Japanese banks liability management before and during the banking crises and macroeconomic fundamentals

Journal of Asian Economics 15 (2004) 373–397

Japanese banks liability management before and during the banking crises and macroeconomic fundamentals Maria Sophia Aguirre*, Reza Saidi1 Department of Business and Economics, The Catholic University of America, Washington, DC 20064, USA Received 25 December 2002; received in revised form 29 December 2003; accepted 25 February 2004

Abstract In this paper, we aim to shed lights on the Japanese banking crisis by means of an Error Correction Model in two ways. First we determine how the dynamic behavior of key macroeconomic variables identified by the literature played in the Japanese banks’ liability management before and during the banking crisis. Second, we focus on whether and how this management affected the balance of payments. We find that exchange rate, interest rates, and foreign reserves were important variables in explaining the short-term behavior of Japanese banks’ liability management at the time of the banking crisis. The causality runs from these variables to debentures (bank notes) and foreign liabilities—two of the three relevant instruments used by Japanese’s banks for liability management. We also find that the use of these instruments affected the balance of payments because of its feedback on the exchange rate and, to a lesser extent, on the foreign reserves. This effect, however, is only of a short-term nature. The stock market plays a key role in explaining the direction of this causality. # 2004 Elsevier Inc. All rights reserved. JEL classification: F32; G15 Keywords: Liability; Macroeconomic; Short-term

1. Introduction Due to the spread of financial crisis during the 1990s and the worsening of the financial markets in both developed and developing countries, studies have focused once again on * Corresponding author. Tel.: þ1-202-319-4957; fax: þ1-202-319-4426. E-mail addresses: [email protected] (M.S. Aguirre), [email protected] (R. Saidi). 1 Tel.: þ1-202-319-4692.

1049-0078/$ – see front matter # 2004 Elsevier Inc. All rights reserved. doi:10.1016/j.asieco.2004.02.007

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the importance of consistency between monetary and fiscal policy because of the effect that its absence can have on international capital markets. The study of the Asian crisis has not been an exception.2 Furthermore, the Japanese experience has brought into the study of financial crisis, the attention to the role played by the solvency in the banking system in the development of these crises.3 Miller (1998a) finds that bad loans have been an important factor of Japan’s banking crisis, but does not seek to define any causality.4 Kanava and Woo (2000) find as the root of the Japanese banking crisis an accelerated deregulation and deepening of capital markets without the appropriate adjustment in regulations. Rosengren (1997) and Miller (1998b) present empirical evidence for a more than ever increased financial stability dependency on what happens abroad. In this paper, we attempt to shed light on the existing literature on the Japanese banking crisis in two ways. First, we determine how the dynamic behavior of the macroeconomic key variables identified by the literature played in the Japanese banks’ liability management before and during the banking crisis.5 Second, we focus on whether and how the instruments used for liability management affected the balance of payments. We do this by means of a cointegration system methodology. Estimation of these models consists of a two-step procedure, which isolates long-run responses from short-run dynamic behavior.6 The rationale for analyzing the long-run or permanent components of the Japanese’s banks liability management vis a vis different fundamentals is that, by doing so, one can determine which mean-reverting components can account for a significant portion of the variations from the long-run equilibrium. This is certainly useful for policymaking as Japan struggles to bring solvency and stability to a very weak and insolvent banking system. An understanding of the short-term dynamic, on the other hand, provides useful information not only for bank operators but also for regulators as well. This is so because it allows operators in the market to understand the dynamic relationship between certain macroeconomic fundamentals and the instruments used by banks for liability management. We find that the exchange rate7, interest rates, and foreign reserves were important fundamentals in explaining the short-term dynamics of Japanese banks’ liability management. The causality runs from these variables to bank notes and foreign liabilities, two instruments used by Japanese banks to manage their liability. We also find that these instruments affected Japan’s balance of payments because of their feedback on the exchange rate and, to a lesser extent, on foreign reserves. The effect, however, is only 2 Hosono, Sugihara, and Mihira (2000), Zaman (2000), and Wongbangpo and Sharma (2002), Eichengreen and Rose (1999), Rose (1999), Bergin (1999), Eichengreen (1997, 1999, 2000), Eichengreen and Areta (2000) are some of the most resent studies. 3 Craig (1998), Kaminsky and Reinhart (1998), Kanava and Woo (2000), Wood (1999) are some of the most resent studies. 4 Along the same lines, Peek and Rosengren (2002) examine the ‘‘evergreen’’ of loans by Japanese banks, particularly to keiretsu firms. 5 Eichengreen (2000) provides a good review and analysis of these fundamentals. 6 In this paper, rather than using a stochastic model to capture the dynamic behavior, we study the change in the variables over time. 7 In this paper, the exchange rate is defined as yen/dollars.

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of a short-term nature. The stock market plays a key role in explaining the direction of this causality. In the second part of the paper we present the analytical frameworks and the data used in this study. The presentation and discussion of the results follow in the third section, ending with the concluding remarks.

2. Framework of analysis An important consideration in the study of Japan’s liability management and its relation to the external balance is the unique structure that its banks balance sheet had at the time. Japanese banks had long held equity securities, known also as ‘‘hidden reserves,’’ as cushions for their capital.8 These large keiretsu-based cross-shareholdings of Japanese banks made them susceptible to downturns in the stock market.9 With a growing economy, low domestic interest rate, and a strong yen, Japanese banks were able to expand aggressively during the second part of the 1980s, both domestically and overseas. Furthermore, the increase in the value of the Nikkei during the 1980s, enabled these banks to increase capital by issuing new equity shares and debt securities at favorable prices. It also enabled banks to sell their stock holdings in other companies that had substantial unrealized gains. The decline of the Japanese stock market at the end of 1989 and thereon, put banks in a compromised financial situation since they held approximately 20% of Japanese common stock.10 As a consequence, their bank total assets declined steadily during the 1990s and the keiretsu-based cross-holdings declined between 1991 and 2001.11 Yet, because the bank-firm lending relationship in Japan is particularly strong, banks were reluctant to reduce credit to their long-standing domestic customers.12 If the banks did not have international operations, a necessary loan shrinkage domestically would have followed. However, since the banks had such operations, Japanese banks borrowed funds abroad while, at the same time, they significantly reduced their lending overseas in an effort to avoid the reduction of domestic credit.13 Eventually, the combination of the 8

During the second half of the 1980s, the implementation of the Basle Accord, an international agreement among bank regulators, Japanese banks were held to higher standards of capital adequacy. The stock holdings of the Japanese banks at the time were thought by Japanese regulators to provide a capital cushion that this accord required. This provision created an obvious relationship between the degree of Japanese ability to expand and the health of the Japanese stock market. 9 The Keiretsu-based structure underwent a significant decreased in the late 1980s and 1990s due to the financial system deregulations that were implemented in five major areas: bond issuance restrictions, new product introductions, foreign exchange transactions, interest rate controls, and stock market regulations. The change in all five areas benefited corporate borrowers by providing them new options while they generally hurt the banks as they were being displaced. 10 Prowse (1990) and French and James (1991). 11 In 1990, total risk-based capital ratios of most of the major Japanese banks temporarily fell to 5%, i.e., below the minimum required level (8%) set by the Basle Accord. 12 Studies of these close relationship in lending includes Gibson (1995), Hoshi and Scharfstein (1990), and Hoshi and Kashyap (2001) among others. 13 This was the path initially followed in the early 1990s (Peek & Rosengren, 1997, 2000) and it continued until 1996 when the yen began to devalue after 10 years of a significant appreciation (between 1985 and the beginning of 1996, the yen appreciated by a factor of 3.2.).

