In this appendix, we demonstrate the effects of three monetary policy shocks on the economy using the SVAR model in order to obtain a deeper understanding of the estimation result for the maturity and spread model discussed in Section 5. We follow the strand of literature that exploits the instrumental variable approach and combines a shock from micro-data with a VAR model to identify the effect of a shock on the economy (see for example Stock & Watson (2012)). More concretely, we assume that the dynamics of financial and macroeconomic variables, which are denoted byn×1vectorYt, is approximated by a VAR system withpnumber of lags as follows:
Yt=
P
∑
p=1
βpYt−p+νt, (6)
whereνt denotes reduced form shocks of the VAR system. We assume that νt is represented by a linear combination of some fundamental shocks,µt: I.e. we assume that νt = Bµt holds with n×nmatrixB. Without loss of generality, we assume that the three monetary policy surprises are defined as the first three elements of vector µt. Moreover, we defineΣµ as a variance-covariance matrix of the fundamental shocksµtas follows,
E(µtµ′t) = Σµ=D (7)
whereDis an×ndiagonal matrix.
Using the identified three column vectorsbi(i=1,2,3) in matrixB, we can calculate the impulse response functions (IRFs),IRFhi, to monetary policy shockiin time horizonhas follows,
IRFhi=ψhbi (8)
where ψh denotes the impulse response function to non-orthogonalized shocks for horizon h in reduced form of the VAR model.
As endogenous variables in the SVAR, we include the eight financial market variables, two macroeconomic variables, and the current account balances (CABs). The eight financial variables consist of the monthly change rates of yen-U.S. dollar exchange rate, the NIKKEI225 stock index, and the monthly changes of the three-month TIBOR future, and one-year, two-year, five-year, ten-year, and 30-year swap rates. The monthly growth rate of the aggregate current account balances of banks is included in order to capture the unconventional monetary policy where the BOJ ex-pands its balance sheet. The two macroeconomic variables include the monthly growth rate of the consumption price index (CPI; less foods and energy, adjusted for the consumption tax increases) and Indices of Industrial Production (IIP). Both are seasonally adjusted before the calculation of the growth rate. All the variables are illustrated as a percentage base. The length of the lag in the SVAR model is chosen as one based on the AIC. Our monthly dataset is from January 2000 until December 2016.
To estimate the VAR model, we used the bootstrap method by resampling 3,000 times. Figure B1 illustrates the IRFs to a positivetarget interest rate shockof one standard deviation, indicating that a positive shock shifts the entire yields curve upwards. Furthermore, it decreases the current account balance although it increases slightly for the first few months. The CPI inflation rate and the IIP decrease although they are not statistically significant. The result implies that the target interest rate shockcaptures the conventional monetary policy shock, which affects the economy through the level of interest rates in all maturities.
Figure B2 illustrates that a positive steepening curve shockdecreases short-term rates and in-creases long-term rates. However, it does not significantly change the CAB although the CAB gradually increases after the shock hits the economy. Furthermore, the CPI inflation rate decreases while the IIP increases. In this paper, the real effects of unconventional monetary policy are not our focus. The growing literature suggests the definition of unconventional monetary policy shocks and a method of identifying them (see, for example,Swanson(2015) andNakashima et al.(2018)).
However, there is still no consensus for the definition and methodology. Hence, here we only pro-vide some possible hypotheses. One explanation is that in a low interest environment, a tiny de-crease in short-term rates without changing the central bank’s balance sheet is not so effective that the inflation expectation does not change, although it stimulates production temporarily. Instead, because the central bank has private information that other agents do not, it signals the informa-tion to the other agents that the inflainforma-tion would be lower than previously expected through the
implementation of the monetary policy.16
Finally, Figure B3 illustrates that a positiveexpansionary twist shockincreases short-term rates while it decreases long-term rates. In addition, it increases the CAB substantially by stimulating the inflation rate and production. In others words, the estimation results imply that theexpansionary twist shock stimulates the economy by purchasing long-term bonds and expanding its balance sheet. Given this backdrop, we can reinterpret the results for the impact of theexpansionary twist shock on the loan maturity in Section 5; the expansionary twist shock increases output, thereby reducing the incentive of firms to raise funds for precautionary motives.
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0 10 20 30 40 50
0 500 1,000 1,500 2,000 2,500
2000 2002 2004 2006 2008 2010 2012 2014 2016
Number of syndicated loan deals Syndicated loan volume (JPY trillion) New loans for fixed investment (JPY trillion)
Proceeds (JPY trillion) Number of deals
Figure 1: Trends of syndicated loans in Japan
N otes: This figure shows the number of annual syndicated loan deals and annual proceeds (JPY in trillion) of total new loans for fixed investment, and the syndicated loans issued in Japan from 2000 through 2016. The number of deals is shown as a solid line, and the proceeds are shown as bars.
