Japanese Foreign Exchange Interventions, 1971-2018:
Estimating a Reaction Function Using the Best Proxy a
Takatoshi Ito b and Tomoyoshi Yabu c
December 12, 2019
Abstract
We analyze the history of Japanese foreign exchange interventions from 1971 to 2018. First, we
provide the best proxy for monthly interventions for the period from 1971 to 1990, when theintervention timings and amounts were not officially disclosed. The accuracy of the proxy is tested forthe period when the statistics were disclosed after 1991. The proxy explains 99.8% of actual
settlement-based interventions. Second, we examine conditions under which the Japanese monetary
authorities are likely to intervene by estimating a policy reaction function, using the long-term data,spanning the period when intervention data have been officially disclosed and the period where our
proxy is available. Third, we analyze intervention timings and amounts for Japan, the US, andGermany. Fourth, we present the episode of international coordination represented by the Plaza andLouvre agreements as a case study of notable interventions during the period.
JEL Classification: F31; E52; E58; G15
Keywords: Central bank intervention; foreign reserves; ordered probit model; policy coordination;
structural break

1. Introduction
Japan is known to have resisted rapid yen appreciation by occasionally conducting foreign exchangeinterventions since it departed from the Bretton Woods system in August 1971. The frequency andscale of interventions have been relatively large for a developed country, and Japan has been a popularcountry for studies in the intervention literature. The daily data of interventions for the period sinceApril 1, 1991 have been officially made available to the public. However, intervention data before
March 1991 have not been officially disclosed. Some researchers have relied on newspaper reports
based on leaks and market rumors in order to analyze particular episodes. Others have used either
changes in foreign reserves or the data titled “Treasury Funds and Others/Foreign Exchange” releasedby the Bank of Japan as a proxy for intervention. However, changes in foreign reserves, for example,

a We would like to thank Takeshi Hoshikawa, Jouchi Nakajima, and Tsutomu Watanabe for helpful comments. We also thank Shota

Arai and Naoto Shimano for research assistance. Ito gratefully acknowledges financial support from the JSPS through Grants-in-Aid
for Scientific Research (A-25245044). Yabu gratefully acknowledges financial support from the JSPS through Grants-in-Aid for
Scientific Research (No. 17K03663). The authors are solely responsible for any remaining errors. The views and opinions expressedin this paper are those of the authors.
b School of International and Public Affairs, Columbia University. Email: ti2164@columbia.edu.
c Department of Business and Commerce, Keio University. Email: tyabu@fbc.keio.ac.jp.

1include receipts of interest income on the foreign reserves, which are not interventions.

Ito and Yabu (2017) identified an accurate proxy for the monthly intervention amount for the period
for which official data have not been disclosed. The accuracy of different possible proxies was tested
against “transaction-based” interventions which took place during the period from which data are
available. The best proxy, identified by Ito and Yabu, is more accurate than other popular proxies usedin the existing literature, which include change in foreign reserves and the Bank of Japan’s "Treasury
Funds and Others/Foreign Exchange."

In this paper, we first show that, in testing for the best proxy, it is important to distinguish “transactionbased” intervention from “settlement-based” interventions. Due to the market practice in the foreignexchange market, settlement is done two business days later than transactions—a rule known as 𝑡𝑡 +2 . We find that Ito and Yabu’s (2017) best proxy captures the movements of settlement-based

interventions very well, explaining 99.8% of settlement-based interventions. Our first contribution isthat we provide reliable data for intervention, from August 1971—the end of the Bretton Woods
system—to March 1991. From April 1991, the official data for intervention are available.

Second, we analyze the conditions under which interventions are most likely to occur for the period
from August 1971 to March 2018. The officially disclosed data are used for April 1991 to March 2018
and our proxy is used from August 1971 to March 1991. Since reaction functions, which represent themonetary authorities’ tolerance of and resistance to exchange rate volatility, are expected to have
evolved and transformed over the 47 years under consideration, possible structural breaks in reaction
functions will be carefully examined in this paper.

Third, the US and German monetary authorities have disclosed daily intervention data for the period
beginning in 1973 and 1976, respectively. By analyzing intervention timings and amounts in the three
countries, insights can be obtained on policy coordination or lack thereof, among the G3, in the past
four decades.

Fourth, we present the episode of international coordination represented by the Plaza and Louvre
agreements as a case study of notable interventions during the period under consideration.

The rest of the paper is organized as follows. Section 2 will review the foreign exchange intervention
literature. Section 3 will review Ito and Yabu (2017) and show that in testing for the best proxy, it isimportant to distinguish transaction-based intervention from settlement-based intervention. Section 4
will provide an overview of the yen/dollar rates and interventions over the past 47 years. Section 5
2

will analyze the conditions under which the Japanese monetary authorities are likely to intervene in
the foreign exchange market. Section 6 will present a case study on notable interventions during theperiod.

2. Literature
In July 2001, the Japanese Ministry of Finance released daily intervention records from April 1991 to
the present. Since then, the Ministry of Finance has updated these records every quarter. The new data
has stimulated academic research on Japanese intervention. One group of studies examined theeffectiveness of intervention, i.e., whether intervention impacted exchange rate movements. This
includes Ito (2003), Kearns and Rigobon (2005), Fatum and Hutchison (2006), Hoshikawa (2008),
Chen, Watanabe, and Yabu (2012), and Fatum and Yamamoto(2014). Estimates vary across studies as
to the estimated effect that one-trillion yen-selling interventions have had on the yen/dollar rate,
ranging from 0.4 to 1.8 percent depreciation of the yen.

Another group of papers, including Ito (2003), Frenkel, Pierdzioch, and Stadtmann (2003, 2005), and
Ito and Yabu (2007), examined the reaction function, i.e., under what financial conditions monetary
authorities are more likely to intervene. Ito (2003) and Ito and Yabu (2007) identified factors that seemto trigger interventions: the deviation in the yen/dollar rate from long-run trends and recent movementsof the yen/dollar rate. Moreover, the person or people in charge of interventions seem to influence thefrequency and magnitude of interventions. For example, since June 1995, when Eisuke Sakakibara
became Director General of the International Finance Bureau, the frequency of interventions lowered
substantially but, when they occurred, the daily intervention amount increased significantly. The
frequency and daily amount of intervention also had a distinctive pattern; for example, there weremore frequent interventions during the 2003-04 period when Zenbei Mizoguchi was in charge ofintervention.

However, given that intervention data before April 1991 has not been disclosed, we have to use a proxyto estimate past intervention data if, for example, we wish to examine and analyze interventions during
the 1970s and 1980s.
In the literature several proxies have been proposed and adopted. These proxies can be obtained on amonthly basis. The most popular proxy is changes in the foreign reserves, namely, “Total Reserves
excluding Gold,” also known as “Change in Reserve” for short. In an earlier study of intervention,
this proxy was used by Dornbusch (1980), Takagi (1991) and others. Dornbusch (1980) and Takagi(1991) found that the Japanese monetary authorities followed a lean-against-the-wind policy during
the 1970s. This is also a popular proxy when an international cross-country comparison is attempted,
3

as the statistics are readily available. For example, Dominguez, Hashimoto, and Ito (2012), in theirlarge cross-country study, used “Change in Reserve,” but adjusted for interest income receipts.

However, a proxy that has been considered to be more accurate is data from the Bank of Japan titled“Treasury Funds and Others/Foreign Exchange” (hereafter, “BOJ FE”). Quirk (1977) was one of theearliest papers to argue that the “BOJ FE” was a better proxy than “Change in Reserve.” He also
argued that the monetary authorities followed a lean-against-the-wind policy and that there was no“target” for the exchange rate in the 1970s. Meanwhile, Hutchison (1984) and Watanabe (1992) bothused “BOJ FE.” Hutchison (1984) found that the monetary authorities adopted a lean-against-the-wind
policy but were biased against yen appreciation in the 1970s. Watanabe (1992) concluded that before
April 1980, the monetary authorities adopted a lean-against-the wind policy, whereas after April 1980,
they began to try to achieve target levels of exchange rates.

Another proxy similar to “BOJ FE” is the Ministry of Finance statistics contained in the fiscal
account “Receipts and Payments of the Treasury Funds; Foreign Exchange Equalization Fund”(hereafter, “MOF FEEF”). The difference between the “BOJ EF” and the “MOF FEEF” is that thelatter include transactions related to issuance and redemption of government securities for the IMF
quota. When an adjustment is made to the account by subtracting the balance of “Issuance andRedemption of Financial Bills in the Financial Market” (hereafter, “Adjusted MOF FEEF”), then it
is believed to be much more representative of actual interventions. Both Suda (1999) and Nakada(1998) used “Adjusted MOF FEEF.” Suda (1999) pointed out a structural problem with the foreignexchange special account where profits were transferred to the general account while unrealized
losses were carried over, whereas Nakada (1998) constructed a neural network model in order toforecast interventions.

At the time these proxies were proposed and adopted by the various authors mentioned above, actualintervention data had not yet been disclosed. After the actual intervention data disclosure, anacademic interest in proxies was lost. However, actual intervention data have only been disclosed forthe period after April 1991. Proxies are still needed for research and analysis of interventions thattook place before April 1991. Since we have actual intervention data, we can test the proxies against
the official data using a “test sample period” where both proxies and actual interventions areavailable. In fact, Ito and Yabu (2017) conducted such as test and identified the best proxy for actualinterventions. 1
1 A similar test of proxies against the actual data was carried out by Neely (2000) for interventions in the United States,

Switzerland and Germany. Neely used the correlation coefficient to judge whether the change in “Total Reserve excluding Gold” isa good proxy of true interventions. However, a test using a simple correlation is inconclusive at best. A regression can determine
whether a proxy is an unbiased estimator.