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emergence of a Japanese banks premium in 199514 and the devaluation of the yen, made foreign borrowing too expensive for Japanese banks. Consequently, they turned to the use of yen denominated debentures as a means of creating credit capabilities and strengthen their balance sheet.15 It follows, that these three important instruments used by Japanese banks to manage their liability, i.e., stocks (MI), debentures or bank notes (BN), and banks’ foreign liabilities (FL) can be good tools to capture this management before and during the Japanese bank crisis.16 Although the theoretical and empirical evidence available thus far does not provide an unambiguous answer as to what the causal linkages between economic fundamentals and the banking crisis are, it does indicate which economic indicators should provide insights about this underlying causality. Since here we are interested in addressing the banks’ liability management before and during the Japanese bank crisis, we utilized these same economic indicators. The literature has pointed to the following economic fundamentals: stock index, the exchange rate, international reserves, and interest rates.17 As previously mentioned, in this paper we want to determine how the dynamic behavior of the macroeconomic fundamentals identified by the literature played in the Japanese banks’ liquidity management and whether and if so how this management affected the balance of payments. In trying to capture the causality of macroeconomic fundamentals in the Japanese banks’ liquidity management, the technique used needs to be able to capture two important elements: the direction of the causality, including the possibility of a two way causality, and the fact that the influence of one fundamental on the banking sector can last for several periods after the initial shock dies out. An errorcorrection model (ECM) captures both of these elements well. This model is part of the cointegration systems methodology developed by Engle and Granger (1987), Engle and Yoo (1987), and others. Two timeseries variables, X and Y, are cointegrated if they are both homogenous of degree one (I(1)), and if there is a vector a, called the cointegrating vector, such that 14 Global financial markets began to suspect that Japanese banks were not healthy, and therefore they increased the cost of funds for these institutions. The premium increased by a factor of five between September and November of 1995 (Peek and Rosengren, 2002). 15 Hoshi and Kashyap (2001) present a good review of the Japanese’s banking and finance history for the period covered in this paper. Commercial banks often were the major purchasers of the debentures issued by the then long-term banks because they used them as collaterals for loans from the Bank of Japan that carried lower rates than the call rate. It is important to note that the role of bank debentures as a source of funding went through a significant change after the 1996 liberalization reform that was followed, in 1997, with the Japanese banks being allowed to issue foreign currency denominated debentures. However, this change did not affect the use of yen denominated debentures which are the ones included in our data. Between 1995 and 1999 the yen devalued and therefore, foreign denominated debentures were not favorable as means of domestic liability management. During 1999, the yen appreciated but the following year, once again, it began to depreciate again. 16 The literature has extensively studied the impact of bad loans in the Japanese bank crises. Here we do not intent to deny that these, together with the bank liberalization in 1996, played a significant role in the banking crisis. Specifically, Hoshi and Kashyap (2001) estimate that when the Financial Supervisory Agency’s definition of bad loans is used to define the amount of bad loans in the Japanese Bank’s assets, for 2000, this amount summed to 63 trillion of yen or 12.8% of the GDP. However, it is important to keep in mind that our focus is on liability management and not on assets composition or on the sources of the Japanese banking crisis. 17 As previously indicated Eichengreen (2000) provides a good review and analysis of these variables.

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{Y  aX} is stationary in levels. An ECM incorporates both the short and the longrun dynamics and it is represented as follows: DYt ¼ b0 þ b1 wt1 þ b2 DVt þ et where DVt are I(0) explanatory variables, wt1 are deviations from the longrun equilibrium relationship represented by the cointegrating vector {Yt  aXt ¼ wt }, and et is a stochastic error term. The explanatory variables can include both current and lagged values of DX and lagged polynomials of DY as proposed by Engle, Grager, and Hallman (1989). In order to test whether the series are cointegrated, it is first necessary to check that each series is I(1). For this we used an augmented Dicky–Fuller test. Then we tested for cointegration by testing whether the residuals from the cointegrating regression are I(0). We did this by running a set of unit root tests in sequence, attempting to classify the series based upon trend and unit root properties. We used both the augmented Dicky–Fuller test and Perron and Phillip (1988). To have a better understanding of how the three instruments used by Japanese banks as tools for liability management are linked with one another, we first ran cointegration tests against each of these variables by pairs. Then, we analyzed the dynamic of the fundamentals, which include interest rates (i), exchange rates (XR), international reserves (IR), and the Nikkei Stock Index (MI) simultaneously, on the different instruments used by the Japanese banks (BN, FL, and MI.) In this case, the regression run is DBNt ¼ b0 tt1 þ

I X

ri DBNti þ

i¼1

I X i¼1

di DIRti þ

I X

fi DMIti þ

i¼1

I X þ yi DXRti þ et

I X li Diti i¼1

(1)

i¼1

where tt1 are deviations from the long-run equilibrium relationship represented by the cointegrating vector of all the variables used, i is the number of lags, and et is a stochastic error term. Finally, in order to test whether and if so how the banks liability management affected the balance of payments, we tested for cointegration between exchange rates and foreign reserves and the instruments used by the Japanese banks to manage their insolvency. We therefore ran the following regression changing accordingly IR for XR when the variable used was the exchange rate and MI for FL and BN, respectively. DIRt ¼ b0 wt1 þ

I I X X di DIRti þ fi DMIti þ gD þ et i¼1

(2)

i¼1

where wt1 are deviations from the long-run equilibrium relationship represented by the cointegrating vector {IRt  aMIt ¼ wt }, i is the number of lags and et is a stochastic error term. 3. Data The monthly data used was obtained from Datastream database. The data covered January 1985 to December 2001. As a proxy for stock holdings of banks, we used the

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Nikkei Stock Index. In order to minimize the problem of non-synchronicity, we used total holdings at the end of the period. Exchange rate used was in terms of yen per US dollar. Since we are interested in the dynamics behavior of these variables over time, we use first differences. We found a significant shift in the Nikkei and in foreign liabilities in 1991.18 We created a dummy variable (D) to capture this shift. Because of this break in the series, we first ran the cointegration test and the ECM over the whole period introducing the dummy variable and then we separated the regression into two sub-periods, 1985:1–1990:12 and 1991:1– 2001:12. As previously indicated, we define the exchange rate in terms of yen/dollar.

4. Empirical results We tested the presence of unit roots in all the variables used in the cointegration test and in all cases we found the presence of unit roots at the 5% level of significance but we find that they are homogenous of degree 1. The augmented Dicky–Fuller test and Perron and Phillip (1988) results are included in Table 1. After corroborating the unit root and the homogeneity of degree 1, we focus on the analysis of the relationship between stock, debentures, and foreign liabilities and whether they are linked with each other. We found w to be I(0) in all cases and significant at the 1% level, and thus we proceeded to run the ECM between these variables. We found no cointegration or any other causality between the stock index and debentures or the stock index and foreign liabilities. We found, however, cointegration between debentures and foreign liabilities for the whole period as well as in the sub-periods. The results for these last two variables are presented in Table 2.19 The results indicate that the debentures bring the foreign liabilities to the long-run value but not vice versa. This implies that it was the foreign liabilities of the Japanese banks that accounted for a significant portion of the variations from the long-run equilibrium of the instruments used by the banks for liability management. Furthermore, the sign of the lag values of the debentures indicates an inverse relationship between these two components of the banks’ liability (Table 2, columns 1, 3, and 5). During the first period (1985–1990), as the yen appreciated, banks sought foreign liabilities or foreign bonds. As the yen devalued, banks substituted foreign borrowing by domestic debentures.20 To confirm this thesis, we tested the existence of cointegration between the exchange rate and foreign liabilities. These results are also presented in 18

Wood (1999) identifies a structural break in the Japanese banking system in 1997 due to the banking crisis eruption, the beginning of the implementation of the 1996 financial reform, and the Foreign Exchange Control Act revision in May of 1997. As a consequence of this last revision, capital controls that had been in place since 1980 were eliminated in April of 1998. While this structural break is present in our data, the only significant shift in the variables mentioned above is shown in 1991. This seems to be consistent with the fact that the lost value of the Japanese stock in the 1990s forced a change in liability management on the part of Japanese banks. 19 Because of space constraints we do not report the results for the stock index. The results of the ECM for the stock index and both debentures and foreign liability are available upon request. 20 The yen did not significantly devalue until 1995. This could be one explanation of why, in the second period, DBNt1 (change in bank notes or debentures) is only significant at the 10% level.

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Table 1 Test for unit roots—augmented Dicky–Fuller test (ADF), Phillip and Perron (PP) Variable

1985:01–2001:12 ADF

LFL LMI LBN LXR LI LIR

2.01 1.75 1.01 1.34 1.41 0.33

DFL DMI DBN DXR Di DIR

11.34** 5.31** 2.03** 3.81** 6.31** 6.02**

PP 2.67** 1.99** 2.84** 1.99** 1.07** 0.92** 5.26 3.91 11.50 4.02 3.17 5.69

Notes. LFL: log of foreign liabilities; LMI: log of market index; LBN: debentures; LXR: log of exchange rate (yen/dollar); LIR: log of international reserves; and Li: log of interest rates. (D) Represents the first differential of the respective variables. ** 5% significance level.