Sources: Thomson Reuters and BOJ.
80 85 90 95 100
2,000 2,400 2,800 3,200 3,600
2000 2002 2004 2006 2008 2010 2012 2014 2016 CR4 Herfindahl-Hirschman Index (HHI)
CR4 HHI
Figure 2: Concentration measures for lead arranger in Japanese syndicated loans
N ote: This figure shows the annual four-firm concentration ratio (CR4) and the annual HHI of the Japanese syndicated loan lead arrangers from 2000 through 2016. Source: Own calculation using data from the Thomson One database.
‐0.5
‐0.4
‐0.3
‐0.2
‐0.1 0 0.1 0.2 0.3 0.4 0.5 0.6
USD/JPY AUD/JPY JASDAQ Nikkei Index
TIBOR 3month
TIBOR 3m, future
Swap 1 year
Swap 2 year
Swap 5 year
Swap 10 year
SWAP 30 year
Target interest rate shock Steepening curve shock Expansionary twist shock
Figure 3: Factor loading of monetary policy shocks on financial market variables
N ote: Each bar indicates the loadings on financial market variables used to extract common factors as monetary policy shocks.
34
-15
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Target interest rate shock
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Steepening curve shock
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Expansionary twist shock
Figure 4: Development of monetary policy shocks
N ote: Each monetary policy shock is extracted as a common component using 11 financial market variables through the principal component analysis, as described in the text.
Table 1: Descriptive statistics
Obs. 25% Median Mean 75% Std Dev
Maturity (months) 11,411 12 36 41.7 60 50.4
No of Lenders 11,411 2 4 4.8 6 3.8
Subordinated (dummy) 11,411 0 0 0 0 0.03
Volatility (30days) 11,411 22.5 31.7 35.8 44.4 20.5
ROE (%) 11,411 1.6 4 5.5 7.5 165.6
Long term debt to equity 11,411 0.1 0.2 0.2 0.3 0.1
Total debt to EBITDA 11,411 2.2 4.5 6.9 8.9 18.7
Sales growth (%) 11,411 -3 3 6.5 9.9 36
EBITDA growth (%) 11,411 -9.8 6.4 32.9 25.8 286.2
Total assets (JPY millions) 11,411 50,977 177,277 898,613 762,117 2,074,571 Rating(dummies)
AAA 7,837 0 0 0.019 0 0.14
AA 7,837 0 0 0.12 0 0.34
A 7,837 0 0 0.42 1 0.49
BBB 7,837 0 0 0.37 1 0.48
BB 7,837 0 0 0.06 0 0.23
B 7,837 0 0 0.009 0 0.09
Spread (basis points) 580 46 70 82.31 100 57.05
Tenor (years) 580 1 3 3.48 5 4.12
Rating(dummies)
AAA 543 0 0 0.004 0 0.06
AA 543 0 0 0.13 0 0.33
A 543 0 1 0.58 1 0.49
BBB 543 0 0 0.21 0 0.40
BB 543 0 0 0.08 0 0.28
B 543 0 0 0.004 0 0.06
Notes: The full sample is from January 1, 2000, to December 31, 2016. Owing to data restric-tions as discussed in Section 3, a smaller sample is used to analyze loan spreads. Thus, for Spread, T enor, and Rating dummies, descriptive statistics is calculated using a smaller sample.
Rating dummy is a 0–1 dummy variable taking the value of unity when the issuing firm has a rating from AAA to B, and zero otherwise. Spreadis measured in basis points over TIBOR.
