
σ1m1/2
j+m/21
∑
t=jm/2
∆Lt,h(2)
where ˆ
σis the HAC robust estimator of the standard error of
the mean of ∆Lt,h, with a sample of Pforecasts and using
window length m.
11 / 38
One-time reversal test (1)
IA potential change in forecast performance is often due to a
single structural break (e.g., the introduction of a new
forecasting model, or a policy that is not well understood by
one forecasting agent), rather than ‡uctuations over time.
IThus the ‡uctuation test creates an unnecessary loss in
power, compared to a test that explicitly models a single
structural break.
IThe one-time reversal test is an entire testing procedure
composed of three separate tests.
12 / 38
One-time reversal test (2)
IFirst test statistic : a straightforward full sample test:
LM1=ˆ
σ2P1"P
∑
t=1
∆Lt#2
(3)
ISecond test statistic : actual structural break statistic based
on the di¤erence between loss di¤erences in di¤erent
subsamples:
LM2=max
j2f0.15P,...,0.85PgLM2(j)(4)
where LM2(j)=
ˆ
σ2P1(j/P)1(1j/P)1h∑j
t=1∆Lt(j/P)∑P
t=1∆Lti2
13 / 38
One-time reversal test (3)
IThird, the joint test-statistic with the null hypothesis of equal
performance at any point in time:
φ=LM1+LM2(5)
ICorrespondingly, if the third test statistic is rejected, we can
reject equal performance at every point in time.
IOnly then we assess the individual underlying statistics LM1
and LM2.
IIf only LM1is rejected, this indicates the permanent
superiority of one model.
IIf only LM2is rejected, this indicates the reverse, in which one
model is superior only for a certain subsample.
14 / 38
Conditional relative performance test (1)
IWe now assess the reasons for the variation by testing for
conditional forecast performance.
IDenoting the set of conditions that potentially explain the
di¤erence in performance at time tby the row vector ht, the
test statistic is given by
T=P P1P
∑
t=1
ht∆Lt,h!ˆ
Ω1 P1P
∑
t=1
ht∆Lt,h!0
(6)
IIn this standard statistic the individual coe¢ cients are
bilateral correlations between the elements of hand ∆L.
INull hypothesis : forecast performance is not related to any of
the indicators collected in h.
15 / 38
Conditional relative performance test (2)
IWe do not assess which kind of shock at t+his
unforeseeable for certain forecasters
IWe assess conditions at twhen the forecast is made.
IWe choose the preferred forecast, ex ante, that is, when the
forecast is made, rather than later when the realization is
known.
IIncluding several indicators in hdoes not “control”for those
in the sense of regression analysis: coe¢ cients are simple
bilateral correlations rather than regression coe¢ cients.
IWe also run an ad hoc variation of this test, in which we use
regression coe¢ cients rather than correlation coe¢ cients and
the corresponding covariance matrix, and we run a Wald test
on the coe¢ cient(s) of interest only.
16 / 38
Data
IForecasts :
I1Y Breakeven in‡ation, 1Y Fwd in‡ation, 1Y Fwd USD/ILS.
I1Y Professional forecasts of in‡ation and USD/ILS.
IEconomic data :
IIn‡ation rate.
IUSD/ILS exchange rate.
IFinancial data :
ITASE 100.
IOil (Brent)
IGas.
ICRB index.
ITerrorism data :
IGTD: text-mining based.
IMFA: geography based.
INII: citizenship based.
17 / 38
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
5
10
15
20
25
30
35
%
Spread V olatility
Figure: Financial uncertainty measured as the TA100’s 1-month rolling
window volatility (standard deviation) and the daily spread (high-low
spread).
18 / 38
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
10
20
30
40
50
60
%
CRB index Gas O il (Brent) USD/ ILS
Figure: Financial uncertainty measured as the 1-month rolling window
volatility (standard deviation) of the CRB index, gas and oil prices in
shekel, and the USD/ILS exchange rate.
19 / 38
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
0
20
40
60
80
100
120
140
160
Quant ity
Figure: Number of terrorist attacks in Israel (2000–2016). Source:
National Consortium for the Study of Terrorism and Responses to
Terrorism (START). Global Terrorism Database.
20 / 38
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
0
100
200
300
400
500
600
Quant ity
Figure: Number of killed and wounded during terrorist attacks in Israel
(2000–2016). Source: National Consortium for the Study of Terrorism
and Responses to Terrorism (START). Global Terrorism Database.