Table 3. Once again, we found a unidirectional cointegration between these two variables and this ran from foreign liabilities to the exchange rate, thus supporting the proposed interpretation (Table 3, columns 1, 3, and 5). The results also show the presence of shortterm or bandwagon expectations (where past deviations reinforce present deviations away from its equilibrium) for the exchange rate (DXRt1 is positive and significant with regard to DXR). When significant, the relationship between the foreign liabilities and the exchange rate was negative indicating that as banks’ foreign borrowing increased the yen appreciated and vice versa, as in fact occurred before and after 1995, respectively. We also find that the first lag of the exchange rate is significant and negative when it is run against the foreign liability, thus providing support for the previous thesis that banks substituted foreign borrowing by domestic borrowing as the yen depreciated and vice versa (Table 3, columns 2, 4, and 6.) It also indicates the presence of a short-term feedback between foreign borrowing and the yen. Finally, the results show distributed-lag expectations for foreign liabilities during the fourth period (Table 3, column 4) for the second and third lag. This behavior indicates that when banks over or under borrowed, they adjusted their positions within 2 months. From a regulator point of view, these findings are of interest because they speak to the importance of requiring banks, as they manage their liability, to hedge against foreign currency exposure in a floating rate system. It also speaks to the effect that heavy borrowing from banks can have on the exchange rate value. We then turned to the determination of the relevant fundamentals that underlined the banks liability management. With this purpose we ran an ECM, which included the exchange rate and the money market interest rate, in addition to international reserves and the stock index. We ran them against the FL, BN, and MI. We present these results in Tables 4(A–C) and 5 (A–C). Once again, the results are first presented for the whole period

380

Table 2 Error correction model 1985:01–2001:12 DFL (1)

1985:01–1990:12 DBN (2)

*

**

DFL (3) ***

1991:01–2001:12 DBN (4)

DFL (5)

DBN (6)

Constant wt1 DFLt1 DFLt2 DFLt3 DFLt4

2.4748 (0.4975) 0.5287** (0.2543) 0.4895** (0.1948) 0.1416*** (0.0836)

1.1514 (0.5019) 0.1720 (0.4038) 0.3677 (0.3135) 0.2674** (0.1296) 0.2616** (0.1318) 0.2346*** (0.1267)

1.3706 (0.6893) 0.3931* (0.1312) 0.3601** (0.1646) 0.0284 (0.1373) 0.0586 (0.1330)

1.1260 (1.2257) 0.1367 (0.2332) 0.2471 (0.2926) 0.2329 (0.2441) 0.4722** (0.2364)

0.3845 (0.4179) 0.0510** (0.0254) 0.1461 (0.1166) 0.1449 (0.1187) 0.1060 (0.1196)

1.3111** (0.6303) 0.0312 (0.1152) 0.4279* (0.1898) 0.1842 (0.1794) 0.1489 (0.1813)

DBNt1 DBNt2 DBNt3 DBNt4

0.3920* (0.1073) 0.1016 (0.0653) 0.0476 (0.0623) 0.0950*** (0.0508)

0.8800* (0.1734) 0.4857* (0.0961) 0.3101* (0.0838)

0.3276* (0.0925) 0.1159 (0.0974) 0.0611 (0.0812)

0.8278* (0.1645) 0.5689* (0.1731) 0.3798** (0.1444)

0.1257*** (0.0701) 0.0150 (0.0826) 0.0211 (0.0710)

0.8523* (0.1087) 0.5113* (0.1262) 0.3368* (0.1080)

Dummy

2.9790* (0.6538)

Notes. FL: foreign liabilities; BN: bond notes or debentures; wt1 : error correction component. DYt ¼ b0 þ b1 wt1 þ b2 DVt þ et where Y is the regressor and V is the set of regressand. * 1% significance level. ** 5% significance level. *** 10% significance level.

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Regressors

Regressors

1985:0–2001:12 DXR (1)

1985:01–1990:12 DFL (2)

DXR (3) **

1991:01–2001:12 DFL (4) *

DXR (5)

DFL (6)

Constant wt1 DXRt1 DXRt2 DXRt3 DXRt4

0.3082 (0.2344) 0.30337* (0.0112) 0.3194* (0.0850) 0.1084 (0.0892) 0.0839 (0.0865) 0.0494 (0.0788)

1.7705 (0.6979) 0.0192 (0.0207) 0.0301* (0.0210) 0.1545 (0.1309) 0.0529 (0.1272) 0.0613 (0.1156)

0.4116 (0.6149) 0.0343** (0.0154) 0.3791* (0.1345) 0.1749 (0.1438) 0.2145 (0.1461) 0.0671 (0.1408)

2.9632 (0.7784) 0.0160 (0.0196) 0.1352* (0.1003) 0.0592 (0.1821) 0.0426 (0.1849) 0.0456 (0.1783)

0.0265 (0.2520) 0.1213* (0.0405) 0.3069* (0.1122) 0.0544 (0.1108) 0.1370 (0.1080) 0.0566 (0.0918)

0.4912 (0.4342) 0.0492 (0.0698) 0.1385* (0.1034) 0.1645 (0.1910) 0.407 (01861) 0.0493 (0.1581)

DFLt1 DFLt2 DFLt3 DFLt4

0.1949* (0.0601) 0.0669 (0.0600) 0.1000*** (0.0602) 0.1207** (0.0629)

0.0623 (0.0892) 0.1140 (0.0898) 0.2301** (0.0904) 0.1057 (0.0941)

0.0838 (0.1061) 0.1314 (0.1028) 0.0912 (0.1033) 0.1829*** (0.1032)

0.0330 (0.1343) 0.2810** (0.1301) 0.1174 (0.1307) 0.3066** (0.1307)

0.3054* (0.0710) 0.0580 (0.0786) 0.1265 (0.0768) 0.1619** (0.0816)

0.0143 0.1471 0.1715 0.0020

Dummy

2.4242** (0.9920)

Notes. FL: foreign liabilities; XR: exchange rates (measured as yen/dollar); wt1 : error correction component. DYt ¼ b0 þ b1 wt1 þ b2 DVt þ et where Y is the regressor and V is the set of regressand. * 1% significance level. ** 5% significance level. *** 10% significance level.

(0.1223) (0.1355) (0.13230 (0.1405)

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Table 3 Error correction model

381

382

Table 4 Error correction model Regressors

DMI (2)

DXR (3)

DIR (4)

DI (5)

0.1338 (0.3439) 6.6498** (2.6432) 0.0820 (0.0804) 0.03740 (0.08055) 0.2300* (0.0807)

0.4934 (0.6374) 5.7382 (4.8995) 0.0645 (0.1490) 0.0610 (0.1493) 0.02692 (0.1496)

0.3873 (0.2695) 2.3171 (2.0716) 0.0863 (0.0630) 0.1135*** (0.0631) 0.0111 (0.0632)

0.5692*** (0.3161) 1.9194 (2.4298) 0.0629 (0.0739) 0.1695** (0.0740) 0.0513 (0.07417)

0.0235 (0.0227) 0.1481 (0.1744) 0.0058 (0.0053) 0.0042 (0.0053) 0.0150* (0.0053)

DXRt1 DXRt2 DXRt3

0.0829* (0.0200) 0.2360*** (0.1277) 0.1446 (0.1776)

0.0847 (0.2223) 0.3600 (0.2367) 0.4115*** (0.2180)

0.3535* (0.0940) 0.0665 (0.1001) 0.1112 (0.0922)

0.0670 (0.1103) 0.0005 (0.1174) 0.05167 (0.1081)

0.0118 (0.0079) 0.0021 (0.0085) 0.0196** (0.0078)

DMIt1

0.0476 (0.0466)

0.03533 (0.0865)

0.0137 (0.0366)

0.0269 (0.0429)

0.0032 (0.0031)

**

*

0.0164** (0.0069) 0.0083 (0.0069) 0.01400** (0.0066) 0.0054 (0.0061)

DIRt1 DIRt2 DIRt3 DIRt4

0.1125 (0.1042) 0.1362 (0.1049) 0.1746** (0.1003) 0.3059* (0.0923)

