Quantitative Easing and Liquidity
in the Japanese Government Bond Market
Kentaro Iwatsubo
Tomoki Taishi

September 2016
Discussion Paper No.1623

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANQuantitative Easing and Liquidity in the Japanese
Government Bond Market

Kentaro Iwatsubo,a,* and Tomoki Taishi aaKobe University, Japan

September 2016

Abstract
The “Quantitative and Qualitative Monetary Easing” enacted immediately after theinauguration of the Bank of Japan Governor Kuroda brought violent fluctuations in theprices of government bonds and deteriorated market liquidity. Does a central bank’s
government bond purchasing policy generally reduce market liquidity? Do conditions
exist that can prevent this decrease? This study analyzes how the Bank of Japan’spurchasing policy changes influenced market liquidity. The results revealed that three
specific policy changes contributed significantly to improving market liquidity: 1)
increased purchasing frequency; 2) a decrease in the purchase amount per transaction;and 3) a reduced variability in the purchase amounts. These policy changes facilitatedinvestors’ purchase schedule expectations and helped reduce market uncertainty. The
evidence supports the theory that the effect of government bond purchasing policy onmarket liquidity depends on the market’s informational environment.

JEL classification: G14
Keywords: Monetary Policy; Quantitative Easing; Liquidity; Government Bond*

Corresponding author. 2-1 Rokko Nada, Kobe, 657-8501 Japan.
Tel.: +81-78-803-6875 E-mail address: iwatsubo@econ.kobe-u.ac.jp (K. Iwatsubo).
11. Introduction
In April 2013, the newly inaugurated Bank of Japan Governor Kuroda accelerated thequantitative easing program and initiated the purchase of long-term government bonds. Thiswas called the “Quantitative and Qualitative Monetary Easing (QQE).” Even though theJapanese financial market responded strongly to this monetary easing, the government bondmarket recorded historically violent fluctuations. Rates on mid-term Japanese government
bonds (JGBs), such as two-year and five-year bonds, rose and then slowly decreased over time.
Rates on long-term (10-year) and super long-term (20-year) JGBs fell briefly then rose again.
The market was surprised to see that the significant purchases of government bonds led to adecrease in bond prices (i.e., a rise in interest rates) rather than an increase (i.e., a decrease ininterest rates). In response to the increases in market rates, banks raised the prime lending rates
to compensate, meaning that the monetary conditions tightened rather than loosened for a shortperiod of time (Figure 1).
In addition, the implied volatility calculated from option prices of JGBs rose significantly(Figure 2). The violent fluctuations in government bond prices continued from April into May,
during which the circuit breaker in the JGB futures market was triggered five times in
mid-April and further three times in May.
Possible side effects of large-scale government bond purchasing have been addressed bymany academician and central bankers. Former Federal Reserve Board Chairman Ben
Bernanke stated in his Jackson Hole speech that “if the Federal Reserve became too dominant
a buyer in a market, trading among private agents could dry up, degrading liquidity and pricediscovery” (Bernanke, 2012).
Since the introduction of monetary easing, the Bank of Japan's bond holdings have exceeded
giant government bond holders, such as life and non-life insurance firms, due to their rapid
purchasing, resulting in a skewed bond distribution with a focus on 5- , 10- , 20- , and 30-year
newly issued bonds. Accordingly, the market participants became concerned over the possiblelack of floating government bonds in the market.
In general, does outright purchasing of government bonds by financial authorities deteriorate
market liquidity? Do conditions exist that can prevent this decrease? During and post thesecond phase of the QQE policy, announced in October 2014, no confusion was evident, nor
was any anxiety around liquidity apparent during the first phase of the April 2013 monetaryeasing. What was the difference?
While the motives and effectiveness of large-scale asset purchase programs have been
intensely debated, the effect of these trades on market quality has received much less attention.
Studies from the United States and European countries are split into two views: the theory thata purchasing policy has a negative effect on market liquidity (Harvey and Huang, 2002;2

Andersson, 2010; Inoue, 1999) and the theory that it improves market liquidity (Pasquariello,
et al., 2014; Brunetti et al., 2010; Christensen and Gillan, 2014). A consensus has not beenreached on this issue.
In this study, we show that the large change in the Bank of Japan's purchasing policy, since
the start of the monetary easing in April 2013, had an improved influence on market liquidity
in the government bond market, as evidenced by the decrease in quote spreads and Amihud’s(2002) ILLIQ. The purchasing policy brought about three specific changes: 1) an increase in
the frequency of purchases; 2) a decrease in the purchase amount per transaction; and 3) adecrease in the variability in the purchase amounts when purchased multiple times in one day.
We argue that these types of policy changes eased investors’ forecast on the purchasingschedule and helped reduce market uncertainty. We find a significant rise in the adverse
selection component of the effective spread in response to the large-scale government bondpurchases right after the start of the QQE, but the impact gradually decreases and becomes
negative as the purchasing policy changes. The model free implied volatility also shows asimilar pattern. Together with the downward trend of the dispersion of the JGB yield forecasts
among market participants, these pieces of evidence support the theory that central banks’communication and transparency play a significant role in the large-scale government bondpurchases in terms of market liquidity.
The rest of the paper is organized as follows. Section 2 provides a brief literature review anda theoretical motivation for asymmetric information frameworks. Section 3 details theexecution of government bond purchases included in the QQE and Section 4 describes theliquidity measures. Section 5 presents the empirical strategy and results. Section 6 discusses
the endogeneity issues and Section 7 concludes the paper.
2. Literature Review and Hypotheses
Although numerous studies have examined how open market operations (hereafter, OMOs),a monetary policy used by financial authorities, affect asset value and the macroeconomy, few
studies have investigated its influence on financial market liquidity. Harvey and Huang’s(2002) research appears to be the earliest study on the subject. Using intraday data on
government bond prices from 1982 to 1988, they show that the OMOs of the United States
increased the volatility of government bond prices. In subsequent studies, Andersson (2010)
confirms a strong upsurge in intraday bond market volatility at the time of the release of themonetary policy decisions by the Federal Reserve Board, while Inoue (1999) discovers thatJapan’s OMOs increased the trade volume in the government bond market and the volatility of
government bond prices.
However, using data covering 2001 to 2007, Pasquariello et al. (2014) show that the OMOs
3

