Two Financial Tsunamis hitting in Japan*
byTatsuyoshi Miyakoshi a,Toyoharu Takahashi b ,Junji Shimada c, Yoshihiko Tsukuda dAbstract
Japan was hit by the Japanese and US financial tsunamis. There are two hypotheses byusing the number of bankruptcy and profit decreases. The Japanese tsunami damaged thefinancial industry, while the US tsunami did the manufacturing industry closely related to
the US and the other countries through the international trade. We retest these hypotheses
by using the evaluation of investor or market: a risk premium for bonds issued by suchindustries. If both hypotheses are correct, relief should be prescribed with an emphasis in
manufacturing industry while the lessons from the Japanese tsunami are not necessarily
applicable to the slump by the US tsunami.

Keywords: Financial Tsunami; Japanese financial industry; risk premium
JEL Classification Number: C10; G01; G12aOsaka School of International Public Policy, Osaka University
Faculty of Commerce, Chuo University / Visiting Fellow, AJRC, Crawford School, CAP, ANU
c
School of Management, Aoyama Gakuin UniversitydSchool of Economics, Tohoku University* Earlier version of this paper was presented at the seminar held by the Science Council of Japan.
We are very grateful to Katsuto Iwai, Shin-Ichi Fukuda, Yuzo Honda, Kazuo Ogawa, and Komei
Sasaki for their helpful comments. The first and second authors‟ research were respectivelybsupported by and Grant-in-Aid for Scientific Research(20530271) from the Ministry of
Education, Culture, Sport, Science and Technology of Japan and Grant-in-Aid for Scientific
Research (19530287) from Japan Society for the Promotion of Science.
Correspondence: Toyoharu Takahashi, Chuo University, 742-1, Higashinakano, Hachioji City,
Tokyo, 192-0393, Japan. Tel:+81-(0)42-674-3612; Fax: +81-(0)42-674-3612
E-mail: toyohal@tamacc.chuo-u.ac.jp

11. Introduction
Japan was hit by the Japanese and US financial tsunamis.1 We define financial
tsunamis as the inflow of risk. The Japanese tsunami was caused by the Japanese stocks andland price bubble collapses, December 1989. This tsunami with several big waves sunk theJapanese economy down into serious stagnation, so-called “Lost Decade of Japan”, until
July 2006. The US tsunami was caused by the sub-prime loan problem in USA, June 2007.
This tsunami leads the world economy including Japan down into serious depression, i.e.,“a second Great Depression” named by Krugman, until now. There are two hypotheses byusing the number of bankruptcy and profit decreases. The Japanese tsunami damaged thefinancial industry and caused the systemic risk in financial markets: Bayoumi (2001),
Caballero, Hoshi and Kashyap (2006), Miyakoshi and Tsukuda (2004, 2007) and
Miyakoshi (2009). The US tsunami hit the manufacturing industry closely related to the US
and the other countries through the international trade, as reported by Mass media.2
It will be necessary to retest the hypotheses by using an alternative viewpoint ofevaluation by investors or markets. The evaluation by investor or market reflects the truthof economy, and on the other hand will drive the truth. Eg., Nadenichek (2007) and Kim(2008) showed that these push the economies into the crisis through a self-fulfilling
expectation. When the appropriate data (for the number of bankruptcy and profit decreases)
is not obtained or is not ready yet, this view point is more significant. Furthermore,available data is restricted because the US tsunami is just starting.
The purpose of this paper is to retest two hypotheses (two tsunamis‟ effects on
Japanese economy) by using the evaluation of investor or market: a risk premium for bonds
issued by such industries. Retesting is significant. If both hypotheses are correct, relief
should be prescribed with an emphasis in manufacturing industry while the lessons fromthe Japanese tsunami are not necessarily applicable to the slump by the US tsunami.
The paper is organized as follows: Section 2 briefly summarizes the events andmonetary-banking policy and corporate policy around the two tsunamis in Japan. Section 3
describes data and does visual inspections. Section 4 explains the statistical methodology
and discusses the results of the test hypotheses. Section 5 concludes the paper.

2. Events, monetary-banking policy, and corporate policy around two
tsunamis
Table 1A shows events around two tsunamis. By using detailed materials,
Miyakoshi (2009, p.28-34) explained the background for events as follow. Japanese banks
were strongly supported by the collateral of land and stock evaluations. When the bubble
collapsed, land and stock prices fell, pushing down the value of collateral, which in turn
We saw the word of ’financial tsunami’, for the first time, in the theme of All China
Economics International Conference held at Hong Kong, Dec.2009.
1

2

There are no papers for the US tsunami except for Longstaff (2010), Duchin et. al.(2010),
Bordo and Haubrich (2010) which investigate the contagion to the other domestic markets.