0.4410 (0.1931) 0.0531 (0.1944) 0.0039 (0.1859) 0.0585 (0.1710)

0.0513 (0.0817) 0.0099 (0.0822) 0.0578 (0.0786) 0.1832** (0.0723)

0.2645 0.1493 0.0739 0.1061

Dit1 Dit2 Dit3

2.9646** (1.2654) 1.6976 (1.3100) 2.4700*** (1.3058)

1.4697 (2.3456) 1.2175 (2.4276) 1.9428 (2.4205)

0.2445 (0.9918) 0.4834 (1.0264) 0.4645 (1.0234)

1.8118 (1.1633) 2.8677** (1.2039) 1.3068 (1.2004)

0.1697** (0.0835) 0.0874 (0.0864) 0.1313 (0.862)

Dummy

2.9789* (0.6538)

8.025* (1.3965) (1.4823) (21.1429) (0.2979) (0.2760) (0.2897)

0.0512 (0.6713) 27.5424* (9.5278) 0.0370 (0.1324) 0.0417 (0.1218) 0.0691 (0.1296)

0.5761 (0.7806) 35.5172* (11.1347) 0.0680 (0.1569) 0.0121 (0.1453) 0.2017 (0.1526)

0.07433 (0.0609) 1.4608*** (0.8685) 0.0073 (0.0122) 0.0289** (0.01133) 0.01189 (0.0119)

0.0470 (0.3520) 0.2303 (0.3743) 0.19553 (0.3463)

0.4349* (0.1577) 0.1629 (0.1649) 0.2221 (0.1593) 0.1275 (0.1622) 0.0444 (0.1589)

0.1748 (0.1854) 0.0631 (0.1971) 0.1237 (0.1824)

0.0132 (0.0145) 0.00434 (0.0154) 0.0267*** (0.0142)

0.0294 0.1536)

0.02617 (0.0679)

0.1148 (0.0809)

(B) 1985:10–1990:12 Constant wt1 DFLt1 DFLt2 DFLt3

2.2064* (0.7407) 28.3968* (10.5116) 0.06500 (0.1460) 0.0198 (0.1343) 0.1405 (0.1430)

DXRt1 DXRt2 DXRt3 DXRt4 DXRt5

0.1143 (0.1740) 0.0853 (0.1819) 0.0569 (0.1758) 0.2526 (0.1790) 0.3391*** (0.1753)

DMIt1

0.0090 (0.0750)

2.2987 16.6527 0.0965 0.4552 0.0391

(0.0958) (0.0940) (0.0922) (0.0848)

0.0028 (0.0063)

M.S. Aguirre, R. Saidi / Journal of Asian Economics 15 (2004) 373–397

(A) 1985:01–2001:12 Constant wt1 DFLt1 DFLt2 DFLt3

DFL (1)

0.3408** (0.1486) 0.2363 (0.1394) 0.2187 (0.1364) 0.0843 (0.1336)

0.0129 (0.01160) 0.0145 (0.0109) 0.0196*** (0.0106) 0.0115 (0.0104)

4.2266 (303130) 2.1644 (309622) 6.6252*** (307240)

0.7693 (1.5429) 3.4950*** (1.9325) 0.3787 (1.6635)

3.8902** (1.7447) 0.5887 (2.0867) 0.2206 (1.9612)

0.3331** (0.1361) 0.0743 (0.1628) 0.0836 (0.1530)

0.6118 1.1523 0.0853 0.4062 0.0622

0.0629 23.0974 0.1855 0.0786 0.1384

0.8424 (0.4622) 11.9150 (10.3283) 0.0259 (0.1142) 0.1811*** (0.1086) 0.0298 (0.1063)

0.0550*** (0.02819) 0.1108 (0.6299) 0.0075 (0.0070) 0.0034 (0.0066) 0.0085 (0.0065)

0.0813 (0.1392) 0.2432*** (0.1369) 0.1065 (0.1264) 0.1795 (0.1315) 0.3265** (0.1282)

0.2845 0.0438 0.1604 0.1221

Dit1 Dit2 Dit3

6.3452* (1.7011) 2.5700 (201320) 1.9574 (1.8352)

(C) 1991:01–2001:12 Constant wt1 DFLt1 DFLt2 DFLt3

1.1238** (0.5541) 28.8068** (12.3800) 0.1118 (0.1369) 0.0614 (0.1302) 0.2828** (0.1274)

(0.2822) (0.2648) (0.2589) (0.2536)

(1.0710) (23.9311) (0.2516) (0.2462)

(0.3806) (8.5040) (0.0940) (0.0894) (0.0875)

DXRt1 DXRt2 DXRt3

0.0995 (0.1893) 0.3621*** (0.1912) 0.1052 (0.1648)

0.1127 (0.3660) 0.6319*** (0.3695) 0.6919** (0.3186)

0.2648 (0.1301) 0.0831 (0.1313) 0.0598 (0.1132)

0.0059 (0.1580) 0.2064 (0.1595) 0.0769 (0.1375)

DMit1

0.02154 (0.0628)

0.0083 (0.1214)

0.0225 (0.0431)

0.0330 (0.0524)

DIRt1 DIRt2 DIRt3 DIRt4 DIRt5

0.1231 (0.1592) 0.1754 (0.1648) 0.4074** (0.1571) 0.4614* (0.1512)

0.3441 (0.3077) 0.2818* (0.1186) 0.1478 (0.3036) 0.1059 (0.2923)

0.0325 0.0038 0.0393 0.0950

Dit1 Dit2 Dit3

2.7090 (2.3195) 1.6818 (2.13250) 0.2076 (2.0746)

3.4850 (4.4837) 2.9202 (4.1222) 6.0553 (4.0104)

(0.1093) (0.1132) (0.1079) (0.1039)

0.2193 (1.5933) 1.6867 (1.4649) 1.1105 (1.4251)

0.0111 (0.0096) 0.0026 (0.0097) 0.0127 (0.0084) 0.0071** (0.0032)

(0.1328) (0.1375) (0.1310) (0.1262)

0.0164** (0.0081) 0.0006 (0.0084) 0.0165** (0.0080) 0.0036 (0.0077)

0.2724 (1.9351) 1.3511 (1.7791) 0.5865 (1.7308)

0.1489 (0.1180) 0.1694 (0.1085) 0.2719** (0.1056)

0.2641 0.0428 0.1038 0.1574

Notes. FL: foreign liabilities; XR: exchange rates (measured as yen/dollar); MI: market index; IR: international reserves; BN: bond notes; i: money market rates; wt1 : error correction component. DYt ¼ b0 þ b1 wt1 þ b2 DVt þ et

M.S. Aguirre, R. Saidi / Journal of Asian Economics 15 (2004) 373–397

0.1359 (0.1262) 0.03618 (0.1241) 0.0978 (0.1146) 0.1286 (0.1192) 0.0074 (0.1162)

DIRt1 DIRt2 DIRt3 DIRt4 DIRt5

where Y is the regressor and V is the set of regressand. * 1% significance level. ** 5% significance level. *** 10% significance level.