21 / 38
(a) Expert forecast (b) Market-based forecast
Figure: Value (plain) and critical value (dashed) of the unbiasedness test
for CPI in‡ation. Note: Signi…cance level: 5%. Short warfare (dark gray):
Second Intifada (A), Lebanon war (B), Operation Cast Lead (C), Operation
Pillar of Defense (D), Operation Protective Edge (E). Relatively long terrorism
period (light gray): rockets from Gaza and Lebanon (T1), mortars and rockets
from Gaza, Flotilla episode and terrorist attacks (T2), terrorism attacks
(including abroad), mortar and rockets from Gaza (T3), and Stabbing Intifada
mixed with periods of rockets and mortars from Gaza (T4).
Cannot exclude a reversal in 2004
(a) Fluctuation test (b) One-time reversal test
Figure: Value (plain) and critical value (dashed) of the ‡uctuation and
one-time reversal tests for CPI in‡ation. Note: Signi…cance level: 5%.
23 / 38
In‡ation forecasts (1Y)
GTD MFA NII
Terror Control variable CPA terr or contro l CPA te rror cont rol CPA terror co ntrol
Killed 0.02 0.01 0.02
TA100 vol. 0.03 1.34 2.47 0.03 1.92 2.34 0.03 1.32 2.46
TA100 spread 0.03 2.87 0.85 0.02 3.33 1.07 0.03 2.94 1.03
USD/ILS vol. 0.03 2.23 0.82 0.03 2.93 0.95 0.04 2.47 0.96
Oil* (WTI) vol. 0.03 1.41 2.51 0.03 1.75 2.29 0.04 1.14 2.43
Gas* vol. 0.04 1.93 2.12 0.03 2.57 1.82 0.04 1.70 2.05
CRB* vol. 0.03 3.02 1.64 0.03 3.21 1.60 0.03 2.80 1.63
Oil (WTI) vol. 0.03 1.54 2.22 0.03 2.32 2.14 0.04 1.48 2.21
Gas vol. 0.04 1.93 2.14 0.03 2.58 1.86 0.04 1.71 2.08
CRB vol. 0.03 2.52 1.20 0.03 2.98 1.26 0.03 2.61 1.32
Wounded 0.01 0.01 0.01
TA100 vol. 0.03 1.86 2.32 0.03 1.81 2.37 0.02 3.01 2.72
TA100 spread 0.03 3.18 0.99 0.03 3.29 1.07 0.02 3.02 0.84
USD/ILS vol. 0.03 2.70 0.82 0.03 2.88 0.95 0.02 2.98 0.84
Oil* (WTI) vol. 0.03 2.02 2.34 0.03 1.64 2.32 0.02 1.97 2.48
Gas* vol. 0.04 2.66 2.09 0.03 2.45 1.87 0.02 2.50 1.89
CRB* vol. 0.03 3.06 1.63 0.03 3.18 1.61 0.02 3.20 1.62
Oil (WTI) vol. 0.03 2.35 2.11 0.03 2.16 2.15 0.02 2.38 2.26
Gas vol. 0.04 2.68 2.11 0.03 2.46 1.90 0.02 2.49 1.91
CRB vol. 0.03 2.83 1.20 0.03 2.94 1.27 0.02 2.89 1.14
Table: Breakeven and expert in‡ation forecasts predictive ability tests (1)
24 / 38
In‡ation forecasts (1Y)
GTD MFA NII
Terror Control variable CPA terror cont rol CPA terror co ntrol CPA ter ror contro l
Total 0.01 0.01 0.01
TA100 vol. 0.03 1.78 2.35 0.03 1.81 2.37 0.02 2.90 2.66
TA100 spread 0.03 3.21 0.97 0.03 3.29 1.07 0.02 3.24 0.91
USD/ILS vol. 0.03 2.68 0.82 0.03 2.88 0.95 0.02 3.13 0.86