2

meant the banks ceased lending, or called in loans from all companies to which they hadloaned money, including risk-averse companies that had not engaged in speculation. The
number of business bankruptcies began to increase from 1990 to 1992, and reached a highof 20,000 per year in 2002. The money involved in bankruptcy was much bigger than thosethat occurred in the 1980s, reaching 26 trillion yen in 2002. On the other hand, the banks
caused a credit crunch for other borrowers. The total number of credit crunch claims
between the fourth quarter of 2002 and the second quarter of 2005 was 1,786 cases, which
created huge turmoil. Many financial institutions failed. The number of Japanese bank andcredit cooperative failures rapidly increased during the 1990s. In the 2000s, the bank slump
continued and worsened, but the failure of financial institutions finally stopped in 2003.
From the 1990s to 2003, 181 failures were recorded. Taking into account the banks
acquired and merged, the number of financial institutions in 2003 was 131 banks(compared with 151 in 1991), 306 Shinkin-banks (reduced from 451 in 1991) and 181
credit cooperatives (reduced from 407 in 1991). Thus, the banking industry itself largely
descended into bankruptcy. Events around the US tsunami are reported a little since the UStsunami has just started. The Industrial Production Index decreases to 69.5 from 110 at May
2008. Most of export companies announced the current account deficits. The Real GDP(Quarterly) growth shows: -8.9,-5.8,-5.1% and the Real Export growth: -36.5, -29.3,-22.3% at the first to the third quarter in 2009.
We define the period of two tsunamis. The starting point of the Japanese tsunami is
Jan 4, 1990 to March 18, 2001, based on the zero interest policy of the BOJ (Bank ofJapan). They say that the zero interest rate policy resolute the Japanese tsunami. This policy
continues during March 19, 2001 to July 14, 2006. On the other hand, the US tsunami
caused by the sub-prime loan problem in June 2007 is actualized by the bankruptcy ofLehman Brothers, Merrill Lynch and the American International Group in September 15
and 17 2008. It is so-called “Lehman Shock”. However, we suppose the starting point of
the US tsunami as the end of the zero interest rate in Japan (July 15, 2006), since thesample period is not sufficient for analysis.3[INSERT Table 1A ]Table 1B shows monetary-banking and corporate policy around two tsunamis. The
Bank of Japan (BOJ) introduced a zero interest rate policy, which encouraged the overnight
call rate to fall as low as possible from 1999. However, investment did not respond to thezero interest rates. Consequently, in March 2001, the BOJ introduced a new monetary
policy framework. The BOJ‟s new monetary policy (referred to as „quantitative easing
policy‟) changed the operating target from the call rate to the current account balance inorder to produce inflation. To do this, the BOJ purchased Japanese Government bonds. The
W should use the “Bank of Japan Monetary Policy Meetings: Announcement of Decisions” as a
guide and split our data into three sub-periods. Admittedly, economic situation does not shift
overnight. The splitting of our sample, however, is consistent with a broader picture of data infigures in the text.
33

private bank reserve at the BOJ consisted of legal reserves and excess reserves. The BOJ‟spurchase of government bonds increased the level of excess reserves, which pushed up theprice level and produced inflation. The target level was set at five trillion yen in March
2001, and gradually increased to 30–35 trillion yen in 2004. The CPI eventually began toincrease after 2005. The BOJ withdrew from its quantitative easing policy in March andceased its zero interest rate policy in July 2006. Eventually, the monetary policy provides
banks with sufficient money. The Financial Service Agency of Japan publicly announced
what is referred to as the Program for Financial Revival (PFR) in October 2002, although
the program had begun operating before that date. This is the Banking Policy which
consisted of grants, the purchase of assets and capital injections for banks. The grant,
amounting to around 19 trillion yen, could not be recovered. However, the purchase of
assets (i.e., the government purchases of bad loans from the banks) amounted to about 10
trillion yen. The last main measure was a capital injection. The government injected about
12 trillion yen into many banks between 1998 and 2004. The government purchased thepreferred stocks and subordinated bonds issued by the banks. On the other hand, forcorporate policy, the Industrial Revitalization Corporation of Japan (IRCJ) purchase badloans from the 25 big companies approved for assistance from April 2003 until September
2004. The monetary-banking and corporate policies around the US tsunami are follows.
The BOJ starts outright purchases of CP and corporate bonds with one trillion yen. The
main policy is related to corporate policy for manufacturing industry. There is no otherstriking policy since the US tsunami has just started.

[INSERT Table 1B ]Thus, the events in Table1A seem to support that the hypothesis that the Japanese
tsunami damaged the financial industry, while the US tsunami did the manufacturingindustry closely related to the US and the other countries through the international trade. As
a result, relief seems to be prescribed with an emphasis in manufacturing industry.
However, the evaluation by investor or market reflects the truth of economy, and on
the other hand will drive the truth, as pointed by Nadenichek (2007) and Kim (2008). Then,
when the available data (for the number of bankruptcy and profit decreases) is not obtained
or is not ready yet because the US tsunami is just starting, the view point of evaluation by
investor or market is significant. We retest the hypotheses by using the evaluation of
investor or market ( a risk premium for bonds issued by such industries).