383

384

Table 5 Error correction model DBN (1)

DMI (2)

DXR (3)

(A) 1985:01–2001:12 Constant wt1 DBNt1 DBNt2 DBNt3

235.2762* (23.732) 2.9110** (1.2830) 0.6567* (0.0974) 0.459* (0.1015) 0.2337* (0.0852)

416.0574 (153.8748*) 16.6821** (8.3751) 1.4164** (0.6356) 1.8170* (0.6626) 0.9215*** (0.5561)

104.6677 (66.7278) 5.0146 (306319) 0.4281 (0.2756) 0.1967 (0.2874) 0.4170*** (0.2412)

0.0371 (0.0326) 0.1107* (0.0347) 0.0577*** (0.0332)

0.0660 (0.2131) 0.3255 (0.2265) 0.4879** (0.2169)

0.3434* (0.0924) 0.1182* (0.0982) 0.1479 (0.0941)

DMit1

0.01354 (0.01300)

0.0426 (0.0846)

0.0330 (0.0429)

0.0018 (0.0031)

DIRt1 DIRt2 DIRt3 DIRt4

0.0008 (0.0289) 0.0682** (0.0288) 0.0186 (0.0280) 0.0464*** (0.0257)

0.3144*** (0.1886) 0.1085 (0.1877) 0.0351 (0.1827) 0.0126 (0.1680)

0.1097 0.0219 0.0431 0.1735

(0.0818) (0.0814) (0.0792) (0.0728)

0.3062* (0.0956) 0.1165 (0.0951) 0.0884 (0.0926) 0.0650 (0.0851)

0.0159** (0.0070) 0.0050 (0.0069) 0.0100 (0.0067) 0.0059 (0.0062)

Dit1 Dit2 Dit3

0.2217 (0.3740) 0.2829 (0.3801) 0.3164 (0.3838)

2.1937 (2.4412) 3.9387 (2.4813) 0.7082 (2.5053)

0.4657 (1.0586) 0.6104 (1.0760) 0.7842 (1.0864)

1.9850 (1.2374) 3.6538* (1.2577) 0.1422 (1.2699)

0.2265 (0.0901) 0.0947 (0.0916) 0.0880 (0.0925)

DXRt1 DXRt2 DXRt3

0.0231 (0.0367)

DI (5)

21.3903 (77.9944) 3.6126 (4.2451) 0.0205 (0.3221) 0.2420 (0.3359) 0.4700*** (0.2819)

4.7993 0.0266 0.0385 0.0279 0.0183

(5.6783) (0.3091) (0.0235) (0.0245) (0.0205)

0.0218 (0.1080) 0.0271 (0.1148) 0.0767 (0.1099)

0.0096 (0.0079) 0.0002 (0.0084) 0.0191 (0.0080)

0.7826* (0.0929)

Dummy (B) 1985:01–1990:12 Constant wt1 DBNt1 DBNt2 DBNt3 DXRt1

DIR (4)

92.9772 (68.9752) 23.1556* (7.7103) 0.0757 (0.3352) 0.0145 (0.2546) 0.0166 (0.1682) 0.1189*** (0.0596)

124.2818 29.5889 0.0138 0.6014 0.6625 0.1941

(465.6148) (52.0485) (2.2629) (1.7185) (1.1352) (0.4021)

85.9976 (207.8474) 1.0380 (23.2341) 0.1351 (1.0101) 0.4079 (0.7671) 0.5768 (0.5068) 0.3328*** (0.1795)

60.6150 17.0462 0.1665 0.0035 0.7662 0.2059

(252.9037) (28.2707) (1.2291) (0.9334) (0.6166) (0.2184)

14.0701 0.1722 0.0898 0.0654 0.0152 0.0005

(19.1060) (2.1358) (0.0929) (0.0705) (0.0466) (0.01650)

M.S. Aguirre, R. Saidi / Journal of Asian Economics 15 (2004) 373–397

Regressors

0.0939 (0.0612) 0.0603 (0.0553)

0.2274 (0.414) 0.4667 (0.3734)

0.2660 (0.1846) 0.2293 (0.1667)

0.1640 (0.2246) 0.0463 (0.2028)

DMIt1

0.0035 (0.0223)

0.0554 (0.1505)

0.0550 (0.0672)

0.0467 (0.0817)

DIRt1 DIRt2 DIRt3 DIRt4 DIRt5

0.0307 0.0630 0.0448 0.0151

Dit1 Dit2 Dit3

0.7327 (0.6210) 1.3974*** (0.7270) 0.0756 (0.6255)

(C) 1991:01–2001:12 Constant wt1 DBNt1 DBNt2 DBNt3 DBNt4 DBNt5 DBNt6

(0.0446) (0.0399) (0.0393) (0.0381)

0.1244 0.0035 0.0130 0.0308

(0.3011) (0.2692) (0.2651) (0.2575)

4.1560 (4.1921) 0.0125 (4.9074) 2.9068 (4.2221) 531.6606 (361.2313) 37.0906 (34.2461) 2.1445* (1.0024) 2.2048* (1.038) 0.9575* (0.7098)

DXRt1 DXRt2 DXRt3 DXRt4 DXRt5

0.0059 (0.0480) 0.0891*** (0.0467) 0.0447 (0.0470) 0.0279 (0.0463) 0.0556 (0.0382)

0.0287 (0.3286) 0.6231*** (0.3382) 0.5700*** (0.3293)

DMIt1

0.0022 (0.0159)

DIRt1 DIRt2 DIRt3 DIRt4

0.0013 (0.0429) 0.0765*** (0.0434) 0.0383 (0.0427) 0.0474 (0.0434)

0.0285 (0.1191) 0.4424 0.2827 0.0350 0.1340

(0.3048) (0.3269) (0.3086) (0.2785)

0.2334 0.1748 0.1840 0.1971

1.8858 (1.8713) 0.7188 (2.1906) 0.9048 (1.8847)

0.8307 (2.2770) 2.9328 (206655) 0.8774 (2.2933)

192.8513 15.1500 1.0631 0.4537 0.4074

(148.7096) (14.09820) (0.7008) (0.5510) (0.3745)

0.3060** (0.1353) 0.1029 (0.1392) 0.0113 (0.1356)

(0.1635) (0.1462) (0.1440) (0.1398)

231.5222 (152.8116) 22.8082 (14.4871) 1.3087*** (0.7202) 0.4864 (0.5662) 0.5121 (0.3849)

0.1611 (0.1390) 0.0921 (0.1431) 0.0123 (0.1393)

0.0138 (0.0490)

0.0126 (0.0504)

0.0442 (0.1255) 0.0015 (0.1346) 0.0209 (0.1270) 0.2483** (0.1147)

0.3656* (0.12890 0.0342 (0.1383) 0.0673 (0.1306) 0.1371 (0.1178)

0.0042 (0.0062) 0.0083 0.0117 0.0057 0.0025

(0.0124) (0.0110) (0.0109) (0.0106)

0.4110** (0.1720) 0.1306 (0.2014) 0.01334 (0.1733) 10.0465 (9.4074) 0.7396 (0.8919) 0.0277 (0.0443) 0.0263 (0.0349) 0.0459*** (0.0237)

0.0078 (0.0086) 0.0007 (0.0088) 0.0110 (0.0086)

0.00678* (0.0031) 0.0172** (0.0079) 0.0038 (0.0085) 0.0128 (0.0081) 0.0060 (0.0073)

385

126.5056 (117.8552) 14.0509** (5.7670) 0.0071 (0.3285) 0.0552 (0.2772) 0.0886 (0.2386) 0.0478 (0.2030) 0.2131 (0.1620) 0.1485 (0.1227)

0.1091 (0.1344) 0.0089 (0.1201) 0.0034 (0.1184) 0.2169*** (0.1149)

0.0009 (0.0170) 0.0231 (0.01532)

M.S. Aguirre, R. Saidi / Journal of Asian Economics 15 (2004) 373–397

DXRt2 DXRt3 DXRt4 DXRt5

386

Regressors

DBN (1)

DMI (2)

DXR (3)

DIR (4)

DI (5)

***

DIRt5 DIRt6

0.0717 (0.0406) 0.0374 (0.0410)

Dit1 Dit2 Dit3

0.2741 (0.5904) 0.5243 (0.5529) 0.1050 (0.5673)

1.1453 (4.4875) 1.9583 (4.1614) 6.41906 (4.1842)

1.0819 (1.8474) 1.3767 (1.7131) 0.2757 (1.7225)

0.9138 (1.8984) 1.5627 (1.7604) 0.4149 (1.7700)

0.0815 (0.1169) 0.2328** (0.1084) 0.2441** (0.1090)

Notes. FL: foreign liabilities; XR: exchange rates (measured as yen/dollar); MI: market index; IR: international reserves; BN: bond notes; i: money market rates; wt1 : error correction component. DYt ¼ b0 þ b1 wt1 þ b2 DVt þ et where Y is the regressor and V is the set of regressands. * 1% significance level. ** 5% significance level. *** 10% significance level.

