Bank of Japan Working Paper Series

Broad
Perspective Broad-Perspective Review Series
Review

The Impact of Quantitative andQualitative Easing and Yield Curve
Control on the Functioning of theJapanese Government Bond Market
Noritaka Fukuma*
noritaka.fukuma@boj.or.jp

Tomiyuki Kitamura*tomiyuki.kitamura@boj.or.jp

Kohei Maehashi*
kouhei.maehashi@boj.or.jp

Naoki Matsuda*1
naoki.matsuda@boj.or.jp

Keita Takemura*2
keita.takemura@boj.or.jp

Kota Watanabe*3
kouta.watanabe@boj.or.jp

No.24-E-9
August 2024

Bank of Japan
2-1-1 Nihonbashi-Hongokucho, Chuo-ku, Tokyo 103-0021, Japan*

Financial Markets Department (1 Currently, Secretariat of the Policy Board, 2Currently, Financial System and Bank Examination Department, 3 Currently,
Research and Statistics Department)
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The Impact of Quantitative and Qualitative Easing andYield Curve Control on the Functioning of
the Japanese Government Bond Market
Noritaka FUKUMA†

Tomiyuki KITAMURA‡

Naoki MATSUDA#

Keita TAKEMURA**

Kohei MAEHASHI§
Kota WATANABE***

August 2024
Abstract
This paper examines the impact of the Bank of Japan (BOJ)’s Quantitative andQualitative Easing (QQE) and Yield Curve Control (YCC) on the functioning of theJapanese government bond (JGB) market using panel data for JGB issues. The
main results can be summarized in the following three points. First, regarding theimpact on transaction volume in the JGB market, JGB purchases by the BOJ (i.e.
increase in flow) increase transaction volume on average, while the BOJ’s
increased holdings of JGBs (i.e. increase in stock) and its conduct of continuous
fixed-rate purchase operations decrease transaction volume. However, if the BOJ
conducts JGB purchases when its share of JGB holdings exceeds a certain threshold,transaction volume will decrease. Second, regarding the impact on bid-ask spreads
in the JGB market, while JGB purchases by the BOJ reduce these spreads, theincrease in the share of JGBs held by the BOJ will lead to a nonlinear widening of
the spreads. Third, regarding the impact on the shape of the yield curve, an increasein the BOJ’s holdings of certain JGB issues and its conduct of continuous fixed-rate
purchase operations will lead to a downward distortion in the yield curve.
JEL classification numbers: C23, D4, D53, E58, G12
Key words: QQE, YCC, JGB market, market functioning, market liquidityThis paper is based partly on the content of a presentation at the first session (on financial markets) of the firstworkshop for the Review of Monetary Policy from a Broad Perspective, titled “The Effects and Side Effects ofUnconventional Monetary Policy,” held on December 4, 2023. In writing this paper, we received valuablecomments from Masuhiro Awai, Alexander Duering, Kenji Fujita, Kentaro Iwatsubo, Sohei Kaihatsu, Izuru Kato,
Takuji Kawamoto, Taiki Kubo, Yoshiyuki Kurachi, Daisuke Miyakawa, Teppei Nagano, Takashi Nagahata,
Seiichi Shimizu, and BOJ staff members. We also received help from Fumiko Muramoto in preparing the figures.
We would like to express our deep appreciation to everyone for their assistance. However, any errors in thispaper are those of the authors themselves. Additionally, the views expressed herein are those of the authors anddo not necessarily reflect the official views of the Bank of Japan.
†
Financial Markets Department (E-mail: noritaka.fukuma@boj.or.jp)‡
Financial Markets Department (E-mail: tomiyuki.kitamura@boj.or.jp)§
Corresponding author, Financial Markets Department (E-mail: kouhei.maehashi@boj.or.jp)#
Financial Markets Department (currently, Secretariat of the Policy Board, E-mail: naoki.matsuda@boj.or.jp)**
Financial Markets Department (currently, Financial System and Bank Examination Department, E-mail:keita.takemura@boj.or.jp)***
Financial Markets Department (currently, Research and Statistics Department, E-mail:kouta.watanabe@boj.or.jp)

1

1. Introduction
The Bank of Japan (BOJ)’s large-scale monetary easing policies of the past decade or so,such as Quantitative and Qualitative Easing (QQE), which was introduced in April 2013, andYield Curve Control (YCC) under the framework of QQE with YCC, which was introducedin September 2016, had the effect of boosting the economy and pushing up prices by lowering
real interest rates. 1 , 2 However, it has been suggested that under QQE and YCC, thefunctioning of the Japanese government bond (JGB) market diminished as the BOJ increased
its holdings of JGBs and its influence over market interest rates. In fact, according to thefindings of the BOJ’s Special Survey of the Bond Market Survey (November 2023), bondmarket participants assessed that the functioning of the bond market had declined
considerably since the introduction of QQE (Figure 1).
Against this backdrop, this paper empirically analyzed the impact of QQE and YCC on thefunctioning of the JGB market. To conduct this analysis, we first need to set some analyzable
quantitative indicators for the somewhat abstract concept of “market functioning.” Many
metrics exist for measuring market functioning, but here we focus on two types: liquidity
indicators (such as the bid-ask spread) and relative price indicators (such as the deviationfrom the spline interpolation of the yield curve).3 Specifically, we used three indicators asmarket function metrics: transaction volume and the bid-ask spread as liquidity indicators andyield curve distortion for the relative price indicator. For these indicators, we used panel data
for JGB issues to perform a regression analysis on the impact of the BOJ’s monetary easing
policies.
The results of the panel data analysis confirmed the following three points. First, from theanalysis using transaction volume as the dependent variable, we found that JGB purchases by
1

At its March 2024 Monetary Policy Meeting, the BOJ decided to shift to the monetary policy framework inwhich the primary policy tool would be to guide the short-term interest rates under the price stability target of
2%, in the belief that its large-scale monetary easing had fulfilled its role.
2

The effects of monetary policy and developments in economic activity and prices after the introduction of
QQE were analyzed and assessed in “Comprehensive Assessment: Developments in Economic Activity andPrices as well as Policy Effects since the Introduction of Quantitative and Qualitative Monetary Easing (QQE)”(2016) and “Assessment for Further Effective and Sustainable Monetary Easing” (2021). The Review ofMonetary Policy from a Broad Perspective, which is now in progress, also includes analytical results.
3

Lorie Logan, president of the Federal Reserve Bank of Dallas, states that to measure whether a market isfunctioning requires not just looking at one indicator, but using several different metrics in a comprehensive way(Logan, 2020). She suggests dividing these indicators into liquidity metrics and relative price metrics, as we doin this paper.

2the BOJ (i.e. increase in flow) increased transaction volume on average, while an increase in
the share of JGBs held by the BOJ (i.e. increase in stock) and its conduct of continuous
fixed-rate purchase operations caused transaction volume to decline. However, it was foundthat transaction volume tended to decline if JGB purchases were made when the BOJ’s
holdings of JGBs exceeded a certain threshold. Second, from the analysis using the bid-ask
spread as the dependent variable, we confirmed that while BOJ purchases of JGBs reduced
bid-ask spreads, an increase in the proportion of JGBs held by the BOJ caused a nonlinear
widening of bid-ask spreads. Third, from the analysis using the distortion of the yield curve as
the dependent variable, we found that an increase in the BOJ’s holdings of some JGB issues
and its conduct of continuous fixed-rate purchase operations caused a downward distortion of
the yield curve.
The remainder of this paper is organized as follows. Section 2 discusses its relationshipwith the existing literature. Section 3 reviews the developments in the functioning of the JGB
market. Section 4 describes the estimation methodology and data for analyzing the impact of
the BOJ’s monetary policies of QQE and YCC on the functioning of the JGB market. Section
5 gives the results of an empirical analysis using panel data for JGB issues. Section 6 presents
our conclusion.