3. Data and Visual Inspections
How should we define and measure the risk in financial and manufacturingindustries? We use four risk premiums for bonds issued by industries.
(1) Nikkei Bond Index (Koshasai Index) spread for all industries in Japan:
9/30/1992/-10/9/2009
The short-term bond index (consisting of bond selected by the Japan Securities Dealers4Association and the Nikkei Media Marketing) is used for new issues with maturities lessthan three years, a medium-term bond (public and private bonds) index for issues withmaturities between three and seven years, and a long-term bond index for those withmaturities more than seven years. 4(2) Euro JPY bond (AAA) for all industries in other countries: 10/8/1998-11/2/2009
A Euro JPY bond (rated AAA) that is denominated in Japanese yen and issued by a
non-Japanese company outside of Japan.
(3) Interest Rate Swap Rate (ISDAFIX®) for financial industries in Japan:
3/29/1996/-9/16/2009
The growth of interest rate derivatives from 1990 to 2006 is conspicuous. Interest rate
options increased greatly to 44 trillion USD, while interest rate swaps soared over one
trillion to 230 trillion USD.5 The swap buyer makes a fixed interest payment in exchange
for a variable cash flow based upon a floating London Interbank Offered Rate. The interest
rate that determines the fixed payment is the swap rate. Its rate includes the risk for main
counter party, i.e., financial industry.
(4) Composite AA for manufacturing industries in Japan: 7/12/2004/-11/2/2009
This index includes the bond in Industrial, Telephone, Utilities, Transportation industries
with rating AA.

The maturities for bonds are 2-, 3-, 5-, 7- and 10-year. The risk premium for each bond ismeasured by using the spread with the government-bond (risk-free) and each bond yieldwith same maturity. Then, the risk for all industries in Japan, is represented by the Nikkei
Bond Index spread (1), the risk for non-Japanese company outside of Japan by the EuroJPY bond spread (2), the risk for financial industries in Japan by the Interest Rate Swap
spread (3), and the risk for the financial industries in Japan by the Composite AA spread(4).6 Data (1) is from Nikkei NEEDS Financial Quest, (2)-(4) from Bloomberg.
These risk premiums inflow into the Japanese companies in financial andmanufacturing industries like a tsunami with several waves. These tsunamis of risks behave
like stock price index (in logarithm form) in a sense that those have a persistent volatility
and a spiked peak. As shown in Figure 1, the difference of the risk (risk change), whichcorresponds to the stock return (the difference of stock price index in logarithm form), may
be analyzed by the EGARCH model. We can check the characters of risk change by
The data of Nikkei Bond index from 8/6/2002 to 9/24/2002 are missing. The number ofmissing data is 33. We omitted these periods from our analysis.
5 See, Bank for International Settlements (2007, pp.7, Table1).
6
The Nikkei Bond Index spread two-year(three-year), five-year (seven year) and ten-year
is respectively defined as a difference between a short-term bond index, a medium-termbond index and a long-term bond index and government-bond yield two-year(three-year),and ten-year.
45

examining the summary statistics in Table 2. In general, as well as our definition, the risk
premium is defined by Lekkos and Milas (2001), Huang and Chen (2007) and Afonso andStrauch (2007).
Table 2 lists the risk change with several maturities (the bond spread differences)and the non-normality of the unconditional distribution of daily risk changes. The table
reports the mean, the standard deviation of risk change, the Kendall-Stuart skewness, theexcess kurtosis, and their tests. The Ljung-Box Q-statistics Q2(12) are reported under thenull hypothesis of non-serial correlation tests in daily squared risk changes. At asignificance level of five per cent, the null hypotheses (skewness = 0 or excess kurtosis = 0)
and non-serial correlation are rejected. Thus, the time series have the same typical features
as well as stock returns, displaying a fat tail distribution, a spiked peak, and the persistenceof variance.7 Therefore, the ARCH-type model including such features seems to beappropriate for analyzing these series, compared with the AR model.