Table 2: Determinants of loan spreads (Dependent Variable: Loan spread)
(1) (2) (3) (4)
Variable Coeff. t-ratio Coeff. t-ratio Coeff. t-ratio Coeff. t-ratio
AA rating -60.09 1.61 -101.42 4.16∗∗∗
A rating -96.78 6.96∗∗∗ -102.29 5.46∗∗∗
BBB rating -69.39 5.50∗∗∗ -77.05 4.17∗∗∗
BB rating -27.06 1.43 -30.51 1.48
Volatility 0.55 1.42 0.81 2.00∗∗
ROE 0.14 0.17 1.42 1.84∗
Long term debt to equity -14.62 0.24 -28.03 0.51
Total debt to EBITDA -0.09 0.68 -0.11 0.80
Sales growth -0.05 0.70 -0.04 0.68
EBITDA growth 0.01 1.29 0.01 0.23
ln(Total assets) -12.71 1.60 -19.23 2.30∗∗
Tenor 4.82 10.53∗∗∗ 4.54 9.18∗∗∗ 5.54 12.30∗∗∗ 5.27 8.49∗∗∗
Secured -2.26 0.26 -10.25 1.41 -0.63 0.10 -7.59 1.39
No. of Lenders 0.08 0.21 0.19 0.41 0.27 0.62 0.40 0.71
Tranche type YES YES YES YES
Lender countries YES YES YES YES
Borrower FE YES YES YES YES
Industry-year FE YES YES
Observations 543 580 438 412
R-squared 0.67 0.50 0.49 0.29
Notes: This table reports results with borrower fixed effects and two-digit NAICS industry-by-year fixed effects for loan spread. The dependent variable isSpreadover TIBOR (in basis points). The estimated equations (1) and (2) also include a set of annual dummy variables. We drop singleton groups from the regression sample. t-ratios are the absolute values oft-statistics computed using robust standard errors. R-squared reports the withinR-squared. *, **, *** denote significance at the 10%, 5%, and 1% levels, respectively. Estimates in this table use data from January 1, 2000, to December 31, 2016.
Table 3: Determinants of loan maturity
(Dependent Variable: Loan maturity in months)
(1) (2) (3)
Variable Coeff. t-ratio Coeff. t-ratio Coeff. t-ratio
AAA rating 17.21 1.67
∗14.38 1.22
AA rating 3.07 0.38 1.46 0.15
A rating 5.96 1.16 4.20 0.75
BBB rating 6.57 1.48 4.60 0.94
BB rating -4.36 1.16 -4.18 0.98
Volatility -0.04 2.09
∗∗-0.05 0.97
ROE 0.001 0.85 -0.002 0.33
Long term debt to equity -4.95 0.55 -7.59 0.41
Total debt to EBITDA 0.02 0.68 0.03 0.61
Sales growth 0.02 2.11
∗∗0.004 0.25
EBITDA growth -0.003 2.78
∗∗∗-0.004 1.78
∗ln(Total assets) 1.72 0.94 -1.33 0.28
No. of Lenders -0.46 2.57
∗∗∗-0.59 3.47
∗∗∗-0.58 2.78
∗∗∗Subordinated 327.92 3.63
∗∗∗420.33 4.17
∗∗∗472.23 5.05
∗∗∗Tranche type YES YES YES
Lender countries YES YES YES
Borrower FE YES YES YES
Industry-year FE YES YES YES
Observations 7,837 11,411 6,427
R-squared 0.20 0.23 0.23
Notes: This table reports results with borrower fixed effects and two-digit NAICS industry-by-year fixed effects for loan maturity. The dependent variable isM aturityin months. We drop singleton groups from the regression sample. t-ratios are the absolute values oft-statistics computed using robust standard errors. R-squared reports the withinR-squared. *, **, *** denote significance at the 10%, 5%, and 1% levels, respectively. Estimates in this table use data from January 1, 2000, to December 31, 2016.
Table 4: Credit supply conditions and loan spread (Dependent Variable:Loan spread)
(1) (2) (3) (4)
Variable Coeff. t-ratio Coeff. t-ratio Coeff. t-ratio Coeff. t-ratio Economic expansion -150.87 1.36 38.17 0.17 -137.48 1.58 66.03 0.18 Lending attitude -1.62 1.83∗ -2.79 2.14∗∗ -1.61 1.86∗ -3.24 1.72∗
AA rating -44.28 1.20 -86.93 3.09∗∗∗
A rating -86.46 6.40∗∗∗ -95.63 4.52∗∗∗
BBB rating -56.26 3.93∗∗∗ -66.16 2.90∗∗∗
BB rating -18.81 0.97 -31.76 1.35
Volatility 0.47 1.28 1.16 1.84∗
ROE 0.11 0.13 5.45 1.34
Long term debt to equity -17.50 0.29 -13.55 0.24
Total debt to EBITDA -0.07 0.50 0.38 1.44
Sales growth -0.07 0.85 0.04 0.20
EBITDA growth 0.01 1.46 -0.35 1.07
ln(Total assets) -13.47 1.73∗ -54.27 2.49∗∗
Tenor 4.85 10.69∗∗∗ 4.57 9.63∗∗∗ 4.70 9.99∗∗∗ 5.00 7.34∗∗∗
Secured -1.81 0.21 -8.88 1.16 -2.74 0.54 -8.85 1.33
Borrower FE YES YES YES YES
Industry-year FE YES YES
Bank-year FE YES YES
Observations 543 580 391 365
R-squared 0.68 0.51 0.40 0.23
Notes: This table reports regression results obtained with macroeconomic control variables and bank-by-year fixed effects in addition to borrower fixed effects and two-digit NAICS industry-by-year fixed effects. The dependent variable is Spread over TIBOR in basis points. Economic expansion is the growth rate of the Coincident index (CI), which is an index of Business Conditions published monthly by the Cabinet Office, government of Japan. Lending attitudeis a statistical survey response about Lending Attitude of Financial Institutions in T ankan (Short-term Economic Survey of Enterprises in Japan) conducted quarterly by the BOJ. Responses are aggregated into Diffusion Index by subtracting the percentage share of enterprises responding (3) Severe from that of (1) Accommodative. Estimates use the value for small enterprises. The estimated equations also include lender country dummies and tranche-type dummies.t-ratios are the absolute values oft-statistics computed using robust standard errors. R-squared reports the withinR-squared. *, **,
*** denote significance at the 10%, 5%, and 1% levels, respectively. Estimates in this table use data from January 1, 2000 to December 31, 2016.