of the United States lowered the bid-ask spread and question the results of previous studies
claiming that monetary policies worsens market liquidity. In the paper, Pasquariello et al.
(2014) focus on the fact that FRB Chairman Alan Greenspan has made the FOMC increasingly
transparent by announcing the monetary policy intentions and disclosing the federal funds
target rate to the public. This change made OMOs virtually uninformative about the Federal
Reserve’s future monetary policy stance.
Among the recent growing literature on the large-scale asset purchase programs (LSAP),
Kandrac and Schlusche (2013) find no significant liquidity effects associated with Treasury
purchases. In contrast, Christensen and Gillan (2014) analyze the effect the Treasury
inflation-protected securities (TIPS) purchases, included in the Federal Reserve Board Q2
program, had on the functioning of the market for TIPS and the related market for inflation
swaps, and find that the liquidity premium is reduced due to the TIPS purchase.
In this way, it has not been settled whether OMOs worsen or improve government bondmarket liquidity. First, let us briefly explain the theory that OMOs reduce market liquidity byfollowing Chari (2007). The basis of this theory is the adverse selection model, which is usedeven in studies on central bank intervention in foreign exchange markets (Bhattacharya andWeller, 1997; Naranjo and Nimalendran, 2000).
In the microstructure model with strategic informed traders, Chari (2007) assumes that
central banks are informed insiders since they have an informational advantage about thefundamentals of government bond prices (Bhattacharya and Weller, 1997). Furthermore,
central banks have utility functions different from standard profit maximizing agents in thatthey can choose to incur losses on their intervention operations. In doing so, central banks
weigh the expected cost of their bond transactions against their success in achieving target
objectives. On the other hand, rational speculators (i.e., strategic informed traders) in thegovernment bond market also have private information with respect to central bank objectives.
Furthermore, the following two conditions are set so that information may differ acrossparticipants in the market when central banks intervene (Kyle, 1985; Bhattacharya and Speigel,1991). First, central banks and speculators as a group can differ in their interpretation of thefundamentals. Second, individual traders’ private signals about the fundamentals may differ
across traders. These two effects can lead to an increase in market uncertainty if the target priceimplied by the intervention signal is not consistent with the fundamentals, causing speculators
facing the central bank’s transactions to trade more cautiously. As a result, uncertainty on the
future prices increases and market liquidity worsens. The uncertainty is especially intensified
when central bank interventions are unexpected. In such case, bid-ask spreads will increase due
to adverse selection risk and price volatility also increases.
Conversely, the theoretical model developed by Pasquariello et al. (2014) shows that the4

OMOs improve market liquidity and the magnitude of this impact depends on the market’s
informational environment. The critical assumption of this theory is that, even though central
banks are informed traders facing a trade-off between policy motives (a non-public anduninformed price target) and the expected cost of interventions, there is no information related
to the fundamentals in the OMOs. The reason is that they already release their monetary policy
decisions and policy details to the market before conducting OMOs. Therefore, the central
banks’ OMOs mitigate adverse selection concerns for market makers because they are noise
trades and thus induce speculators to trade more aggressively on their private signals, reducing
uncertainty about future prices. Consequently, price volatility decreases and the bid-ask spreadnarrows.
The essential difference in assumptions between the two competing theories is whether
OMOs have any information on fundamentals. Japanese experience allows us to test these
theories since the Bank of Japan changed their JGB purchasing policy to enable traders toforecast the timing and scale of OMOs and estimate fundamentals easily following the reduced
market liquidity experienced during the April 2013 monetary easing. The Bank of Japan madetheir interventions more transparent by pre-announcing the monthly rough schedule ofpurchases, increasing the frequency of trades and decreasing the purchase amount pertransaction. Therefore, we expect that government bond purchases do not worsen or even
improve market liquidity after the Bank of Japan changed their policy, as the theory by
Pasquariello et al. (2014) predicts.
3. Changes in the Bank of Japan’s Outright Purchasing Policy
With the aim of overcoming deflation that has lasted for nearly 15 years, the Bank of Japan
entered a new phase of monetary easing in terms of both quantity and quality in April 2013. It
began to double the monetary base and the amounts outstanding of JGBs as well as
exchange-traded funds (ETFs) in two years, and more than double the average remaining
maturity of JGB purchases. The Bank of Japan purchased JGBs from financial institutions inthe secondary market so that their amount outstanding increased at an annual rate of about 50
trillion yen1. In addition, the average remaining maturity of the Bank's JGB purchases was
extended from slightly less than three years to about seven years - equivalent to the average
maturity of the amount outstanding of JGBs issued.
However, the massive JGB purchases increased price volatility and worsened market
liquidity. The Bank of Japan responded to the market turbulence by changing the rate of its
1

The Bank of Japan increased the amount of purchases of JGBs at an annual rate of about 80
trillion yen at the start of the second phase of the QQE policy announced in October 2014.
5

purchases of government bonds. It increased the frequency of its purchases and lowered thepurchase amount per transaction. Table 1 shows that through these measures, the purchaseamount per transaction fell steadily from April 2014. The average number of purchases per day
was fixed at 2.6, the number of days on which bonds were purchased in a month at 10, and thetotal number purchases in one month at 26 after June. Another apparent characteristic is thatthe variability in the purchasing amount decreased then leveled off in cases where there were
multiple purchases in one day.
Furthermore, the Bank of Japan began to announce the monthly purchasing rate ex ante.
Although the detailed schedule of the Bank of Japan’s auctions is not disclosed in advance, abroad pattern has been shared with market participants.
The following is an example of actual business procedures of OMOs. At 10:10 in themorning, the Bank of Japan offers outright purchases of JBGs to eligible counterparties.
Around noon on the same day, it decides on the successful bids for the purchase and notifies
the bidders of the results on the total amounts of the bids, amounts of successful bids, and theaverage successful bid rate via the Bank of Japan’s and others’ websites. The purchases are
executed generally two business days after the auction day.
We call the day when the target bond for outright purchasing and the purchase amount areannounced, “auction day,” and the day when the government bond purchase is executed,“settlement day.” Since all the information about bond purchasing is revealed on the auctiondays, it is expected that the impact on market liquidity occurs only on the auction days.
Since the frequency of OMOs has recently increased, it is often the case that the auction dayof new purchases coincides with the settlement day of the previous purchases. During thesample period from April 4, 2013, to June 30, 2014, there are 307 business days. Among thesedays, 74 days are when the auction day and the settlement day coincide.
4. Data
We measure liquidity using bid-ask spreads, effective spreads, one-minute realized spreads,
one-minute adverse selection, and Amihud’s (2002) ILLIQ.
Although the intraday data on the prices of the JGB futures traded on stock exchanges areavailable, the intraday price data on over-the-counter trading of active JGBs (spot) are notwholly available in Japan. Among various OTC brokers dealing with JGBs, the biggest broker,
Japan Bond Trading CO., Ltd. (BB), discloses only execution prices and yields, which makes it
impossible to measure liquidity. As an alternative to the broker’s data, we use intraday datafrom Tradeweb, which is one of the top JGB brokers, operating electronic, over-the-countermarket places. The data is provided by Thomson Reuters and covers all bid and ask quotes.
Accordingly, we use the intraday quotes of 5- , 10- , 20- , and 30-year on-the-run JGBs to
6