2. Relationship with existing literature
Although a fair amount of research has been done both in Japan and overseas on the impact
of government bond purchases by central banks and the impact of monetary policy on market
functioning, there has been little consensus among their conclusions.
First, among the prior studies of periods when traditional monetary policies were being
implemented, Harvey and Huang (2002) and Andersson (2010) pointed out that the Fed’s
open market operations led to increased volatility in government bond prices.4 For Japan aswell, Inoue (1999) reported that the BOJ’s open market operations caused transaction volume
in the JGB market to increase and the volatility of JGB prices to go up.5 Meanwhile,
4

Harvey and Huang (2002) analyzed the period from 1982 to 1988, while Andersson (2010) looked at theperiod from 1999 to 2006.
5

Iwatsubo and Taishi (2018) described the mechanism whereby central bank purchases of government bonds
increase the volatility in the government bond market as follows. In a situation where the central bank possesses
some special (private) information about government bond prices, market participants watching the open market

3

Pasquariello et al. (2020) asserted that from 2001 to 2007, the Fed’s open market operations
caused bid-ask spreads to narrow and market liquidity to improve.6
Among prior overseas studies of periods when unconventional monetary policies werebeing implemented on a broad scale, there are no definitive assessments of the impact that
central bank government bond purchases or monetary policies have on market functioning.
Kandrac and Schlusche (2013) reported being unable to find any evidence that the Fed’s
Large Scale Asset Purchases (LSAP) reduced market liquidity. In addition, De Pooter et al.
(2018) argued that the Securities Market Programme (SMP) of the European Central Bank
(ECB) reduced government bond liquidity premiums in the Eurozone peripheral countries andimproved market functioning. In contrast, Schlepper et al. (2020) asserted that the ECB’s
Public Sector Purchase Programme (PSPP) had the negative effect of reducing the depth of
the German government bond market. Additionally, Finlay et al. (2023) reported that under
the Reserve Bank of Australia’s Yield Target, government bond purchases had an adverse
effect on market liquidity, as they reduced the volume of government bonds circulating in themarket and caused bid-ask spreads to widen.
Of the research on the unconventional monetary policies implemented by the BOJ,
Iwatsubo and Taishi (2018) reported that since the introduction of QQE, liquidity in the JGB
market improved when the BOJ increased the frequency of its JGB purchases, reduced theamount it purchased each time, and reduced the variability of the amounts purchased per day.
In addition, according to Pelizzon et al. (2018), BOJ purchases of JGBs have both positive
and negative effects on market liquidity, the former being called the spotlight effect and thelatter being called the scarcity effect. The spotlight effect occurs when the BOJ purchases aoperations will think that these open market operations are using some special information that is not yet
reflected in the market prices. As a result, trading in the market becomes cautious, and the volatility of thegovernment bond market increases with rising uncertainty over future prices anticipated by market participants.
Bhattacharya and Weller (1997) and Chari (2007) also pointed out that in the foreign exchange market, the same
type of mechanism occurs through foreign exchange intervention by the currency authorities.
6

Regarding the background to such improvements in market liquidity, Pasquariello et al. (2020) commented
that the transparency of monetary policy improved after former Fed Chair Alan Greenspan started announcing
what the Federal Open Market Committee (FOMC) had decided and the Fed’s monetary policy stance at a press
conference following a FOMC meeting, and that there was no additional information regarding the Fed’smonetary policy stance in the open market operations themselves. When open market operations possess no
additional information, market participants who are watching the open market operations view them as “noise
trades” and start to engage in more arbitrage trading, which lowers the volatility of the government bond market
because it reduces the uncertainty over future prices anticipated by market participants (Iwatsubo and Taishi,
2018).

4certain bond issue and it becomes easier for market participants to sell JGBs from that issue,
leading to an increase in inter-dealer transactions and improved market liquidity. On the other
hand, the scarcity effect occurs when the BOJ’s JGB purchases cause the volume of JGBs
circulating in the market to decline, leading to a reduction in transaction volume and lessmarket liquidity. Furthermore, Han and Seneviratne (2018) noted that the impact of the BOJ’s
JGB purchases on market liquidity changes depending on the level of the BOJ’s holdings of
JGBs.
Meanwhile, the BOJ has also been analyzing the liquidity and market functioning of the
JGB market (Nishizaki et al., 2013; Kurosaki et al., 2014; Kinugasa and Nagano, 2017;
Sakiyama and Kobayashi, 2018; Kobayashi et al., 2019; Genma and Inamura, 2019). Some ofthese papers proposed new monitoring indicators for measuring the liquidity of the JGB
market in a more multifaceted manner. These liquidity indicators are now being posted on the
BOJ’s website about once per quarter.7
The present paper is based on this strand of prior studies. However, we also include in thescope of our analysis several key issues that were not addressed in prior research. First of all,while most prior research focused on the liquidity metric such as the bid-ask spread, we use
the relative price metric of yield curve distortion as well as the liquidity metrics of transaction
volume and the bid-ask spread in the analysis. This enables a broader examination of how the
BOJ’s monetary easing policy affected the market’s functioning. Second, as for the impact of
unconventional monetary policy, we analyze not only that of QQE, which was based onlarge-scale JGB purchases, but also that of fixed-rate purchase operations that were conductedin order to accomplish the long-term interest rate target of YCC. This allows us to form aclearer picture for our assessment of the BOJ’s monetary easing policy, which is thought tohave reduced the market’s functioning. Third, our panel data analysis used data through June
2023, so the analysis includes the samples of increased volatility in international financial
markets due to the outbreak of COVID-19 or the interest rate hikes by overseas central banks
since 2022. As we review in Section 3, pronounced deteriorations of market functioning, suchas the widening of bid-ask spreads and distortion of the yield curve, were observed in this7

https://www.boj.or.jp/en/paym/bond/index.htm#p02

5

period. Thus, having this period as part of our analysis enables us to conduct a morecomprehensive assessment of how QQE and YCC affected market functioning.8

3. Developments in JGB market functioning
In this section, we use liquidity indicators and relative price indicators to review thefunctioning of the JGB market.
First, Figure 2 shows the results of a survey on the major causes of price changes identified
by bond market participants when trading. From the late 1990s to the early 2000s, bondmarket participants focused on “Economic situation” and “Supply/demand for bonds” when
they were trading, but after around 2013, when QQE was introduced, they started to pay
attention to “Monetary policy” when trading. Then, from 2016, when YCC began, until
around 2021, market participants’ expectations for 10-year interest rates three months forward
started to trend generally within the YCC range of fluctuation (Figure 3). Starting fromaround 2022, overseas interest rates rose and expectations for domestic inflation increased.
This further increased bond market participants’ focus on monetary policy and also led
market participants to expect that interest rates would go above the YCC range of fluctuation.
Against this backdrop, transaction volumes in the JGB cash market declined withfluctuation following the introduction of QQE (Figure 4 (1)). For some time after the
introduction of YCC, the bid-ask spread improved as market participants’ expectations forinterest rates trended to be within the YCC range of fluctuation, but then it widened
significantly starting in 2022 (Figure 4 (2)). Also, from early 2022 to early 2023, pronounced
distortions in the yield curve were seen in the JGB cash market. Figure 5 shows the deviation
between the yield curve smoothed with spline interpolation and the actual market yield (foron-the-run 10-year bonds), showing that a major distortion in the yield curve occurred in
2022.98

At its July 2023 Monetary Policy Meeting, the BOJ decided to conduct YCC with greater flexibility, partly inorder to reduce the risk of affecting bond market functioning in light of future upside and downside risks, and nofurther bidding for fixed-rate purchase operations took place subsequently. As this paper focuses on examining
the negative effects of QQE and YCC on market functioning (which are side effects of the monetary easing
policy), we have not included the period after July 2023 in our analysis.
9

Figure 5 uses a cubic spline, but approximately the same results can be obtained with a spline that uses the
Nelson-Siegel model or a spline that uses locally weighted scatterplot smoothing (LOWESS). Furthermore, in
calculating the yield curve distortion, we could use a spline that a priori excludes issues thought to generate price6

In this way, the JGB market’s liquidity indicators deteriorated with the introduction of
QQE. Then, from the start of YCC until around 2021, some of the liquidity indicators such as
the bid-ask spread improved, as market participants’ expectations for interest rates moved
basically in tandem with the YCC range of fluctuation. However, from around 2022, overseasinterest rates began rising and expectations of domestic inflation increased, so liquidity
indicators deteriorated overall as market participants’ expectations for interest rates deviatedfrom the YCC range of fluctuation, and yield curve distortion became pronounced.

