This paper investigates the determinants of forecast heterogeneity in the Yen-US dollar market using a panel data set from Consensus Economics. Regardless of the particular model specification and consideration of control variables we find that exchange rate misalignments increase forecast dispersion, while foreign exchange intervention of the Japanese Ministry of Finance dampens expectation heterogeneity.Exchange rates, forecast heterogeneity, survey data

Reitz, Stefan

Stadtmann, Georg

Taylor, Mark P.

English

Research Papers in Economics

The E®ects of Japanese Interventions on FX-Forecast
Heterogeneity
Stefan Reitza, Georg Stadtmannb, Mark P. Taylorc
May 2009
Abstract
This paper investigates the determinants of forecast heterogeneity
in the Yen-US dollar market using a panel data set from Consen-
sus Economics. Regardless of the particular model speci¯cation and
consideration of control variables we ¯nd that exchange rate misalign-
ments increase forecast dispersion, while foreign exchange intervention
of the Japanese Ministry of Finance dampens expectation heterogene-
ity.
JEL classi¯cation: F31, D84, C33
Keywords: Exchange rates, forecast heterogeneity, survey data
Address:
a Deutsche Bundesbank, Frankfurt Germany. The views expressed here
are those of the authors and are not necessarily those of the Deutsche
Bundesbank.
b¤ corresponding author: Georg Stadtmann
University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark,
Tel. 0045 6550 4479, stadtman@whu.edu
c Mark P. Taylor, The University of Warwick, Barclays Global Investors
(BGI) & CEPR.1
1 Introduction
Since the seminal work of Allen and Taylor (1990) and Ito (1990) survey
data of FX-market participants are used to shed light on the question of
how exchange rate expectations are built. One important stylized fact of the
literature is that { in contrast to the prevailing assumption of the rational
representative agent { real world forecasters are heterogeneous in a number
of aspects. This ¯nding has also gained attention in other strands of the
literature: For instance, Elliot et al. (2008) recently examine heterogeneity
in output projections, while Mankiw et al. (2003) and Capistran and
Timmermann (2009) investigate expectation heterogeneity with respect to
in°ation forecasts. MacDonald and Marsh (1996), Elliot and Ito (1999),
B¶ enassy-Qu¶ er¶ e et al. (2003), and Dreger and Stadtmann (2008) all ¯nd
evidence in favor of foreign exchange forecasters' heterogeneity. Investigating
the determinants of forecast dispersion, Menkho® et al.(2009) ¯nd strong
support for the chartist and fundamentalist approach pioneered by Frankel
and Froot (1990). The authors show that the recent exchange rate change
as well as the degree of misalignment explain the dispersion of forecasts.
From a policy maker's perspective it is important to understand that market
heterogeneity seems to be a major source of exchange rate volatility (Evans,
2002), constituting an additional channel by which central bank inter-
vention may calm disorderly markets (Dominguez and Frankel, 1993). In
addition, the relationship between exchange rate misalignment and forecast
heterogeneity is also important for the so-called coordination channel of
intervention (Reitz and Taylor, 2008). Using Japanese interventions in the
Yen-US dollar market Beine et al. (2007) analyze the in°uence of central
bank operations on the heterogeneity among FX-forecasters. The authors
¯nd that neither expected nor unexpected interventions have an impact
on forecast heterogeneity between 1992 and 1994, while the estimated2
coe±cients based on the period 1996 { 2001 are statistically signi¯cant
but ambiguously signed. Against the backdrop of the results presented in
Menkho® et al. (2009) and studies of Bank of Japan reaction functions
(Frenkel et al., 2005; Ito and Yabu, 2007) these ¯ndings may be due to an
omitted variable problem, as current misalignment and recent returns of the