calculate bid-ask spreads in the spot markets. These bonds reflect the type of long-term
maturity government bonds the Bank of Japan purchased.
We also measure liquidity in the JGB futures market by using the intraday data of the mostactively traded delivery month of JGB futures, particularly the long-term (10-year) government
bond futures, irrespectively the existence of 5- and 20- year bond futures. The data on JGB
futures, which are traded on Osaka Exchanges, are taken from Nikkei Media Marketing. It
includes all bid and ask prices, execution prices, and trading volume, which enables us tocalculate liquidity measures such as various spreads and ILLIQ.
Although intraday data is obtained, we do not focus on the intraday effect of OMOs on
liquidity because our preliminary analysis shows that the instantaneous impacts of the auction
announcements at 10:10 AM are neither significant nor stable. Instead, we analyze the effectusing the daily average of liquidity measures.
The bid-ask (half) spread is defined asbid  ask spreadt  ( Askt  bid t ) / 2mt
where mt is the quote midpoint. The wider the bid-ask spread, the less liquid the government
bond. We use the daily averages of the spreads taken from one-minute intervals.
The effective (half) spread is defined as

effective spreadt  qt ( pt  mt ) / mt
where qt is an indicator variable that equals +1 for buyer-initiated trades and -1 forseller-initiated trades, pt is the trade price, and mt is the quote midpoint prevailing at thetime of trade. We follow the standard trade-signaling approach of Lee and Ready (1991) and
use the contemporaneous quotes to sign trades and calculate effective spreads. For each day,
we use all JGB futures trades and quotes to calculate the average of all trades that day.
We also calculate Amihud's (2002) ILLIQ using the daily closing prices and daily trading
volume:ILLIQt | rt |.
volumetIf the effective spreads widen when JGBs are purchased, it is of interest to decompose thespread along the lines of Glosten (1987) to determine whether the wider spread means
information asymmetry between informed traders and liquidity providers, or more revenue for7liquidity providers.
We estimate revenue to liquidity providers using the one-minute realized spread, whichassumes the liquidity provider is able to close her position at the quote midpoint one-minuteafter the trade2. The realized spread is defined asrealized spreadt  qt ( pt  mt 1 ) / mt
where mt 1 is the quote midpoint one minute after the trade. In contrast, adverse selection ismeasured using the one minute price impact of a trade, defined using the same variables asadverse selection t  qt (mt 1  mt ) / mt .
Note that there is an arithmetic identity relating the realized spread, the adverse selection, andthe effective spread:effective spreadt  realized spreadt  adverse selection t .
In addition to asymmetric information, we use the dispersion of JGB yield forecasts among
traders as a measure of information heterogeneity and the implied volatility from option prices
as a measure of fundamental uncertainty. These three measures are expected to decrease if the
JGB purchasing policy change mitigates market uncertainty when the Bank of Japan intervenes
in the market.
The JGB yield forecasts are obtained from Quick Survey System (QSS), which conducts amonthly paper-based survey of forecasts made by professional forecasters as well as their
attributes in Japanese financial markets. The number of respondents is between 130 and 150.
The implied volatility we use is the S&P/JPX VIX, which is provided by the Tokyo Stock
Exchange and it measures a 30-day forecast of the variability of the long-term (10-year) JGB
futures price.
Table 2 contains summary statistics and correlation coefficients for liquidity measures. There
are some points worth mentioning. First, on average the adverse selection is higher than therealized spread. This is also evident from Figure 3. Both the bid-ask spreads and effective
spreads fluctuated unstably for some months from April 2013, but are stabilized afterwards.
The adverse selection is extremely high at the beginning of the sample period, while therealized spread takes a negative value. This suggests that the degree of information asymmetry2

We also analyzed using the same measures at five-minutes realized spreads and adverse
selection and obtained similar results to those one-minute measures.
8

is very high immediately after the QQE started. Further, the correlation between the adverse
selection and the realized spread is negative and close to -1.
Second, as Table 2 shows, the correlations between the Amihud’s (2002) ILLIQ and thebid-ask spreads on spot and futures markets are positive and high, but the degree ofcorrelations are higher between JGB VIX and these spreads. Especially the VIX has highcorrelations with the bid-ask spread and the effective spread on the futures market, whose
correlation coefficients are 0.86 and 0.78, respectively.
5. Estimation Results
In this section we analyze whether permanent OMOs generally reduce market liquidity andwhat conditions prevent this decrease by utilizing the Bank of Japan’s outright JGB purchases.
We proceed in two steps. First, we test whether the change in purchasing policy by the Bank ofJapan contributes to improving market liquidity. Second, we test whether the change mitigates
the information asymmetry between informed traders and liquidity providers and thus
enhances market liquidity.
5-1. Outright JGB Purchases and Market Liquidity
We examine how the Bank of Japan’s purchasing policy had an effect on market liquidity as
their policy changed. To test this, we use an event study methodology following previous
literature (Andersen et al., 2003). Specifically, we set the auction day dummy (day t) and thesettlement day dummy (day t+2) as explanatory variables to compare the effects of bondpurchases; however, the effects may depend on the way that the Bank of Japan purchases.
Hence, we suppose that the coefficients of the auction and the settlement dummies are
dependent on the number of purchases in one day, the purchase amount per transaction, and thestandard deviation of the purchase amounts for one day.
Furthermore, we call the day adjacent to the auction day, “following day,” and include thefollowing day dummy (day t+1) as an additional explanatory variable to see if the impact onliquidity lasts beyond a single day. We suppose that the effect of following day also depends onvariables related to the purchasing policy analogously.
Therefore, our estimation regression is as follows.
Liquidity measuret αβ1t Auction day dummyt β2t Following day dummyt β3t Settlement day dummyt  i  4βi X it  et
10

where
β1t β10 β11number in a day t β12 average amountt β13 s.d . of amountt
β2t β20 β21number in a day t 1 β22 average amountt 1 β23 s.d . of amountt 1
β3t β30 β31number in a day t  2 β32 average amountt  2 β33 s.d . of amountt  2