4. Framework and data for the empirical analysis
This section presents the framework for our empirical analysis and describes the data used.
As stated previously, various metrics exist for measuring the degree of the JGB market’sfunctioning, but this paper focuses on two types: liquidity indicators (e.g. the bid-ask spread)and relative price indicators (e.g. the deviation from the spline of the yield curve). Specifically,
we use three indicators as our metrics for the degree of market functioning: transaction
volume and the bid-ask spread as liquidity indicators and yield curve distortion as the relative
price indicator. In addition, we performed regression analyses using these indicators as
dependent variables and issue-by-issue panel data for cash market JGBs (2-, 5-, 10-, 20-, 30-,
and 40-year bonds) in order to examine the impact of QQE and YCC on the functioning of the
JGB market (Figure 6 shows the developments of the dependent variables in the following
analysis).

4-1. Estimation model using transaction volume as the dependent
variableLet us first use the following equation for the estimation model with transaction volume as
the dependent variable. We call this estimation model Model (1).
𝑇𝑟𝑎𝑑 , = 𝑐 + 𝛼 ∙ 𝑃𝑢𝑟 , + 𝛽 ∙ 𝐻𝑜𝑙 ,+ 𝛾 ∙ 𝑌𝐶𝐶 ,(1)+𝐶𝑜𝑛𝑡𝑟𝑜𝑙 + 𝜇 + 𝜇 + 𝜀 ,premiums for individual bond issues (such as on-the-run 10-year bonds). However, to keep from being arbitrary,
we have not made any such adjustments in this paper.

7

Here, the dependent variable 𝑇𝑟𝑎𝑑 , is the transaction volume of issue 𝑖 at time 𝑡 divided
by the outstanding issuance amount. 10 As for the independent variables, 𝑃𝑢𝑟 , is thepercentage of JGB purchases by the BOJ, that is, the amount of JGB purchases by the BOJ(which is a flow variable) divided by the outstanding issuance amount. 𝐻𝑜𝑙 , is thepercentage of JGBs held by the BOJ, that is, the balance of JGBs held by the BOJ (which is astock variable) divided by the outstanding issuance amount. 𝑌𝐶𝐶 , is the YCC range offluctuation upper bound dummy, which takes 1 if, (i) the issue 𝑖 could be purchased throughthe BOJ’s continuous fixed-rate purchase operations at time 𝑡 (or in other words, it is subject
to the operations), AND (ii) the difference between the yield on the issue 𝑖 and the interest
rate level for continuous fixed-rate operations is within 3 basis points at time 𝑡. 𝐶𝑜𝑛𝑡𝑟𝑜𝑙
denotes the control variables, specifically, the on-the-run issue dummy (which is 1 when theissue 𝑖 is a new issue), the first off-the-run issue dummy (which is 1 when it is the firstseasoned issue), and the cheapest dummy (which is 1 when it is the cheapest-to-deliver bondfor JGB future contracts). Additionally, 𝜇 is the individual issue fixed effect and 𝜇 is thetime period fixed effect.
The second term (𝑃𝑢𝑟 , ) and third term (𝐻𝑜𝑙 ,) on the right-hand side of equation (1) try

to capture the impact of QQE, which is based on the large-scale JGB purchases. As mentioned
above, according to Pelizzon et al. (2018), BOJ purchases of JGBs can have both a positiveeffect (spotlight effect) and negative effect (scarcity effect) on market liquidity. In the
spotlight effect, when the BOJ purchases a certain JGB issue, it becomes easier for market
participants to sell that issue, so inter-dealer transactions increase and market liquidity
improves. This effect can be captured by 𝑃𝑢𝑟 , , the share of JGB purchases. In the case of
the scarcity effect, when the BOJ purchases JGBs, the volume of bonds circulating in themarket declines, which reduces transaction volume and causes market liquidity to deteriorate.
This effect can be captured by 𝐻𝑜𝑙 ,, the share of JGBs held by the BOJ. 𝐻𝑜𝑙 ,islagged by one period in order to avoid overlap with the effect of the current period’s share of
JGB purchases (𝑃𝑢𝑟 , ).
The fourth term (𝑌𝐶𝐶 , ) tries to capture the impact of continuous fixed-rate purchase
operations conducted so that the YCC’s long-term interest rate target can be met. Continuous
10

Figure 6 (1) shows the chronological development of the average of all issues for the transaction volume ofissue i at time t divided by the outstanding issuance amount (𝑇𝑟𝑎𝑑 , ).

8

fixed-rate purchases are operations for limitless purchases of JGBs of a certain maturity overa certain time period at an interest rate designated by the BOJ in order to prevent large interest
rate increases.11 If the yield on issues subject to continuous fixed-rate purchase operations
approaches the interest rate level for continuous fixed-rate operations, market participants are
cognizant of possible conducts of the continuous fixed-rate operations while they trade.
Specifically, when the yield on issues subject to continuous fixed-rate purchase operations isapproaching the interest rate level for continuous fixed-rate operations, trading may ease off
due to expectations that the continuous fixed-rate purchase operations will take place and theyield on that issue will hit the interest rate level for continuous fixed-rate purchase operations,and that the resulting price change will be restricted.12 𝑌𝐶𝐶 , is a dummy variable forassessing these impacts of continuous fixed-rate purchase operations. It takes 1 when the yieldon the issue 𝑖 subject to continuous fixed-rate purchase operations is within 3 basis points of
the interest rate level for continuous fixed-rate purchase operations, and 0 otherwise.
In addition to Equation (1), we use Equation (2) below, which takes into account thepossibility that the coefficient of the share of JGB purchases by the BOJ (𝑃𝑢𝑟 , ) changes withthe level of the share of JGBs held by the BOJ (𝐻𝑜𝑙 ,). This estimation model, which wecall Model (2), is a more flexible model in which the impact of the share of JGBs purchasedby the BOJ (𝑃𝑢𝑟 , ) on transaction volume (𝑇𝑟𝑎𝑑 , ) can vary as the share of JGBs held by the
BOJ (𝐻𝑜𝑙 ,) changes. Note that the coefficient 𝜃 in Equation (2) is the coefficient for the

cross term of the share of JGBs purchased by the BOJ (𝑃𝑢𝑟 , ) and the share of JGBs held by
the BOJ (𝐻𝑜𝑙 ,).

𝑇𝑟𝑎𝑑 , = 𝑐 + 𝛼 + 𝜃 ∙ 𝐻𝑜𝑙 ,∙ 𝑃𝑢𝑟 , + 𝛽 ∙ 𝐻𝑜𝑙 ,+ 𝛾 ∙ 𝑌𝐶𝐶 ,(2)+𝐶𝑜𝑛𝑡𝑟𝑜𝑙 + 𝜇 + 𝜇 + 𝜀 ,11The decision to conduct continuous fixed-rate purchase operations was made at the March 2021 Monetary
Policy Meeting. Later, the April 2022 Monetary Policy Meeting specified that these fixed-rate purchase
operations would take place every business day, unless it is highly likely that no bids will be submitted, at a yieldof 0.25% for 10-year JGBs. Then, the December 2022 Monetary Policy Meeting revised the operation so thatthey would be conducted continuously at a yield of 0.5% for 10-year JGBs. The YCC range of fluctuation upperbound dummy ( 𝑌𝐶𝐶 , ) is constructed for the period after April 2022, when the interest rate levels forcontinuous fixed-rate operations were clarified.
12

According to Kobayashi et al. (2019), a positive correlation exists between market volatility and liquidity
metrics such as transaction volume. The mechanism here is that when market volatility increases, the pricechange expands, which leads to increased trading by stimulating inter-dealer price hedging transactions andshort-term trading.