43. In search of the best proxyThis section first introduces candidate proxies. Then, we will give an overview of the yet to betranslated Japanese paper by Ito and Yabu (2017) and review their testing procedure and estimation
results. Moreover, this section establishes that it is important to distinguish transaction-based
intervention from settlement-based intervention in the testing procedure. This has not been discussed
at all in the existing literature.

3.1．Candidates for Proxies
Data regarding actual interventions of the Japanese monetary authorities have been available withdaily frequency since April 1, 1991. However, only proxies with a monthly frequency can be used forthe period prior to April 1, 1991.

As discussed in section 2, four proxies have been used in the literature. Since actual intervention data
are available on and after April 1, 1991, Ito and Yabu (2017) compared these proxies to actual
transaction-based interventions for a period from April 1991 to March 1999. The end of the sample
period for the test was determined by the point at which one of the proxies had a discontinuity in theclassification. Each proxy is explained below in detail. (Table 1 lists brief summary of the proxies.)<Table 1 about here>(1) Change in Reserve
When large cross-country studies on interventions are conducted, foreign reserves (less gold) are often
used. What is included and what is not is standardized by the International Monetary Fund (IMF) andthe variable is readily available from the IMF (see IMF, 2013 for details). One drawback of using thisas a proxy is that changes in foreign reserves include an amount of interest income received. Hence,in order to recover the amount of intervention, the estimated interest income should be deducted fromthe change in foreign reserves. However, for such an estimation, a researcher needs currency andmaturity information on assets that are held as foreign reserves; this information is not available.

Conceptually, the first proxy for interventions is defined as follows:�𝐵𝐵𝑡𝑡 − 𝐵𝐵𝑡𝑡−1 − 𝑖𝑖𝑡𝑡𝑈𝑈𝑈𝑈(𝐵𝐵𝑡𝑡 + 𝐵𝐵𝑡𝑡−1 )� 𝑆𝑆𝑡𝑡2where 𝐵𝐵𝑡𝑡 is the end-of-month t amount of foreign reserves (less gold) in USD; 𝑆𝑆𝑡𝑡 is the yen/dollar

rate average for the month; and 𝑖𝑖𝑡𝑡𝑈𝑈𝑈𝑈 is the average yield of foreign reserve assets in month t.

Dornbusch (1980) and Takagi (1991) used the 3-month T-bill rate as 𝑖𝑖𝑡𝑡𝑈𝑈𝑈𝑈 . However, portfolios are notdisclosed, meaning that the appropriateness of the 3-month T-bill rate cannot be tested. More recently,

it can be inferred from interest incomes that the foreign reserves of several countries include treasury
5bonds as well as T-bills. In order to check robustness with regard to portfolio interest rates, threedifferent cases are considered: Change in Reserve, with T-bill (3 months); Change in Reserve, with Tbond (3 years); and Change in Reserve, with T-bond (10 years).

(2) BOJ EF
The BOJ EF captures an item in the flow of funds, the need of the government account. Hutchison(1984) and Watanabe (1992) used this data set in order to analyze the intervention record, policy
intention, and effectiveness. The data are available from the Bank of Japan’s website (http://www.statsearch.boj.or.jp/ssi/cgi-bin/famecgi2?cgi=$nme_s050_en). The series code is MD06'MASDM26.

(3) MOF FEEF
This series captures the Ministry of Finance’s account that is used for all transactions in the foreignexchange market. This data is published monthly by the Ministry of Finance in Ministry of Finance
Statistics Monthly. This is the counterpart of the Bank of Japan’s account that is used for alltransactions in the foreign exchange market. The data is available in Japanese on the Ministry of
Finance’s website (https://www.mof.go.jp/pri/publication/zaikin_geppo).

(4) Adjusted MOF FEEF
The source of this data set is the same as that for the MOF FEEF. The data set is adjusted for an itemthat is not related to intervention; namely, “Balancing of issuance and redemption of Financial Billsin the financial market,” which is not related to intervention activities but rather to fees that the
Ministry pays to issuing and redeeming securities. An adjustment is made to MOF FEEF bysubtracting from it “Receipts and Payments of the Treasury Funds: Foreign Exchange Equalization
Fund” in the fiscal operations statistics. Suda (1999) and Nakata (1998) used the Adjusted MOF
FEEF.
3.2．Ito and Yabu (2017)
We review the testing procedure and estimation results of Ito and Yabu (2017). In order to test theaccuracy of the proxies, monthly data of the four proxies from April 1991 to March 1999 are tested
against actual interventions in the same period. The disclosed data regarding interventions wereobserved with daily frequency but have been aggregated to monthly values for the purposes of testing.
The sample period ends March 1999, because in April 1999, “MOF FEEF” becomes impossible toadjust due to changes in categorization.

The tests are conducted with the following regression:
𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡 = 𝛽𝛽0 + 𝛽𝛽1 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑡𝑡 + 𝑢𝑢𝑡𝑡
6(1)

where 𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡 denotes the transaction-based intervention amount (𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡 > 0 implies selling yen, buying

dollars) and 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑡𝑡 denotes a particular proxy. Both variables are measured in units of 100 million

yen. The null hypothesis is 𝛽𝛽0 = 0 and 𝛽𝛽1 = 1. If the null is not rejected, it implies that a proxy is
an unbiased estimator of actual interventions. Among the proxies that are shown to be unbiased
estimators, the best estimator is the one in which 𝑅𝑅 2 is closest to 1.

Proxies to be tested are (1) Change in Reserve, 3-month T-bills; (2) Change in Reserve, 3-year Tbonds; (3) Change in Reserve, 10-year T-bonds; (4) BOJ EF; (5) MOF FEEF; and (6) Adjusted MOF
FEEF. Table 2 shows the estimated results of Eq. (1) with the null hypothesis of 𝐻𝐻0: 𝛽𝛽0 = 0 and 𝐻𝐻0:

𝛽𝛽1 = 1.

<Table 2 about here>

The null hypothesis of 𝐻𝐻0 : 𝛽𝛽1 = 1 is rejected when we adopt “BOJ EF” or “MOF FEEF.” Thus, they
are not unbiased estimators. Their coefficients of determination (𝑅𝑅 2) are low as well. This is surprising,

given that alternatives to “Change in Reserve” were popular among researchers before actualintervention data were made available to the public. The null hypothesis of an unbiased estimator is
not rejected when either “Change in Reserve” with an interest income adjustment or “Adjusted MOF
FEEF” is used. The estimated coefficient of 𝛽𝛽0 is closest to 0 and 𝛽𝛽1 is closest to 1 when “Adjusted

MOF FEEF” is adopted. Moreover when "Adjusted MOF FEEF” is adopted, 𝑅𝑅 2 is 0.969 and higher
than those with “Change in Reserve” (with any of the three possible interest rate assumptions): 0.928.

Therefore, “Adjusted MOF FEEF” is concluded to be the best proxy for transaction-based
interventions.

In order to determine the accuracy of the best proxy, Figure 1 shows the time series of transactionbased intervention amounts (𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡 ) and the estimated errors of the proxy (Adjusted MOF FEEF), which

are defined as:

𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑡𝑡 = 𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡 − 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑡𝑡 .

The error is small overall; however, there is a negative serial correlation in the error (𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑡𝑡 ), meaningthat when the error is positive in one month, there is a negative error of a similar amount the following
month. This may suggest some accounting issue: When actual intervention takes place near the end of
the month, settlement and booking may spill over into the next month. This may explain the negative
correlation.
<Figure 1 about here>

3.3. Important adjustments in the test
In testing for the best proxy, it is important to distinguish transaction-based interventions from7

settlement-based interventions. Due to the market practice in the foreign exchange market, settlement
is done two business days after transactions—a rule known as 𝑡𝑡 + 2.
The following is true of any transaction in the foreign exchange market: For example, if Bank A buys
yen from Bank B in exchange for US dollars on Monday, the transactions are recorded as having takenplace on Monday. However, the currency is not actually sent to until Wednesday. Transactions between
the monetary authority and its counterpart in the market are no exception. Supposing that the monetary
authority intervenes in the market to purchase foreign currency (say, US dollars) by selling domestic
currency (say, yen) on Monday, the market impact should be immediate. In the market, if it isimpressed by the authority’s move, foreign currencies will appreciate (preventing further depreciation)
on Monday. However, actual payments of yen and receipts of the foreign currency by the authority do
not take place until Wednesday. Official records, the balance sheets of the central banks, or thegovernment’s fiscal account that records actual changes in government’s assets and liabilities do notchange until Wednesday. Thus, any interventions that are so recognized by the market and so recordedin the official statistics of interventions during the last two business days of the month (say, 𝑚𝑚) will

not be recorded as a change in the balance sheet or fiscal accounts of the central bank and thegovernment in month 𝑚𝑚, but rather in month 𝑚𝑚 + 1. This fact is important in evaluating the precisionof a monthly proxy, by using daily intervention amounts for the period that daily intervention amounts
are officially disclosed.
Let us denote interventions conducted during the last two business days of the month by 𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡𝐸𝐸𝐸𝐸𝐸𝐸 .

These amounts are recorded in the fiscal accounts that are used in the monthly proxy of month 𝑡𝑡 + 1.