9(1)

As liquidity indicators, we use the bid-ask half spreads for current 5- , 10- , 20- , and 30-yearbonds for JGB spot markets and the bid-ask spread, effective spread and ILLIQ for JGB futures.
As control variables, we enter a Bank of Japan policy meeting day dummy, a US Federal Open
Market Committee (FOMC) one-day lag dummy, and a Ministry of Finance (5-, 10-, 20-, and30-year) tender day dummies. The lagged trading volume in the JGB futures market is also
included as a control variable, although the trading volume data is not available for the JGB
spot market.
Our analysis subject is confined to outright purchases of JGBs, excluding short-term
purchases such as those with sell-back conditions. We also exclude purchases of treasury
discount bills and the securities lending facility operation. The information on JGB auctions,such as their schedule and volume, is obtained from the website of Tokyo Tanshi Corp.
As Table 2 shows, since a number of liquidity measures are serially correlated, we use
Newey-West estimators in OLS regressions to take into account autocorrelation andheteroskedasticity in the error terms in the models.
Table 3 reports the results on the bid-ask spreads for 5-, 10-, 20-, and 30-year JGBs. The
estimated coefficients on the cross terms between the day dummies and the variables relating
to the purchasing policy indicate that increasing the frequency of purchases, decreasing theamount, and leveling off the amounts lead to lower spreads, thus improving market liquidity
both on the auction and following days. The effect of lowering the amount continues to besignificant even on the settlement days. From the coefficients, it is evident that the impacts are
greater for longer maturity bonds. Table 4 shows that the bid-ask spread, the effective spread,and ILLIQ for JGB futures are also sensitive to the change in purchasing policy. These results
suggest that the way the central bank intervenes has a significant bearing on market liquidity.
These effects do not die out only on the auction days but last at least three days until theoperations are executed.
In order to assess whether permanent OMOs improve or reduce market liquidity, we
calculate the time-varying coefficients representing the liquidity effect ( ̂ 1t , ̂ 2t , ̂ 3t inEquation (1)) by exploiting the estimated coefficients obtained from the regressions andmonthly averages of the number of purchases in one day, the purchase amount per transaction,and the standard deviation of the purchase amounts for one day. We rely on all estimatedcoefficients even though they are not significant.
Figures 5 and 6 show the time-varying ˆt s from April 2013 to June 2014. All graphs showthat the impacts are positive and high in April 2013 and gradually shift downward. Second, it isalso clear that the impacts on some liquidity measures, such as the bid-ask spread and the10effective spreads, become negative in the latter half sub-sample period. Taking into account theconfidence interval, we find that the negative time-varying coefficients in the period are notsignificantly different from zero, while those in the beginning of the sample period (from April
to May 2013) are significantly greater than zero 3 . Therefore, it is reasonable that thepurchasing policy changes contribute to improving liquidity to the degree that the JGB
purchases do not worsen the market liquidity. Lastly, the impact on liquidity is the highest on
the auction days, the second highest on the following days and the lowest on the settlement
days for the bid-ask spread, the effective spread, and ILLIQ.
5-2. The effects on asymmetric information, implied volatility, and dispersion of forecasts
We have shown that the Bank of Japan’s purchasing policy change has an improved
influence on market liquidity. However, what is the mechanism under which this policy change
works? Following the theories by Chari (2007) and Pasquariello et al. (2014), we argue that it
facilitates informed traders to forecast the time and scale of the next JGB purchases and to
estimate the fundamentals, and thus, mitigates market uncertainty so that they can further
engage in risk-arbitrage in the market.
To test this implication, we assess whether the following three variables are affected by the
Bank of Japan’s purchasing policy change: 1) the asymmetric information component of theeffective spread; 2) the implied volatility obtained from option prices; and 3) the dispersion of
forecasts on government bond yields. The asymmetric information and the implied volatility
are obtained on a daily basis, while the forecasts are collected on a monthly basis.
Table 5 reports the results of the regressions whose dependent variables are the adverse
selection, the realized spread, and the implied volatility. Leveling off the amount decreases theadverse selection and increases the realized spread on the auction and following days. On theother hand, a small amount interventions decrease the adverse selection on the settlement days.
For the implied volatility, a smaller amount and leveling off the amount decrease the implied
volatility on the auction, following, and settlement days.
In Figure 7, the graphs depict the time-varying coefficients of the three day dummies. The
adverse selection is positive and very high in April 2013 when intervening in the market, while
it becomes negative after September 2013. By contrast, the realized spread is negative but
becomes positive as time goes by. Both effects are evident on the auction and following days,
while the effect is not significant on the settlement days. In addition to this downward trend oftime-varying coefficients of the adverse selection, the effective spread and the adverse
selection has a high correlation (0.67), as shown at Table 2. The above evidence suggests that

3

The empirical results are not displayed in this paper but provided upon request.
11

the adverse selection is the key component in determining the effective spread.
The effect on the JGB VIX gradually becomes negative in March 2014. The impacts of thethree-day dummies are close to each other, but the magnitude is the highest on the auction days,
the second highest on the following days and lowest on the settlement days.
Since the forecasts on government bond yields are collected on a monthly basis, we are notable to use the survey data in the regression analysis. However, Figure 8 shows that thestandard deviations of both 1-month and 3-month forecasts decline for 5-year, 10-year, and20-year JGB yields. These pieces of evidence suggest that the Bank of Japan’s purchasing
policy change has a significant contribution to mitigating market uncertainty and enhancing
market liquidity.
6. Endogeneity Issues
A concern regarding the analysis presented above is the possibility that the purchases of JGBs
are endogenous. If the Bank of Japan reacts to worsening liquidity positions by purchasing
JGBs, then coefficient estimates will be biased. We conducted the Granger-causality using
daily data and found that JGB purchases are not caused by yesterday’s liquidity conditions4.
Another possibility is that the Bank of Japan may respond to market liquidity on an intraday
basis. However, this is impractical as purchases are relatively large, occur early in the morning,
and would require a simultaneous response to many different liquidity indicators.
Another concern is that if the purchasing policy change enables traders to forecast the next
intervention exactly, the purchasing day dummies become endogenous. In practice, however,what the Bank discloses is not the detailed schedule of purchases but rather a monthly roughschedule. Therefore, the exact timing and size of interventions remains uncertain for traderswhen the Bank intervenes in the market.
7. Conclusion
In this study, we investigate how the Bank of Japan’s outright JGB purchases has an effecton market liquidity. Although there are the two opposing theories on the effect, their criticaldifferences are whether OMOs have any information related to fundamentals. The large change
in the Bank of Japan's purchasing policy after the monetary easing in April 2013 is a naturalexperiment to allow us to test the implication from the theories. The purchasing policy brought
about three specific changes: 1) an increase in the frequency of purchases; 2) a decrease in thepurchase amount per transaction; and 3) a decrease in the variability in the purchase amounts
when purchased multiple times in one day. These types of policy changes eased investors’