94-2. Estimation model using the bid-ask spread as the dependent
variable
Next, we consider the following equation as an estimation model that uses the bid-ask
spread as the dependent variable.
𝐵𝐴𝑆 , = 𝑐 + 𝛼 ∙ 𝐷𝑃𝑢𝑟 , + 𝛽 ∙ 𝐻𝑜𝑙 ,(3)+𝛾 ∙ 𝑌𝐶𝐶 , + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 + 𝜇 + 𝜇 + 𝜀 ,Here, the dependent variable 𝐵𝐴𝑆 , is the bid-ask spread of issue i at time t, and the
independent variable 𝐷𝑃𝑢𝑟 , is a dummy for JGB purchases by the BOJ (which is 1 whenthe BOJ has conducted JGB purchases).13 The other variables are the same as in Model (1),where transaction volume was the dependent variable.
In addition to the estimation model of Equation (3), we run a model that takes into accountthe possible nonlinearity of the scarcity effect by adding the square of the share of JGBs heldby the BOJ (𝐻𝑜𝑙 ,), as in Equation (4) below. In this estimation model, the impact of theshare of JGBs held by the BOJ (𝐻𝑜𝑙 ,) on the bid-ask spread (𝐵𝐴𝑆 , ) is captured by the

combined value of the linear effect’s elasticity 𝛽 and the nonlinear squared term’s elasticity
𝛽 . This equation allows us to explore the possibility that the scarcity effect can change
nonlinearly depending on the BOJ’s share of JGB holdings.
𝐵𝐴𝑆 , = 𝑐 + 𝛼 ∙ 𝐷𝑃𝑢𝑟 , + 𝛽 ∙ 𝐻𝑜𝑙 ,+ 𝛽 ∙ 𝐻𝑜𝑙 ,(4)+𝛾 ∙ 𝑌𝐶𝐶 , + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 + 𝜇 + 𝜇 + 𝜀 ,4-3. Estimation model using yield curve distortion as the dependent
variable
Finally, we consider the following equation as an estimation model in which the distortionof the yield curve is the dependent variable.

13

In the estimation model where the bid-ask spread is the dependent variable, if we use the share of JGBs
purchased by the BOJ (𝑃𝑢𝑟 , ) as the variable for capturing the spotlight effect, we verify that its coefficient is
not significant. Because the bid-ask spread cannot be 0 or less, this result may be caused by the fact that theeffect of reducing the bid-ask spread may be limited when the spreads are already small even if the BOJ
purchases large volumes of JGBs. For this reason, in the estimation model where the bid-ask spread is thedependent variable, we use a dummy that is 1 when the BOJ has conducted JGB purchases (𝐷𝑃𝑢𝑟 , ) as thevariable for capturing the spotlight effect.

10𝐷𝑖𝑠𝑡 , = 𝑐 + 𝛼 ∙ 𝐻𝑜𝑙 , + 𝛽 ∙ 𝐹𝑅𝑂 , + 𝛽 ∙ 𝐹𝑅𝑂𝐶 ,(5)+𝐶𝑜𝑛𝑡𝑟𝑜𝑙 + 𝜇 + 𝜇 + 𝜀 ,Here, the dependent variable 𝐷𝑖𝑠𝑡 , represents the distortion of the yield curve for issue i attime t. This yield curve distortion is measured as the deviation of the actual yields from theyield curve in which the yield on each issue has been smoothed with a spline. For example,
looking at the distortions in the case of the yield for on-the-run10-year JGB issues (Figure 6(3)), in 2022, although the entire yield curve was under upward pressure, continuous
fixed-rate purchase operations held down increases in the 10-year interest rate so that thedeviation becomes significantly negative. In such a situation where the actual yield ismarkedly lower than the yield on the spline-smoothed yield curve, it can be said that there is a
downward distortion in the yield curve. The independent variable 𝐻𝑜𝑙 , is the share of JGBs
held by the BOJ, 𝐹𝑅𝑂 , is the fixed-rate purchase operations dummy (which is 1 when thebid for fixed-rate purchase operations have been submitted), and 𝐹𝑅𝑂𝐶 , is the continuous
fixed-rate purchase operations dummy (which is 1 when the bid for continuous fixed-rate
purchase operations have been submitted).14, 15 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 denotes the control variables, which
include the SLF utilization dummy (which is 1 when the Securities Lending Facility (SLF) isused), the on-the-run issue dummy, the first off-the-run issue dummy, and the cheapest issue
dummy.

14

As indicated by Sudo and Tanaka (2021), because the impact of JGB purchases by the BOJ on interest rates is
considered to be more important in terms of the stock effect than the flow effect, in Model (5) we omit the flow
variable of JGB purchases by the BOJ. Instead of this, for the share of JGBs held by the BOJ, which is a stock
variable, we add the value of the flow during the current period (𝐻𝑜𝑙 , ).
15

The fixed-rate purchase operations dummy and the continuous fixed-rate purchase operations dummy used inModel (5) indicate the actual occurrence or non-occurrence of purchases through these operations, which differsfrom the YCC range of fluctuation upper bound dummy used in Models (1) through (4). The YCC range offluctuation upper bound dummy not only indicates whether an actual operation was conducted, but also captures
any increased expectations of market participants regarding the conduct of continuous fixed-rate purchase
operations. On this point, for the shape of the yield curve, actual purchases through these operations should havea greater impact than market participants’ increased expectations regarding the conduct of these operations,because actual purchases also directly affect the balance of supply and demand for JGBs in the relevant maturity.
In fact, we did not obtain any statistically significant coefficient on the YCC range of fluctuation upper bounddummy when we added it to Model (5). Therefore, for Model (5), we used the fixed-rate purchase operations
dummy and the continuous fixed-rate purchase operations dummy, which indicates whether purchase operations
were actually conducted or not.

11

4-4. The data
This subsection describes the data used in the estimations. In all estimation models, Models(1) through (5), we used issue-by-issue panel data for cash JGBs (2-, 5-, 10-, 20-, 30-, and40-year bonds) (the horizontal divisions are individual bonds and the time divisions aremonths).
First, in Models (1) and (2), transaction volume (𝑇𝑟𝑎𝑑 , ) is inter-dealer transaction
volume (monthly average) divided by outstanding issuance amount (at month-end). Because
the transaction volume is among dealers, there is no way that purchases by the BOJ will
directly increase transaction volume (𝑇𝑟𝑎𝑑 , ). The sample period is from January 2012 toJune 2023, and the frequency of observations is monthly. The share of JGB purchases by the
BOJ (𝑃𝑢𝑟 , ) is the amount of JGB purchases by the BOJ (calculated from the difference in
the BOJ’s holdings of JGBs at month-end) divided by outstanding issuance amount (atmonth-end). Also, the share of JGBs held by the BOJ (𝐻𝑜𝑙 , ) is the BOJ’s holdings of JGBs
(at month-end) divided by outstanding issuance amount (at month-end). The YCC range offluctuation upper bound dummy (𝑌𝐶𝐶 , ) is 1 when the difference between the monthly
average yield on the issues subject to continuous fixed-rate purchase operations and theinterest rate levels for continuous fixed-rate purchase operations is within 3 basis points. The
on-the-run issue dummy is 1 when the relevant issue is a new issue, the first off-the-run issue
dummy is 1 when the relevant issue is a first seasoned issue, and the cheapest issue dummy is
1 when the relevant issue is the cheapest-to-deliver bond for JGB futures contracts.
In Models (3) and (4), the bid-ask spread (𝐵𝐴𝑆 , ) is the bid-ask spread for inter-dealer
transaction. The sample period is from October 2015 to June 2023, and observation values per
second are converted to monthly averages. The dummy for JGB purchases by the BOJ(𝐷𝑃𝑢𝑟 , ) is 1 when so ascertained from the difference from the prior month’s JGB holdings
by the BOJ. The other variables are the same as in Models (1) and (2).
In Model (5), the yield curve distortion (𝐷𝑖𝑠𝑡 , ) is calculated from the yield curvesmoothed by a spline (using a cubic spline) and the actual yields on each issue of JGBs, theresult being a deviation of the actual yield on each issue from the splined yield curve. The
sample period is from January 2008 to June 2023, and daily observation values are converted
to monthly averages. Also, the fixed-rate purchase operations dummy (𝐹𝑅𝑂 , ) is 1 when thebid for fixed-rate purchase operations is submitted, the continuous fixed-rate purchase12