39
Table 5: Credit supply conditions and loan maturity
(Dependent Variable: Loan maturity in months)
(1) (2) (3)
Variable Coeff. t-ratio Coeff. t-ratio Coeff. t-ratio Economic expansion 61.71 2.20
∗∗31.64 1.71
∗50.61 1.73
∗Lending attitude -0.42 1.45 -0.24 1.27 -0.55 1.80
∗Economic expansion×
0.11 2.10
∗∗0.47 0.55 0.20 0.55 Total Debt to EBITDA
AAA rating 12.17 1.07 10.91 0.93
AA rating -0.11 0.01 1.03 0.11
A rating 4.45 0.77 3.47 0.57
BBB rating 3.92 0.78 3.23 0.60
BB rating -3.66 0.82 -3.61 0.77
Volatility -0.05 2.41
∗∗-0.07 1.35
ROE 0.001 1.17 -0.001 0.16
Long term debt to equity -2.85 0.29 -9.96 0.49
Total debt to EBITDA 0.01 0.51 -0.001 0.29
Sales growth 0.02 2.10
∗∗0.02 1.14
EBITDA growth -0.003 2.81
∗∗∗0.0002 0.87
No. of Lenders -0.52 2.66
∗∗∗-0.60 3.60
∗∗∗-0.55 2.73
∗∗∗Subordinated 439.51 4.06
∗∗∗381.13 3.41
∗∗∗439.70 4.05
∗∗∗Borrower FE YES YES YES
Industry-year FE YES YES YES
Bank-year FE YES YES YES
Observations 6,615 11,198 6,305
R-squared 0.20 0.21 0.20
Notes: This table reports regression results obtained with macroeconomic control variables and bank-by-year fixed effects in addition to borrower fixed effects and two-digit NAICS industry-by-year fixed effects. The definition of macroeconomic control variables are in Table4. The dependent variable isM aturityin months. The estimated equations also include lender country dummies and tranche-type dummies.t-ratios are the absolute values oft-statistics computed using robust standard errors. R-squared reports the withinR-squared. *, **, *** denote significance at the 10%, 5%, and 1% levels, respectively. Estimates in this table use data from January 1, 2000 to December 31, 2016.
40
Table 6: Monetary policy shocks and loan spread (Dependent Variable:Loan spread)
All observations With access to bonds Without access to bonds
(1) (2) (3) (4) (5)
Target interest rate shock 4.09 1.01 -1.78 0.69 6.66
(1.95)∗ (0.68) (0.76) (0.44) (0.43)
Steepening curve shock -6.72 -0.44 -0.78 -1.24 -7.75
(2.52)∗∗ (0.38) (0.39) (1.09) (0.87) Expansionary twist shock 10.21 -0.66 -0.49 -0.91 34.14 (2.38)∗∗ (0.37) (0.16) (0.48) (1.13) Economic expansion 16.93 -137.08 -67.75 -137.87 922.73
(0.05) (1.43) (0.44) (1.55) (0.73)
Lending attitude -2.36 -1.43 -1.53 -1.43 -8.26
(1.24) (1.66)∗ (0.89) (1.67)∗ (0.93)
Long term debt to equity -0.47 -0.55
(1.48) (2.41)∗∗
Total debt to EBITDA 0.89 0.32
(3.01)∗∗∗ (1.67)∗
Tenor 4.70 4.71 4.63 4.38 8.98
(6.82)∗∗∗ (9.63)∗∗∗ (7.70)∗∗∗ (9.37)∗∗∗ (5.39)∗∗∗
Continued
41
Table 6: Continued
All observations With access to bonds Without access to bonds
(1) (2) (3) (4) (5)
Secured -9.28 -8.87 -13.37 -5.33 1.90
(0.82) (1.62) (2.51)∗∗ (0.99) (0.11)
Lender countries YES YES YES YES YES
Borrower FE YES YES YES YES YES
Industry-year FE YES YES YES YES YES
Bank-year FE YES YES YES YES YES
Observations 327 353 231 321 220
R-squared 0.29 0.44 0.51 0.44 0.29
Notes: This table reports regression results obtained with the monetary policy shock variables and explanatory variables in Table 4. Monetary policy shock variables are three principal components along with the three largest eigenvalues from the changes in 11 financial asset variables, as shown in Figure3. The dependent variable isSpreadover TIBOR in basis points. The estimated equations also include a set of tranche-type dummies. Macroeconomic control variables are the same set as in Table4. A set of explanatory variables in (5) includes neither rating variables nor financial indicator variables to maximize the sample size. t-ratios reported in parentheses are the absolute values of t-statistics computed using robust standard. R-squared reports the within R-squared. *, **, ***
denote significance at the 10%, 5%, and 1% levels, respectively. Estimates in this table use data from January 1, 2000, to December 31, 2016.