4

The empirical results are not displayed in this paper but provided upon request.
12

expectations on the schedule of the next purchases and their estimations of the fundamentals,
reducing market uncertainty. The regression results show that these policy changes
significantly contributed to the improvement of the market liquidity. In addition, the adverse
selection, the implied volatility, and the dispersion of forecasts are all reduced by the policychange. These pieces of evidence suggest that central banks’ communication and transparency
play an important role in the large-scale government bond purchases in terms of market
liquidity.
Appendix
We thank Takasi Hatakeda, Kazuhito Ikeo, Bernd Hayo, Kazuhiko Ohashi, Wataru Ohta,
Paolo Pasquariello, Jie Qin, Ghon Rhee, Toshio Serita, Hideki Takada, Toyoharu Takahash,
Yasuhiko Tanigawa, Kazuo Ueda, Toshiaki Watanabe, Toshinao Yoshiba, and seminar
participants at the Bank of Japan Finance Workshop, University of Hawaii, the 11th
International Conference on Asian Financial Markets and Economic Development at Nagasaki
University, and Asian Finance Association Meeting. We are also grateful to Editage for English
language editing. This work was supported by JSPS KAKENHI Grant Number 16K03742.

13

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Inoue, H. (1999). The effects of open market operations on the price discovery process in theJapanese government securities market: an empirical study, Working Paper.
https://www.bis.org/publ/cgfs11ino_c.pdf
Kandrac, J. and B. Schlusche (2014). Flow effects of large-scale asset purchases, Economics
Letters 121, 330-335.
Kyle, A. S. (1985). Continuous Auction and Insider Trading, Econometrica, 53, 1315-1385.

14

Lee, C. M. C., and M. J. Ready (1991). Inferring trade direction from intraday data, Journal of
Finance 46, 733-746.
Naranjo, A. and M. Nimalendran (2000). Government Intervention and adverse selection costs
in foreign exchange markets, Journal of Finance, 13, 2, 453-477.
Pasquariello, P., J. Roush, and C. Vega (2014). Government intervention and strategic tradingin the U.S. treasury market, Working Paper, University of Michigan.
http://webuser.bus.umich.edu/ppasquar/openmarket.pdf

15

Table 1 Changes of Purchasing Strategy of JGB
The units are the number of times, and billion yen. The data is from the website of the Tokyo
Tanshi Corp.
(times, billion yen)
Date of auctions
Jan.-March 2013
April
May
June
July
AugustSeptember
OctoberNovember
DecemberJan.-June 2014

Average
Average Average
Average of standard
Average days
Amount in
numbernumber in amount per
deviation of amountin a montha monthin a daya month transaction
in a day
2.00
2.50
2.29
2.60
2.60
2.60
2.60
2.60
2.60
2.60
2.63

4.0
6.0
7.0
10.0
10.0
10.0
10.0
10.0
10.0
10.0
10.0

8.0
15.0
16.0
26.0
26.0
26.0
26.0
26.0
26.0
26.0
26.3

16

2,181
4,426
4,146
3,208
2,716
2,791
2,853
2,851
2,724
2,626
2,445

17,44868,14665,45081,70971,01571,72074,02575,262
72,03568,22865,368

527
1,784
1,690
1,314
1,173
951
917
768
847
714
734

Table 2 Descriptive Statistics and Correlation Coefficients
This table reports summary statistics for liquidity measures and others from April 4, 2013 to June 30, 2014. Spreads are all unit-free as they are divided by the midquote. The unit of
the JGB VIX is percentage (%). The JGB VIX meaures the market estimate of the price fluctuation of 10-year JGB Futures over the next 30 days.
JGB spot
JGB futures
JGB Option
Bid-ask spread Bid-ask spread Bid-ask spread Bid-ask spread
Bid-ask spread Effective spread ILLIQ
Adv. selection Realized spread VIX(5 year)(10 year)(20 year)(30 year)
Mean
0.000300.000590.001520.001810.00004
0.00004
4.05E-08
0.000020.00001
3.083
Median0.000300.00058
0.001520.001710.00004
0.00004
2.84E-08
0.000020.00001
2.680
Maximum
0.000390.00094
0.002070.002740.00005
0.00005
3.10E-07
0.00008
0.00002
6.550
Minimum
0.000160.000400.00087
0.001020.000030.00003
0.00E+00
0.00001-0.00003
1.730
Std. Dev.
0.000030.000070.000210.00036
2.45E-06
1.65E-06
4.06E-08
5.84E-06
4.89E-06
1.154
Skewness-0.21506
0.96051-0.34359
0.800432.518023.741812.17899
3.88369-2.92610
1.28917
Kurtosis3.46636.3235
3.52833.11209.893624.1694
10.7157
32.6192
22.4761
3.6715
Autocorr.
0.68600.60400.78600.84800.78800.64900.14200.20200.09900.9520Observations
307
307
307
307
307
307
307
307
307
307
Bid-ask (5 yr)
1.000
Bid-ask (10
0.187
1.000
yr)
Bid-ask (20
0.347
0.522
1.000
yr)
Bid-ask (30
0.225
0.453
0.667
1.000
yr)
Bid-ask-0.046-0.151-0.179-0.172
1.000(Futures)
Effective
0.348
0.222
0.383
0.265
0.903
1.000
spred (futures)
ILLIQ (futures)
0.193
0.228
0.283
0.265
0.348
0.432
1.000
Adv. Selection
0.204
0.124
0.274
0.203
0.520
0.669
0.308
1.000
(futures)
Realized sp.
-0.126-0.074-0.198-0.153-0.317-0.461-0.221-0.9681.000(futures)
JGB VIX
0.427
0.312
0.488
0.400
0.856
0.777
0.392
0.378
-0.1901.000