operations dummy (𝐹𝑅𝑂𝐶 , ) is 1 when the bid for continuous fixed-rate purchase operations
is submitted, and the SLF utilization dummy is 1 when the SLF is used. The other variables
are the same as in Models (1) through (4).

5. Estimation results
5-1. Estimation results when transaction volume is the dependent
variable
Figure 7 (1) shows the regression results for Models (1) and (2), in which transaction
volume is the dependent variable. In Model (1), the coefficient of the share of JGBs
purchased by the BOJ (𝑃𝑢𝑟 , ) is positive and statistically significant at the 1% level. This
indicates that transaction volume increases when the BOJ purchases JGBs, and hence
confirms that there is a spotlight effect. Meanwhile, the coefficient of the share of JGBs heldby the BOJ (𝐻𝑜𝑙 ,) is negative and statistically significant at the 1% level, and we canconfirm the existence of a scarcity effect that reduces transaction volume when the share of
JGBs held by the BOJ increases. In addition, the coefficient of the YCC range of fluctuation
upper bound dummy (𝑌𝐶𝐶 , ) is negative and statistically significant at the 1% level, and we
observe that when continuous fixed-rate purchase operations are conducted or market
participants are aware of possible conducts of the operations, the price change of JGB issues
that will be subject to continuous fixed-rate purchase operations are expected to become
limited in the future, and as a result, transaction volume declines.
To consider the implications of the results of Model (1), we examine the situation in which
the BOJ purchases 1% of the outstanding issuance amount of JGBs at time 𝑡 . In this case,noting that the estimated parameters are α = 0.0065 and β = −0.0026, at time 𝑡 , thespotlight effect will cause transaction volume (as a percentage of total issuance) to increase by+0.0065 = +0.0065 × 1(%) . At this point, the scarcity effect has not yet appeared. In thefollowing period, 𝑡 , the spotlight effect goes away, and transaction volume declines by−0.0026 = −0.0026 × 1(%) due to the scarcity effect from the JGB holdings that werepurchased in the previous period. Because JGBs purchased by the BOJ are generally held tomaturity, the scarcity effect occurs at time 𝑡 and subsequent periods as well, so thattransaction volume declines −0.0026 each period until the bonds mature. In other words,
while the spotlight effect is transient, the scarcity effect continues until maturity.

13

Looking at the estimation results of Model (2), where the spotlight effect can change
depending on the share of JGBs held by the BOJ (𝐻𝑜𝑙 ,), the coefficient for the cross termof the share of JGBs purchased by the BOJ (𝑃𝑢𝑟 , ) and the share of JGBs held by the BOJ(𝐻𝑜𝑙 ,) is negative and statistically significant at the 1% level. The coefficients of the shareof JGBs purchased by the BOJ (𝑃𝑢𝑟 , ), the share of JGBs held by the BOJ (𝐻𝑜𝑙 ,), and the

YCC range of fluctuation upper bound dummy (𝑌𝐶𝐶 , ) are almost the same as those in Model(1). Based on these results, Figure 7 (2) shows how the spotlight effect (𝛼 + 𝜃 ∙ 𝐻𝑜𝑙 ,changes depending on the level of the share of JGBs held by the BOJ (𝐻𝑜𝑙 ,). We see thatthe spotlight effect is positive until the share of JGBs held by the BOJ (𝐻𝑜𝑙 ,around 70%, but once the share of JGBs held by the BOJ (𝐻𝑜𝑙 ,)) reaches) exceeds around 70%, thespotlight effect becomes negative.
The spotlight effect makes it easier for market participants to sell the specific JGB issue
that the BOJ is purchasing, and it stimulates inter-dealer transactions. The above results
suggest that when the share of a certain JGB issue held by the BOJ reaches a certain level,
market participants will realize that inter-dealer market liquidity has decreased, there will be
no inducement to trade outside of auctions conducted through BOJ operations, and a negative
spotlight effect will occur (i.e., transaction volume will decline). In addition, in light of theabove results, it can be said that if the share of JGB held by the BOJ exceeds a certain level, anegative signaling effect can occur with respect to the market liquidity in the future, and this
could work to constrain JGB transactions.

5-2. Estimation results when the bid-ask spread is the dependent
variable
Figure 8 (1) shows the regression results for Models (3) and (4), in which the bid-ask
spread is the dependent variable. In Model (3), the coefficient of the dummy for JGB
purchases by the BOJ (𝐷𝑃𝑢𝑟 , ) is negative and statistically significant at the 1% level. This
indicates that the bid-ask spread tightens when the BOJ purchases JGBs, and confirms againthat there is a spotlight effect. Meanwhile, the coefficient of the share of JGBs held by the
BOJ (𝐻𝑜𝑙 ,) is positive and statistically significant at the 1% level, and a scarcity effect isagain confirmed whereby the bid-ask spread widens when the share of JGBs held by the BOJ
increases. Additionally, the coefficient of the YCC range of fluctuation upper bound dummy(𝑌𝐶𝐶 , ) is negative and statistically significant at the 1% level. However, we think that thisresult only reflects the following rather mechanical movement: When continuous fixed-rate
14

purchase operations are conducted or market participants become aware of possible conductsof these operations, market participants expect the price changes of the issues subject tocontinuous fixed-rate purchase operations to be limited in the future, resulting in tightening of
bid-ask spreads.
To consider the implications of the results of Model (3), as we did for Model (1), we
examine the situation in which the BOJ purchases 1% of the outstanding issuance amount of
JGBs at time 𝑡 . In this case, noting that the estimated parameters are α = −0.0337 and
𝛽 = 0.0062, the spotlight effect causes the bid-ask spread to tighten by −0.0337 (=−0.0337 × 1). At this point, the scarcity effect has not yet appeared. In the following period,𝑡 , the spotlight effect goes away, and the bid-ask spread widens by +0.0062 =+0.0062 × 1(%) due to the scarcity effect from the JGB holdings that were purchased in
the previous period. Because JGBs purchased by the BOJ are generally held to maturity, thescarcity effect occurs in time 𝑡 and subsequent periods, so that the bid-ask spread continues
to widen +0.0062 each period until the bonds mature. It should be noted that the spotlighteffect is transient, whereas the scarcity effect continues until maturity. Taking into accountsuch differences in the persistence of these effects, we calculated the impact of the BOJ’s JGB
purchases (a spotlight effect) and JGB holdings (a scarcity effect) on the bid-ask spread ofon-the-run 10-year JGBs during the sample period, and found it to average about +0.2 basis
points.16
When we look at the regression results for Model (4), which takes into account the possible
nonlinearity of the scarcity effect, we find that the coefficient 𝛽 of the share of JGBs heldby the BOJ (𝐻𝑜𝑙 ,) is negative and statistically significant at the 1% level, and thecoefficient of its square 𝛽 is positive and also statistically significant at the 1% level. Figure
8 (2) shows the scarcity effect when the simple term and squared term for the share of JGBs
held by the BOJ (𝐻𝑜𝑙 ,) are combined. Here, the bid-ask spread does not fluctuate much

until the share of JGBs held by the BOJ reaches about 40%, but as it exceeds 50% andcontinues to grow, the bid-ask spread tends to widen in a nonlinear fashion.