Oil* (WTI) vol. 0.03 1.93 2.38 0.03 1.64 2.32 0.02 1.97 2.43
Gas* vol. 0.04 2.61 2.10 0.03 2.45 1.87 0.03 2.62 1.89
CRB* vol. 0.03 3.10 1.64 0.03 3.18 1.61 0.02 3.39 1.64
Oil (WTI) vol. 0.03 2.23 2.13 0.03 2.16 2.15 0.02 2.42 2.22
Gas vol. 0.04 2.63 2.12 0.03 2.46 1.90 0.03 2.61 1.91
CRB vol. 0.03 2.84 1.20 0.03 2.94 1.27 0.02 3.07 1.17
Number 0.02 0.01
TA100 vol. 0.09 1.14 2.86 0.05 3.45 2.31
TA100 spread 0.09 0.67 0.47 0.05 3.93 1.09
USD/ILS vol. 0.08 0.34 0.77 0.05 3.52 0.66
Oil* (WTI) vol. 0.08 0.06 2.84 0.05 2.68 1.63
Gas* vol. 0.04 0.69 2.21 0.04 3.68 1.82
CRB* vol. 0.09 0.94 1.52 0.05 4.10 1.73
Oil (WTI) vol. 0.07 -0.38 2.52 0.05 2.76 1.50
Gas vol. 0.04 0.70 2.23 0.04 3.67 1.80
CRB vol. 0.08 0.40 1.12 0.05 3.51 0.96
Table: Breakeven and expert in‡ation forecasts predictive ability tests (2)
25 / 38
In‡ation forecasts (1Y)
GTD MFA NII
Terror Control variable CPA terr or contro l CPA te rror cont rol CPA terror co ntrol
Killed 0.05 0.06 0.05
TA100 vol. 0.01 3.01 0.50 0.01 2.61 0.68 0.01 2.76 0.58
TA100 spread 0.00 3.05 -0.37 0.00 2.69 -0.02 0.00 2.86 0.18
USD/ILS vol. 0.00 3.08 -1.30 0.00 2.62 -0.72 0.00 2.76 -0.64
Oil* (WTI) vol. 0.04 2.99 0.25 0.04 2.59 0.21 0.04 2.75 0.15
Gas* vol. 0.08 3.03 2.26 0.08 2.56 2.10 0.08 2.74 2.07
CRB* vol. 0.01 3.01 -0.62 0.01 2.63 -0.74 0.01 2.77 -0.63
Oil (WTI) vol. 0.03 2.97 0.40 0.03 2.59 0.46 0.03 2.75 0.46
Gas vol. 0.08 3.02 2.29 0.07 2.55 2.14 0.07 2.73 2.12
CRB vol. 0.00 3.05 -0.88 0.00 2.64 -0.45 0.00 2.78 -0.35
Wounded 0.04 0.04 0.01
TA100 vol. 0.01 3.35 -0.16 0.01 3.08 0.20 0.02 2.62 1.40
TA100 spread 0.00 3.40 -0.24 0.00 3.16 -0.11 0.00 2.59 -0.96
USD/ILS vol. 0.00 3.41 -1.49 0.00 3.10 -0.90 0.01 2.54 -1.35
Oil* (WTI) vol. 0.04 3.30 -0.08 0.04 3.06 -0.09 0.03 2.54 0.29
Gas* vol. 0.08 3.40 2.20 0.08 3.06 1.80 0.05 2.47 1.91
CRB* vol. 0.01 3.34 -1.10 0.01 3.10 -1.19 0.01 2.56 -1.31
Oil (WTI) vol. 0.03 3.26 0.12 0.03 3.05 0.32 0.02 2.56 0.55
Gas vol. 0.08 3.40 2.26 0.08 3.05 1.86 0.05 2.45 1.96
CRB vol. 0.00 3.38 -1.18 0.00 3.11 -0.89 0.01 2.58 -1.42
Table: Forward and expert in‡ation forecasts predictive ability tests (1)
26 / 38
In‡ation forecasts (1Y)
GTD MFA NI I
Terror Control variable CPA t error con trol CPA terr or contro l CPA terror co ntrol
Total 0.04 0.04 0.01
TA100 vol. 0.01 3.31 -0.03 0.01 3.04 0.27 0.02 2.77 1.31
TA100 spread 0.00 3.36 -0.25 0.00 3.12 -0.05 0.00 2.75 -0.76
USD/ILS vol. 0.00 3.37 -1.45 0.00 3.05 -0.86 0.01 2.71 -1.26
Oil* (WTI) vol. 0.04 3.27 -0.01 0.04 3.01 -0.05 0.03 2.69 0.22