17Table 3 The Effect of Purchasing Policy Change (JGB Spot)
This table reports the OLS slope coefficients of the following regression with the daily average of bid-ask spreads
for the on-the-run JGBs:

Liquidity measuret αβ1t Auction day dummyt β2t Following day dummyt β3t Settlement day dummyt  i  4βi X it  et9where
β1t β10 β11number in a day t β12 average amountt β13 s.d . of amountt
β2t β20 β21number in a day t 1 β22 average amountt 1 β23 s.d . of amountt 1
β3t β30 β31number in a day t  2 β32 average amountt  2 β33 s.d . of amountt  2
The t-statistics in parentheses are calculated from Newy-West standard errors. The symbols ***, ** and * denotesignificance at the 1%, 5% and 10% levels, respectively. The sample is April 4, 2013 to June 30, 2014. A constantterm is not displayed.

Auction day dummy
Auction day dummy×# of auctions in a day
Auction day dummy×
average amount
Auction day dummy×standard deviation
Following day dummy
Following day dummy×# of auctions in a day
Following day dummy×
average amount
Following day dummy×standard deviation
Settlement day dummy
Settlement day dummy×# of auctions in a day
Settlement day dummy×
average amount
Settlement day dummy×standard deviation
BOJ meeting
FOMC dummy (-1)
Tender (5year JGB)
Tender (10year JGB)
Tender (20year JGB)
Tender (30year JGB)
Observations
Adj. R-squared

5 years
1.24E-05(0.870)-8.18E-06 *(-1.814)
8.18E-10(0.197)
9.90E-09 ***(2.288)
2.19E-05(1.448)-1.59E-05 ***(-3.297)
6.37E-09 *(1.851)
9.33E-09 **(2.052)-1.23E-05(-0.846)-4.42E-06(-1.188)
1.00E-08 ***(3.016)-5.97E-10(-0.136)-6.29E-07(-0.064)-1.91E-05 *(-1.659)-5.75E-06(-0.702)
1.23E-05(1.463)-1.43E-05(-1.241)
1.48E-05(1.861)307
0.053

Bid-ask spreads of JGB spot markets
10 years
20 years-1.25E-05-2.91E-04 ***(-0.372)(-4.442)-1.54E-05-4.90E-05 *(-1.315)(-1.769)
1.24E-08 *6.67E-08 ***(1.648)(3.426)
1.11E-08 ***6.61E-08 ***(2.408)(2.859)-1.90E-05-3.33E-04 ***(-0.463)(-4.621)-5.73E-06
5.93E-05 **(-0.482)(2.166)
6.86E-09
6.84E-08 ***(0.779)(3.333)
7.77E-09
6.69E-08 ***(0.970)(2.922)-2.72E-05-2.87E-04 ***(-0.942)(-3.631)-1.35E-05 *1.72E-05(-1.704)(0.649)2.41E-08 ***9.25E-08 ***(3.384)(4.341)-7.49E-09
3.26E-08(-0.655)(1.334)
2.25E-06
4.65E-06(0.123)(0.116)-5.75E-06-2.16E-05(-0.349)(-0.355)
1.31E-05
1.39E-04 ***(0.547)(3.381)
1.87E-05
9.31E-05 **(1.264)(2.311)-9.66E-06
9.17E-05(-0.528)(1.496)-1.75E-05
5.48E-05(-0.878)(0.867)
307
307
0.057
0.203

18

30 years-1.81E-04(-1.182)-6.49E-05(-1.303)
1.64E-07 ***(4.530)1.28E-08(0.316)-3.19E-04 **(-2.216)-2.75E-05(-0.536)
1.55E-07 ***(3.943)
3.10E-08(0.704)-3.06E-04(-2.292)-3.71E-05(-0.877)
1.56E-07 ***(4.244)
5.25E-10(0.011)
4.31E-05(0.617)-8.89E-05(-1.154)
6.33E-05(0.587)
1.80E-04 ***(2.678)-3.41E-05(-0.399)
1.17E-04(1.085)307
0.145

Table 4 The Effect of Purchasing Policy Change (JGB Futures)This table reports the OLS slope coefficients of the following regression of daily liquidity measures for JGB futures:

Liquidity measuret αβ1t Auction day dummyt β2t Following day dummyt β3t Settlement day dummyt  i  4βi X it  et
10

where
β1t β10 β11number in a day t β12 average amountt β13 s.d . of amountt
β2t β20 β21number in a day t 1 β22 average amountt 1 β23 s.d . of amountt 1
β3t β30 β31number in a day t  2 β32 average amountt  2 β33 s.d . of amountt  2
The bid-ask spreads are calculated by taking the 1 min. average of the bid-ask spreads. The effective spread is thedaily average. ILLIQ is Amihud’s (2002) illiquidity measure. The t-statistics in parentheses are calculated from
Newy-West standard errors. The symbols ***, ** and * denote significance at the 1%, 5% and 10% levels,
respectively. The sample is April 4, 2013 to June 30, 2014. A constant term is not displayed.

Auction day dummy
Auction day dummy×# of auctions in a day
Auction day dummy×
average amount
Auction day dummy×standard deviation
Following day dummy
Following day dummy×# of auctions in a day
Following day dummy×
average amount
Following day dummy×standard deviation
Settlement day dummy
Settlement day dummy×# of auctions in a day
Settlement day dummy×
average amount
Settlement day dummy×standard deviation
BOJ meeting dummy
FOMC dummy (-1)
Tender (5year JGB)
Tender (10year JGB)
Tender (20year JGB)
Tender (30year JGB)
Volume(-1)
Observations
Adj. R-squared

Bid-ask spread-3.00E-06 ***(-3.362)-1.86E-07(-0.560)
5.60E-10 **(2.409)1.99E-09 ***(5.321)-1.83E-06 *(-1.868)-3.93E-07(-1.224)
5.01E-10 **(2.067)
1.15E-09 ***(3.653)-1.57E-06(-1.579)-4.14E-07 *(-1.639)
7.74E-10 ***(3.091)
3.45E-10(1.214)
4.51E-08(0.090)-3.89E-07(-0.658)-1.68E-07(-0.240)
1.84E-07(0.476)-5.05E-07(-1.069)
5.73E-07(0.593)
6.8E-11 ***(2.757)306
0.362