16

The average bid-ask spread for on-the-run 10-year JGBs during the sample period (shown in Figure 6 (2))
was about 0.7 bps.

15

5-3. Estimation results when yield curve distortion is the dependent
variable
Finally, Figure 9 (1) shows the regression results for Model (5), in which yield curve
distortion is the dependent variable. The coefficient of the share of JGBs held by the BOJ(𝐻𝑜𝑙 ,) is negative and statistically significant at the 1% level. This indicates that, if theshare of a certain issue of JGBs held by the BOJ increases, the interest rate on that issue
declines, causing a downward distortion in the yield curve.17 Additionally, we see that thecoefficient of the continuous fixed-rate purchase operations dummy (𝐹𝑅𝑂𝐶 , ) is also negativeand statistically significant at the 1% level. This implies that the yield curve experienced adownward distortion due to the strong pressure that the continuous fixed-rate purchase
operations exert on rising interest rates.
Figure 9 (2), which is based on the estimation results of Model (5), divides the sample
period into three intervals—start of QQE (from April 2013 to September 2016), start of YCC(from October 2016 to December 2021), and from 2022 on (from January 2022 to June2023)—and shows the causes of the yield curve distortions for on-the-run 10-year JGBs ineach period.18 In the case of the period from 2022 on, when the yield curve experienced adownward distortion, the results show that the conducts of continuous fixed-rate purchase
operations significantly contributed to the downward distortion.

5-4. Robustness check
This section reports results of robustness check on the empirical results of the previous
sections regarding the sample periods and the calculation methods of the yield curve
distortion.

17

The direction of the impact of central bank purchases of government bonds on yield curve distortion is notclear a priori. A central bank’s purchase of a certain issue of government bonds reduces the volume ofgovernment bonds circulating in the market (supply), and this can cause a downward distortion of the yield curve.
At the same time, however, because reduced volume of government bonds circulating in the market (supply)
may increase the liquidity premium, demand for that issue can decrease, and an upward distortion of the yield
curve can take place. Nevertheless, our analytical results suggest that the yield curve experiences a downward
distortion when the BOJ purchases JGBs and its holdings increase.
18

In calculating the historical decomposition shown in Figure 9 (2), we used the coefficients of the estimation
results from the whole sample (the results in Figure 9 (1)), and the subsample averages of the independent
variables.

16

First, as we saw in Section 3, because the functioning of the JGB market deteriorated
significantly from 2022 on, the samples from 2022 on may heavily influence the empiricalresults. If this is true, then the impact of QQE and YCC discussed in the previous sections
should be interpreted for the limited period from 2022 on. Figure 10 shows the estimation
results for samples through December 2021 for Models (1) through (5). Looking at theestimated coefficients and their significance levels, one can see that the subsample results
have few significant differences from the full sample results (Figures 7 to 9). This means thatthe impact of QQE and YCC on the JGB market’s functioning discussed above extends overthe entire sample period.
Second, the computation of the yield curve distortion in Model (5) uses a cubic spline tosmooth the yield curve. As other methods can be used to smooth the yield curve (e.g., a splinethat uses the Nelson-Siegel model or a spline that uses LOWESS), the choice of thesmoothing method may affect the results of the empirical analysis. Figure 11 shows theestimation results of Model (5) with the yield curve distortion calculated using the yield curvesmoothed by the Nelson-Siegel model and LOWESS. The coefficients for the share of BOJ
holdings (𝐻𝑜𝑙 , ) and the continuous fixed-rate purchase operations dummy (𝐹𝑅𝑂𝐶 , ) arenegative and statistically significant. This is the same as in the case where the cubic spline
was used (Figure 9), and consistent with our discussion in the previous sections.

6. Conclusion
The present paper reviewed the developments in the functioning of the JGB market andexamined the impact of the QQE and YCC policies of the BOJ on the JGB market’sfunctioning. Specifically, we conducted an empirical analysis of how QQE and YCC affected
the JGB market’s transaction volume, bid-ask spreads, and yield curve distortion using panel
data for JGB issues. The analysis yielded the following three key findings. First, according to
the results of the analysis where transaction volume is the dependent variable, JGB purchasesby the BOJ increase transaction volume on average, while the BOJ’s increased share of JGB
holdings and its conduct of continuous fixed-rate purchase operations decrease transaction
volume. However, if the BOJ conducts JGB purchases when its share of JGB holdings
exceeds a certain threshold, transaction volume will decrease. Second, the results where thebid-ask spread is the dependent variable shows that, while JGB purchases by the BOJ reducethe bid-ask spreads, the increase in the share of the BOJ’s holdings of JGBs will lead to a17

nonlinear widening of the spreads. Third, the results where the yield curve distortion is thedependent variable shows that an increase in the BOJ’s share of a certain issue of JGB
holdings and continuous fixed-rate purchase operations will lead to a downward distortion in
the yield curve.
At its Monetary Policy Meeting in March 2024, the BOJ decided to shift to a monetary
policy framework in which the primary policy tool is to guide the short-term interest rates
under the price stability target of 2%, in the belief that its large-scale monetary easing had
fulfilled its role. Nonetheless, as the results of our empirical analysis show, among the effectsof the large-scale monetary easing on the functioning of the JGB market, the effect of theshare of JGBs held by the BOJ (which is a stock effect) will continue to remain. Furthermore,as the BOJ continues purchasing long-term JGBs, if the developments of the financial markets
shift, the functioning of the JGB market can experience other new changes. In light of thispoint, the functioning of the JGB market should continue to be monitored closely.

18

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Reserve and ECB monetary policy decisions,” International Journal of Central Banking,6(2), pp.117-146.
Bank of Japan (2016), “Comprehensive Assessment: Developments in Economic Activity andPrices as well as Policy Effects since the Introduction of Quantitative and Qualitative
Monetary Easing (QQE)”Bank of Japan (2021), “Assessment for Further Effective and Sustainable Monetary Easing”
Bhattacharya, U. and P. Weller (1997), “The advantage to hiding one’s hand: speculation andcentral bank intervention in the foreign exchange market,” Journal of Monetary
Economics, 39(2), pp.251-277.
Chari, A. (2007), “Heterogeneous market-making in foreign exchange markets: evidence fromindividual bank responses to central bank intervention,” Journal of Money, Credit andBanking, 39(5), pp.1131-1162.
De Pooter, M., R. F. Martin, and S. Pruitt (2018), “The liquidity effects of official bondmarket intervention,” Journal of Financial and Quantitative Analysis, 53(1), pp.243-268.
Finlay, R., D. Titkov, and M. Xiang (2023), “The yield and market function effects of theReserve Bank of Australia’s bond purchases,” Economic Record, 99(326), pp.359-384.
Genma, Y. and Y. Inamura (2019), “Kokusai shijyou ni okeru meigarakann no soutai
kakakusa ni tsuite,” Bank of Japan Working Paper Series, No.19-J-8. (only in Japanese)Han, F. and D. Seneviratne (2018), “Scarcity Effects of Quantitative Easing on Market
Liquidity: Evidence from the Japanese Government Bond Market,” IMF Working Paper,
No.18/96.
Harvey, C. R. and R. D. Huang (2002), “The impact of the Federal Reserve Bank’s open
market operations,” Journal of Financial Markets, 5(2), pp. 223-257.
Inoue, H. (1999), “The Effects of Open Market Operations on the Price Discovery Process in
the Japanese Government Securities Market: An Empirical Study,” Bank for International
Settlements (ed.), Market Liquidity: Research Findings and Selected Policy Implications,No. 11, part 2-Ⅲ, pp.1-21.
Iwatsubo, K. and T. Taishi (2018), “Quantitative easing and liquidity in the Japanese
Government Bond Market,” International Review of Finance, 18(3), pp.463-475.
Kandrac, J. and B. Schlusche (2013), “Flow effects of large-scale asset purchases,”
Economics Letters, 121(2), pp.330-335.