𝐸𝐸𝐸𝐸𝐸𝐸
Similarly, 𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡−1
is recorded in the monthly proxy of month 𝑡𝑡. Thus, the proxy of month 𝑡𝑡 should

be compared against settlement-based intervention amounts, calculated as:
𝐸𝐸𝐸𝐸𝐸𝐸�.
𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 = 𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡 − �𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡𝐸𝐸𝐸𝐸𝐸𝐸 − 𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡−1

In order to see the point of this discussion, the estimated errors of the best proxy (𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑡𝑡 = 𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡 −𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑡𝑡 ) are examined. We argue that this is mostly explained by the end-of-month interventions andthe 𝑡𝑡 + 2 difference between transaction and settlement. We define the 𝑡𝑡 + 2 difference as

𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑡𝑡 (abbreviation of “end of month difference”):

𝐸𝐸𝐸𝐸𝐸𝐸
𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑡𝑡 = 𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡𝐸𝐸𝐸𝐸𝐸𝐸 − 𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡−1.

Figure 2 shows the time series of 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑡𝑡 and 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑡𝑡 where 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑡𝑡 is again computed using“Adjusted MOF FEEF” as a proxy. As expected, the “Errors” appear to be explained mostly as a resultof end-of-month interventions and the 𝑡𝑡 + 2 settlement. To be statistically accurate, a regression isconducted:

8

𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑡𝑡 = −8.200 + 1.074 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑡𝑡(7.166) (0.083)

𝑅𝑅 2 = 0.956

Here HAC standard errors are in brackets. 2 Neither 𝐻𝐻0 : 𝛽𝛽0 = 0 nor 𝐻𝐻0 : 𝛽𝛽1 = 1 is rejected; thedeviation of the proxy from transaction-based interventions is well explained by the end-of-monthinterventions. In fact, 95.6% of the deviation is due to end-of-month interventions, as shown by 𝑅𝑅 2 =0.956.

<Figure 2 about here>

We further examine how close settlement-based intervention data are to various proposed proxies forthe period after the disclosure, April 1991. Table 3 shows the regression results of Eq. (1) with thedependent variable being settlement-based interventions (𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 ). As shown, the explanatory

power of proxies is much improved, compared to the one in Table 2. For the regression using “Adjusted
MOF FEEF,” the 𝑅𝑅 2 improves from 0.968 in Table 2 to 0.998 in Table 3. 3 This is a confirmation thatthe proxy is an accurate measure, explaining 99.8% of the movements of settlement-based

interventions.
<Table 3 about here>

The above detailed examination in search of the best proxy for interventions is thus concluded withboth encouraging and discouraging results. We are encouraged to have found that “Adjusted MOF
FEEF” is the best proxy with 99.8% accuracy. However, our enthusiasm must be tempered slightly
given that this accuracy is measured against settlement-based interventions. Hence, considering thatdaily data are not available prior to April 1991, the available monthly interventions data for months(𝑡𝑡) covers the period from the last two days of month (𝑡𝑡 − 1) to the third to last business day of month(𝑡𝑡). This may pose a problem in analyzing the effectiveness of interventions or the reaction functionof the monetary authorities when intervention actions are concentrated at the end of the month.
However, an adjustment can be applied when such circumstances are suspected based on the volatilityof the exchange rate; for example, one can use the exchange rate from the third to last business day as
the monthly exchange rate. This is what we will do in section 5 when estimating the reaction functionof the monetary authorities.

2 The long-run variance is constructed based on the quadratic spectral window with the truncation parameter being selected via the

Andrews’ (1993) plug-in method.
3 For the regression adopting “Change in Reserve,” the 𝑅𝑅 2 improves from 0.928 in Table 2 to 0.950 in Table 3, whichmeans that this popular proxy is for settlement-based interventions, not for transaction-based interventions. Researchers interested inthis proxy for interventions in other countries should keep this point in mind.

94. Patterns of intervention
Figure 3, panels (a) and (b), show monthly data of the yen/dollar rate and amounts of interventions by
the Japanese monetary authorities. 4 Panel (a) shows the movement of the yen/dollar rate. The dottedline represents the past-five-year moving-average of the yen/dollar rate.
<Figure 3 about here>

Panel (b) shows that in the 1970s and 1980s, interventions were very “frequent and symmetric”: yenselling interventions as well as yen-buying interventions were frequently conducted. The yen had been
on a long-term appreciation trend, as shown in panel (a), throughout the 1970s and 1980s. It has been
well documented that the authorities intervened in an attempt to stop this appreciation, presumably toprevent adverse effects on export industries.

However, it was little publicized that yen depreciation was also resisted by selling the dollar. Threehypotheses could explain the symmetric interventions. First, the authorities genuinely believed thatthe long-run trend could not be resisted, yet it was the authorities’ role to reduce “excess” volatilities,
upside and downside. Second, merely resisting yen appreciation by buying US dollars, would only
have resulted in ever-accumulating foreign reserves that would have been exposed to increasing
evaluation losses, which could have triggered political criticism. Third, the interest rate in Japan was
similar or even higher than the US interest rate during the 1970s and 1980s; hence, building up foreign
reserves meant that interest income might not cover domestic interest payments to fiscal bills that are
liabilities in the foreign exchange special accounts. The “quasi-fiscal” costs were real concerns inJapan during the 1970s and 1980s.

For the reasons mentioned above, whenever the yen depreciated to some extent, like in 1973 and 1979
(due to the oil crises), 1981-82, and 1989, the yen was bought back, which appears to slow the yen’s
depreciation and to keep foreign reserves at a reasonable level. This attitude of symmetricinterventions changed in the 1990s. After 1992, the only instance of sizable dollar-selling interventions
was 1997-98 when the banking crisis of Japan caused a sharp yen depreciation. The magnitude ofinterventions became distinctively large while the frequency of interventions was reduced. In
particular interventions in 2003-04 and 2011 were very large.

Table 4 shows the details of interventions by fiscal year (April to March): intervention frequencies(the number of months), intervention (cumulative) amounts (by fiscal year), and the average (per4 In the analysis below, intervention amounts below 30 billion yen for the period from August 1971 to March 1991 are assumed tobe 0, as intervention amounts for the period are estimated based on the proxy and the proxy is likely to contain a certain degree of
estimation error. The intervention amounts are in units of 100 million yen. The monthly yen/dollar rate is the Tokyo close rate on thethird to last business day for the period before April 1991, since the proxy is for settlement-based interventions. After April 1991, we
use the end-of-month New York close rate for the monthly yen/dollar rate.

10

month) magnitude of interventions (year total divided by the number of intervention months). This
clearly shows that the frequency has declined, but also that the per-month magnitude has becomelarger since 1995. Prior to 1995, there were 178 months featuring intervention, amounting to 46 trillion
yen in total. Thus, average per-month intervention was 257 billion yen. After 1995, interventions
occurred during 41 months, with intervention totals reaching 78 trillion yen. The average per-monthintervention amount was 1.9 trillion yen—more than 7 times the amount before 1995. Neely (2011)
described and examined the fact that interventions had become infrequent over time in majorcountries: frequent interventions were ceased in the UK in 1993; in the US, Germany, and Switzerland
in 1995; in Canada in 1998, and in Japan in March 2004.
<Table 4 about here>

Figure 4 shows the changing ceiling and floor exchange rates for intervention over time. The last
selling rate is the yen/dollar rate when the dollar was sold by the Japanese monetary authorities, while
the last buying rate is the yen/dollar rate when the dollar was bought by the Japanese monetary
authorities. The band between the two prices implies the tolerance range, or target zone, for thefluctuation of the exchange rate for the authorities. Between 1971 and 1990, both the ceiling and floorof the tolerance band shifted gradually toward yen appreciation. The figure shows that the Japanese
authorities had a moving band of symmetric interventions. Although the band shifted over time, thefloor and ceiling had a significant spread most of the time. Until 1992, interventions were symmetric,
but the range had shifted, gradually allowing for a long-run trend of yen appreciation.
<Figure 4 about here>

The above figure shows that after 1991, the last selling rate consistently remained above 125, while
the last buying rate stayed below 125. Since the last dollar-selling intervention was in 1998, the last
selling rate has not changed. Since 1991, the last buying rate only went down toward the appreciatedlevel, while the last selling rate only went up toward the depreciated level. Thus, the tolerance bandhas widened to the range of 77.5-140.6, which is a spread of 63 yen.

Intervention became more unilateral after 1998, only changing the point of intervention on theappreciation side, mitigating the speed of yen appreciation and defending the particular level, such as
75 to 80 yen per dollar in 2010-12.

5. Reaction Function
In this section, we estimate the monetary authorities’ reaction function using the data from August
1971 to March 2018. Most papers analyzing interventions before March 1991 have used “Change inReserve” or “Treasury Funds and Others/Foreign Exchange” as a proxy for interventions. What11

differentiates this paper from these previous papers is the use of the proxy Ito and Yabu (2017)
identified and also the adoption of a state-of-the-art reaction function proposed by Ito and Yabu (2007).