[INSERT Figure 1 and Table 2 ]Visual Inspection
In Figure 1, Bond Index spreads show risks for all industries in Japan. On the other
hand, the Euro JPY bond spreads show risks for all industries in foreign countries,
measured in Japanese yen where Japanese yen rate risk is deleted, while the data of theJapanese tsunami is very short. The US tsunami (7/15/2006-) has damaged the foreigncountries much more than Japan, because the zero risks for the foreign countries aredramatically increased to 1.2 to 1.4. However, the US tsunami has damaged Japan
relatively a little, compared with the Japanese tsunami (1/4/1990-3/18/2001). The exception
is a short-run risk with 2-year maturity. Nevertheless, we have to study which industries are
more serious by the US tsunami, in order to know in which industry relief should beprescribed with an emphasis.
The swap spreads show risks for financial industries in Japan, while the composite
AA spreads show risks for non-financial industries in Japan. If possible in comparison withsize, the US tsunami has damaged non-financial industry with much larger risks thanfinancial industry still now: 0.6-0.8 compared with 0.2-0.4. In particular long-run risk with
5-,7-,10-year maturity overcome the short-run risks, suggesting the US tsunami persistinglarger risks for a long time. This finding seems to support:
H1: the US tsunami damaged the non-financial industry.
Relief should be prescribed with an emphasis in manufacturing industry. On the other hand,
if we can get more data for the early periods of the composite AA spreads and the finding
support:7

All computations in the paper have been performed with the computer package WinRATS
Version 5.

6

H2: the Japanese tsunami damaged the financial industry.
The previous experiences in the Japanese tsunami are not necessarily applicable to theslump by the US tsunami. We check two hypotheses by using EAGARCH model in a next
section.
4. Statistical Methodology and Discussion of Hypotheses

Methodology
The specification of risk change process can be expressed as:

Rt  a    R t-1    R t-1   t : t=1,2,…,T(1)

 t | It 1 ~ N (0,  t2 )(2)log  t2   0  1 (| zt 1 |  E(| zt 1 |))   2 z t 1   3 log  t21

(3)

 t  t z t :(4)

z t ~ N (0,1)

where Rt is the risk change (the first difference of risk), Rt1 = max{ Rt 1 , 0}, and Rt1= min{ Rt 1 , 0}, (a,   ,   ) is the constant in (1) and I t -1 denotes the information set of
the time t-1 in (2). We assume that the disturbance term (  t ) in (3)-(4) has the EGARCH
process proposed by Nelson (1991). We also assume asymmetric returns in (1) proposed byKoutmos (1998, 1999) and Nam et al.(2003,2005).   ,   are not necessarily equal.
The asymmetric volatility of  2  0(  2  0) means that the larger volatility
happens when the risk is rising (falling). The  2  0(  2  0) imply the larger movement ofrisks with pessimistic (optimistic) feeling.
If   (   )  0 , it is called “back-stepping” which forces fall (raise) of risk when therisk increases (decreases) in the previous day. As is obvious later, the estimated coefficientsshow back-stepping   (   )  0 or zero   (   )  0 . The back-stepping of   0 implies investor (market)‟s optimistic prospects for risks because of back-stepping
against the rising risks. The back-stepping of    0 implies investor (market)‟s
pessimistic prospects for risks because of back-stepping against the falling risks. Then,      0 means more pessimistic prospects for risks in particular phases, suggesting
more heavy risks. On the other hand, the   (   )  0 means Rt  a   t , which implies
that the risk show a random walk. Investors (markets) are uncertain about the movement ofrisks and anxious for it.
Thus, in order to compliment the degree of damage in a view point of size of risks,
we recognize the asymmetric volatility and the persistent volatility:7

Financial industry was more damaged than Non-Financial one in a strong volatility
persistence when 3F  3 NF(5)Financial industry was more damaged than Non-Financial one in the larger movement ofrisks with pessimistic feeling when  2 F  2 NF .
(6)
We also recognize the truth in future from investor‟s prospects:
Investors have pessimistic (optimistic) prospects, driving the truth sowhen       0 (       0 ).

(7)

Investors are uncertain about risks, which move randomly when   (   )  0 .

(8)

The required joint density in (9) is obtained.

pdf(R1 ,..., R T |  )   p d f (t R
;  | I t -1 )
t T

(9)

The model of (1)-(4) is estimated by the maximum likelihood estimation method using thejoint density of (9).The   ( ,   ,   , a0 , a1 , a2 , a3 ) are unknown parameters. For the sakeof simplicity, the estimated ̂ is assumed to be consistent and asymptotically normal.

Discussion of Hypotheses
We now discuss the estimation results, in order to support visual inspections anddescriptive analysis for two hypotheses H1 and H2, by using (5)-(8).Table 3 reports theestimated results in equation (1)-(4) for each of 2-,3-,5-7-,and 10- year maturity. Most ofcoefficients are significant at 5% level.
By investigating the Bond Index spreads for Japan risks and the Euro JPY bondspreads for the other countries risks, the volatility persistence on other countries by the UStsunami is larger than that on Japan and more damaged, based on (5). On the other hand,the  2 in both countries is negative in Japan implying larger movement of risks with thepessimistic feeling, while the results are only seen in two maturities out of five, based on(6). As supported by the volatility persistence, Japan was not so damaged by the UStsunami, compared with the other countries. Finally,       0 (except for 5-year
maturity) for the effect of the US tsunami on Japan means more optimistic prospects forrisks in future, driving lighter risks than the Euro JPY bond spreads for the other countries
risks. Nevertheless, we have to study which industries are more serious by the US tsunami,
in order to know in which industry relief should be prescribed with an emphasis.
By investigating the Swap spreads for the financial industry‟s risks and theComposite AA spreads for the non-financial industry‟s risks, the volatility persistence on

