HIAS-E-126

Central Bank Information Effects inJapan:The Role of Uncertainty Channel

Ryo Matsumoto(a) , Hiroshi Morita (b) , Taiki Ono (c)
(a)
(b)
(c)

Hitotsubashi University
Hosei University
Hitotsubashi University

November 2022

Hitotsubashi Institute for Advanced Study, Hitotsubashi University
2-1, Naka, Kunitachi, Tokyo 186-8601, Japan
tel:+81 42 580 8668 http://hias.hit-u.ac.jp/
HIAS discussion papers can be downloaded without charge from:https://hdl.handle.net/10086/27202
https://ideas.repec.org/s/hit/hiasdp.html

Central Bank Information Effects in Japan:
The Role of Uncertainty Channel∗
Ryo Matsumoto1 , Hiroshi Morita† ‡2 , and Taiki Ono1
1

Hitotsubashi University
2
Hosei University
November 28, 2022

Abstract
Central bank information effect have been analyzed in the recent literature on
monetary policy. In this study, we apply the identification method by Jarociński
and Karadi (2020) to the Japanese data to empirically examine the macroeconomic
effects of central bank information shock and pure monetary policy shock. These
shocks are identified by combining of high-frequency identification and sign restriction. The empirical results support the presence of central bank information
effects in Japan. Particularly, the central bank information shock accompanying
monetary tightening decreases economic uncertainty and increases stock prices andoutput, suggesting that central bank’s optimistic outlook is conveyed through contractionary monetary actions. The results of the forecast error variance decomposition indicate that the central bank’s information effect may be spread throughchanges in uncertainty. Finally, the total effect of monetary policy and information
shocks on the variables are much larger than that of the shocks identified by theconventional Cholesky decomposition. These findings are important for evaluating
the true effects of monetary actions on the economy.

Keywords: Monetary policy, Information effect, High-frequency data, VAR model.
JEL classification: C32, D83, E44, E52, G14.
∗This research was funded by Grant-in-Aid for Early-Career Scientists (19K13652). The authors haveno conflicts of interest directly relevant to the content of this study. Errors, if any, are entirely theauthors’ responsibility.
†
Corresponding author.
‡
Address: Hosei University, Faculty of Economics, 4342 Aihara, Machida, Tokyo 194-0298, Japan.
E-mail address: morita-h@hosei.ac.jp1

1

Introduction

Recently, an increasing interest has been paid to central bank (CB) information effect inmonetary policy analysis (Melosi, 2017; Nakamura and Steinsson, 2018; Jarociński and
Karadi, 2020). The CB information effect refers to monetary policy actions conveying
information to private agents about the central bank’s economic outlook beyond information about policy changes due to asymmetric information between the central bank andprivate agents (Romer and Romer, 2000). For instance, an announcement about tightening monetary policy is considered to possibly convey the central bank’s optimistic outlookthat the economy has sufficiently improved to withstand an increase in the interest rate,
thereby leading to expansionary reactions of the variables to tightening monetary actions(see, Nakamura and Steinsson, 2018). These reactions contrast with the conventional
reactions to monetary policy shocks expected from theoretical models such as the New
Keynesian model, highlighting the importance of considering the CB information effectto correctly identify the effects of monetary policy shocks. In other words, without considering the existence of the CB information effect, the identified monetary policy shockis likely to be a mixed estimate of pure monetary policy shock and CB information shock.
In this paper, we investigate the effects of monetary policy shock in Japan by applying the method of Jarociński and Karadi (2020), in which both pure monetary policy
and CB information shocks are separately identified by combining the high-frequency
identification (HFI) method and sign restrictions in the structural VAR framework. Although a growing body of literature on the CB information effects focuses on the U.S.
and European economies, information effects in Japan are scarcely analyzed and are only
mentioned in Kubota and Shintani (2022) and Nakamura et al. (2021). Therefore, thisstudy primarily aims to examine whether the CB information effect is detected in Japanas in the U.S. and Europe. Furthermore, we focus on the transmission mechanism of
the CB information effect, which has not been focused in Jarociński and Karadi (2020).
Particularly, the transmission through market uncertainty is central to our analysis. Because some studies (e.g., Bauer et al., 2021; Mumtaz and Theodoridis, 2020) indicate thenexus between monetary policy and uncertainty, it is intuitively reasonable to supposethat private agents are more willing to rely on information conveyed from the central