5.1. Ordered Probit Model
Almekinders and Eijffinger (1996) were the first to derive a reaction function from a loss function ofmonetary authorities. Ito and Yabu (2007) extended their model by allowing for a more realistic
formulation of the target exchange rate and a cost function of interventions to derive a reaction function
as an ordered probit model.
The ordered probit model of Ito and Yabu (2007) is the following 5:
𝜇𝜇2 < 𝑦𝑦𝑡𝑡∗
𝜇𝜇1 < 𝑦𝑦𝑡𝑡∗ < 𝜇𝜇2
𝑦𝑦𝑡𝑡∗ < 𝜇𝜇1+1 𝑖𝑖𝑖𝑖
𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡𝑡𝑡 = � 0 𝑖𝑖𝑖𝑖
−1 𝑖𝑖𝑖𝑖(2)

where 𝑦𝑦𝑡𝑡∗ = 𝑋𝑋𝑡𝑡 𝛽𝛽 + 𝜀𝜀𝑡𝑡 with 𝜀𝜀𝑡𝑡 ∼ 𝑖𝑖. 𝑖𝑖. 𝑑𝑑. 𝑁𝑁(0, 𝜎𝜎 2 ) and𝑀𝑀𝑀𝑀) + 𝛽𝛽3 𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡𝑡𝑡−1.
𝑋𝑋𝑡𝑡 𝛽𝛽 = 𝛽𝛽1 (𝑠𝑠𝑡𝑡−1 − 𝑠𝑠𝑡𝑡−2 ) + 𝛽𝛽2 (𝑠𝑠𝑡𝑡−1 − 𝑠𝑠𝑡𝑡−1

Here 𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡𝑡𝑡 takes a value of +1 if there is dollar-purchasing intervention, −1 if there is dollar-

selling intervention, and 0 if there is no intervention. The log of the monthly yen/dollar rate is denotedby 𝑠𝑠𝑡𝑡 and the log of the moving average yen/dollar rate over the past five years by 𝑠𝑠𝑡𝑡𝑀𝑀𝑀𝑀 . 6 The lag of
𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡𝑡𝑡 is included to explain a positive autocorrelation of interventions, i.e., when there is intervention

one month, it is likely that intervention will take place in the following month. The latent variable 𝑦𝑦𝑡𝑡∗

represents the optimal amount of intervention and intervention does not take place as long as 𝑦𝑦𝑡𝑡∗ isinside a “neutral band” of no intervention, i.e., [𝜇𝜇1 , 𝜇𝜇2 ].

Regarding the parameters of the model, the parameter 𝛽𝛽1 takes a negative value (𝛽𝛽1 < 0) when thereis lean-against-the-wind intervention and 𝛽𝛽2 takes a negative value (𝛽𝛽2 < 0) when policy makers

have a long-run target in mind. On the other hand, the parameter 𝛽𝛽3 takes a positive value (𝛽𝛽3 > 0)

when there is intervention one month and intervention is likely to take place in the following month.
Here, 𝜇𝜇1 represents the cost of dollar-selling intervention while 𝜇𝜇2 is the cost of dollar-buying
intervention. The cost of interventions is introduced to explain the fact that interventions do not takeplace every month. 7 We estimate this model using the maximum likelihood method but identify only
5 Ito and Yabu (2007) include the daily change in the yen/dollar rate as an explanatory variable. However, this paper analyzes

monthly data and thus omits the daily change in the yen/dollar rate.

6 We also adopted different windows to check the robustness of results such as the past one year as well as the past three years.

However, our main results did not depend on the choice of the window.

7 Ito and Yabu (2007) explained the cost of interventions as “Political costs reflect costs of discussion with Minister of Finance ofown country and other major countries of intervention currencies. In order to carry out intervention, an explanation to the Ministerof Finance, and in some cases to other ministers, including the Prime Minister, is necessary and a tacit approval, if not coordinatedintervention, of other countries has to be sought. Political costs are most likely independent of the magnitude of the intervention.

12the normalized parameters 𝛽𝛽𝑖𝑖∗ ≡ 𝛽𝛽𝑖𝑖 /𝜎𝜎 and 𝜇𝜇𝑖𝑖∗ ≡ 𝜇𝜇𝑖𝑖 /𝜎𝜎, not the parameters 𝛽𝛽𝑖𝑖 and 𝜇𝜇𝑖𝑖 themselves.
5.2. Structural Break Test
Before Ito and Yabu (2007), most papers estimated a model similar to the following:𝑀𝑀𝑀𝑀) + 𝜑𝜑3 𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡𝑡𝑡−1 + 𝜈𝜈𝑡𝑡
𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡𝑡𝑡 = 𝜑𝜑0 + 𝜑𝜑1 (𝑠𝑠𝑡𝑡−1 − 𝑠𝑠𝑡𝑡−2 ) + 𝜑𝜑2 (𝑠𝑠𝑡𝑡−1 − 𝑠𝑠𝑡𝑡−1(3)This model is considered to be a linearized version of Eq. (2). Note that 𝜑𝜑1 < 0 when there is alean-against-the wind intervention and 𝜑𝜑2 < 0 when policy makers have a long-run target in mind;
𝜑𝜑3 > 0 when there is a positive autocorrelation in interventions.

Ito and Yabu (2007) analyzed the data from April 1991 to December 2002 to find that there was astructural break in the reaction function in June 1995 when Eisuke Sakakibara took charge ofinterventions. Ito (2007) and Watanabe and Yabu (2013) asserted that when Zenbei Mizoguchi wasin charge of the intervention from January 2003 to July 2004, the frequency and daily amount ofintervention had a distinctive pattern—more frequent interventions of greater magnitude. We takethese observations as given and examine whether there exist any additional structural breaks
between August 1971 and May 1995.

Here we analyze the data from August 1971 to May 1995 to conduct Andrews’ (1993) sup F test andsearch for an unknown structural break date. 89 The possible structural break dates are assumed to be
the middle 70% of the sample period. The first and last 15% of dates are excluded from breakcandidates. (We set the trimming parameter at 0.15). The standard Chow test is conducted to obtainthe F-statistic at each break candidate.

Figure 5 shows the sequence of F-statistics for each break candidate. In the figure, the break dates
are given on the horizontal axis and the F-statistics on the vertical axis. As shown, the F-statistic has
the largest value in November 1981 at 4.80. This is larger than the 5% critical value of 4.09 but
slightly lower than the 1% critical value of 5.12. Therefore, we reject the null hypothesis of no break
and accept the alternative of a structural break. In addition, based on the figure, the F-statistic issingle-peaked, implying that there are no other structural breaks. 10 The structural break date ofNovember 1981 is a year and a half after a break date pointed out by Watanabe (1992) without
However, once the approval is secured, then intervention can be carried out over the course of several days in a row, if the situation
does not change dramatically.”
8 There is a possibility of endogeneity in this analysis. However, Perron and Yamamoto (2015) found that we can test for structuralchanges and consistently estimate the break dates using the standard methods even when endogenous regressors are present. This isbecause when true parameters change, the probability limits of the OLS parameter estimates also change simultaneously.
9 The sample period starts from August 1971 when the Bretton Woods system collapsed, but some may argue that the sample
period should start from March 1973 when the floating rate system started. Therefore, we checked the sensitivity of our results tothe starting date to find that our main results are robust to the selection of the starting date.
10 We also conducted the Bai and Perron (1998) multiple break tests for the period from August 1971 to May 1995 and confirmedthat there was only one break, which occurred in November 1981. This result is consistent with our visual impression from Figure 5.

13

conducting any structural break test.

5.3. Conventional Regression
We split the full sample into five subperiods and estimate a linearized version of the reaction
function Eq. (3) for each subsample. As discussed in section 5.2, the sample before May 1995 isdivided into two periods: 1) August 1971 to November 1981; and 2) December 1981 to May 1995
just before Eisuke Sakakibara took charge of interventions. In addition, taking into account largescale and frequent interventions by Zenbei Mizoguchi, the sample after June 1995 is divided into
three periods: 3) June 1995 to December 2002; 4) January 2003 to July 2004; and 5) August 2004 to
March 2018.

Table 5 summarizes the estimated results of Eq. (3) for each subsample. In the first period (August
1971 to November 1981), the parameter 𝜑𝜑1 is significantly negative, but 𝜑𝜑2 is not significantly

different from zero. The monetary authorities adopted a lean-against-the-wind policy without setting
any long-run targets. They were only intent on reducing monthly volatility in the exchange rate.
These results are consistent with the findings of Quirk (1977), Hutchison (1984), and Watanabe(1992). On the other hand, the parameter 𝜑𝜑3 is significantly positive and thus there was a positive
autocorrelation in interventions.

<Table 5 about here>
In the second period (December 1981 to May 1995), the parameter 𝜑𝜑2 is significantly negative, but

𝜑𝜑1 is not significantly different from zero. That is, the monetary authorities had long-run targets in

mind, but they were not mindful of reducing monthly volatility in the exchange rate. These results
are sharp contrast to the results of the first period but are consistent with the findings of Watanabe(1992). The parameter 𝜑𝜑3 has a large value of 0.713, a strong positive autocorrelation ofinterventions. Adjusted 𝑅𝑅 2 has the largest value of 0.598 among the subsamples. Interventions wereeasiest to predict among the subsamples.

In the third period (June 1995 to December 2002), we obtain results similar to Ito and Yabu (2007),
which analyzed the same period using daily data. Both parameters of 𝜑𝜑1 and 𝜑𝜑2 are significantly

negative. In other words, the monetary authorities paid attention to changes in the exchange rate aswell as long-run targets. In contrast to the results of the first and second periods, the parameter 𝜑𝜑3 isnow close to zero and no longer significant. There was no evidence of autocorrelation ofinterventions in this period. 11 Adjusted 𝑅𝑅 2 is smaller than it was in the previous two periods and

11 Ito and Yabu (2007) found using daily data that the parameter 𝜑𝜑 was close to zero yet significantly different from zero. The
3

reason we missed this autocorrelation is partly because the number of observations is much smaller than in Ito and Yabu (2007).

14

thus interventions became more difficult to predict.
In the fourth period (January 2003 to July 2004), the parameter 𝜑𝜑3 is significantly positive, whileboth parameters 𝜑𝜑1 and 𝜑𝜑2 are not significantly different from zero. The monetary authorities,

thus, intervened only because they had intervened the month before. As Ito (2007) and Watanabe andYabu (2013) have pointed out, the monetary authorities may have had other motives to conduct
interventions.
For the last period (August 2004 to March 2018), the parameter 𝜑𝜑2 is significantly negative, whileboth parameters 𝜑𝜑1 and 𝜑𝜑3 are not significantly different from zero. Therefore, the monetary
authorities had long-run targets in mind but were not intent on reducing the volatility of theexchange rate. In addition, there was no positive autocorrelation of interventions. Therefore,
interventions during the previous month did not significantly increase the likelihood of interventions
occurring during the following month. Adjusted 𝑅𝑅 2 has the smallest value of 0.069 among the

subsamples because interventions took place during only 5 months of the 14-year period which also
shows the difficulty of predicting interventions.