exchange rate seem to be important not only for central bank intervention
activity, but also for forecast heterogeneity.
This paper investigates the determinants of forecast heterogeneity in the
Yen-US dollar market by applying a large panel data set from Consensus
Economics Inc. We consider a number of control variables such as the
prevailing volatility regime as well as the risk premium at the foreign
exchange market. Moreover, the data set contains expectations for GDP
and CPI allowing for cross-sectional correlation between dispersion of
exchange rate forecasts and forecast dispersion of related macro variables.
We ¯nd that increasing misalignments raise forecast heterogeneity, while
the estimated coe±cient of the recent exchange rate return is positive, but
generally insigni¯cant. In addition, central bank interventions reduce fore-
cast dispersion, regardless of the particular speci¯cation and consideration
of control variables.
The paper is structured as follows: Section 2 describes the data set. In section
3, we explain our econometric set up and present the regression results. The
last section concludes.
2 Description of the data set
The panel data set of foreign exchange forecasters stems from Consensus
Economics Inc. The survey takes place on a monthly basis and we concen-
trate on the one-month-ahead forecast. As a consequence, we do not run3
into a problem of overlapping forecast horizons. We focus on the currency
pair of the Yen against the US dollar. The sample under consideration rang-
ing from 10/1995 to 12/2007 includes 146 monthly forecasts of 31 individual
forecasters. Thus, our data set signi¯cantly extends the one used in Beine
et al. (2007) ending in 2001. The Japanese Ministry of Finance (JMoF)
intervened on 173 days (Direction: 167 buy US dollar/ 6 sell US dollar).
The Federal Reserve intervened only once, so that this intervention is not
considered in our study. The daily average size of intervention was about
34.46 Million Yen.1
3 Empirical analysis
In Speci¯cation I we regress the dispersion measure dispt on a constant and
on the absolute volume of JMoF interventions jJMoFtj during the preceding
month. Dispersion is measured as the standard deviation of all individual
exchange rate forecasts at each point in time. In Speci¯cation II we estimate
the following equation:
dispt = ® + ¸jJMoFt¡1j + ¯jst ¡ st¡1j + °jst ¡ s116j + ²t; (1)
and additionally control for the recent change in the exchange rate, st¡st¡1,
as well as the deviation of the exchange rate from an exchange rate target
(st¡s116). To determine an exchange rate target, we apply the methodology
suggested by Benassy-Quere et al. (2003) and use the average nominal
exchange rate level of 116 Yen/USD that prevailed during the time period
of our study.
The results are displayed in Table 1 and can be interpreted as follows: The
coe±cients of both the return and misalignment variable have a positive
sign and are signi¯cantly di®erent from zero. The latter ¯nding points
1See Ito (2002) for an detailed description of the Japanese intervention behavior.4
into the direction that forecasters' heterogeneity increases as misalignments
grow. Importantly, the coe±cient of the intervention measure is signi¯cantly
negative, which implies a lowering of uncertainty among FX-forecasters
in the presence of Japanese intervention operations. This is in contrast
to Beine et al. (2007), who ¯nd that neither expected nor unexpected
interventions have an impact on forecast heterogeneity between 1992 and
1994, while the estimated coe±cients based on the period 1996 { 2001 are
statistically signi¯cant but ambiguously signed.2
In Speci¯cation III we add a risk and a volatility measure as additional
control variables. Risk is measured as the absolute di®erence between the
one month forward rate and the mean expected exchange rate level. This