2

bank, which has information superiority, under high uncertainty.
Specifically, our analysis, along with Jarociński and Karadi (2020), proceeds as follows.
First, we begin by constructing a novel dataset containing the surprises in the interest
rate and equity prices within a 30-min window around the Bank of Japan’s (BOJ’s) monetary policy meetings (MPMs) from 2007 to 2021. The series of high-frequency surprises is
considered as a desirable method to resolve the issues of reverse causality and endogeneity
inherent in the VAR analysis conducted thus far, on the ground that variations in asset
prices unrelated to the monetary announcements are plausibly eliminated, as emphasizedin the seminal paper of Gürkaynak et al. (2005). We then estimate the structural VAR
model including the constructed high-frequency surprises of financial variables, uncertainty index, and other macroeconomic variables of interest. As discussed above, bothpure monetary policy and CB information shocks are separately identified by imposing
sign restrictions. Following Jarociński and Karadi (2020), these shocks are distinguishedby the signs of co-movements between interest rate and equity prices. Monetary policy shock is assumed to cause a negative co-movement between interest rate and equity
price, while the CB information shock is assumed to cause a positive co-movement between them. For comparative purposes, we show the results derived from the recursive
restriction using Cholesky decomposition, which corresponds to the conventional analysis ignoring the CB information effect. The effects of monetary policy and the CB
information shocks are evaluated qualitatively and quantitatively using impulse response
functions (IRFs) and forecast error variance decomposition (FEVD).
The results obtained from the IRFs are as follows. First, a monetary tightening shockraises market uncertainty and lowers stock prices and GDP. This result is consistent
with that of existing studies in the literature that monetary tightening (easing) raises(reduces) macroeconomic and market uncertainty. Next, in response to an information
shock that occurs when monetary policy is tightened, market uncertainty decreases, andstock prices and GDP rise. This result is intuitive. The CB information shock that
occurs during monetary tightening reduces market uncertainty because private sectors
anticipate an improvement in economic conditions. Furthermore, this result implies that
the CB information shock, which occurs when monetary policy is easing, increases market
uncertainty. In other words, monetary easing transmits to private sectors that current
3economic conditions are bad, and stock prices and GDP will decline. Under the Cholesky
decomposition, the effect of monetary policy shock is confounded with that of CB information shock, leading to a small estimate of the impact of monetary policy. However, ourresults overcome this problem by identifying these two shocks. In addition, we confirm
the robustness of our findings against the coverage of the MPMs, measures of uncertainty
and macroeconomic variables, and sample period.
The results of the FEVD also show that monetary policy shock identified by ourstrategy is different from that identified by the Cholesky decomposition. Monetary policy shock under the Cholesky decomposition indicates almost no contribution to thehigh-frequency movements in TOPIX futures, suggesting that this conventional identification method may fail to capture the true monetary policy shock in terms of the lackof transmission to equity prices. Moreover, the results highlight the significant role of
CB information shock contained in monetary announcements, showing the contributionof the CB information shock to about 40% movements in Euroyen and TOPIX futures
within the narrow window around the announcement. In addition, the CB information
shock shows relatively high contributions to the movements of uncertainty index. Thus,
we conclude that the CB information effects propagates through the uncertainty channel. Finally, the total contribution of monetary policy and CB information shocks tofinancial and macroeconomic variables are estimated to be larger than that of the shocks
identified by the Cholesky decomposition. This implies, as in the IRFs analysis, that theidentification method using a simple recursive restriction may underestimate the impact
of monetary actions in the economy, despite using the HFI method.
These results suggest that the central bank information shock may have different
consequences than that intended by policymakers. In other words, monetary easing tostabilize the economy may instead increase uncertainty and worsen economic conditions.
Therefore, policymakers need to be careful in communicating information to the private
sector.
Our study is related to the following three strands of the literature. The first is
related to the discussion on the information effects of monetary policy. Our study isclosely related to Jarociński and Karadi (2020), who identify monetary policy and CB
information shocks by applying sign restriction to high-frequency data on futures rates
4and stock prices. Their analysis reveals that CB information shocks bias the results ofthe standard HFI of monetary policy shocks. We employ a similar approach and identify
two shocks. They also present several episodes that indicate high economic uncertainty
during the period of high CB information shocks (see Jarociński and Karadi, 2020, p.17).
Therefore, we include market uncertainty as a variable and analyze how information
effects spill over through uncertainty. Miranda-Agrippino and Ricco (2021) and D’Amicoand King (2015) investigate the response of key macroeconomic indicators to monetary
policy announcements using Greenbook forecasts and a survey of expectations. Thesestudies generally indicate the existence of information effects.
The second is related to the studies on monetary policy and uncertainty. Bekaert
et al. (2013) decompose the VIX into a “risk aversion” and an “uncertainty” component and show that monetary easing policy reduces both risk aversion and uncertainty.
Mumtaz and Theodoridis (2020) analyzes the impact of monetary policy shocks on uncertainty from both theoretical and empirical perspectives, and shows that a 1% increasein the policy rate raises the volatility of the unemployment rate and the inflation rateby about 15%. These studies show that monetary policy affects uncertainty. However,they only focus on monetary policy shock and do not separate monetary policy shocks
and CB information shocks. Therefore, it is unclear how CB information affects market
uncertainty. We fill this gap by separating the two shocks and analyzing their effects onmarket uncertainty.
The third is related to the analysis of monetary policy in Japan. Nakamura et al.
(2021) constructed an index of monetary policy shocks using the three-month Euroyen
futures rates. They show that changes within a 30-min window around the monetary
policy meetings contain information on asset prices, such as long-term interest rates, andcan be used as instruments to estimate the impact of monetary policy on macroeconomic
variables. Kubota and Shintani (2022) constructed two factors, called “target factor” and“path factor”, following Gürkaynak et al. (2005). These studies apply the standard HFIof monetary policy shocks, but they do not distinguish monetary policy shocks from CB
information effects. However, Nakamura et al. (2021) and Kubota and Shintani (2022)
suggested the existence of information effects. To the best of our knowledge, the current
study is one of the few studies to analyze information effects in Japan.
5