5.4. Ordered Probit Regressions
Table 6 presents the estimated results of the ordered probit model Eq. (2) for each subsample. Whenthese results are compared with the ones from the linearized model in section 5.3, the signs of theestimated coefficients and their significances are almost the same. Consequently, we only discuss
estimates of new parameters in the ordered probit model: 𝜇𝜇1∗ and 𝜇𝜇2∗ , which respectively represent thecost of dollar-selling interventions and the cost of dollar-buying interventions. Note that in the last two
periods, there was no dollar-selling intervention at all and thus, the parameter 𝜇𝜇1∗ is not estimated in

the regression.

<Table 6 about here>
In the first period, 𝜇𝜇1∗ is significantly negative while 𝜇𝜇2∗ is not significantly different from zero. The

monetary authorities were accordingly more inclined to buy rather than sell dollars due to thedifference in cost. This point was also raised by Hutchison (1984).
For the second period, the parameter 𝜇𝜇2∗ is significantly positive. Due to international politicalpressure, it might have been difficult for the monetary authorities to purchase dollars to induce yen
depreciation. The parameter 𝜇𝜇1∗ is significantly negative and larger in terms of absolute values thanthe corresponding parameter from the first period, which means that there was a higher cost for dollarselling interventions. Moreover, 𝜇𝜇2∗ is larger than 𝜇𝜇1∗ in terms of absolute values and thus, the cost15

of dollar-selling interventions was higher than the cost of dollar-buying interventions. The monetary
authorities are more inclined to resist yen appreciation than yen depreciation. The neutral band of nointervention is wider here than that of the first period.
For the third period, both parameters 𝜇𝜇1∗ and 𝜇𝜇2∗ are significantly different from zero. As was thecase for the second period, 𝜇𝜇1∗ is larger than 𝜇𝜇2∗ in terms of absolute values and thus, the cost of

dollar-selling interventions was larger than the cost of dollar-buying interventions. The monetary
authorities were more inclined to prevent yen appreciation than yen depreciation, and the neutral bandof no intervention was even wider than that of the second period. Note that 𝜇𝜇2∗ is lower than thecorresponding parameter for the second period, which means that the cost of dollar-buying
interventions becomes lower in this period.

For the fourth period, 𝜇𝜇2∗ is not significantly different from zero. Therefore, there is no evidence of aneutral band, which means that there was no international political pressure from other countries whenthe monetary authorities intervened in the foreign exchange market. As Taylor (2006), who served as
the Under Secretary of the Treasury for International Affairs during the George W. Bushadministration, documented, the US Treasury has been critical of interventions but approved Japaneseinterventions during this period in order to show support for the quantitative easing policy adopted by
the Bank of Japan. (See Taylor, 2006 for details). In fact, Watanabe and Yabu (2013) found that whenyen-selling interventions supplied yen funds to the market, the Bank of Japan offset 60% of the yenfunds through market operations, while the remainder remained in the market for a while.
On the other hand, for the fifth period, 𝜇𝜇2∗ has the largest value of 8.405. Hence, its neutral band was
the widest among all subsamples. This is because interventions took place only on rare occasions, suchas when the yen appreciated sharply due to the Great East Japan Earthquake in March 2011 and when
the yen/dollar rate appreciated to 75 yen per dollar, setting a historical record in August 2011.
Therefore, the monetary authorities were not able to intervene in the market due to international
political pressure, except in cases when it was clear that excess volatility and disorderly movementsin the yen/dollar rate would damage economic and financial stability. In contrast to the results in thelinear regression, this period has higher explanatory power (𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 − 𝑅𝑅 2) than other periods. Thisis due to the fact that the model is now rich enough to predict zero intervention by introducing aneutral band of no intervention.

6. International Coordination or non-Coordination
6.1. Policy coordination on exchange rates
The Group of Seven (G7) monetary authorities were engaged in interventions very frequently during
16

the 1970s and 1980s. However, the frequency of interventions declined during the 1990s. Frequentinterventions by the US, Germany and the UK ceased by 1995. The frequency of Japanese intervention
also declined after 1995, apart from 14 months of frequent interventions from January 2003 to March
2004.

During the period of frequent interventions, there were many cases where two or three of the G3—theUS, Japan and Germany—intervened in the same direction simultaneously. There were also cases of
US-Japan joint interventions; US-German joint interventions and even three-way interventions. Whenengaging in joint interventions, they must have shared the same views in terms of the level andmovement of the exchange rate, such as the idea that since the US dollar is overvalued, the US should
buy German marks and Japanese yen, while Germany and Japan should sell US dollars. When two or
three authorities intervene in the same direction in the same month, we refer to them as “coordinatedinterventions” throughout the rest of the paper.

Figure 6 shows that the number of months per year during which the authorities coordinated theirinterventions from 1976 to 1995. For example, in 1979, US-German joint interventions wereconducted during nine months and US-German-Japanese joint interventions were conducted during
three months. In September 2000, there was a joint ECB-US-Japan intervention in order to stop further
depreciation of the euro. On March 18, 2011, the G7 countries intervened to reverse the direction of
yen appreciation that had progressed following the Great East Japan Earthquake. One of the proposed
reasons for this appreciation was based on speculation that Japanese insurance companies would needto liquidate assets abroad and convert them to yen to pay for claims. (See Neely 2011). The
aforementioned coordinated intervention, the first since 2000, played a crucial role in stabilizing theyen.
<Figure 6 about here>

Policy coordination on exchange rates among the G5 countries occasionally had dramatic effects
during the 1970s and 1980s. The most prominent example of this was the period bookended by thePlaza agreement of September 1985 and the Louvre accord of February 1987.
6.2. Case Study: Policy coordination under the Plaza and Louvre agreements 12
With newly estimated intervention amounts for the Japanese authorities, we are able to examine oneof the most dramatic policy coordination events of the post-Bretton Woods era, namely the Plaza andLouvre agreements.

12This section heavily draws on Ito (2016).

17

The meeting of G5 Finance Ministers and Central Bank Governors was called primarily to rectify an
overvaluation of the US dollar on September 22, 1985. The real effective US dollar appreciated byabout 40% from January 1981 to March 1985, due to the policy of Reaganomics which resulted inlarge fiscal deficits. This meeting resulted in what would come to be known as the Plaza Agreement.
The communiqué stated the following: 13“The Ministers and Governors agreed that exchange rates should play a role in adjusting
external imbalances. In order to do this, exchange rates should better reflect fundamental
economic conditions than has been the case. They believed that agreed policy actions must
be implemented and reinforced to improve the fundamentals further, and that, in view of thepresent and prospective changes in fundamentals, some further orderly appreciation of themain non-dollar currencies against the dollar is desirable. They stand ready to cooperate
more closely to encourage this when to do so would be helpful.” (Underlines by the author.)

The communiqué itself did not mention “intervention” or a targeted level of appreciation for the nondollar currencies, although they were part of the discussion that was kept confidential at the time.
According to Funabashi (1988), the non-paper draft stated that intervention shares were 25% for the
US, Japan, and Germany, followed by 12.5% for the UK and France. With German objection, the US
revised the ratios for the US and Japan to 30%; followed by Germany at 25%; France at 10%; and the
UK at 5%. The obligation to intervene in order to achieve the exchange rate goals was also discussed.
According to Gyohten (2013), 18 billion US dollars were committed over a period of six weeks.
Countries would be relieved of their intervention obligation if the necessary currency adjustments
were achieved. All G5 members committed themselves to these goals and interventions willingly.

The communiqué was announced on Sunday, September 22, 1985. Meanwhile, the Tokyo market was
to be closed on Monday, September 23, for a national holiday. Initial interventions were conducted in
the European and US markets on September 23 and in Tokyo on September 24.14 Gyohten (2013, p.
77) stated in his memoir that, over the seven days that followed the Plaza announcement, Japan soldUSD 1.25 billion; surpassing the USD 635 million sold by France, the USD 480 million sold by the
US; the USD 247 million sold by West Germany, and the USD 174 million sold by the UK. The proxylargely corroborates the numbers in the memoir.

The proxy shows that the Japanese monetary authorities intervened to buy yen, selling the US dollars,

13
The statement is available at
http://warp.ndl.go.jp/info:ndljp/pid/8779816/www.mof.go.jp/english/international_policy/convention/g7/g7_850922.pdf
14 According to Gyohten (2013, pp. 69-70), “As far as [non-dollar] currency appreciation, Japan was the most forthcoming.
Finance Minister Takeshita expressed [during the Plaza meeting] that Japan could accept yen appreciation up to 200 yen/dollar.”18

in the amount of 705 billion yen over three months: September, October and November. The US soldUSD 1.44 billion over the same three months. Consequently, the USD/JPY rate became 200 by theend of November. There were no interventions by the Japanese or US authorities in December. So it
appeared that the target had been achieved.

On the DEM/USD markets, both the US and German authorities intervened to appreciate the German
mark over the same three months. The mark reached 2.5 marks per dollar by the end of November,with interventions of USD 1.86 billion by the US and DEM 3 billion by Germany over the threemonths starting in September. In short, the US, Japanese and German authorities conducted
coordinated interventions for three months and achieved the agreed exchange rate targets by the endof November.