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Table 5 (Continued )

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387

(A) and then for each of the sub-periods (B–C). When these variables were ran against foreign liabilities for the whole period (Table 4A, column 1), we found that exchange rate, international reserves, and interest rates bring the foreign liabilities to equilibrium but not vice versa except for the case of exchange rates in the first period (Table 4B, column 3). This exception for the first period captures the depreciation that the yen suffered between the beginning of 1989 and the end of 1990. The results suggest that the three variables mentioned accounted for a significant portion of the variations from the long-run equilibrium of foreign liabilities, which was used by banks to manage their liability. They also indicate that banks managed their liabilities taking into consideration these economic fundamentals. Analyzing the short-term dynamics we find that foreign liabilities is inversely affected by the lags of exchange and interest rates (Table 4A, column 1.) Interest rate, at the same time, is positively related to its own lag and, for most part, with the lag values of foreign reserves (Table 4A–C, column 4–5.) The first two results are consistent with both the economic theory of prices and with the cointegrated relation previously found between foreign liabilities and the exchange rate. Under a floating exchange rate system, it is this variable which is the adjusting variable. The positive sign of the DIR(1) (Table 4A and B, column 4) indicates the presence of bandwagon expectations. Once again, the stock market showed no significance for any of the sub-periods. Interest rates were significant in the first sub-period, but not in the second sub-period. As the Japanese interest rate increased due to the fall in liquidity that followed the stock market fall, banks resorted to foreign funds but, as the yen depreciated and they switched to debentures and domestic financing, the impact of the interest rate fell. Reserves were also found to be significant in its third and fourth lag. These results are consistent with the fact that during this time, because of the devaluation of the yen, banks substituted foreign borrowing by issuing debentures. When the regressor is the Nikkei Index (Table 4A and C, column 2), we find a positive causality between the index and international reserves and the index and the exchange rate. International reserves, however, shows its effect in the first lag, indicating a quick effect of capital flows on the movements in the stock market. Movements of the exchange rate, on the other hand, only affected the Nikkei after the third lag. From the viewpoint of policy and of bank regulators, this is of interest as it indicates that stocks are vulnerable sources of bank financing since stocks are very sensitive to both movements of capitals and exchange rates. Therefore, a high concentration of this source, as was the case for Japanese banks, is not recommendable. Finally, when significant, the signs of the coefficients of the exchange rate, international reserves, and interest rates, behave as one would have expected based on the theory and the framework of analysis presented in Section 2 (Table 4A and C, columns 3–5). Table 5A–C presents the results for the ECM that includes debentures. Once again, lag values of the exchange rate and international reserves as well as its own lags bring the debentures to the long-run value but not vice versa (Table 5A, column 1.) The coefficient signs, when significant, switched from one lag to the other, suggesting a support for the feedback effect previously noticed between the three components of the banks’ balance sheet and the exchange rate and the foreign reserves. For the period 1985–1990, exchange rates and interest rate show some level of significance in the first and second lag, respectively, (Table 5B, column 1.) Such behavior is consistent with the institutional

388

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and macroeconomic environment within which Japanese banks were working at the time. As it has been previously explained, until 1997, debentures were only issued in domestic currency, thus the decision to hold debentures by large credit banks and commercial banks as a means of liability management, was affected by the interest rate level, as this is the opportunity cost in liability management. At the same time, the positive sign of the exchange rate indicates that as the yen appreciated, the use of debentures decreased and vice versa. During this sub-period, and with the exception of 1989–1990, the yen experienced a significant appreciation; therefore the value of the yen also played an important role in the liability management decision. While the yen was appreciating, foreign funds provided an inexpensive and accessible alternative to debentures as instruments of liability management. On the other hand, during the second sub-period, are exchange rates and international reserves that influence the long-run equilibrium of debentures’ holdings (Table 5C, column 1.). Once again, the signs of the variables reflect the institutional reality. Until 1996 the yen continued to appreciate but, at this point, the trend was reversed and the yen devalued until 1999. In addition, in 1997, debentures were allowed to be issued in foreign denominated currencies while, as it was mentioned earlier in the paper, at this time, foreign banks began to question the solvency of some of the Japanese banks with the consequent introduction of a Japanese premium. It is not surprising then, that the decision to hold debentures as means of liability management was influenced by the value of the yen. Similarly, the negative sign of international reserves is consistent with the fact that, during the second sub-period, as the value of the Nikkei declined, banks resorted to foreign funding to make up for the fall on the available credit at home. The large inflows of capital resulted in a significant increase in international reserves. Interest rate was significant and negative for the 1985–1990 period but insignificant for the 1991–2001 period. This behavior mirrored the one found for the foreign liability case. Overall, results indicate that the debentures responded rapidly to movements in the fundamentals and that the effect, in most cases, lasted for 3 months. Stock was found to have no relationship to foreign liabilities. It shows, however, a clear link to debentures especially during the second period (Table 5A and C, column 3.) In fact, debentures are one of the variables that showed to be significant in bringing the stock index to its long-run equilibrium. Exchange rates and international reserves also play a role but it is not as significant. We found a positive and significant relationship between the stock index and debentures. When present, the lag values of debentures affected the present stock index but not vice versa. This is consistent with the fact that banks used debentures as a collateral for credits from the bank of Japan who carried lower rates than the call rates. As the stock market fell, and banks resorted to foreign funds as a source of liability management, the holdings of debentures declined. This last tactic however, was reversed after 1995, as the yen began to devalue again and a Japanese premium appeared. Banks resorted, once again, to the use of debentures as an instrument of liability management. In summary, the ECM results analyzing the economic fundamentals seem to indicate that international reserves, exchange rates, interest rates, the lag values of debentures, and foreign liabilities played an important role on the short-term dynamics of two of the three important instruments used by Japanese banks’ in their liability management. The exchange rates, reserves, and debentures influenced the stock market short-term dynamics but not the dynamics of banks’ foreign liabilities. Results also suggest that the exchange

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389

rate was affected by the increase on banks’ foreign borrowing activity following the stock market decline. Finally, we turn to the study of whether and, if so, how the liability management instruments used by Japanese banks before and after the banking crisis affected the balance of payments. Under a flexible exchange rate system, as in the case of the yen, the adjustment variable of the balance of payment is the exchange rate. This is why we incorporated it into the previous analysis as one of the relevant fundamentals. As could have been expected, the exchange rate was found to be significant, especially in reference to the foreign borrowing to which banks resorted when liquidity was low in Japan (Table 3). We also found it to be significant when ran against debentures and stock alone (Tables 6–7). In both cases, it contributes to bringing these variables to their long-run equilibrium level. These results closely resemble the finding of Table 5. Technically, under a clean floating system, the foreign reserves should not be affected. Yet, we know that the Japanese Central Bank undertook open market operations in an effort to mitigate the capital outflow that took place following the Nikkei Stock Index decline and the devaluation of the yen from 1995 to 1999. In order to corroborate this causality, we tested for cointegration between foreign reserves, the Nikkei Stock Index, debentures, and banks’ foreign liabilities.21 Once again, we found w to be I(0) in all cases and significant at the 1% level, and thus we proceeded to run the ECM. Tables 8–10 present these results for the three different periods analyzed and for the three instruments used as means of liability management against international reserves. Table 8 reports the results of the Nikkei Stock Index against the international reserves for the period 1985–2001 and sub-periods. The long-run constraint (wt1) is negative and significant for the Nikkei with the international reserves and vice versa. Therefore, although weak, there is evidence of a long-run equilibrium relationship between the international reserves and the Japanese stock market. Results also suggest that the deviations from the long-run equilibrium are self-adjusting.22 As expected, given the shift in the stock market series that occurred in 1991, we also found the dummy variable to be significant. The short-term dynamic of the stock market index and the levels of reserves, were influenced by the first lag of international reserves positively. This is consistent with the institutional details previously addressed. When the sub-periods’ ECM models were analyzed, similar signs but insignificant results were found for the causality of international reserves in the stock for the second period. However, the reverse held, i.e., the stock impacted the international reserves with some lags (Table 8, column 6). This one-sided significance of w in the sub-period 1991–2001 indicates that while the stock index brought international reserves back to the long-run equilibrium, the reverse was not the case. Once again, this is consistent with the findings 21

We therefore ran the following regression: DIRt ¼ b0 wt1 þ

I I X X di DIRti þ fi DMIti þ gD þ et i¼1

i¼1

where once again, wt1 are deviations from the long-run equilibrium relationship represented by the cointegrating vector {IRt -aMIt ¼ wt }, i is the number of lags and et is a stochastic error term. We first used international reserves (IR) and then used the exchange rate (XR). 22 Furthermore, these results suggest that there may be some efficiency gains from the ECM over the VAR model.