The remainder of the paper is organized as follows. Section 2 explains the constructionof the high-frequency dataset and the VAR framework as well as identification strategy.
Section 3 provides the data and specifications for our empirical analysis. Section 4 demonstrates the results of IRFs and FEVDs derived from our benchmark specification. Section
5 confirms the robustness of our main findings by changing the data in the VAR model.
Finally, 6 concludes.

2

Econometric Methodology

Following Jarociński and Karadi (2020), we disentangle monetary policy and central bankinformation shocks by combining high-frequency identification (HFI) and sign restrictions
in structural VAR model. We begin by constructing a novel dataset of Japanese highfrequency financial market surprises around the policy announcements after the BOJ’smonetary policy meetings (MPMs). The dataset is then used to identify the two shocks
by sign restrictions. Specifically, the surprises in interest rate and stock prices around the
BOJ’s announcements are collected, and sign restrictions on the co-movements among
them are used to distinguish between two shocks of interest. We follow Jarociński and
Karadi (2020) and assume that monetary policy shock causes negative co-movements
between interest rate and equity prices, whereas information shock causes negative comovements between the two. This section explains how we build a dataset on highfrequency surprises in financial market and implement the identification of the shocks in
the VAR model.

2.1

High-frequency surprises

Our measures of the surprises in financial market are the changes in Euroyen futures
rates and TOPIX equity futures. These asset-price changes are collected in the narrowwindows around the BOJ’s announcements by using tick-by-tick data. To accurately
identify the impacts of the CB announcements, we need to know the exact timings ofannouncements and focus on the reactions within a narrow window. Among various
types of CB announcements, we consider the statements of monetary policy decisions(MPD) released immediately after the MPMs. The BOJ has been recording the release6