The yen continued to appreciate beyond 200 in 1986. The USD/JPY rate dipped into the 190s in
January 1986, to the 180s in February, and finally to the 170s in March. Given that this movement was
unacceptable to the Japanese authorities, they began to intervene in the opposite direction, namely
selling the yen, in April 1986. However, in 1986, interventions were conducted by the Japanese
authorities alone. It was unilateral intervention as opposed to coordinated intervention. Japan
requested to have another agreement to stabilize the exchange rate.
<Table 7 about here>

The Plaza agreement was followed by the Louvre agreement of February 1987, in which the G7
authorities agreed that “their currencies [are] within ranges broadly consistent with underlying
economic fundamentals.” Thus, “they agreed to cooperate closely to foster stability of exchange rates
around current levels” in their statements. The market interpreted that the authorities have committed
to a target zone around the “current levels.” The authorities indeed started to intervene in a coordinatedmanner; that is, both the Japanese and U.S. authorities intervened, selling the yen and buying the dollar,
to counter yen-appreciation pressure. The coordinated interventions continued through April 1988.

7. Concluding Remarks
This paper confirms that “Adjusted MOF FEEF” is the best proxy for settlement-based interventions.
In fact, the proxy explains 99.8% of the movements of settlement-based interventions. The monthly
intervention data are presented in the appendix and are also available on the authors’ websites. 15 At
least in the monthly frequency, it has become possible to research topics related to Japaneseinterventions from August 1971 to the present day. One caution: most researchers are interested in
15 The data are available at

http://www3.grips.ac.jp/~t-ito/JapaneseIntervention1971-2018.xlsx
http://www.fbc.keio.ac.jp/~tyabu/JapaneseIntervention1971-2018.xlsx

19transaction-based interventions when analyzing the effectiveness of interventions or in the reaction
function of the monetary authorities since the exchange rate reacts to the buy and sell in the market
transactions. The difference is the last two business days of the month. Therefore, when we use
settlement-based interventions as transaction-based interventions, we must use the exchange rate
returns using the third to last business day as the monthly exchange rate.

This paper has analyzed the reaction function of the Japanese monetary authorities. The following
points stand out. First, the reaction functions were not stable over time. In fact, we observed four breakdates and thus five regimes: August 1971 to November 1981, December 1981 to May 1995, June 1995
to December 2002, January 2003 to July 2004, and August 2004 to March 2018. These policy shifts
might be associated with changes in the impact of interventions on the yen/dollar rate. Therefore, weneed to exercise caution when analyzing the effectiveness of interventions. Second, the neutral bandof the reaction function widened over time, apart from during the period in which it did not exist,
between January 2003 to July 2004. This implies that the Japanese monetary authorities haveexperienced increased political pressure from other countries over time concerning their intervention
or proposed intervention in the foreign exchange market. Third, the cost of selling-dollar interventions
was higher than the cost of buying-dollar interventions. This implies that the monetary authorities
were more inclined to resist yen appreciation.

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Fatum, Rasmus, and Yohei Yamamoto, 2014. “Large Versus Small Foreign Exchange Interventions,”
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22

Table 1: List of proxies for interventions
Change in Reserve

Changes in the “Total Reserve Excluding Gold” available in the Balanceof Payments statistics.

BOJ EFBank of Japan. "Treasury Funds and Others/Foreign Exchange,"
Statistical category: Sources of Changes in Current Account Balances at
the BOJ and Market Operations.

MOF FEEF

Ministry of Finance. “Receipts and Payments of the Treasury Funds:
Foreign Exchange Equalization Fund” in the fiscal operations statistics.

Adjusted MOF FEEF

Ministry of Finance. Subtracting “Balancing of Issuance and Redemptionof Financial Bills in the Financial Market” from “Receipts and Paymentsof the Treasury Funds: Foreign Exchange Equalization Fund” in the fiscal
operations statistics.

23

Table 2: Proxies used for transaction-based intervention
Sample period: April 1991 – March 1999
𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡 = 𝛽𝛽0 + 𝛽𝛽1 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑡𝑡 + 𝑢𝑢𝑡𝑡

β0
β1
R2

Change in Reserve
3 months
3 years
10 years-176.544 -27.292
48.409(130.508) (129.000) (128.572)
1.026
1.024
1.025
(0.056)(0.057)(0.057)
0.928
0.928
0.928

BOJ
EF
-24.279(264.463)
0.751**(0.109)
0.757

MOF
FEEF
Adjusted FEEF
-24.2870.448(264.463)(33.421)0.751**
0.995(0.109)(0.020)
0.757
0.969

Note: (1) Both transaction-based intervention amounts and proxies are in units of 100 million yen. (2) HAC standard errors arepresented in brackets; The long-run variance is constructed based on the quadratic spectral window with the truncation parameter
being selected via the Andrew’s (1993) plug-in method. (3) The null hypothesis is 𝐻𝐻0 : 𝛽𝛽0 = 0 and 𝐻𝐻0 : 𝛽𝛽1 = 1, tested independently

with t-statistics; Asterisks (*, **, ***) denote the 10%, 5%, and 1% significance of the hypothesis testing, respectively. (4) The sample

period ends March 1999, since one of the proxies has a discontinuity in the classification.

Table 3: Proxies used for settlement-based intervention
Sample period: May 1991 – March 1999
𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 = 𝛽𝛽0 + 𝛽𝛽1 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑡𝑡 + 𝑢𝑢𝑡𝑡

β0
β1
R2

Change in Reserve
3 months
3 years
10 years-214.191 -64.378
11.486(120.814) (118.523) (117.537)
1.027
1.025
1.026
(0.053)(0.053)(0.054)
0.950
0.950
0.950

BOJ
EF
-28.087(267.028)
0.751**(0.109)0.778

FEEF-28.095(267.028)
0.751**(0.109)0.778

MOF
Adjusted FEEF
-5.264(10.951)
0.996(0.004)0.998

Note: (1) Both settlement-based intervention amounts and proxies are in units of100 million yen. (2) HAC standard errors are presented
in brackets; The long-run variance is constructed based on the quadratic spectral window with the truncation parameter being selected
via the Andrew’s (1993) plug-in method. (3) The null hypothesis is 𝐻𝐻0 : 𝛽𝛽0 = 0 and 𝐻𝐻0 : 𝛽𝛽1 = 1 , tested independently with t-

statistics; Asterisks (*, **, ***) denote the 10%, 5%, and 1% significance of the hypothesis testing, respectively. (4) The sample starts

from May 1991 because we needed information on daily intervention at 𝑡𝑡 − 1 month in order to compute settlement-based

intervention at 𝑡𝑡 month. The sample period ends March 1999. This is because one of the proxies has a discontinuity in theclassification.

24

Table 4: Frequency and magnitude of interventions
FY
Months Total amount Amount per month Yen/Dollar rate
FY
1971
9
33711
3746
304
1995
1972
12
22478
1873
266
1996
1973
12
30499
2542
275
1997
1974
10
7945
795
291
1998
1975
8
10764
1346
300
1999
1976
6
5957
993
277
2000
1977
9
30798
3422
222
2001
1978
8
20376
2547
208
2002
1979
11
40857
3714
250
2003
1980
6
16494
2749
211
2004
1981
6
8670
1445
247
2005
1982
8
19113
2389
239
2006
1983
3
3142
1047
225
2007
1984
4
3062
766
254
2008
1985
3
7046
2349
180
2009
1986
7
42083
6012
149
2010
1987
8
29180
3648
124
2011
1988
2
4745
2373
133
2012
1989
11
47734
4339
159
2013
1990
2
2622
1311
138
2014
1991
12
1909
159
133
2015
1992
4
5664
1416
115
2016
1993
7
30917
4417
103
2017
1994
10
32990
3299
87
Sub-total
178
4587562577
Sub-total

Months Total amount Amount per month Yen/Dollar rate
7
47672
6810
107
0
0
0
124
1
10591
10591
133
3
37033
12344
119
7
79626
11375
103
1
13854
13854
126
1
31455
31455
133
5
62837
12567
118
11
327865
29806
104
0
0
0
107
0
0
0
118
0
0
0
118
0
0
0
100
0
0
0
99
0
0
0
93
2
28174
14087
83
3
136046
45349
83
0
0
0
94
0
0
0
103
0
0
0
120
0
0
0
113
0
0
0
111
0
0
0
106
41

775153

18906

Note: “Fiscal year (FY)” is from April (in year 𝑡𝑡) to the end of March (in year 𝑡𝑡 + 1). “Yen/dollar rate” uses the Tokyo close rateon the third to last business day of March before April 1991 as well as the New York close rate at the end of March after April 1991.
“Months” denotes the number of months in which the intervention amount exceeds 30 billion yen. Interventions are in units of 100

million yen.

25

Table 5: Estimating the reaction function for various sample periods
𝑀𝑀𝑀𝑀) + 𝜑𝜑3 𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡𝑡𝑡−1 + 𝜈𝜈𝑡𝑡
𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡𝑡𝑡 = 𝜑𝜑0 + 𝜑𝜑1 (𝑠𝑠𝑡𝑡−1 − 𝑠𝑠𝑡𝑡−2 ) + 𝜑𝜑2 (𝑠𝑠𝑡𝑡−1 − 𝑠𝑠𝑡𝑡−1

1971/8-1981/11 1981/12-1995/5
φ00.118-0.073*(0.107)(0.041)-7.184 ***-1.536
φ1(2.406)(1.114)0.733-0.389*φ2(0.681)(0.214)
φ30.373 ***
0.713 ***
(0.097)(0.063)
0.598
Adjusted R2 0.291

Sample period
1995/6-2002/12
0.179 ***
(0.044)-2.425*(1.251)-1.607 ***(0.419)0.121(0.127)0.282

2003/1-2004/7
0.561 **
(0.223)-1.992(3.648)4.119(2.608)0.440*(0.250)0.194

2004/8-2018/3
0.023**(0.011)-0.169(0.371)-0.275**(0.129)0.113(0.181)
0.069

Note: (1) HAC standard errors are presented in brackets; The long-run variance is constructed based on the quadratic spectral
window with the truncation parameter being selected via the Andrew’s (1993) plug-in method. (2) Asterisks (*, **, ***) denote the
10%, 5%, and 1% significance of the hypothesis testing, respectively.