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Kinugasa, K. and T. Nagano (2017), “SC repo shijyou kara mita kokusai no kishousei,” Bankof Japan Working Paper Series, No.17-J-5. (only in Japanese)
Kobayashi, S., S. Kobayashi, and Y. Inamura (2019), “Genbutsu kokusai shijyou ni okeru
borateiritei to torihikidaka no kannkei,” Bank of Japan Working Paper Series, No.19-J-7.
(only in Japanese)
Kurosaki, T., Y. Kumano, K. Okabe, and T. Nagano (2015), “Liquidity in JGB Markets: An
Evaluation from Transaction Data,” Bank of Japan Working Paper Series, No.15-E-2.
Logan, L. K. (2020), “The Federal Reserve’s Market Functioning Purchases: From
Supporting to Sustaining,” remarks at SIFMA Webinar.
Nishizaki, K., A. Tsuchikawa, and T. Yagi (2013), “Indicators Related to Liquidity in JGB
Markets,” Bank of Japan Review 2013-E-3.
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trading in the U.S. Treasury Market,” Journal of Financial and Quantitative Analysis,
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20Figure 1. Comparison of bond market functioning by period
80

DI, percentage points

60

Bond market functioning DI

40
20
0
-20
-40
-60
-80
Period Ⅰ

Period Ⅱ

Period Ⅲ

Period Ⅳ

Period Ⅴ

Note: Based on the Bond Market Survey, Results of Special Survey (November 2023).
Period Ⅰ: before the introduction of QQE (from late 1990s to April 2013)
Period Ⅱ: after the introduction of QQE (from April 2013 to January 2016)
Period Ⅲ: after the introduction of the negative interest rate policy (from January 2016 to September
2016)
Period Ⅳ: after the introduction of YCC (from September 2016 to December 2021)
Period Ⅴ: from 2022 on
Source: BOJ.

21

Figure 2. Major causes of price changes identified by bond market participants
100% of total

90
80
70
60
50
40
30
20
10
0
98 00 02
Monetary policy
Foreign exchange
Stock prices

04

06

08 10 12 14 16
PricesSupply/demand for bonds

18

20 22 24 Year
Overseas interest rates
Economic situation

Note: Survey subjects were banks, brokerage firms, institutional investors, and other market participants.
Source: QUICK, "QUICK Monthly Survey (Bonds)."

22

Figure 3. Long-term interest rates after the introduction of YCC
2.0%
Market interest rate expectations (market forecasts)
Yield on 10-year bonds (actual)
YCC range of fluctuation
YCC reference range of fluctuation
Strict cap for long-term interest rates
Upper bound for long-term interest rates as a reference

1.5
1.0
0.5
0.0
-0.5
-1.0
16

17

18

19

20

21

22

23

24 Year

Note: Survey subjects were banks, brokerage firms, institutional investors and other market
participants. “Market interest rate expectations (market forecasts)” is the expected range for theyield of on-the-run 10-year JGBs three months forward. Until March 2021, “YCC range offluctuation” is based on the views of market participants and fixed-rate purchase operations at thetime. From September 2016 through July 2018, the range is set to be ±0.1%, and from August
2018 to March 2021, it is set to be ±0.2%.
Sources: QUICK, "QUICK Monthly Survey (Bonds)"; Bloomberg.

23

Figure 4. JGB market transaction volumes and bid-ask spreads(1) Transaction volumes
0.9
0.8

trillion yen / daytrillion yen / day
CashFutures (right-hand scale)

3.4
3.3

0.7

3.2

0.6

3.1

0.5

3.0

0.4

2.9

0.3

2.8
Pre-QQE

Start of QQEStart of YCC

From 2022 on(2) Bid-ask spread
0.4

reverse scale, bps

reverse scale, JPY cents↑ More liquidity

Cash
Futures (right-hand scale)

0.5

0.9
1.0

0.6

1.1

0.7

1.2

0.8

1.3

0.9

1.4

1.0

1.5
↓ Less liquidity

1.1

1.6
Pre-QQE

Start of QQEStart of YCC

From 2022 on

Notes: 1. Figures are period averages (Pre-QQE: from January 2012 to March 2013; Start of QQE: from April
2013 to September 2016; Start of YCC: from October 2016 to December 2021; From 2022 on: fromJanuary 2022 to January 2024; the same applies hereinafter).
2. For the cash market in (1), total daily transaction volume (inter-dealer transactions) for 2-, 5-, 10-,20-, 30-, and 40-year bonds. For the cash market in (2), the average bid-ask spread within a 1-second
frequency for on-the-run 10-year bonds (inter-dealer transactions). For the futures market in (2), theaverage for the widest 10 percent of the bid-ask spread within a 1-minute frequency (the average of
the widest 10%).
Sources: QUICK; Japan Bond Trading; Osaka Stock Exchange, Inc.; Nikkei Inc., "Nikkei NEEDS."

24

Figure 5. Yield curve distortion (for on-the-run 10-year bonds)
0.10

percentage points

0.05
0.00
-0.05
-0.10
-0.15
-0.20
02

04

06

08

10

12

14

16

18

20

22

24 Year

Note: The differential between the yield curve estimated by using a cubic spline interpolation for the yield curvefor each individual issue and the actual yield curve for each individual issue.
Source: Japan Securities Dealers Association.

25

Figure 6. Developments in dependent variables in the panel regression analysis(1) Transaction volume / outstanding issuance amount (average for all issues)
3

%

2

1

0
12

13

14

15

16

17

18

19

20

21

22

23 Year(2) Bid-ask spread (for on-the-run 10-year bonds)
4

bps

3
2
1
0
15

16

17

18

19

20

21

23 Year22(3) Yield curve distortion
10

bps

5
0
-5
On-the-run 5-year bonds-10

On-the-run 10-year bonds-15

On-the-run 20-year bonds

-20
08

09

10

11

12

13

14

15

16

17

18

19

20

21

22

23 Year

Note: (1) is inter-dealer transactions (monthly average value) divided by the outstanding issuance amount (atmonth-end). These are simple averages for all individual issues. (2) is the monthly average of the bid-ask
spread within a 1-second frequency for inter-dealer transactions. (3) is the deviation from the cubic splineof the yield curve for each individual issue.
Sources: QUICK; Japan Bond Trading; Japan Securities Dealers Association.