times of the MPD statements in minutes since July 2007; thus, we focus on 190 eventswith unique timestamps in our sample period from July 2007 to December 2021. In
addition, consistent with previous studies, the surprises are measured in a 30-min window,
starting 10 min before and 20 min after the announcements (e.g., Gürkaynak et al., 2005;
Nakamura and Steinsson, 2018; Kubota and Shintani, 2022).1
The tick-by-tick data of Euroyen futures rates and TOPIX futures are obtained fromthe CQG Data Factory and JPX Data Cloud, respectively. The VAR analysis below uses
monthly aggregates of the intraday surprises in interest rate and stock prices; thus, thesurprises measured in the tick-by-tick data are transformed into monthly frequencies inthe VAR analysis. Specifically, a minute-by-minute weighted average prices is first computed by multiplying the prices and transaction volumes for every minute on each MPM
day. There are multiple contracts with different maturities in the Euroyen futures rates;
thus, the prices of the contract month with the highest trading volume are selected forconstructing a minute-by-minute weighted average price. We then calculate the changes
in prices around the announcements, and finally add them for each month to produce
monthly frequency price-change data.
Figure 1 shows, as in Jarociński and Karadi (2020), the scatter-plot of the surprises inthe Euroyen and TOPIX futures. Similarly, positive co-movements between interest rates
and stock prices are observed in Quadrants I and III of this figure. This co-movement canbe attributed to the CB information effect. For example, on September 8, 2006, the BOJ
announced that it would maintain the level of short-term interest rates. At the time,
the BOJ was aiming to break away from zero-interest rate policy2 . However, the rise in
the CPI published in August 2006 had contracted. Although this contraction reduced
the market’s expectations of an interest rate hike, the market expected the BOJ to raiseinterest rates in the future, and futures rates rose despite no change in the BOJ’s policy.
Although this rise in futures rates depressed stock prices, stock prices rose because the
BOJ was simultaneously projecting a positive outlook for a gradual economic recovery.
1

The Japanese market has a lunch break, and there are cases in which a 30-min window is entirely or
partially included in the lunch break. In such cases, we measure the 30-min window by trading hours,
as in Kubota and Shintani (2022).
2
The BOJ had raised short-term interest rates to 0.25% on July 14, 2006.

7

Surprise in TOPIX future

4

II

I

2

September 8, 2006

0

-2

-4
-0.1

III

IV
-0.05

0

0.05

0.1

Surprise in Euroyen future

Figure 1: Scatter plot of Euroyen futures and TOPIX futures surprises

2.2

VAR model with sign restrictions

Let mt and yt be a vector of surprises in interest rate and stock prices and a vector of
financial and macroeconomic variables observed in month t, respectively. As noted above,the surprises in the interest rate and stock prices are incorporated into the VAR model by
aggregating their intraday movements on the day of the MPM announcement monthly.
Therefore, our VAR model is formulated as follows:     
P
X
0
0
0
mt−p
um
um

 = + +  t  ,  t  ∼ N (0, Σ) .
yt
cy
BYp M BYp Y
yt−p
uyt
uyt
p=1


mt



(1)

y ′
where ut = [umt , ut ] is the reduced-form residuals, which follows the normal distribution

with variance-covariance matrix Σ. mt is assumed to have zero mean without any lag
dependence, which is justified if surprises in financial variable are unpredictable, as in
Jarociński and Karadi (2020). We estimate equation (1) by using a Bayesian method.
As discussed in the seminal paper of Uhlig (2005), the posterior distribution for the VAR
coefficients and the inverse of variance-covariance matrix can be given by the NormalWishart distribution under the diffuse priors.
Given the posterior draws of coefficients and variance-covariance matrix, the structural
shocks are identified by the sign restriction method. We use QR decomposition proposed8

by Rubio-Ramı́rez et al. (2010). Specifically, we first calculate a lower triangular matrix
A0 using the Cholesky decomposition of Σ and generate the orthonormal matrix Q suchas QQ′ = I by the QR decomposition of the random matrix whose elements are drawn
from the standard normal distribution. Subsequently, multiplying A0 by Q provides
the new contemporaneous matrix, A = A0 Q. For each combination of VAR coefficientsand variance-covariance matrix, we randomly generate Q matrix 100 times and save thedraw if all the sign restrictions are satisfied for the generated matrix A. The processof generating the coefficients and variance matrix is repeated until we have 5000 valid
draws.
Table 1 summarizes sign restrictions adopted in this study. As discussed above, monetary policy and central bank information shocks are identified by the signs of highfrequency co-movements between the interest rate and stock prices. First, a tightening
monetary policy shock is assumed to increase the interest rate and decrease stock prices,leading to a negative co-movement between the interest rate and stock prices. Second, CB
information shock along with a tightening monetary policy is assumed to have positiveeffect on stock prices, meaning that the announcement about a tightening policy conveys
the BOJ’s positive economic outlook to the private agents. Finally, shocks other thanmonetary policy and central bank information shocks are assumed to have no effects on
surprises in both the interest rate and stock prices.3 These sign and zero restrictions canfully distinguish monetary policy and CB information shock from other possible structural shocks. In addition, all restrictions are only imposed on the surprise in interest rateand stock prices. Thus we let the data determine the responses of the variables included
in yt .
3