390

Regressors

1985:01–2001:12 DBN (1) *

1985:01–1990:12 DXR (2)

Constant wt1 DBNt1 DBNt2 DBNIt3

2.3059 0.1540* 0.7707* 0.5145* 0.3166*

DXRt1 DXRt2 DXRt3

0.3189 (0.2496) 0.1269* (0.0612) 0.0603** (0.0453)

(0.9571) (0.0194) (0.4352) (0.2546) (0.1872)

**

0.6857 (0.3260) 0.0030 (0.0147) 0.2864 (0.6844) 1.0765 (1.3890) 0.6778** (0.4436) 0.5435* (0.0709) 0.1326* (0.0785) 0.1877 (0.1978)

DBN (3)

1991:01–2001:12 DXR (4)

DBN (5)

DXR (6)

6.7445 (4.4536) 0.5342* (0.0826) 0.0067 (0.1643) 0.3377 (0.4930) 0.0942 (0.2287)

1.3067 (0.9672) 1.0196 (0.8029) 0.5422* (0.9876) 0.6543 (0.7890) 0.5930 (0.4733)

2.9916 (2.4716) 0.3791* (0.2003) 0.1324 (0.1357) 0.5836 (0.6751) 0.5748 (0.6713)

0.6779 7.0456 3.2536 1.1228 0.7343

0.2354** (0.1008) 0.0854 (0.1389) 0.0698 (0.1371)

0.3589* (0.1026) 0.7321 (0.6031) 0.3239 (0.4272)

0.0052 (0.1048) 0.6814* (0.1340) 0.0894 (0.0798)

*

Notes. BN: bond notes; XR: exchange rates (measured as yen/dollar); wt1 : error correction component. DYt ¼ b0 þ b1 wt1 þ b2 DVt þ et where Y is the regressor and V is the set of regressands. * 1% significance level. ** 5% significance level.

(0.6353) (6.4644) (2.9147) (1.1161) (0.9511)

0.4598* (0.0729) 0.8121 (0.7818) 0.8564 (0.9082)

M.S. Aguirre, R. Saidi / Journal of Asian Economics 15 (2004) 373–397

Table 6 Error correction model

Regressors

1985:01–2001:12 DMI (1)

1985:01–1990:12 DXR (2)

Constant wt1 DXRt1 DXRt2 DXRt3

0.2906 (0.3042) 0.7806* (0.0679) 0.4016 (0.4531) 0.6519 (0.5425) 0.5780* (0.0254)

1.0484 (0.4612) 0.7001 (0.6781) 0.3828 (0.3982) 0.1038 (0.2039) 0.5461* (0.2321)

DMIt1 DMIt2 DMIt3

0.8783 (0.9080) 0.9650 (0.9808) 0.7800 (0.8746)

0.0867 (0.0899) 0.0653 (0.0727) 0.0338 (0.0761)

Dummy

0.3904* (0.0857)

0.3687* (0.0850)

DMI (3) 2.4944 1.7800 0.0965 0.6745 0.9727

1991:01–2001:12 DXR (4)

(1.997) (1.8652) (0.0987) (0.8739) (0.8999)

0.9526 (0.9957) 0.8632 (0.9601) 0.6430 (0.7529)

0.9691 (0.9755) 2.4867 (1.2670) 0.3173** (0.1292) 0.1252 (0.1256) 0.6514 (0.7268) 0.4103 (0.4983) 0.4729 (0.4980) 0.3964 (0.4503)

Notes. XR: exchange rates (measured as yen/dollar); MI: market index; wt1 : error correction component. DYt ¼ b0 þ b1 wt1 þ b2 DVt þ et where Y is the regressor and V is the set of regressand. * 1% significance level. ** 5% significance level.

DMI (5)

DXR (6) *

1.8930 (0.9021) 0.9164* (0.4082) 0.7035 (0.8038) 0.6036* (0.3402) 0.6645 (0.5641) 0.0096 (0.0876) 0.0043 (0.0274) 0.0056 (0.0311)

0.9156 0.5672 0.7899 0.3944 0.1013

(1.5601) (0.6387) (0.9414) (0.3911) (0.1114)

0.0611 (0.0589) 0.03861 (0.0394) 0.0226 (0.0243)

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Table 7 Error correction model

391

392

Table 8 Error correction model Regressors

1985:01–2002:12 DIR (2)

Constant wt1 DIRt1 DIRt2 DIRt3 DIRt4 DIRt5 DIRt6 DIRt7 DIRt8

1.5170 (0.9210) 0.0002** (0.0001) 0.3085*** (0.1614) 0.0735 (0.1649) 0.1418 (0.1656) 0.0450 (0.1611)

DMIt1 DMIt2 DMIt3 DMIt4 DMIt5 DMIt6 DMIt7 DMIt8

0.0143 0.0918 0.0006 0.0027

Dummy

2.8410** (1.1685)

DMI (3) *

0.8882 (0.3176) 0.0001* (0.00005) 0.1948** (0.0826) 0.0555 (0.0848) 0.1065 (0.0837)

(0.0842) (0.0852) (0.8451) (0.0837)

0.0700 0.0036 0.0156 0.0384

(0.0433) (0.0438) (0.0430) (0.04288)

where Y is the regressor and V is the set of regressands. * 1% significance level. ** 5% significance level. *** 10% significance level.

DIR (4)

DMI (5) **

DIR (6)

1.1863 (1.0724) 0.0002** (0.0001) 0.3500* (0.1175) 0.0354 (0.2187) 0.0356 (0.2179) 0.0023 (0.2145)

1.5602 (0.6535) 0.0002** (0.00007) 0.2607** (0.1283) 0.0590 (0.1282) 0.2046 (0.1280) 0.1433 (0.1299)

1.4164 0.0005 0.1287 0.0345 0.2316 0.1507 0.4675 0.0684

0.0545 (0.1332) 0.1161 (0.1482) 0.0459 (0.1711) 0.3073*** (0.1719)

0.0556 (0.0809) 0.0807 (0.0869) 0.0089 (0.1016) 0.1472 (0.1015) 0.2159** (0.1049)

0.0070 (0.1211) 0.0027 (0.1187) 0.0417 (0.1087) 0.0964 (0.1020) 0.0292 (0.1000) 0.1918*** (0.0995)

Notes. IR: international reserves; MI: market index; wt1 : error correction component. DYt ¼ b0 þ b1 wt1 þ b2 DVt þ et

1991:01 to2002:12

(0.9393) (0.0003) (0.2754) (0.2735) (0.2785) (0.2796) (0.2766) (0.2782)

0.7043*** (0.4215) 0.0004** (0.0002) 0.1372 (0.1213) 0.0800 (0.1200) 0.1300 (0.1212) 0.0079 (0.1198) 0.1770 (0.1194) 0.0028 (0.1239) 0.1709 (0.1211) 0.0139) 0.0907 (0.0547) 0.0908 (0.0545) 0.0512 (0.0483) 0.0314 (0.0438) 0.0363 (0.0433) 0.0740*** (0.0433) 0.0339 (0.0438) 0.0234 (0.0436)

M.S. Aguirre, R. Saidi / Journal of Asian Economics 15 (2004) 373–397

DMI (1)

1985:01–1990:12

Regressors

1985:01–2002:12 DFL (1)

1985:01–1990:12 DIR (2)

*

DFL (3) **

Constant wt1 DIRt1 DIRt2 DIRt3 DIRt4

2.2095 (0.5971) 0.0450* (0.0149) 0.1132 (0.0938) 0.0300 (0.0947) 0.0568 (0.0949) 0.1922** (0.0933)

0.7997 (0.3260) 0.0302** (0.0127) 0.2339* (0.0844) 0.0765 (0.0850) 0.0378 (0.0836)

2.9025 0.0242 0.0046 0.0357 0.0041 0.0908

DFLt1 DFLt2 DFLt3 DFLt4

0.0383 (0.0822) 0.2019** (0.0795) 0.1445*** (0.0808) 0.0938 (0.0844)

0.0145 (0.0709) 0.1392** (0.0687) 0.1332*** (0.0696) 0.1754** (0.07048)

0.0239 (0.1332) 0.2654** (0.1289) 0.0990 (0.1301) 0.2579*** (0.1313)

Dummy

2.8675* (0.7089)

(0.7646) (0.0296) (0.1255) (0.1300) (0.1298) (0.1268)

Notes. FL: foreign liabilities; IR: international reserves; wt1 : error correction component. DYt ¼ b0 þ b1 wt1 þ b2 DVt þ et where Y is the regressor and V is the set of regressand. * 1% significance level. ** 5% significance level. *** 10% significance level.