measure is motivated by Menkho® et al. (2009) referring to the noise
trader theory. We also control for the exchange rate volatility. Exchange
rate volatility is measured as the standard deviation of daily exchange
rate returns during the preceding month and signi¯cantly explains forecast
heterogeneity. Moreover, these variables seem to dominate the in°uence of
the recent change in exchange rate, as the latter becomes insigni¯cant in all
subsequent speci¯cations.
In Speci¯cation IV we also introduce expected changes of exchange-rate-
related macro-variables. We control for the standard deviation of all
individual CPI and GDP forecasts at each point in time for the two
economies under consideration. The hypothesis is that a higher dispersion
with respect to, for example, CPI forecasts should also lead to a larger
dispersion for the FX-forecasts. However, all forward looking control
variables are insigni¯cant, only the Japanese CPI forecast is borderline
2Our results may di®er from Beine et al. (2007) due to a signi¯cantly extended sample
and the fact that we use the absolute amount of intervention instead of intervention
frequency. Subsequently, we provide a robustness check regarding the di®erent choices
of the intervention variable.5
signi¯cant.
Speci¯cations I { IV in Table 1 have been estimated using ordinary least
squares. Signi¯cant autocorrelation of the error term was present at a
lag of one and three while the partial autocorrelation was signi¯cant only
at lag one. As a consequence, we also speci¯ed two AR speci¯cations
with a lag of one and three and estimated the equation via maximum
likelihood. These speci¯cations are presented as speci¯cation V and
VI. The estimation results are quite robust against this modi¯cation of
the econometric technique. In addition, we tested whether or not our
results are driven by the choice of the dispersion measure. Following
Beine et al. (2007) we used the variation coe±cient, de¯ned as standard
deviation of all forecasters divided by the mean forecast at each point
in time, as an alternative measure of dispersion. The results presented
as Speci¯cation VII in Table 1 seem to be robust regarding this modi¯cation.
The last robustness check considers the number of intervention days in the
previous month instead of the cumulated absolute volume of intervention.
Re-estimation of models I { VII yields parameter estimates as presented in
Table 2. Consistent with Beine et al. (2007) the coe±cient of intervention
frequency remains statistically insigni¯cant in most of the model speci¯-
cations. This result suggests that a high frequency of intervention may
not be su±cient to diminish controversies among FX-forecasters. A high
intervention volume seems to be important to in°uence the foreign exchange
market. This is in line with Ito (2002) showing that Japanese intervention
have been more e®ective after the policy regime shift in the mid 1990s
towards larger but less frequent operations.6
4 Conclusion
In this paper we analyze the impact of central bank intervention on
exchange-rate forecast heterogeneity. We control for misalignments as well
as recent returns of the exchange rate as these variables have proven to
be important for the heterogeneity among forecasters on foreign exchange
markets. Based on a panel data set provided by Consensus Economics we
¯nd empirical evidence that Japanese Ministry of Finance interventions
exhibited a dampening e®ect on the dispersion among FX-forecasts. Partic-
ularly, the volume of intervention rather than intervention frequency seems
to be capable of reducing forecast heterogeneity.
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Table 1: Dispersion with absolute volume of BoJ interventions
I II III IV V VI VII
2.75*** 2.31*** 1.45*** 1.39*** 1.81*** 1.72*** 1.62*** constant
(32.64) (15.12) (7.07) (5.52) (5.44) (4.51) (5.19)
-0.015** -0.016** -0.015** -0.014** -0.017* -0.016* -0.012* jIntBoJt¡1j