To achieve zero restrictions on high-frequency surprises in interest rate and equity, we specify thematrix Q, following Jarociński and Karadi (2020), as
 ∗

Q 0
Q=
,
0 I
where only Q∗ is generated by the QR decomposition of arbitrary 2 × 2 matrix.

9

Table 1: Sign restrictions
Monetary policy

CB information

Other

Interest rate

≥0

≥0

0

Stock prices

≤0

≥0

0

Other variables

∗

∗

∗

Notes: The marks ≥ 0 and ≤ 0 literally indicate non-negative and non-positive sign restrictions, respectively. Similarly, zero restriction is represented by 0. The asterisk mark, ∗, means no restrictions on thevariables.

3

Data and specification

Our sample period is from July 2006 to December 2021 because of the data availability.
We can obtain the exact release times of the BOJ’s statements after the MPMs from
July 2006. The BOJ held 190 meetings during our sample period, including scheduled
and unscheduled meetings. For each MPM, we collect surprises in Euroyen and TOPIX
futures within 30-min window. However, the vector of macroeconomic variables in the
VAR model comprises uncertainty index, stock prices, output measure, and prices, withthe uncertainty index and stock prices capturing changes in expectations, and output andprices measuring the macroeconomic impact of the shock. In the benchmark analysis, we
use Nikkei Volatility Index (Nikkei VI), Tokyo Stock Price Index (TOPIX), monthly real
GDP published by Japan Center of Economic Research, and consumer price index (CPI,
all items less fresh food). The variables other than the CPI are included in their natural
logarithms multiplied by 100, while the CPI is included in the form of the year-on-year
inflation rate. As shown in Section 5, the robustness is also checked by limiting theevents only to scheduled MPMs, using the other measure of the variables, and changing
the sample period.
Regarding the model specification, the VAR model in equation (1) is estimated witha constant term and 12 lags. The information criterion selects the shorter lags (actually
only one lag), but we select 12 lags to capture the dynamics of the variables sufficiently.
In addition, draws in which the roots of the VAR coefficients lie within the unit circle are
saved in the sampling process to ensure stationarity of the VAR system.

10

4

Empirical results

4.1

Impulse responses

Figure 2 depicts the impulse responses to monetary policy and CB information shocks inthe first and second column, respectively. First, we observe almost opposite signs of the responses in financial and macroeconomic variables to monetary policy and CB information
shocks, meaning that two structural shocks are clearly distinguished by sign restrictions
imposed on high-frequency movements in the interest rate and stock prices. Furthermore,
the responses to CB information shock are clearly different from the “conventional ” responses to monetary policy shock, which is usually expected from a theoretical model.
This evidence supports the presence of CB information effects in the BOJ’s monetaryannouncements.
A closer look at the responses shown in Figure 2 reveals the following results. The
first column shows the impulse responses to a monetary shock. As described in Section
2.2, this tightening shock is a combination of the rise of Euroyen futures rate and thedrop in TOPIX equity futures within a narrow window. After the sudden drops, Nikkei
VI positively reacts to the shock in the short or medium run. This is due to the nervous
perception of private agents in response to the shock. The result that the uncertainty
measure rises regarding a monetary tightening shock is consistent with that of Bekaert
et al. (2013).4 With monetary policy tightening, stock prices show a long term and statistically significant downturn. Output and price levels significantly increase in the short
run and decline in the medium run. In summary, at least for the medium-term, impulse
responses to a monetary shock are consistent with conventional responses expected fromtheoretical models.
4

Bekaert et al. (2013) show that the uncertainty measure declines immediately in response to amonetary easing shock and rises in the medium to long term. The difference between their results andours may be due to the order of variables and use of real interest rates.