8the financial industry show stronger one (more damaged) than the non-financial industry inshort-term risks, while it is weaker (less damaged) in long-run risks, based on (5). On theother hand, based on (6), there is no difference of asymmetric volatility between two
industries, suggesting equally damaged in both industries. In these sense, volatility
persistence support the visual inspection where the non-financial industry is more damaged
in long-run risks. On the other hand, based on (8),       0 (mostly with all year
maturities) show a random walk in risks, suggesting that investors are uncertain about the
movement of risks and anxious for it. Based on (7), the composite AA for the non-financial
industry shows back-stepping in down-turn markets with 2- and 3- year maturity. Also,
based on (8), the risks with 5- and 7-year maturity show a random walk (investors are
uncertain about risks). Compared with the financial industry, in future the large risks
continue.
Thus, we can find some evidences supporting Hypotheses 1 and 2 in the estimation
results by EGARCH model.

[INSERT Table 3 ]5. Concluding Remarks
We investigated two hypotheses: H1: the US tsunami damaged the non-financial
industry. H2: the Japanese tsunami damaged the financial industry. By using alternative
viewpoint from previous research, i.e., by using the evaluation of investor or market (a risk
premium for bonds issued by such industries), we can find several evidences supporting
two hypotheses in visual inspection and EGARCH estimation with asymmetric risks. The
sizes of risks with 2-,3-,5-,7- and 10-year maturity bonds in non-financial industry arelarger than those in financial industry. In particular long-run risk with 5-,7-,10-year
maturity overcome the short-run risks, suggesting the US tsunami persisting larger risks fora long time. In addition, if we can get more data for the early periods of the composite AA
spreads and the finding strongly confirm H2. Due to the EGARCH estimation, thecomposite AA for the non-financial industry shows back-stepping in down-turn markets
with 2- and 3- year maturity. Also, the risks with 5- and 7-year maturity show a random
walk (investors are uncertain about risks). Compared with the financial industry, in future
the large risks continue.
As a result, relief should be prescribed with an emphasis in manufacturing industry
while the lessons from the Japanese tsunami are not necessarily applicable to the slump by
the US tsunami.

9References
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10Figure 1. Graphs of variables
Nikkei Bond Index Spreads
Index Spreads (%)
2.50

Japanese tsunami

2.00
1.50

US tsunami

1.00
0.50
0.00
-0.50
1992/09/30

1996/01/22

1999/05/20

2Y

2002/10/30

3Y

5Y

7Y

2006/02/28

2009/06/29

10Y

2yr
3yr
5yr
7yr
10yr

Difference of spread with 5-year maturity
5Y
0.40
0.30
0.20
0.10
0.00
-0.10
-0.20
-0.30

2007/10/30

2005/05/06

2002/11/01

2000/03/21

1997/09/19

1995/03/27

1992/10/01-0.40Euro JPY bond spreads
JP Eurobond Spread (AAA) (%)

2yr
3yr
5yr
7yr
10yr

1.6
1.2
0.8
0.4

Japanese tsunami

US tsunami

0-0.4
08-Oct-98

08-Oct-01
2Y

3Y

08-Oct-04
4Y

7Y

08-Oct-07

10Y

Difference of spread with 5-year maturity
AAA5Y-JGB5Y Difference

0.3
0.2
0.1
0
-0.1

11

Oct-09Oct-08

Oct-07

Oct-06

Oct-05

Oct-04

Oct-03Oct-02Oct-01

Oct-00Oct-99Oct-98-0.2

Swap Spreads
Swap Spread
0.6

(%)

US tsunami

Japanese tsunami

0.4

3yr
2yr
5yr
7yr
10yr

Zero-interest Rate

0.2

0

-0.2

-0.4
Mar-96

Mar-99
SP2YR

Mar-02
SP3YR

Mar-05

SP5YR

SP7YR

Mar-08

SP10YR

Difference of spread with 5-year maturity
SP5YR
0.2
0.1
0
-0.1
-0.2
-0.3
Mar-96

Mar-99Mar-02Mar-05Mar-08Composite AA Spreads
5yr
7yr
10yr
3yr
2yr

CompositeAA spread(%)