Gas* vol. 0.08 3.36 2.22 0.08 3.01 1.85 0.06 2.67 1.90
CRB* vol. 0.01 3.31 -0.98 0.01 3.06 -1.09 0.01 2.72 -1.15
Oil (WTI) vol. 0.03 3.24 0.18 0.03 3.01 0.34 0.03 2.71 0.50
Gas vol. 0.08 3.36 2.27 0.08 3.00 1.91 0.06 2.65 1.95
CRB vol. 0.00 3.34 -1.11 0.00 3.07 -0.80 0.01 2.74 -1.29
Number 0.00 0.01
TA100 vol. 0.01 1.55 1.36 0.02 4.79 0.11
TA100 spread 0.00 1.42 -1.55 0.00 4.88 -1.00
USD/ILS vol. 0.01 1.63 -1.92 0.01 5.40 -2.91
Oil* (WTI) vol. 0.01 1.40 0.49 0.03 4.71 -1.12
Gas* vol. 0.01 1.50 2.24 0.04 4.91 1.86
CRB* vol. 0.01 1.36 -1.72 0.01 4.82 -1.63
Oil (WTI) vol. 0.01 1.32 0.53 0.03 4.70 -1.00
Gas vol. 0.01 1.52 2.28 0.04 4.93 1.89
CRB vol. 0.01 1.52 -1.81 0.01 5.14 -2.47
Table: Forward and expert in‡ation forecasts predictive ability tests (2)
27 / 38
Both forecasts are biased
(a) Expert forecast (b) Market-based forecast
Figure: Value (plain) and critical value (dashed) of the unbiasedness test
for USD/ILS. Note: Signi…cance level: 5%.
28 / 38
Forecast performances fairly similar
(a) Fluctuation test (b) One-time reversal test
Figure: Value (plain) and critical value (dashed) of the ‡uctuation and
one-time reversal tests for USD/ILS. Note: Signi…cance level: 5%.
29 / 38
USD/ILS forecasts (1Y)
GTD MFA NII
Terror Control variable CPA terr or contro l CPA te rror cont rol CPA terror co ntrol
Killed 0.09 0.11 0.12
TA100 vol. 0.23 4.68 -0.29 0.25 3.46 -0.05 0.25 3.12 -0.04
TA100 spread 0.23 4.62 0.44 0.26 3.66 0.80 0.27 3.26 0.67
USD/ILS vol. 0.21 4.56 1.16 0.22 3.75 1.47 0.22 3.38 1.47
Oil* (WTI) vol. 0.23 4.91 -1.87 0.28 3.71 -1.99 0.29 3.34 -1.92
Gas* vol. 0.25 4.74 0.12 0.30 3.59 0.08 0.31 3.24 0.11
CRB* vol. 0.24 4.63 0.66 0.27 3.49 0.51 0.28 3.15 0.46
Oil (WTI) vol. 0.24 4.78 -1.57 0.28 3.57 -1.39 0.29 3.21 -1.16
Gas vol. 0.25 4.74 0.08 0.30 3.59 0.05 0.31 3.24 0.09
CRB vol. 0.23 4.62 0.79 0.24 3.66 0.99 0.25 3.30 0.97
Wounded 0.10 0.17 0.39
TA100 vol. 0.24 4.37 -0.73 0.28 2.06 -0.09 0.16 0.24 0.65
TA100 spread 0.25 4.26 0.15 0.35 2.11 -0.27 0.55 0.09 -1.40
USD/ILS vol. 0.21 4.16 0.97 0.26 2.22 1.20 0.24 0.24 1.09
Oil* (WTI) vol. 0.26 4.69 -2.50 0.36 2.26 -2.05 0.25 0.26 -0.84
Gas* vol. 0.28 4.41 0.17 0.39 2.17 0.15 0.39 0.17 0.76
CRB* vol. 0.26 4.27 0.08 0.35 2.12 -0.21 0.31 0.22 -0.71
Oil (WTI) vol. 0.27 4.48 -1.84 0.37 2.16 -0.97 0.32 0.24 -0.22
Gas vol. 0.28 4.40 0.14 0.39 2.17 0.12 0.40 0.18 0.70
CRB vol. 0.24 4.24 0.52 0.30 2.16 0.61 0.29 0.23 0.45
Table: Forward and expert USD/ILS forecasts predictive ability tests (1).