11

Figure 2: Impulse responses
Notes: The dashed line indicates the median of impulse response functions. The dark and light shaded
areas correspond to 68% and 90% credible intervals, respectively.

The second column shows the impulse responses to a CB information shock. The
CB information shock accompanying a tightening monetary policy is associated with afall in Euroyen futures rates and rise in TOPIX equity futures. As mentioned above, we
12observe the responses in the opposite direction to a monetary policy shock. The shock
has a negative statistically significant impact on Nikkei VI in the short and medium run.
This response is due to the transmission of optimistic news to private agents that theeconomy is in good condition to tighten the financial market. This information shockleads to the statistically significant increment of the stock price. Moreover, both theoutput and price level increase in the medium run. These effects are contrary to those ofa monetary policy shock.
The third column represents the impulse responses of a monetary policy shock identified by a Cholesky decomposition. These effects are mixtures of shocks identified bya sign restriction. As signs of the responses are opposite between a monetary policy
shock and a CB information shock, the effect of a monetary policy shock identified by
the Cholesky decomposition is relatively neutral.

4.2

Forecast error variance decomposition

Table 2 presents the results of forecast error variance decomposition (FEVD), whichallows us to quantitatively assess the contributions of each structural shock on the variables. The results indicate that the monetary policy shocks identified by us is different
from those which are derived from standard recursive restriction in quantitative perspective. Notably, monetary policy shocks from our identification, shown in the top panel (i),contribute to the high-frequency movements in both the Euroyen and TOPIX futures,
while shocks from recursive restrictions, shown in the bottom panel (iii), only contributeto the movement in the Euroyen futures. As monetary announcements are likely to affect stock prices immediately, the standard HFI fails to capture the contemporaneous
effects on stock prices and thus may only estimate a part of monetary policy shocks.
In addition, our results demonstrate the quantitative role of CB information shocks inmonetary announcements, shown in the middle panel (ii). About 40% of the movements
in both Euroyen and TOPIX futures are explained by CB information shocks, implyingthat the CB information effects contained in monetary announcements has relevant rolein high-frequency movements of asset prices.

13

Table 2: Forecast error variance decomposition(i) Monetary policy shock
Horizons

Euroyen futures

TOPIX futures

Nikkei VI

TOPIX

GDP

CPI

1

0.59

0.57

0.01

0.00

0.00

0.01

5

0.59

0.57

0.07

0.02

0.06

0.04

10

0.59

0.57

0.12

0.11

0.10

0.11

20

0.59

0.57

0.14

0.21

0.15

0.19

30

0.59

0.57

0.15

0.21

0.16

0.21

(ii) CB information shock
Horizons

Euroyen futures

TOPIX futures

Nikkei VI

TOPIX

GDP

CPI

1

0.41

0.43

0.00

0.00

0.00

0.00

5

0.41

0.43

0.13

0.06

0.04

0.05

10

0.41

0.43

0.25

0.12

0.12

0.08

20

0.41

0.43

0.27

0.12

0.17

0.11

30

0.41

0.43

0.26

0.12

0.18

0.13

Horizons

Euroyen futures

TOPIX futures

Nikkei VI

TOPIX

GDP

CPI

1

1.00

0.03

0.01

0.00

0.00

0.00

5

1.00

0.03

0.15

0.03

0.05

0.02

10

1.00

0.03

0.14

0.05

0.09

0.06

20

1.00

0.03

0.17

0.09

0.10

0.10

30

1.00

0.03

0.17

0.10

0.11

0.11

(iii) Cholesky

Notes: The figures in the table indicates the median estimates of each variable in selected horizon.