JGB futures market
Effective spread-1.77E-06 ***(-2.899)-1.80E-07(-0.818)
3.07E-10 **(2.070)1.19E-09 ***(5.267)-5.52E-07(-0.920)-3.44E-07 *(-1.776)
1.32E-10(0.903)
6.90E-10 ***(3.670)-4.56E-07(-0.443)-4.12E-07(-1.586)
4.77E-10 ***(3.225)
1.18E-11(0.038)-1.57E-07(-0.438)-2.31E-07(-0.657)-4.31E-07(-1.474)-2.99E-08(-0.087)-3.10E-07(-0.936)
3.20E-07(0.578)5.08E-11 ***(2.877)
306
0.280

19

ILLIQ
-6.93E-08 ***(-3.277)-1.34E-08 *(-1.747)
7.92E-12 **(2.061)
1.07E-11 **(1.990)-1.56E-08(-0.718)-4.05E-09(-0.598)
6.25E-12(1.254)
6.69E-12(1.002)-3.08E-08 *(-1.750)-2.61E-09(-0.552)
1.19E-11 **(2.514)
7.72E-14(0.014)-9.17E-09(-1.006)
5.35E-09(0.499)1.63E-08(1.120)-1.02E-08(-1.215)
3.00E-09(0.320)-1.56E-08 **(-2.057)-7.91E-14(-0.207)306
0.075

Table 5 The Effect of Purchasing Policy Change on Adverse Selection, Realized Spread, andImplied Volatility
This table reports the OLS estimates of the slope coefficients of the regression:

Liquidity measuret αβ1t Auction day dummyt β2t Following day dummyt β3t Settlement day dummyt  i  4βi X it  et
10

where
β1t β10 β11number in a day t β12 average amountt β13 s.d . of amountt
β2t β20 β21number in a day t 1 β22 average amountt 1 β23 s.d . of amountt 1
β3t β30 β31number in a day t  2 β32 average amountt  2 β33 s.d . of amountt  2
The adverse selection and the realized spreads are daily averages. The t-statistics in parentheses are calculated from
Newy-West standard errors. The symbols ***, ** and * denote significance at the 1%, 5% and 10% levels,
respectively. The sample is April 4, 2013 to June 30, 2014. A constant term is not displayed.

Auction day dummy
Auction day dummy×# of auctions in a day
Auction day dummy×
average amount
Auction day dummy×standard deviation
Following day dummy
Following day dummy×# of auctions in a day
Following day dummy×
average amount
Following day dummy×standard deviation
Settlement day dummy
Settlement day dummy×# of auctions in a day
Settlement day dummy×
average amount
Settlement day dummy×standard deviation
BOJ meeting
FOMC dummy (-1)
Tender (5year JGB)
Tender (10year JGB)
Tender (20year JGB)
Tender (30year JGB)
Volume(-1)
Observations
Adj. R-squared

JGB futures market
Adverse selection
Realized spread-3.79E-06
2.02E-06(-1.435)(0.885)-4.29E-08-1.37E-07(-0.047)(-0.168)
5.02E-12
3.02E-10(0.011)(0.723)
3.47E-09 ***-2.28E-09 ***(5.144)(-3.873)-6.26E-06 ***5.71E-06 ***(-2.764)(2.704)
8.09E-07-1.15E-06 *(1.119)(-1.719)
3.12E-10-1.80E-10(0.682)(-0.421)
2.19E-09 ***-1.50E-09 ***(3.463)(-2.694)
9.03E-07-1.36E-06(0.211)(-0.384)-1.46E-06
1.04E-06(-1.257)(1.083)
1.71E-09 ***-1.23E-09 **(2.708)(-2.197)-2.03E-09
2.04E-09 **(-1.617)(1.992)-5.30E-07
3.73E-07(-0.388)(0.298)-1.56E-06
1.33E-06(-1.150)(0.938)-2.73E-07-1.58E-07(-0.202)(-0.128)-3.60E-07
3.30E-07(-0.252)(0.271)-1.44E-06
1.13E-06(-1.257)(1.174)-8.17E-07
1.14E-06(-0.699)(1.304)
1.07E-10 ***-5.64E-11(2.050)(-1.189)
306
306
0.119
0.054

20

JGB option market
JGB VIX-1.5796 ***(-3.619)-0.0236(-0.160)
0.0004 ***(3.374)0.0006 ***(3.628)-1.4288 ***(-3.919)-0.1341(-0.909)
0.0004 ***(3.773)0.0006 ***(4.493)-1.4826 ***(-3.307)-0.1718(-1.479)0.0005 ***(4.918)0.0004 ***(2.879)-0.0155(-0.061)-0.3164(-1.536)-0.2282(-0.808)0.2901(1.218)-0.0414(-0.139)-0.0004(-0.001)
0.00002 *(1.895)
306
0.423

21

2014/7/4

2014/6/4

2014/5/4

2014/4/4

2014/2/4
2014/3/4

2014/1/4

2013/12/4

2013/11/4

2013/10/4

2013/9/4

2013/8/4

2013/7/4

2013/6/4

2013/5/4

2013/4/4

2013/2/4
2013/3/4

2013/1/4

2014/6/2

2014/5/2

2014/4/2

2014/3/2

2014/2/2

2014/1/2

2013/12/2

2013/11/2

2013/10/2

2013/9/2

2013/8/2

2013/7/2

2013/6/2

2013/5/2

2013/4/2

2013/3/2

2013/2/2

2013/1/2

Figure 1. JGB Yields
2.5

2

1.5
2year

5yrear1
10yrear20yrear

0.5
30yrear0(Data) Bloomberg

Figure 2 Implied Volatility

The Tokyo Stock Exchange (JPX) provides the model-free implied volatility obtained from option prices.