42
Table 7: Monetary policy shocks and loan maturity (Dependent Variable:Loan maturity in months)
All observations With access to bonds Without access to bonds
(1) (2) (3) (4) (5) (6)
Target interest rate shock -1.15 -0.70 -0.90 -0.90 -3.13 0.04 (2.52)∗∗ (2.32)∗∗ (1.88)∗ (1.80)∗ (2.46)∗∗ (0.34)
Steepening curve shock 0.82 0.49 0.74 0.78 4.47 -0.20
(1.91)∗ (1.72)∗ (1.65)∗ (1.67)∗ (1.82)∗ (1.24) Expansionary twist shock -1.23 -0.69 -1.23 -1.25 -3.35 0.20
(2.90)∗∗∗ (2.46)∗∗ (2.72)∗∗∗ (2.58)∗∗∗ (1.33) (0.98)
Economic expansion -6.16 -8.46 15.98 3.94 -201.08 -0.81
(0.17) (0.35) (0.44) (0.10) (1.39) (0.04)
Lending attitude 0.23 0.20 0.05 0.01 -2.08 -0.16
(0.66) (0.79) (0.15) (0.03) (0.63) (0.77)
Economic expansion× 0.12 0.49 0.12 0.33 3.65 1.28
Total Debt to EBITDA (2.23)∗∗ (0.55) (2.25)∗∗ (0.36) (0.46) (0.86)
Long term debt to equity -2.47 -11.58 1.68
(0.26) (0.52) (0.27)
Total debt to EBITDA 0.01 0.03 -0.02
(0.41) (0.55) (0.74)
Sales growth 0.02 0.02 0.01
(2.00)∗∗ (0.99) (0.86)
Continued
Table 7: Continued
All observations With access to bonds Without access to bonds
(1) (2) (3) (4) (5) (6)
EBITDA growth -0.003 -0.01 -0.002
(2.72)∗∗∗ (1.56) (1.84)∗
ln(Total assets) 2.23 -4.22 2.23
(1.09) (0.66) (1.39)
No. of lenders -0.52 -0.60 -0.59 -0.65 -0.54 -0.34
(2.64)∗∗∗ (3.58)∗∗∗ (2.61)∗∗∗ (2.79)∗∗∗ (1.12) (2.39)∗∗
Subordinated 440.34 381.58 411.80 411.12 na 237.25
(4.08)∗∗∗ (3.41)∗∗∗ (3.26)∗∗∗ (3.24)∗∗∗ (1.52)
Lender countries YES YES YES YES YES YES
Borrower FE YES YES YES YES YES YES
Industry-year FE YES YES YES YES YES YES
Bank-year FE YES YES YES YES YES YES
Observations 6,615 11,198 6,135 5,807 394 5,205
R-squared 0.21 0.21 0.20 0.20 0.19 0.37
This table reports regression results obtained with the monetary policy shock variables and explanatory variables in Table5. Monetary policy shock variables are three principal components along with the three largest eigenvalues from the changes in 11 financial asset variables, as shown in Figure3. The dependent variable isM aturityin months. The estimated equations also include
This table reports regression results obtained with the monetary policy shock variables and explanatory variables in Table5. Monetary policy shock variables are three principal components along with the three largest eigenvalues from the changes in 11 financial asset variables, as shown in Figure3. The dependent variable isM aturityin months. The estimated equations also include