0.8

0.6

US tsunami

0.4

0.2

0
Jul-04

Jul-05Jul-062Y

Jul-07

3Y

5Y

Jul-08

7Y

Jul-0910Y

Difference of spread with 5-year maturity
5Y
0.1
0.05
0
-0.05
-0.1
July-04

July-05

July-06

July-07

12

July-08

July-09

Table 1A. Events around two tsunamis
Date1990 Jan.4
1994 Dec.1
Dec.9
Dec.201997 May 1
1999 Feb. 1
Feb. 26
Mar. 122001 Apr. 26
Dec. 1
Dec. 18
2007 Mar.13
Jul. 92008 Sep. 15.
2009 Feb. 1
Mar.31
Sep.16
Dec.9

EventsJapanese tsunami happens: the stock price bubble collapses.
The credit unions began to fail.
The government decided to establish a special bank to rescue failed banks.
The Ministry of Finance announced a decrease in the budget by 2.9% after a 40 year
period without any reductions in the budget.
Small security companies began to fail.
The banks began to fail.
The think tank of the prime minister announced the rescue packages.
The government injected public money of 7 trillion yen to rescue the big banks.
Koizumi Cabinet birth
Many private companies began to fail.
Of the companies having special status (Tokushu-hojin, in Japanese), only 17 wereabolished and 45 privatized.
The US tsunami happens: the US subprime loan problem appeared by the housing
bubble collapse.
Nikkei225 hit 18,261yen after IT bubble collapse at 20,833 yen (April 12,2000)
The Lehman Shock (The bankruptcy of Lehman Brothers) prevailed over the world.
Industrial Production Index decreases to 69.5 from 110 at May 2008.
Most of export companies announced the current account deficits.
Hatoyama Cabinet birth
Real GDP(Quarterly): -8.9,-5.8,-5.1%. Real Export: -36.5, -29.3, -22.3%

13

Table 1B. Chronology of monetary, banking and corporate policies
1999 Feb. 12 Introduction of zero interest rate policy: Encouragement for overnight call rate to be low to reduce
deflation2000 Aug. 11 Withdrawal of zero interest rate policy
2001 Feb. 13 Reduction in official discount rate to 0.35%
Mar. 19 Introduction of new monetary policy framework: Change in operating target from call rate to current
account balance of BOJ, where BOJ changes current account balance until CPI registers zero percent or
higher. Raising of current account balance target to 6 trillion yen from 5 trillion yen
Aug. 14 Reduction in official discount rate to 0.1%
Sep. 19 Raising of current account balance target to 10–15 trillion yen
2002 Oct. 30 The Program for Financial Revival (PFR) starts. Injected money to banks from government are mainly
Grants (19 trillion yen), Purchase of assets (10 trillions),and Capital injection(12 trillions):As of theend of March 2006.
2004 Jan. 20 Raising of current account balance target to 30–35 trillion yen
2004 Sep.28 Industrial Revitalization Corporation of Japan (IRCJ) purchase bad loans from the 25 big companies
approved for assistance from April 2003 until September 2004.
2006 Mar. 9 Withdrawal of quantitative easing policy
Change in operating target from current account balance to call rate. Retention of zero interest rate policyJul. 14 Withdrawal of zero interest rate policy. Raising of overnight call rate to 0.25%
2007 Feb.21 Raising of overnight call rate to 0.5%
2008 Dec.19 Falling of overnight call rate to 0.1%. Official discount rate to 0.3%
2009 Jan.21 Starts of outright purchases of CP
Feb.19 Purchase of corporate bonds with one trillion yen.
Source: Miyakoshi and Tsukuda (2007) , Miyakoshi (2009), Minutes of the Monetary Policy Meetings in Bank of Japan,.
(http://www.boj.or.jp/en/theme/seisaku/mpm_unei/giji/index.htm), Nikkei Kinyu Nenppo (Nikkei Newspaper Company),
SNA (Cabinet Office:http://www.cao.go.jp/index-e.html), and the IRCJ at http://www.ircj.co.jp/shien/index.html .
Note: „Current account balance target‟ refers to the target for the outstanding balance of the current account at the BOJ

14

Table 2. Summary Statistics for Daily Change of Risks
Difference of Koushasai Index Spreads for Japan:
9/30/1992−10/9/2009, sample=4103
maturity
2-Year
3-Year
5-Year
7-Year
10-Yearmean at-value(mean=0) b

St.dev.

Skewness a

Ex-Kurt aQ2(12) c

0.116E(-3)-0.117E(-3)-0.079E(-3)-0.060E(-3)-0.063E(-3)

0.177-0.170-0.103-0.070-0.079

0.042
0.043
0.049
0.055
0.051

-2.46*-0.23*-0.09*
0.07
0.07*

77.33*
12.26*
8.31*
5.64*
5.41*

25.59
743.58873.331565.62
1549.45

Difference of Euro JPY Bonds AAA Spreads for the Other Countries:
10/8/1998−11/2/2009, sample=2860
maturity

2-Year
3-Year
5-Year
7-Year
10-Yearmean at-value(mean=0) b

St.dev.