1991:01–2002:12 DIR (4)

DFL (5)

DIR (6)

0.9276 (0.7671) 0.0169 (0.0291) 0.3555* (0.1264) 0.1237 (0.1306) 0.1048 (0.1282)

1.1616 (0.4706) 0.1091 (0.0802) 0.1474 (0.1417) 0.536 (0.14710)) 0.3490** (0.14630) 0.4099* (0.1508)

0.7992** (0.3633) 0.0145 (0.0644) 0.2653** (0.1143) 0.1282 (0.1171) 0.0343 (0.1150)

0.1719 (0.1338) 0.2549*** (0.1299) 0.1064 (0.1308) 0.4245** (0.1258)

0.0523 0.1413 0.1689 0.0380

0.0085 (0.0828) 0.1218 (0.0815) 0.560 (0.0829) 0.0099 (0.0874)

(0.1084) (0.1013) (0.10280) (0.1100)

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Table 9 Error correction model

393

394

Regressors

1985:01–2002:12 DBN (1)

1985:01–1990:12 DIR (2)

*

Constant wt1 DIrt1 DIrt2 DIRt3 DIRt4

254.9068 (30.5942) 0.061** (0.0025) 0.0163* (0.0053) 0.0195 (0.0257) 0.0278 (0.0255) 0.0281 (0.0252)

DBNt1 DBNt2 DBNt3 DBNt4

0.7318* (0.0870) 0.5396* (0.1035) 0.2671** (0.1039) 0.0073 (0.0846)

DBN (3)

81.4584 (100.4616) 0.0017 (0.0081) 0.2866* (0.0828) 0.1038 (0.0839) 0.1258 (0.0821) 0.3806 0.2838 0.2189 0.3748

(0.2859) (0.3403) (0.3405) (0.2764)

DIR (4)

where Y is the regressor and V is the set of regressand. * 1% significance level. ** 5% significance level. *** 10% significance level.

DBN (5)

DIR (6) *

(53.9387) (0.0065) (0.0397) (0.03950) (0.0392) (0.0392)

96.9091 (175.1735) 0.0048 (0.02060) 0.3173** (0.1292) 0.1522 (0.1295) 0.1614 (0.1268)

193.8930 (54.9526) 0.0164** (0.0082) 0.0339* (0.0083) 0.0361 (0.0400) 0.0045 (0.0401) 0.0484 (0.0386)

92.2156 (161.0104) 0.0208 (0.02388) 0.2999 (0.1141) 0.0954 (0.1187) 0.0130 (0.1164)

0.7521* (0.1517) 0.6070* (0.1820) 0.2702 (0.1857) 0.0089 (0.1521)

0.9103*** (0.4993) 0.1878 (0.5955) 0.5264 (0.6010) 0.4020 (0.4830)

0.4968* (0.1876) 0.3253** (0.1788) 0.1635 (0.1524) 0.0483 (0.1127)

0.6331 (0.5489) 0.0941 (0.5248) 0.270 (0.4503) 0.0826 (0.3327)

262.4944 0.0079 0.0069 0.0202 0.0427 0.0084

Notes. IR: international reserves; BN: bond notes; wt1 : error correction component. DYt ¼ b0 þ b1 wt1 þ b2 DVt þ et

1991:01–2002:12

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Table 10 Error correction model

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395

previously analyzed. In the short-term, the dynamics of the international reserves were also linked to the stock market but after several lags. International reserves were cointegrated with banks’ foreign liabilities for the whole period, but not in the sub-periods (Table 9). Therefore, in this case we also find evidence of a long-run equilibrium relationship between international reserves and banks’ foreign borrowing but this is significantly weaker than for the case of the Nikkei. In the short-term dynamics, captured by the ECM, we see that while there is a significant AR process for international reserves, the FL lags capture most of its dynamics. The FL coefficients are for the most part positive, indicating an increase in reserves as foreign liabilities increased. Turning to the sub-periods, we found neither cointegration nor a short-run dynamic to indicate a causality going from international reserves to foreign liabilities in the first subperiod, but it is present in the second sub-period. Once again, this is consistent with the institutional and statistical results previously presented. This suggests that the instruments chosen by the banks to manage their liability after the banking crisis had an impact on the balance of payments. We found debentures and international reserves to be cointegrated (Table 10). In the case of debentures, the cointegration takes place only in one direction, as was the case with foreign borrowing (i.e., international reserves brought debentures to its long-run equilibrium and not the other way around). Even though the cointegration was present, the shortterm dynamics showed that the linkage between these two variables was weak. The subperiods’ results indicate that the presence of cointegration was due to the second period. This, once gain, is consistent with the results analyzed above and is another indicator that the instruments used by the Japanese banks to manage the liabilities during the crisis had an impact on the balance of payments. In summary, the ECM results of this section indicate that the instruments used by the Japanese banks to balance their liabilities was affected by and, in turn, affected the yen in the short-run. In the long-run, however, the causality ran from the yen to these instruments. A weaker case, as could have been expected under a floating exchange rate system, was found for the long-run effect of these instruments on international reserves. In this sense, our results support the predictions of Obstfeld (1994) that a weak banking system can affect the balance of payments. Since money under a flexible exchange rate is exogenous, we find no support for Calvo’s (1995) suggestion regarding the role of self-fulfilling expectations.

5. Conclusion Attention has been brought to the role played by the solvency (or lack) of the banking system in balance of payments and financial crisis under fixed exchange rate systems. The conclusion of the research so far is that causality may run from either direction, that is, from the balance of payments to the banking system or vice versa. In addition, there has been clear evidence of an increasing degree of dependency, across countries, on what happens abroad. If this is the case, the question of which and how macroeconomic fundamentals affect the instruments used by banks for liability management as well as how and whether these instruments affect the exchange rate and the level of reserves become relevant for any country, whether a country has a fixed or a flexible exchange rate system.

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In this paper, we aimed at investigating the Japanese banking crisis in two ways. First, we investigated how the dynamic behavior of the macroeconomic fundamentals played on the Japanese banks’ liability management. Second, we focused on if and how the instruments used for liability management affected the balance of payments. With this aim, we used an ECM methodology for Japan between 1985 and 2001. We found that in the case of Japan, the instruments used by banks to manage their liability affected the balance of payment only in the short-run through the indirect effect they had on both the exchange rate and international reserves. In the long-run, however, the integration suggest a direction of causality that goes from the exchange rate—and to a lesser extent from international reserves—to debentures, foreign liabilities, and the Nikkei. The behavior found in the exchange rate supports Mishkin (1996) who stipulated that a devaluation of the currency could further weaken the banking system by making banks’ liabilities denominated in foreign currency more vulnerable. In the Japanese case, however, since we were dealing with a flexible exchange rate, the reason for this causality were the means used by the financial institutions to manage their liabilities, i.e., the use of stocks as cushioning of capital rather than other financial reserves traditionally supported by the Basle Accord. In this sense, our results also supported those that maintained that currency and banking crisis have common causes in the policies used and emphasized the importance of bank solvency for financial stability. Regarding the fundamentals, we also found that exchange rate, interest rates, and foreign reserves were important fundamentals in explaining the short-term dynamics of the Japanese banks’ liability management. The causality ran from these variables to bank notes and foreign liabilities, two of the three important instruments used by Japanese banks. In this sense, a clear policy recommendation is that the solvency of banks under a flexible exchange rate system can be strengthened by means of exchange rates interventions or interest rates reductions. The effects of these policies, however, are only short-term and thus a more structural approach is needed to ensure banks’ solvency. Regulations on the banks instruments used to manage their liability are also important if an insolvency of the banks is to be avoided. Regulations regarding hedging, low volatility assets, and the proportion allowed of these assets are some of the regulations that the results of this paper suggest. Acknowledgements We are grateful for the helpful comments of Dr. Dutta and the referees, as well as for the comments received at the 2003 FMA Annual Meeting from the discussant and participants of the session where this paper was presented. The usual disclaimer applies.

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