The results of the FEVD also proves the influence of the shocks on the financial andmacroeconomic variables. Importantly, the standard HFI scheme using the Cholesky
decomposition possibly underestimates the effects of monetary actions. The total contribution of two-shocks, (i) monetary policy shock and (ii) CB information shock, on thevariables always exceed the contribution of “monetary policy shock” identified by (iii) the
Cholesky decomposition. Let us suppose, as in Romer and Romer (2000), Melosi (2017),
Nakamura and Steinsson (2018), and Jarociński and Karadi (2020), that monetary announcements convey the CB’s information. Thus, the monetary policy shock identified in
the conventional manner captures only a part of it. Second, the contribution of monetaryshocks on macroeconomic variables is estimated to be relatively high compared with the14

estimates reported in previous studies on the Japanese economy. For example, Shibamoto(2007) reports that monetary policy shock contributes to 5% and 10% of forecast error
variances in output and prices at the 60-month horizon, respectively. More recently, Nagao et al. (2021) document that 3% of forecast error variances in both output and prices
are explained by monetary policy shock at the 24-month horizon. In contrast, the resultsin Table 2 show that each monetary shock contributes about 10% more to output andprices in the medium- and long-run horizon. Future studies should address whether thesedifferences between are due to differences in identification methods used in this study
and previous studies. Nevertheless, as noted in the first point, we confirm that the sumof contributions of monetary policy and CB information shocks exceeds that of the shock
identified by the Cholesky decomposition. Finally, as shown in the middle panel (ii), CB
information shocks steadily contribute to the movement of Nikkei VI, suggesting that
CB information effects are transmitted to the economic activities through channels of
uncertainty.

5

Robustness check

This section conducts robustness analyses to check whether main findings continue to holdunder the exclusion of unscheduled meetings, different measures of variables, and the useof different sample periods. Figure 3 displays the IRFs obtained with various specifications along with the ones obtained with the benchmark specification. The benchmark
IRFs and confidence intervals associated with them are represented by thick solid lines
and shaded areas, respectively. The IRFs under the different specification lie within
the confidence bands of the benchmark; thus, our benchmark results are robust without
depending on the specifications.

15

Figure 3: Impulse responses: Robustness checks
Notes: The thick solid line and shaded areas correspond to the benchmark results. The impulse responses
obtained in the robustness analyses are drawn by the thin, dashed or the accented lines.

Specifically, the first robustness analysis checks whether the result may change by excluding unscheduled meetings from the sample. As discussed in Nakamura and Steinsson(2018) and Kubota and Shintani (2022), the unscheduled meetings may have been causedby the structural shocks other than monetary policy and CB information shocks. Furthermore, we replace the variables in the VAR model with a possible alternative measure
to check the robustness. All the variables incorporated into the estimation are representative proxies for the variables originally employed in the benchmark analysis, and thedetails on the data are provided in Appendix A. The IRFs obtained in the robust analysis are within the confidence interval of the benchmark IRFs, with the exception that16

the response of the EPU index, alternatives of Nikkei VI, to the CB information shockis slightly outside the shaded areas. For this exceptional response, the IRFs of othervariables is confirmed to trace the benchmark ones, despite using the EPU index as theuncertainty measure. Finally, we limit the sample period until December 2020 because
the Euroyen futures have been barely traded in 2021, with high-frequency surprises inEuroyen futures taking zero for that year.5 The IRFs obtained from this sample period
follow those of the benchmark, confirming the robustness independent of the sample period. Overall, our main findings are robust to changes in the definition of MPMs, data,and sample period.

6

Conclusion

This study examined the effects of monetary policy shocks in Japan by separately identifying pure monetary policy and the CB information shock based on the method of
Jarociński and Karadi (2020). Our empirical results show that a monetary tightening
shock raises economic uncertainty and lowers stock prices and output, consistent withthe effects of monetary policy shown in previous studies. However, the CB information
shocks associated with monetary tightening reduce economic uncertainty and increase
stock prices and output, suggesting that the optimistic outlook of the central bank istransmitted through monetary tightening. Furthermore, the variance decomposition of
forecast errors indicates the significant contribution of CB information shock to economic
uncertainty index, leading to the possibility that central bank information effects may betransmitted through changes in uncertainty. Our findings emphasize that CB information
effects are crucial for evaluating the effects of monetary policy.
Finally, the relationship between the level of uncertainty and the two shocks should be
evaluated in future studies. Several studies have suggested that the effects of monetary
policy shocks differ with the level of uncertainty (Castelnuovo and Pellegrino, 2018; De
Pooter et al., 2021). Therefore, we can discuss the relationship with uncertainty byexamining whether the CB information effects are similar to those of monetary policy
5

The TOPIX futures were traded even in 2021, so that the structural shocks in 2021 were likely tobe identified based on the movements in the TOPIX futures.