30 / 38
USD/ILS forecasts (1Y)
GTD MFA NI I
Terror Control variable CPA t error con trol CPA terr or contro l CPA terror co ntrol
Total 0.10 0.16 0.33
TA100 vol. 0.24 4.50 -0.66 0.28 2.29 -0.10 0.21 0.50 0.62
TA100 spread 0.24 4.39 0.23 0.33 2.36 -0.07 0.52 0.37 -1.32
USD/ILS vol. 0.21 4.30 1.00 0.25 2.48 1.24 0.26 0.52 1.08
Oil* (WTI) vol. 0.25 4.80 -2.37 0.35 2.52 -2.10 0.32 0.53 -0.98
Gas* vol. 0.27 4.54 0.15 0.37 2.42 0.12 0.42 0.45 0.66
CRB* vol. 0.25 4.40 0.20 0.33 2.35 -0.09 0.35 0.48 -0.56
Oil (WTI) vol. 0.26 4.60 -1.82 0.35 2.40 -1.06 0.37 0.51 -0.31
Gas vol. 0.27 4.54 0.12 0.38 2.42 0.09 0.43 0.45 0.61
CRB vol. 0.23 4.37 0.58 0.29 2.40 0.67 0.32 0.50 0.47
Number 0.12 0.14
TA100 vol. 0.28 1.11 0.63 0.29 1.45 0.22
TA100 spread 0.26 1.08 -1.24 0.33 1.47 -1.17
USD/ILS vol. 0.27 0.88 0.93 0.28 1.30 0.62
Oil* (WTI) vol. 0.27 1.12 -1.16 0.32 1.61 -1.60
Gas* vol. 0.29 1.08 0.81 0.34 1.45 0.63
CRB* vol. 0.26 1.03 -0.56 0.33 1.49 -0.67
Oil (WTI) vol. 0.29 1.12 -0.59 0.35 1.58 -0.84
Gas vol. 0.29 1.08 0.76 0.34 1.46 0.58
CRB vol. 0.29 1.04 0.29 0.33 1.46 0.01
Table: Forward and expert USD/ILS forecasts predictive ability tests (2).
31 / 38
Summary
IExpert and market-based forecasts are strongly impacted by
terrorism.
IIn‡ation and USD/ILS expert forecasts are superior to
market-based forecasts during high uncertainty periods.
IStrong evidence that terrorism, rather than other phenomena
(e.g., commodity price ‡uctuations and …nancial distress),
triggers the changes in forecast performance.
32 / 38
Interpretation (1)
IThe impact of terrorism a¤ects the:
Iperception of market players and forecasters, which in turn
impact their implied forecasts.
Ipredictive ability of these forecast providers and their updates
following the events.
Ipredictive ability of market participants considerably more
than professional forecasters.
IFinancial uncertainty is not signi…cant when controlling for
terrorism.
33 / 38
Interpretation (2)
ITerrorism remains robustly signi…cant when controlling for
…nancial risks.
IExchange rate forecasts are signi…cantly impacted by terrorist
attacks, but also by natural gas price volatility.
IUSD/ILS forecasts are more strongly impacted by the number
of fatalities from terrorist attacks, whatever the quantitative
methodology accounting for them.
IOur …nding that terrorism impacts market-based in‡ation
forecasts remains robust when controlling for in‡ation risk.
34 / 38
Policy implications
IInterpreting these forecasts without considering the current
terrorism situation could lead decision makers to erroneous
interpretations.
IMarket-based and expert forecasts have to be interpreted
di¤erently conditional on the current level of terrorism.
35 / 38
Conclusion (1)
IThe consequences of terrorist attacks on expert as well as
market-based forecasts are absent from the literature.
IIn‡ation and exchange rate forecast errors in Israel were
signi…cantly impacted by terrorism over the last 15 years.
IIn‡ation expert forecasts are generally better than
market-based (breakeven and forward) in‡ation forecasts.
IWhen considering the number of terrorist attacks, the picture
is very clear: whatever the type of in‡ation forecast, the
number of terrorist attacks has the best explanatory power
for the relative predictive ability of the considered forecasts.
IMarket-based in‡ation forecasts as well as its risk premium are
both impacted by terrorism.
36 / 38
Conclusion (2)
IOil and TASE100 control variables are sometimes found to
matter, although this …nding depends strongly on the
terrorism indicator.
IExternal market participants in the forex market give higher
weight to attacks in which human lives are lost.
IUncertainty in general and terrorism in particular a¤ects
market participants much more than professional forecasters.
IPolicymakers should pay attention to market-based forecasts
and prefer expert forecasts during terrorism periods.
37 / 38
Thank you
38 / 38