26

Figure 7. Estimation results for the impact on transaction volume(1) Estimation results
Model (1)Model (2)Share of BOJ purchases (%)

0.0065 ***0.0114 ***Share of BOJ holdings (%)-0.0026 ***-0.0024 ***－Share of BOJ purchases (%) x share of BOJ holdings (%)-0.0002 ***YCC range of fluctuation upper bound dummy-0.2517 ***-0.1978 ***On-the-run issue dummy

0.9640 ***0.9622 ***First off-the-run issue dummy

0.2571 ***0.2576 ***Cheapest issue dummy

0.0710 ***0.0704 ***Individual issue fixed effect

Yes

Yes

Time period fixed effect

Yes

Yes

Adjusted R-squared

0.51

0.52

Sample size

41,31341,313

Estimation period

From January 2012 to June 2023

(2) Relation between the spotlight effect and the share of BOJ holdings (Model 2)
0.02

Elasticity of the effect of BOJ purchases (flows) on transaction volume
BOJ purchases (flows) increase transaction volume(spotlight effect)

0.01

0.00

-0.01
BOJ purchases (flows) decrease transaction volume
-0.02
0

10

20

30

40
50
60
70
Share of BOJ holdings, %

80

90

100

Note: In (1), the share of BOJ purchases is the percentage of the amount purchased by the BOJ over theoutstanding issuance amount. The amount purchased by the BOJ is the difference of the amount held by
the BOJ. The share of BOJ holdings is lagged by one period to avoid overlap with BOJ purchases in thecurrent period. The YCC range of fluctuation upper bound dummy becomes 1 when the monthly averageinterest rate for issues subject to continuous fixed-rate purchase operations is within 3 bps of the interest
rate levels for continuous fixed-rate purchase operations. The on-the-run issue dummy is 1 when it is a
new issue, the first off-the-run issue dummy is 1 when it is a first seasoned issue, and the cheapest issue
dummy is 1 when it is the cheapest-to-deliver issue. The estimations use panel data for each issue. In (1),*** denotes significance at the 1% level. (2) was computed based on Model (2). The shaded area denotesthe 99% confidence interval.

27

Figure 8. Estimation results for the impact on the bid-ask spread(1) Estimation results
Model (3)Model (4)BOJ purchases dummy-0.0337 ***-0.0291 ***Share of BOJ holdings (%)

0.0062 ***-0.0039 **－0.0001 ***YCC range of fluctuation upper bound dummy-0.5613 ***-0.4909 ***On-the-run issue dummy

0.0737 **0.0806 **First off-the-run issue dummy

0.01730.0286

Cheapest issue dummy-0.0131-0.0189

Individual issue fixed effect

Yes

Yes

Time period fixed effect

Yes

Yes

Adjusted R-squared

0.05

0.05

Sample size

19,35019,350Share of BOJ holdings (%), squared

From October 2015 to June 2023

Estimation period(2) Impact of share of BOJ holdings (Model 4)0.8Impact of share of BOJ holdings on the bid-ask spread, bps

0.7

Bid-ask spread widens

0.6
0.5
0.4
0.3
0.2
0.1
0.0
-0.1
0

10

20

30

40

50

60

70

80

90

100

Share of BOJ holdings, %Note: In (1), the BOJ purchase dummy is for purchases by the BOJ. BOJ purchases are calculated from thechanges in the amount of BOJ holdings. The share of BOJ holdings is lagged by one period to avoid
overlap with BOJ purchases in the current period. The YCC range of fluctuation upper bound dummy
becomes 1 when the monthly average interest rate for issues subject to continuous fixed-rate purchase
operations is within 3 bps of the interest rate level for continuous fixed-rate purchase operations. The
estimations use panel data for each issue. In (1), *** denotes significance at the 1% level, and ** denotes
significance at the 5% level. (2) was computed based on Model (4).

28

Figure 9. Estimation results for the impact on yield curve distortion(1) Estimation results
Model (5)Share of BOJ holdings (%)-0.0002 ***Continuous fixed-rate purchase operations dummy-0.0033 ***Fixed-rate purchase operations dummy

0.0001

SLF utilization dummy-0.0425On-the-run issue dummy

0.0009First off-the-run issue dummy

0.0018 *Cheapest issue dummy-0.0170 ***Individual issue fixed effectYesTime period fixed effect

Yes

Adjusted R-squared

0.02

Sample size

50,254
From January 2008 to June 2023

Estimation period(2) Breakdown of factors in the changes (on-the-run 10-year bonds)
0.02

percentage points

0.01
0.00
-0.01
-0.02
-0.03
-0.04
-0.05
-0.06

Fixed effect + residualOn-the-run issue dummy
SLF utilization dummy
Continuous fixed-rate purchase operations dummy
Fixed-rate purchase operations dummy
Share of BOJ holdings
Yield curve distortion
Start of QQEStart of YCC

From 2022 on

Note: In (1), the fixed-rate purchase operations dummy is 1 when a bid was submitted for fixed-rate purchase
operations. The continuous fixed-rate purchase operation dummy is 1 when a bid was submitted forcontinuous fixed-rate purchase operations (implementation of this operation began in March 2022). The
SLF utilization dummy is 1 when the Securities Lending Facility is used. The estimations use panel data
for each issue. In (1), *** denotes significance at the 1% level, and * denotes significance at the 10%
level. In (2), the share of BOJ holdings is computed using the share of the BOJ’s holdings of on-the-run
10-year bonds less the simple average of the share of the BOJ’s holdings of all JGB issues (the deviationfrom the average) at each point of time.

29

Figure 10. Estimation results (subsamples)(1) Dependent variable is transaction volume
Model (1)Model (2)Share of BOJ purchases (%)

0.0067 ***0.0122 ***Share of BOJ holdings (%)-0.0032 ***-0.0030 ***－-0.0002 ***Share of BOJ purchases (%) x share of BOJ holdings (%)－－

On-the-run issue dummy

1.0205 ***1.0216 ***First off-the-run issue dummy

0.2727 ***0.2751 ***Cheapest issue dummy

0.0610 ***0.0595 ***Individual issue fixed effect

Yes

Yes

Time period fixed effect

Yes

Yes

Adjusted R-squared

0.51

0.51

35,95435,954

YCC range of fluctuation upper bound dummy

Sample size

From January 2012 to December 2021

Estimation period(2) Dependent variable is bid-ask spread
Model (3)Model (4)BOJ purchase dummy-0.0383 ***-0.0338 ***Share of BOJ holdings (%)

0.0096 ***-0.0050 **Share of BOJ holdings (%), squared－

0.0002 ***YCC range of fluctuation upper bound dummy－－

On-the-run issue dummy

0.1503 ***0.1713 ***First off-the-run issue dummy

0.05370.0765 **Cheapest issue dummy-0.0468-0.0511

Individual issue fixed effect

Yes

Yes

Time period fixed effect

Yes

Yes

Adjusted R-squared

0.04

0.04

Sample size

15,763

15,763

From October 2015 to December 2021

Estimation period(3) Dependent variable is yield curve distortion
Model (5)Share of BOJ holdings (%)-0.0002 ***Continuous fixed-rate purchase operations dummy
Fixed-rate purchase operations dummy－-0.0013

SLF utilization dummy-0.0005 **On-the-run issue dummy-0.0003First off-the-run issue dummy

0.0023 ***Cheapest issue dummy-0.0189 ***Yes

Individual issue fixed effectTime period fixed effect

Yes

Adjusted R-squared

0.01

Sample size

45,325
From January 2008 to December 2021

Estimation period

Note: See Figures 7 to 9 for definitions of the variables. *** denotes significance at the 1% level, and ** denotes
significance at the 5% level.

30

Figure 11. Estimation results (dependent variables are other indicators ofyield curve distortion)
Nelson-Siegel

LOWESS

Share of BOJ holdings (%)-0.0008 ***-0.0001 ***Continuous fixed-rate purchase operations dummy-0.0400 **-0.0365 ***Fixed-rate purchase operations dummy-0.0149-0.0043 ***SLF utilization dummy

0.0020 ***0.0000On-the-run issue dummy-0.0155 ***-0.0006

First off-the-run issue dummy-0.0120 ***-0.0013 **Cheapest issue dummy-0.0253 ***-0.0067 ***Individual issue fixed effect

Yes

Yes

Time period fixed effect

Yes

Yes

Adjusted R-squared

0.02

0.06

Sample size

50,24750,267

From January 2008 to June 2023

Estimation period

Note: See Figure 9 for definitions of the variables. *** denotes significance at the 1% level, and ** denotes
significance at the 5% level.

31