Table 6: Estimating the reaction function based on the ordered probit model+1 𝑖𝑖𝑖𝑖
𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡𝑡𝑡 = � 0 𝑖𝑖𝑖𝑖
−1 𝑖𝑖𝑖𝑖

𝜇𝜇2 < 𝑦𝑦𝑡𝑡∗
𝜇𝜇1 < 𝑦𝑦𝑡𝑡∗ < 𝜇𝜇2
𝑦𝑦𝑡𝑡∗ < 𝜇𝜇1

where 𝑦𝑦𝑡𝑡∗ = 𝑋𝑋𝑡𝑡 𝛽𝛽 + 𝜀𝜀𝑡𝑡 with 𝜀𝜀𝑡𝑡 ∼ 𝑖𝑖. 𝑖𝑖. 𝑑𝑑. 𝑁𝑁(0, 𝜎𝜎 2 ) and𝑀𝑀𝑀𝑀) + 𝛽𝛽3 𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡𝑡𝑡−1
𝑋𝑋𝑡𝑡 𝛽𝛽 = 𝛽𝛽1 (𝑠𝑠𝑡𝑡−1 − 𝑠𝑠𝑡𝑡−2 ) + 𝛽𝛽2 (𝑠𝑠𝑡𝑡−1 − 𝑠𝑠𝑡𝑡−1

β 1*
β 2*
β 3*
μ 2*
μ 1*
pseudo-R2

1971/8-1981/11 1981/12-1995/5-13.928 ***-4.613(5.334)(3.492)
1.059-1.125*(1.186)(0.614)0.574 ***
1.819 ***
(0.158)(0.207)0.264
1.448 ***
(0.179)(0.160)-0.622 ***-1.002 ***(0.186)(0.172)
0.168
0.417

Sample period
1995/6-2002/12-10.773 **(4.505)-7.335 ***(2.017)0.412(0.403)1.110 ***
(0.215)-2.863 ***(0.351)0.302

2003/1-2004/7-6.676(10.781)
16.562(11.313)1.485*(0.796)-0.339(0.628)0.298

2004/8-2018/3
10.219(11.962)-33.315 ***(11.580)-0.251(0.802)8.405***(2.546)0.517

Note: (1) Standard errors are in Brackets. (2) Asterisks (*, **, ***) denote the 10%, 5%, and 1% significance of the hypothesis
testing, respectively. (3) We can estimate only the normalized parameters 𝛽𝛽𝑖𝑖∗ ≡ 𝛽𝛽𝑖𝑖 /𝜎𝜎 and 𝜇𝜇𝑖𝑖∗ ≡ 𝜇𝜇𝑖𝑖 /𝜎𝜎, not the parameters 𝛽𝛽𝑖𝑖 and𝜇𝜇𝑖𝑖 themselves. (4) In the last two periods, there was no dollar-selling intervention at all and thus, the parameter 𝜇𝜇1∗ is not estimatedin the regression.

26

Table 7: From Plaza to Louvre, and beyond

Sep-85Oct-85
Nov-85
Dec-85Jan-86
Feb-86
Mar-86
Apr-86May-86
Jun-86
Jul-86
Aug-86
Sep-86Oct-86
Nov-86
Dec-86
Jan-87
Feb-87
Mar-87
Apr-87May-87
Jun-87
Jul-87
Aug-87
Sep-87
Oct-87
Nov-87
Dec-87
Jan-88
Feb-88Mar-88
Apr-88May-88Jun-88
Jul-88Aug-88
Sep-88Oct-88
Nov-88Dec-88Jan-89Feb-89Mar-89Apr-89May-89Jun-89Jul-89Aug-89Sep-89Oct-89
Nov-89Dec-89Jan-90Feb-90Mar-90Apr-90USD/JPY
Major Events(End of
Month)
222.8 Plaza Agreement
212.9
200.9
202.92194.3
181.85
180
170.2
168.9
166.1
155.7
155.25154.43
159.35
162.5
159.5
151.2
153.55 Louvre Agreement
149
138.1
143.15145.9
150.15142.55144.22140.75 Black Monday
134.75123.5
127.65128.8
124.37124.57124.64130.95
132.5
134.2
134.2
125.9
122
125.27128.65126.29133.25131.47142.88143.55139.35144.15141.35142.75143.65142.1 Peak of the Bubble
143.22148.65158.63
159.25

Japanese Intervention(100 millions of Yen)
-2839
-3303
-904
0
0
0
0
4743
1914
1767
6178
4680
0
0
0
0
13494
0
9307
13930
0
0
0
653
1677
336
6408
3548
2258
0
370
1114
0
0
0
0
0
0
3631
0
0
0
0
0
-6835
-10120
-2145
-1191
-4300
-3606
-764
-665
-4155
-2858
-11095-1342

27

U.S. Intervention(1 Million of Unilateral or CoordinatedDirection of Interventions
USD)-224 Policy Coordination
Buy yen, sell dollars-1138 Policy Coordination
Buy yen, sell dollars-77 Policy Coordination
Buy yen, sell dollars
00000 Japan unilateral
Sell yen; buy dollars
0 Japan unilateral
Sell yen; buy dollars
0 Japan unilateral
Sell yen; buy dollars
0 Japan unilateral
Sell yen; buy dollars
0 Japan unilateral
Sell yen; buy dollars
000050 Policy Coordination
Sell yen; buy dollars
02435 Policy Coordination
Sell yen; buy dollars
1529 Policy Coordination
Sell yen; buy dollars
20 US unilateral
Sell yen; buy dollars
103 US unilateral
Sell yen; buy dollars
0
105 Policy Coordination
Sell yen; buy dollars
285 Policy Coordination
Sell yen; buy dollars
65 Policy Coordination
Sell yen; buy dollars
428 Policy Coordination
Sell yen; buy dollars
1089 Policy Coordination
Sell yen; buy dollars
235 Policy Coordination
Sell yen; buy dollars
0318 Policy Coordination
Sell yen; buy dollars
260 Policy Coordination
Sell yen; buy dollars
0
0
0
0
0
200 US unilateral
Sell yen; buy dollars
1570 Policy Coordination
Sell yen; buy dollars
200 US unilateral
Sell yen; buy dollars
00-100 US unilateral
Buy yen, sell dollars-50 US unilateral
Buy yen, sell dollars-3185 Policy Coordination
Buy yen, sell dollars-3823 Policy Coordination
Buy yen, sell dollars-230 Policy Coordination
Buy yen, sell dollars-595 Policy Coordination
Buy yen, sell dollars-1594 Policy Coordination
Buy yen, sell dollars-1100 Policy Coordination
Buy yen, sell dollars-50 Policy Coordination
Buy yen, sell dollars-100 Policy Coordination
Buy yen, sell dollars-600 Policy Coordination
Buy yen, sell dollars-350 Policy Coordination
Buy yen, sell dollars-1180 Policy Coordination
Buy yen, sell dollars-50 Policy Coordination
Buy yen, sell dollars

Figure 1: Actual intervention and estimated error
30,000(yen 100mil)

Actual Intervention
Error

20,00010,000Dollar Buying Interventions

0

91-10,000

92

93

94

95

96

97

98

99

Dollar Selling Interventions-20,000-30,000

Notes: The intervention amounts are in units of 100 million yen. 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑡𝑡 is defined as 𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡 − 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑡𝑡 where 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑡𝑡 is Adjusted
MOF FEEF.

Figure 2: Estimated error and EoMDiff
10,000(yen 100mil)

EoMDiff

8,000

Error
6,000
4,000
Dollar Buying
Interventions

2,000
0

91

92

93

94

95

96

97

98

99

-2,000Dollar Selling Interventions-4,000-6,000-8,000-10,000
𝐸𝐸𝐸𝐸𝐸𝐸
Notes: 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑡𝑡 is defined as 𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡 − 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑡𝑡 where 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑡𝑡 is Adjusted MOF FEEF. 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑡𝑡 is defined as 𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡𝐸𝐸𝐸𝐸𝐸𝐸 − 𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡−1

where 𝐼𝐼𝐼𝐼𝐼𝐼𝑡𝑡𝐸𝐸𝐸𝐸𝐸𝐸 is interventions conducted in the last two business days of the month 𝑡𝑡.

28

Figure 3: The yen/dollar rate and magnitude of interventions from August 1971 to March 2018(a) Yen/dollar exchange rate
360
340

Spot

320

Moving Average

300
280
260
240
220
200
180
160
140
120
100
80
93

95

97

99

01

03

05

07

09

11

13

15

17

95

97

99

01

03

05

07

09

11

13

15

17

91

93

89

87

85

83

81

79

77

75

73

71

60

(b) Magnitude of Japanese interventions

100000(yen 100mil)

80000

60000
Buy dollars, sell yen

40000

20000

91

89

87

85

83

81

79

77

75

73

71

0

-20000
Sell dollars, buy yen-40000Note: The monthly yen/dollar rate uses the Tokyo close rate on the third-to-last business day for the period before April 1991, since
the proxy is for settlement-based interventions. After April 1991, we use the end-of-month New York close rate for the monthly
yen/dollar rate. Intervention amounts below 30 billion yen for the period from August 1971 to March 1991 are assumed to be 0, asintervention amounts for the period are estimated with the proxy and the proxy is likely to contain a certain degree of estimation
error. The intervention amounts are in units of 100 million yen.