Skewness a

Ex-Kurt aQ2(12) c

0.242E(-3)
0.186E(-3)
0.172E(-3)
0.195E(-3)
0.075E(-3)

0.631
0.476
0.398
0.406
0.143

0.021
0.021
0.023
0.026
0.028

0.48*
0.26*
0.28*
0.05
-0.56*

15.71*
16.86*
13.05*
9.09*
15.14*

367.05463.88448.22303.84219.0215

Difference of Japanese Yen Swap Spreads for Japanese Financial Industry:
3/29/1996−9/16/2009, sample=3219
maturitymean at-value (mean=0) b

St.dev.

Skewness a

Ex-Kurt aQ2(12) c

2-Year
3-Year
5-Year
7-Year
10-Year0.040E(-3)-0.018E(-3)
0.001E(-3)
0.022E(-3)-0.011E(-3)

0.108-0.0480.002
0.053-0.025

0.021
0.022
0.022
0.024
0.025

-0.604*-0.637*-0.454*-0.135*-1.014*15.01*
17.62*
14.91*
11.66*
36.51*

1011.69
1196.931018.121001.76433.36

Difference of Composite Index AA Spreads for Japanese Non−Financial Industry:
7/12/2004−11/2/2009, sample=1361
maturitymean at-value (mean=0) b

St.dev.

Skewness a

Ex-Kurt aQ2(12) c

2-Year
-0.208E(-3)-0.8010.010-1.111*
16.39*
43.69
3-Year
-0.208E(-3)-0.8510.009-1.104*21.12*
30.00
5-Year
-0.270(-3)-1.0790.009-0.959*19.25*
39.24
7-Year
-0.312E(-3)-1.1380.010-1.537*21.77*
23.86
10-Year-0.292E(-3)-0.9630.011-1.445*23.94*
21.43
a
-3
b
c
Notes: E(-x)=10 .
* Statistically significant at 5% level. Distributed as 2(12) under the null hypothesis of non-serial
correlation with lags up to 12. The five per cent critical value is 21.03. JGB=Japanese Government Bond.

16

Table 3.

Japanese Tsunami and the US Tsunami

Rt  a    R t-1    R t-1   t ; log  t2   0  1 (| zt 1 | E(| zt 1 |))   2 z t 1   3 log  t21
Bond Spread Difference for Japanmaturity 9/30/1992-3/18/2001: JP Tsunami









2

3

2-Year-0.324*(-6.11)

3-Year-0.338* -0.156*-0.003* 0.957*(-268.24) (-2.66) (-2.66)(61.54)-0.271*-0.394* -0.002*
0.970*(-13.31) (-65.99) (-3.63) (127.85)-0.334* - 0.256* -0.002* 0.966*(-6.20) (-4.30) (-2.47)(118.16)-0.356*-0.153* -0.005 0.926*(-7.71)(-13.93) (-5.89) (99.74)

5-Year
7-Year
10-Year3/19/2001-7/14/2006:Zero-interest

7/15/2006 to10/ 9/2009:US Tsunami











2

3

-0.188* -0.001*
0.966* -0.068* -0.726* -0.002* 0.971*(-3.26) (-2.18)(119.83) (-2.53) (-13.03) (-20.50) (608.49)-0.268* -0.527*
0.001* 0.982*(-3.39) (-6.92) (3.97) (77.57)-0.169* -0.631*-0.002* 0.820*(-2.57) (-9.89) (-2.38) (26.62)-0.328* -0.530* -0.004* 0.941*(-6.64) (-7.49) (-8.11) (72.19)-0.337* -0.427* -0.005* 0.884*(-6.21) (-6.26) (-5.87) (36.64)-0.299*(-9.93)



2

3

-0.191* 0.004* 0.999*(-10.80) (31.63)(1993)-0.518*-0.406* 0.001 0.971*(-6.76)(-5.85) (0.78) (71.24)-0.434*-0.438*-0.001 0.989*(-7.00)(-19.14) (-1.24) (1819)-0.475-0.432* -0.002 0.981*(-6.40)(-5.83) (-1.42) (91.08)-0.640* -0.419* 0.002*
0.988*(-9.89) (-6.52) (2.63) (142.21)

Euro JPY Bonds AAA Spread Difference for the other countries
maturity 10/18/1998-3/18/2001:JP Tsunami









2

7/15/2006-11/2/2009: US Tsunami











2

3



2

3

2-Year-0.429*(-11.80)0.720*(15.88)-0.024 -0.079* -0.000 0.986*(-0.11) (-17.85) (-1.52) (206.08)