17

shocks. Second, the effect of shocks over time should be analyzed. Hoesch et al. (2022)
points out that the CB information effect weakened around the early to mid-2000s since.
Furthermore, BOJ has adopted various policies such as QQE and NIRP. Therefore, it isalso important to examine how the impacts of monetary policy shocks and CB information
effects vary according to the policy.

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A

Data Appendix

A.1

Dates of monetary policy meeting

As described in Section 2.1, we focus on 190 BOJ’s statement released after the MPMs
held during the period from July 2007 to December 2021. The dates and times of BOJ’spress releases are collected from the BOJ’s website: https://www.boj.or.jp/en/mopo/
mpmdeci/state_all/index.htm/. The BOJ’s MPMs have been scheduled 14 times ayear until 2015 and 8 times a year after 2016; in addition, the BOJ sometimes holds
unscheduled meetings depending on the economic situation. In the benchmark, we include
both scheduled and unscheduled meetings in our sample, and also check the robustness
using only on the scheduled meetings.

A.2

High frequency financial variables

We use high-frequency data of Euroyen futures rates and TOPIX futures as the Japanesecounterparts of high-frequency interest rate and equity prices in Jarociński and Karadi(2020). The data for Euroyen futures and TOPIX futures are downloadable from theCQG Data Factory (https://www.cqgdatafactory.com/) and JPX Data Cloud (http://db-ec.jpx.co.jp/?__lang=en), respectively. Both data sources provide a tick-by-tickdata that records prices for every transaction.

A.3

Monthly financial and macroeconomic data

In addition to high-frequency surprises, our VAR model comprises four types of monthly
financial and macroeconomic data: uncertainty measure, stock prices, output, and prices.
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We use Nikkei VI, TOPIX, monthly real GDP, and CPI (all item, less fresh food) in thebenchmark analysis, and the robustness is also checked by replacing the variables withother indicators. All the data included in yt are collected from the Nikkei NEEDSFinancial Quest provided by Nikkei Media Marketing Inc., unless specifically mentioned.
Uncertainty index
The uncertainty measure in the benchmark is Nikkei VI, which represents the one-monthahead expected volatility of the Nikkei 225 computed based on prices of Nikkei 225
futures and Nikkei 225 options. The higher value in Nikkei VI implies that the market
participants expect the larger fluctuation in the stock market. In the robustness analysis,
we use the Japan Economic Policy Uncertainty Index, which is constructed by Saxegaard
et al. (2022) and released from https://www.policyuncertainty.com/japan_monthly.
html. The EPU index is a newspaper-based uncertainty index. Specifically, this index
represents the relative frequency of occurrence of articles related to “policy uncertainty”
that is constructed by dividing the number of articles reporting about policy uncertainty
by the total number of articles.
Stock prices
As for the stock prices, we use the TOPIX (Tokyo Stock Price Index) in the benchmark
and Nikkei 225 average (Nikkei 225) in the robustness analysis. Both TOPIX and Nikkei
225 are the representative stock market indices of Japan. TOPIX is a weighted averageof the stock prices of companies listed on the First Section of the Tokyo Stock Exchange(TSE), while the Nikkei 225 is composed of 225 stocks selected from the stocks listed on
the First Section of the TSE (recently, the Prime Market of the TSE).
OutputIn the System of National Accounts published by the government, GDP is only released
on a quarterly basis. Thus, we use the monthly estimates of GDP constructed by private
think tanks. The GDP in the benchmark is released by Japan Center for Economic
Research while that in the robustness analysis is by NLI research Institute establishedby Nippon Life Insurance Company.
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Prices
Two types of consumer price indice (CPI) are used for our analyses. The CPI for all
items less fresh food, known as core CPI, is employed in the benchmark, while the CPI
for all items, less food (less alcoholic beverages) and energy, known as core core CPI, isemployed in the robustness analysis. We obtain the consumption tax adjusted CPI fromthe website of Statistics Bureau of Japan: https://www.stat.go.jp/data/cpi/1.html(Japanese page only).

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