29

Figure 4: The yen/dollar exchange rate, last selling rate, and last buying rate
360
340

spot

320
300

Last buying rate

280

Last selling rate

260
240
220
200
180
160
140
120
100
80
60
71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 01 03 05 07 09 11 13 15 17

Figure 5: Structural break test
6.0
Sup-F = 4.80
5.0

4.0

3.0

2.0

1.0

95

94

93

92

91

90

89

88

87

86

85

84

83

81

80

79

78

77

76

75

74

73

72

71

82

Break Date
1981/110.0

Note: The sample period is from August 1971 to May 1995. The trimming parameter is set to 0.15. Therefore, break candidates are
the middle 70% of the sample.

30

Figure 6: Coordinated interventions
12
US-Germany
10
US-Japan
8US-JapanGermany
6

4

2

0
1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995

31

Data Appendix
Table: Japanese Interventions and yen/dollar rates

32

Table: Japanese Interventions and yen/dollar rates (continued)

33

Table: Japanese Interventions and yen/dollar rates (continued)
Japanese Interventions (100mil Yen)
Yen/Dollar Rate
ActualEstimated
131.2
Jan-91NA0
133.1
0Feb-91NA
Mar-91
NA
-1280
138.4
Apr-91
0-489
136.1
May-91
-139
-139
138.5
Jun-91
-424
-424
137.5
Jul-910
0137.4
Aug-9100
136.8
Sep-9100
133.0
Oct-91
0
0
130.8
130.0
0
Nov-910
Dec-9100
124.8
Jan-92
-63
-62
125.8
Feb-92
-896
-896
129.5
Mar-92
-387
-387
132.8
Apr-92
-2036
-1104
133.3
May-92
-1673
-1775
127.6
Jun-92
-1477
-2305
125.7
Jul-92
-478
-478
127.2
Aug-92
-160
-160
123.2
0
0
Sep-92
119.9
0
123.5
Oct-920
Nov-9200
124.6
Dec-92
0
1
124.9
0
0
Jan-93124.7
0Feb-930118.3
Mar-9300114.8
Apr-93
6756
6756
111.1
May-93
4971
2384
107.5
7974
Jun-93
5386
107.3
Jul-93366
0104.9
Aug-93
7835
8262
104.8
Sep-93
218
219
106.2
0
1
Oct-93
108.6
0
109.1
Nov-930
Dec-9300
111.8
Jan-94
0
0
108.5
2445
1869
Feb-94
104.5
3158
2714
102.7
Mar-94Apr-94
2637
2845
101.7
May-94
824
2298
104.8
Jun-94
4678
4083
98.5
Jul-94
1056
1652
99.9
Aug-94
1494
1494
100.2
Sep-94789
789
99.2
Oct-94
1913
1913
97.0
Nov-94
1645
1644
99.0
Dec-940
1
99.8
Jan-9500
99.6
Feb-95
1183
1115
96.7
12116
86.6
Mar-95
16771
Apr-95
4986
9710
84.3
May-95635
084.6
Jun-95
430
1064
84.7
Jul-95
579
579
88.4
Aug-95
8174
8174
97.5
Sep-95
16831
16840
99.7
3
102.0
Oct-950
Nov-9502
102.2
Dec-950
3
103.4

Japanese Interventions (100mil Yen)
Yen/Dollar Rate
ActualEstimated
Jan-9600
107.1
Feb-96
16037
16037
105.2
Mar-9600107.3
Apr-960
186
105.1
May-960-182
108.1
Jun-960
1
109.7
Jul-9600
106.9
Aug-9600
109.0
Sep-9600
111.4
Oct-9601
114.1
Nov-960-1
113.9
Dec-960-1
115.9
Jan-9700
121.4
Feb-970
1
120.4
Mar-9701
123.8
Apr-9701
127.1
May-9700116.3
Jun-9701
114.7
Jul-9700118.8
Aug-9700
120.8
Sep-9700
120.4
Oct-9700120.5
Nov-970
7127.8
Dec-97-10591-10585130.6
Jan-9801
127.2
Feb-9800
126.1
Mar-9800
133.0
Apr-98-28158-28157132.9
May-9800138.8
Jun-98
-2312
-2312
138.8
Jul-9800
144.7
Aug-9800139.2
Sep-9802
136.7
Oct-9801
116.0
Nov-9802
123.0
Dec-9800
113.5
Jan-99
6563
6564
116.4
Feb-990
1
119.2
Mar-9901
118.9
Apr-990NA
119.5
May-990NA
121.4
Jun-99
24996
NA
121.2
Jul-99
13681
NA
114.6
Aug-990NA
109.8
Sep-99
10195
NA
106.4
Oct-990NA
104.1
Nov-99
11328
NA
102.2
Dec-99
3704
NA
102.3
Jan-00
5753
NA
107.4
Feb-000NA
110.2
Mar-00
9969
NA
102.7
Apr-00
13854
NA
108.2
May-000NA
107.7
Jun-000NA
106.2
Jul-000NA
109.4
Aug-000
NA
106.7
Sep-000
NA
108.2
Oct-000
NA
109.0
Nov-000NA
110.4
Dec-00
0
NA
114.5

34

Table: Japanese Interventions and yen/dollar rates (continued)
Japanese Interventions (100mil Yen)
Yen/Dollar Rate
ActualEstimated
116.5
Jan-01
0
NA
117.4
NA
Feb-010
Mar-010NA
126.2
Apr-010
NA
123.6
May-010NA
119.3
Jun-010
NA
124.7
Jul-010NA
125.1
Aug-010
NA
118.9
Sep-01
31455
NA
119.5
Oct-01
0
NA
122.5
123.5
NA
Nov-010
Dec-010
NA
131.7
Jan-020NA
134.6
Feb-020NA
133.4
Mar-020NA
132.8
Apr-020
NA
128.6
May-02
21174
NA
124.3
Jun-02
18750
NA
119.5
Jul-020NA
119.9
Aug-02
0
NA
118.4
0
NA
Sep-02
121.7
NA
122.6
Oct-020
Nov-020NA
122.5
Dec-02
0
NA
118.8
6781
NA
Jan-03119.9
NAFeb-0310614
118.1
Mar-03
5518
NA
118.1
Apr-030NA
119.0
May-03
38997
NA
119.3
NA
Jun-036289
119.8
Jul-03
20271
NA
120.6
Aug-03
4124
NA
117.0
Sep-03
51116
NA
111.5
16687
NA
Oct-03
110.0
NA
109.6
Nov-0315872
Dec-03
26196
NA
107.3
Jan-04
68215
NA
105.8
34766
NA
Feb-04
109.2
45332
NA
104.3
Mar-04
Apr-040NA
110.5
May-040NA
109.6
Jun-040NA
108.8
Jul-040NA
111.4
Aug-040NA
109.2
Sep-040NA
110.1
Oct-040NA
106.0
Nov-040NA
103.1
Dec-040NA
102.7
Jan-050NA
103.7
Feb-05
0
NA
104.6
107.2
Mar-050NA
Apr-050
104.8
NAMay-050NA
108.6
Jun-050NA
111.0
Jul-050NA
112.5
Aug-050NA
110.6
Sep-05
0
NA
113.5
NA
116.4
Oct-050
Nov-050NA
119.8
Dec-050NA
117.8

Japanese Interventions (100mil Yen)
Yen/Dollar Rate
ActualEstimated
Jan-060NA
117.2
Feb-060NA
115.8
Mar-060NA
117.8
Apr-060NA
113.8
May-060NA
112.6
Jun-060
NA
114.4
Jul-060NA
114.7
Aug-060NA
117.4
Sep-060NA
118.1
Oct-060NA
116.9
Nov-060NA
115.8
Dec-060NA
119.1
Jan-070NA
120.7
Feb-070NA
118.5
Mar-070NA
117.8
Apr-070NA
119.5
May-070NA
121.7
Jun-070NA
123.2
Jul-070NA
118.6
Aug-070NA
115.8
Sep-070
NA
114.8
Oct-070NA
115.4
Nov-070NA
111.2
Dec-070NA
111.7
Jan-080NA
106.5
Feb-080NA
103.7
Mar-080NA
99.7
Apr-080NA
103.9
May-080NA105.5
Jun-080NA
106.2
Jul-080NA
107.9
Aug-080NA
108.8
Sep-080NA106.1
Oct-080NA
98.5
Nov-080NA
95.5
Dec-080NA
90.7
Jan-090
NA
89.9
Feb-090NA
97.6
Mar-090
NA
99.0
Apr-090
NA
98.6
May-090NA95.4
Jun-090
NA
96.4
Jul-090
NA
94.7
Aug-090NA
93.2
Sep-090
NA89.7
Oct-090NA
90.1
Nov-090NA
86.4
Dec-090NA
93.0
Jan-100NA
90.3
Feb-100NA
88.9
Mar-100NA
93.5
Apr-100NA
93.9
May-100NA91.5
Jun-100NA
88.5
Jul-100NA
86.5
Aug-100NA84.2
Sep-10
21249
NA
83.5
Oct-100NA80.4
Nov-100NA
83.7
Dec-10
0
NA
81.2

35

Table: Japanese Interventions and yen/dollar rates (continued)

Note: The intervention amounts are in units of 100 million yen. The monthly yen/dollar rate is the Tokyo close rateon the third to last business day for the period before April 1991, since the proxy is for settlement-based
interventions. After April 1991, we use the end-of-month New York close rate for the monthly yen/dollar rate.

36