0.056(30.80)3-Year

-0.103-0.466*-0.003* 0.912*(-1.17)(-5.66)(-3.01)(32.20)-0.351*-0.553* 0.002* 0.979*(-4.17)(-9.14)(5.10) (81.67)
0.059- 0.507* -0.003* 0.994*(0.84) (-6.89) (-16.51) (920.98)-0.301* -0.394* -0.002* 0.953*(-3.75) (-3.97) (-4.46) (670.76)-0.030 -0.183*-0.000* 0.973*(-0.53) (-2.83) (-2.03) (110.88)-0.235* -0.367* 0.000 0.985*(-3.28) (-6.79) (0.01) (109.41)-0.138* -0.244* -0.005 0.977*(-2.16) (-3.28) (-1.67) (174.30)-0.263* -0.207* -0.000 0.955*(-4.11) (-2.81) (-1.38) (79.62)-0.567*-0.234* -0.003* 0.965*(-7.47)(-2.73) (-8.11) (79.93)
0.013-0.245* 0.000 0.993*(0.17) (-4.15)(1.87) (4334)-0.012-0.264* 0.001 0.998*(-0.16)(-3.24) (1.88) (244.92)
0.008-0.379* 0.000 0.995*(0.18) (-5.00) (1.48) (282.94)

5-Year
7-Year
10-Year-0.172 -0.007*(-1.81) (-5.68)

3

3/19/2001-7/14/2006:Zero-interest

Notes: * Statistically significant at 5% level. The number of parentheses is t-values.

17-0.639-0.002* 0.875*(-7.13) (-3.54)(34.50)Swap Spread Difference for Japanese Financial Industry
maturity 3/29/1996-3/18/2001:JP Tsunami









2

3

2-Year

-0.351*(-5.02)

3-Year-0.519* -0.128*
0.001 0.976*(-9.07)(-2.39)(0.10) (167.36)-0.337* -0.168* 0.001* 0.985*(-5.42)(-7.22) (2.41) (167.79)-0.355* - 0.378* 0.001 0.960*(-7.12) (-10.61)(1.49) (66.83)-0.281*-0.197* 0.002* 0.862*(-2.91)(-2.65) (2.12) (21.12)

5-Year
7-Year
10-Year-0.322* 0.002*
0.956*(-4.74)(3.41)(83.06)

3/19/2001-7/14/2006:zer-interest

7/15/2006-9/16/2009:US Tsunami-0.252*(-5.51)



2

3



0.073 0.000(0.96) (0.62)

0.993*(257.12)0.120(1.27)

-0.163* -0.074-0.000 0.989*(-2.36) (-1.02) (-0.05) (258.10)-0.214* -0.192*
0.000* 0.970*(-5.57) (-3.47) (5.20) (618.58)-0.360* -0.181* -0.000 0.968*(-5.76) (-11.21) (-0.50) (628.04)-0.092* -0.153* 0.000 0.950*(-2.93) (-2.95) (1.17) (85.57)



2

3

-0.1450.001* 0.992*(-1.65) (2.28) (132.02)-0.000-0.0420.001* 0.992*(-0.00)(-0.49) (6.65) (206.69)
0.027-0.0840.000* 0.980*(1.61) (-1.07)(2.38) (86.31)-0.273* 0.098 0.001* 0.967*(-3.49)(1.16) (3.30) (110.63)-0.0730.018 -0.000
0.966*(-0.85) (0.50) (-0.30) (85.12)

Composite AA Spread Difference for Japanese Non−Financial Industry
maturity 3/29/1996-3/18/2001:JP Tsunami


2-YearData Non

3-YearData Non

5-YearData Non

7-YearData Non

10-YearData Non



2

3

3/19/2001-7/14/2006:zer-interest

7/15/2006-9/16/2009:US Tsunam







2

3

-0.270*-0.009 0.000* 0.998*(-6.86)(-0.10) (8.49) (10540)-0.674* 0.041 0.000 0.977*(-7.45) (0.81) (1.17) (1549)
0.257* -0.102* 0.000* 0.642*(13.19) (-1.95) (6.05) (19.75)-0.0080.228 -0.000* 0.994*(-0.06) (0.00) (-2.28) (1608)-0.763* -0.000 -0.001* 0.900*(-9.96) (-0.00) (-3.25) (55.25)

Notes: * Statistically significant at 5% level. The number of parentheses is t-values.

18



2

3

-0.127 -0.112* 0.000* 0.989*(-1.76) (-2.27) (2.05) (367.38)-0.081* -0.299* 0.000* 0.990*(-2.09)(-3.57) (6.00) (501.59)-0.200* -0.155 0.000
0.987*(-2.92)(-1.84) (1.60) (294.46)-0.127* -0.135 0.000*
0.995*(-2.77)(-1.55) (15.05) (326.33)-0.141
0.046* 0.000 0.980*(-1.68) (3.14) (1.50) (4782)