(-1.99) (-2.22) (-2.29) (-2.15) (-1.91) (-1.89) (-1.74)
{ 8.00** 1.73 2.85 1.98 2.44 2.20 jst ¡ st¡1j
(2.19) (0.50) (0.78) (0.69) (0.91) (0.92)
{ 4.34** 3.02* 3.48** 5.83*** 5.43** 3.49* jst ¡ s116j
(2.52) (1.91) (2.10) (2.66) (2.36) (1.91)
{ { 11.58 10.65 13.54** 10.23* 8.84* jft ¡ ¹ Et[st+1]j
(1.61) (1.46) (2.33) (1.78) (1.91)
{ { 146.16*** 140.02*** 60.00* 73.72** 60.54* FX-Volatility Yen/USD
(4.60) (4.34) (1.90) (2.38) (1.93)
{ { { 1.32 1.19 0.76 0.67 Stand:Dev:Et;i[JCPIt+1]
(1.51) (1.51) (0.80) (0.89)
{ { { -0.87 -1.16 -0.92 -0.98 Stand:Dev:Et;i[UCPIt+1]
(-0.93) (-1.05) (-0.82) (-1.04)
{ { { -0.15 -0.10 0.11 0.03 Stand:Dev:Et;i[JGDPt+1]
(-0.36) (-0.20) (0.22) (0.08)
{ { { 0.03 0.06 0.05 0.067 Stand:Dev:Et;i[UGDPt+1]
(0.26) (0.09) (0.07) (0.11)
R2
adjust: 0.0201 0.0808 0.2488 0.2403 { { {
{ { { { 0.45*** 0.39*** 0.37***
AR(1) (6.47) (5.12) (4.34)
{ { { { { 0.045 -0.023
AR(2) (0.05) (-0.23)
{ { { { { 0.18** 0.125
AR(3) (2.04) (1.46)
Log.Likel. { { { { -163.8 -161.6 534.11
Obs. 146 146 146 146 146 146 146
Note: t-statistics in parentheses. The coe±cients in Speci¯cation VII are
multiplied by 100. *** (**, *) = signi¯cant at the 1 (5, 10) percent level.9
Table 2: Dispersion with intervention frequency
I II III IV V VI VII
2.74*** 2.31*** 1.46*** 1.39*** 1.83*** 1.73*** 1.62*** constant
(32.46) (14.87) (6.93) (5.43) (5.47) (4.49) (5.14)
-0.042* -0.040* -0.034 -0.031 -0.044 -0.039 -0.027 CountBoJt¡1 (-1.68) (-1.65) (-1.51) (-1.37) (-1.58) (-1.49) (-1.24)
{ 7.72** 1.51 2.66 1.96 2.39 2.20 jst ¡ st¡1j
(2.09) (0.43) (0.72) (0.67) (0.89) (0.90)
{ 4.14** 2.83* 3.31** 5.53** 5.20** 3.27* jst ¡ s116j
(2.39) (1.78) (1.99) (2.43) (2.27) (1.79)
{ { 11.94 10.99 14.22** 10.48* 8.96* jft ¡ ¹ Et[st+1]j
(1.65) (1.49) (2.43) (1.80) (1.93)
{ { 144.52*** 138.21*** 54.03* 67.29** 55.98* FX-Volatility Yen/USD
(4.49) (4.24) (1.68) (2.16) (1.76)
{ { { 1.36 1.14 0.72 0.63 Stand:Dev:Et;i[JCPIt+1]
(1.53) (1.30) (0.75) (0.83)
{ { { -0.94 -1.24 -0.90 -0.97 Stand:Dev:Et;i[UCPIt+1]
(-1.00) (-1.10) (-0.79) (-1.01)
{ { { -0.13 -0.019 0.18 0.09 Stand:Dev:Et;i[JGDPt+1]
(-0.31) (-0.04) (0.36) (0.21)
{ { { 0.040 0.066 0.056 0.070 Stand:Dev:Et;i[UGDPt+1]
(0.31) (0.09) (0.08) (0.11)
R2
adjust: 0.0124 0.0670 0.2332 0.2253 { { {
{ { { { 0.45*** 0.38*** 0.35***
AR(1) (6.56) (5.08) (4.28)
{ { { { { 0.026 -0.0026
AR(2) (0.27) (-0.03)
{ { { { { 0.177** 0.126
AR(3) (2.08) (1.50)
Log.Likel. { { { { -165.8 -163.3 532.5
Obs. 146 146 146 146 146 146 146
Note: t-statistics in parentheses. The coe±cients in Speci¯cation VII are
multiplied by 100. *** (**, *) = signi¯cant at the 1 (5, 10) percent level.

Biases in macroeconomic forecasts: irrationality or asymmetric loss;

Chartists, fundamentalists, and trading in the foreign exchange market,

Charts, Noise, and Fundamentals in the London Foreign Exchange Market;

Currency Forecasters Are Heterogeneous: Con¯rmation and Consequences,

Disagreement about in°ation expectations;

Disagreement and biases in in°ation expectations;

Does foreign-exchange intervention work?,

Foreign exchange rate expectations: micro survey data,

FX trading and exchange rate dynamics;

Heterogeneity in Exchange Rate Expectations: Evidence on the ChartistsFundamentalist Approach;

Is Foreign Exchange Intervention E®ective? The Japanese Experiences in the 1990s. NBER Working Paper 8914.

Models of Exchange Rate Expectations: How Much Heterogeneity?;

The Coordination Channel of Foreign Exchange Intervention: A Nonlinear Microstructural Analysis,

The effects of Japanese foreign exchange market interventions on the yen/U.S. dollar exchange rate volatility.

The Impact of Central Bank Intervention on Exchange-Rate Forecast Heterogeneity;

What prompts Japan to intervene in the Forex market? A new approach to a reaction function,

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The Effects of Japanese Interventions on FX-Forecast Heterogeneity

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