S&P/JPX JGB VIX

7.0

6.0

5.0

4.0

3.0

2.0

1.0

0.0

Figure 3 Comparison of Spreads and Spread Components

1.0E-04
8.0E-05
6.0E-05bid-ask spread
4.0E-05

effective spread

2.0E-05

adverse selection
realized spread
2014/6/1

2014/4/1

2014/2/1

2013/12/1

2013/10/1

2013/8/1

2013/6/1-2.0E-05

2013/4/1

0.0E+00-4.0E-05(Data) The JGB futures price and volume data are from Nikkei Media Marketing

Figure 4 Amihud’s (2002) Illiquidity Measure, ILLIQ

ILLIQ
3.5E-073.0E-07
2.5E-072.0E-07
1.5E-07
1.0E-07
5.0E-08(Data) The JGB futures price and volume data are from Nikkei Media Marketing22

2014/6/1

2014/5/1

2014/4/1

2014/3/1

2014/2/1

2014/1/1

2013/12/1

2013/11/1

2013/10/1

2013/9/1

2013/8/1

2013/7/1

2013/6/1

2013/5/1

2013/4/1

0.0E+00

Figure 5 Liquidity Impacts of Interventions in the JGB Spot Market(5-, 10-, 20-, 30-Year JGBs)
Liquidity measuret αβ1t Auciton day dummyt β2t Following day dummyt β3t Settlement day dummyt  i  4βi X it  et9where
β1t β10 β11number in a day t β12 average amount t β13 s.d . of amount t
β2t β20 β21number in a day t 1 β22 average amount t 1 β23 s.d . of amount t 1
β3t β30 β31number in a day t  2 β32 average amount t  2 β33 s.d . of amount t  2
These graphs show the time-varying coefficient of the auction day dummy ( ˆ1t ), that of the following day dummy( ̂ 2 t ), and that of the settlement day dummy ( ̂ 3t ). These coefficients are calculated with the regression estimates
and the monthly averages of the number of purchases in a day, the monthly averages of the purchasing amount andthe monthly average of the standard deviation of the daily amounts.
10 year JGB

5 year JGB
4.0E-04

4.0E-04

3.0E-04

3.0E-04

2.0E-04

2.0E-04

1.0E-04

1.0E-04

0.0E+00

0.0E+00-1.0E-04-1.0E-04
Auciton day impact

Following day impact

Auciton day impact

Settlement day impact

Following day impact

Settlement day impact

30 year JGB

20 year JGB
4.0E-04

4.0E-04

3.0E-04

3.0E-04

2.0E-04

2.0E-04

1.0E-04

1.0E-04

0.0E+00

0.0E+00-1.0E-04-1.0E-04

Auciton day impact

Following day impact

Auciton day impact

Settlement day impact

23

Following day impact

Settlement day impact

Figure 6

Liquidity Impacts of Interventions in the JGB Futures Market(Bid-ask Spread, Effective Spreads, ILLIQ)

Liquidity measuret αβ1t Auciton day dummyt β2t Following day dummyt β3t Settlement day dummyt  i  4βi X it  et
10

where
β1t β10 β11number in a day t β12 average amount t β13 s.d . of amount t
β2t β20 β21number in a day t 1 β22 average amount t 1 β23 s.d . of amount t 1
β3t β30 β31number in a day t  2 β32 average amount t  2 β33 s.d . of amount t  2
These graphs show the time-varying coefficient of the auction day dummy ( ˆ1t ), that of the following day dummy( ̂ 2 t ), and that of the settlement day dummy ( ̂ 3t ). These coefficients are calculated with the regression estimates
and the monthly averages of the number of purchases in a day, the monthly averages of the purchasing amount andthe monthly average of the standard deviation of the daily amounts.
Bid-ask spread

Effective spread

5.0E-06

5.0E-06

4.0E-06

4.0E-06

3.0E-06

3.0E-06

2.0E-06

2.0E-06

1.0E-06

1.0E-06

0.0E+00

0.0E+00-1.0E-06-1.0E-06-2.0E-06-2.0E-06
Auciton day impact

Following day impact

Settlement day impact

Auciton day impact

ILLIQ
4.0E-083.0E-082.0E-08
1.0E-08
0.0E+00-1.0E-08
Auciton day impact

Following day impact

Settlement day impact

24

Following day impact

Settlement day impact

Figure 7 Impacts of Interventions in the Futures and Option Markets(Adverse Selection, Realized Spreads, JGB VIX)
Measuret αβ1t Auciton day dummyt β2t Following day dummyt β3t Settlement day dummyt  i  4βi X it  et
10

where
β1t β10 β11number in a day t β12 average amount t β13 s.d . of amount t
β2t β20 β21number in a day t 1 β22 average amount t 1 β23 s.d . of amount t 1
β3t β30 β31number in a day t  2 β32 average amount t  2 β33 s.d . of amount t  2
These graphs show the time-varying coefficient of the auction day dummy ( ˆ1t ), that of the following day dummy( ̂ 2 t ), and that of the settlement day dummy ( ̂ 3t ). These coefficients are calculated with the regression estimates
and the monthly averages of the number of purchases in a day, the monthly averages of the purchasing amount andthe monthly average of the standard deviation of the daily amounts.
Realized spread

Adverse selection
5.0E-06

5.0E-06

4.0E-06

4.0E-06

3.0E-06

3.0E-06

2.0E-06

2.0E-06

1.0E-06

1.0E-06

0.0E+00

0.0E+00-1.0E-06-1.0E-06-2.0E-06-2.0E-06-3.0E-06-3.0E-06
Auciton day impact

Following day impact

Auciton day impact

Settlement day impact

JGB VIX
2.5

2.0
1.5
1.0
0.5
0.0
-0.5
Auciton day impact

Following day impact

Settlement day impact

25

Following day impact

Settlement day impact

Figure 8

Dispersion of JGB Yield Forecasts among Traders

The below graphs depict the standard deviations of one-month and three-month forecasts for 5-, 10-, and 20-year
JGB yields. The Quick Survey System (QSS) conducts a monthly paper-based survey of forecasts made byprofessional forecasters as well as their attributes in Japanese financial markets. The number of respondents is
between 130 and 150.

S.D. of 1 month forecasts

5 year JGB
10 year JGB

20 year JGB

2013/01
2013/022013/032013/04
2013/05
2013/06
2013/072013/08
2013/09
2013/102013/112013/12
2014/01
2014/02
2014/03
2014/04
2014/05
2014/06

%
0.18
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0

S.D. of 3 month forecasts
0.18
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0

5 year JGB
10 year JGB

2013/01
2013/022013/032013/04
2013/05
2013/06
2013/072013/08
2013/09
2013/102013/112013/12
2014/01
2014/02
2014/03
2014/04
2014/05
2014/06

20 year JGB

26