Banks’ maturity mismatch, financial stability, and macroeconomic dynamics

Figures & data

Figure 1. Maturity structure of Chinese Commercial Banks loan.

Source: Original data is from the People’s Bank of China.

Figure 2. 2007Q1–2019Q4 The changes of LMI in the banking system of China. Note: The original data is from the People’s Bank of China.

Table 1. Benchmark calibration.

Parameter	Implication	Value	Source
β	Household discount factor	0.99	Chen et al. (2018)
b	Habit formation parameter	0.65	Christiano et al. (2005)
${\varphi }_c$	Household risk aversion	1	Yang et al. (2019)
${\varphi }_1$	Inverse Frisch Elasticity	1	Yang et al. (2019)
θ	Capital share	0.33	Andreasen et al. (2013)
δ	Depreciation rate	0.025	Chen et al. (2018)
${\alpha }_p$	Calvo price adjustment	0.75	Gertler and Kiyotaki (2010)
η	Substitution elasticity	6	Chen et al. (2018)
k	Capital adjustment parameter	2.5	Christiano et al. (2005)
Λ	Fraction of transferred asset	0.20	Yang and Yi (2020)
σ	Bank survival rate	0.972	Yang and Yi (2020)
ε	Start-up fund for new banks	0.002	Yang and Yi (2020)

Source: the parameter estimation results are obtained using Dynare 4.5.7.

Table 2. Prior and posterior distributions of the parameters.

Parameters	Prior			Posterior
	Distribution	Mean	SD	Mean	90% C.I.
${\phi }_{\pi }$	Normal	2.37	0.5	2.8186	2.0699	3.5083
${\phi }_y$	Normal	0.04	0.01	0.0217	0.0098	0.0329
${\rho }_r$	Beta	0.84	0.1	0.7159	0.6312	0.8057
${\rho }_a$	Beta	0.8	0.2	0.9235	0.8004	0.9891
${\rho }_o$	Beta	0.37	0.1	0.3526	0.1981	0.5105
${\sigma }_a$	Inverse gamma	0.1	Inf	0.0298	0.0244	0.0346
${\sigma }_r$	Inverse gamma	0.1	Inf	0.0249	0.0178	0.0318
${\sigma }_o$	Inverse gamma	0.1	Inf	0.6327	0.4581	0.8076

Source: the parameter estimation results are obtained using Dynare 4.5.7.

Table 3. Marginal data density.

1	2	3	4	5	6	7	8	9	10
287.122932	287.267206	287.482702	287.751874	288.067836	288.425303	288.81948	289.245639	289.699231	290.175568
11	12	13	14	15	16	17	18	19	20
290.670561	291.18042	291.700726	292.228631	292.760434	293.293536	293.824948	294.352024	294.872138	295.383377

Note: The log data densities are obtained from the Bayesian estimation of models with different values of D.

Figure 3. Impulse responses to technological shock.

Source: the simulation results are calculated by the authors with Dynare 4.5.7.

Table 4. Relative volatility of variables in Baseline model.

		Real variables			Financial variables
		Output	Consumption	Investment	Bank revenue	Net worth	Credit	Leverage	Spread
Technological shock	D = 12	0.9952	0.996	0.9896	1.002	1.0003	1.002	1.2664	7.3302
	D = 20	0.9919	0.9932	0.9833	1.0081	1.0059	1.008	1.3335	14.9216
Monetary policy	D = 12	0.2428	0.4387	0.0518	0.5037	0.4701	0.5391	0.644	0.832
	D = 20	0.1185	0.5391	0.1337	0.518	0.5235	0.5603	0.7827	2.2645
Net worth shock	D = 12	0.4624	0.4603	0.4265	0.9978	1.0002	1.0034	0.9793	8.4387
	D = 20	0.1279	0.1267	0.1106	1.0113	1.0134	1.0211	0.9774	18.036

Notes: 1. The results above are the relative rate of the model with maturity mismatch to the Basic Model without maturity mismatch, calculated in a second-order approximation.

2. Relative volatility higher than 1 indicates the variable has greater volatility compared to the basic model.

Source: the simulation results are calculated by the authors with Dynare 4.5.7.

Figure 4. Impulse responses to monetary policy.

Source: the simulation results are calculated by the authors with Dynare 4.5.7.

Figure 5. Impulse responses to bank net worth shock.

Source: the simulation results are calculated by the authors with Dynare 4.5.7.

Table 5. Relative volatility of variables under macroprudential policy.

		Real variables			Financial variables
		Output	Consumption	Investment	Capital price	Net worth	Credit	Leverage	Spread
Technology shock	D = 1	0.4558	0.5586	0.0906	0.09	0.079	0.0829	0.1644	81.2082
	D = 12	0.455	0.5582	0.0881	0.0893	0.078	0.0823	0.1311	11.42
	D = 20	0.4537	0.5573	0.0858	0.0888	0.0774	0.0818	0.1279	5.8191
Monetary policy	D = 1	0.4668	0.4811	0.3704	0.3955	0.3611	0.3837	0.5839	1.0597
	D = 12	0.7156	0.9671	0.0494	0.5122	0.3226	0.5148	0.5501	0.8954
	D = 20	0.9656	1.0876	1.7466	0.563	0.3308	0.5616	0.5142	0.783
Bank net worth shock	D = 1	0.0938	0.0961	0.1547	0.2883	0.6308	0.2902	0.891	0.3282
	D = 12	0.0077	0.0078	0.013	0.3084	0.6295	0.3105	0.8818	0.5169
	D = 20	0.2456	0.253	0.4106	0.3259	0.6246	0.3279	0.8658	0.5308

Notes: 1. The results above are the relative rate of the model with macroprudential policy to the Baseline Model without policy, calculated in a second-order approximation.

2. A relative volatility higher than 1 indicates the variable has greater volatility compared to the basic model.

Source: the simulation results are calculated by the authors with Dynare 4.5.7.

Figure 1. Loan Proportions in Total Bank Loans of 9 Major Industries

Note: The original data is collected from Wind Financial Database.

Table 1. Investment Horizon and Loan Proportions of Industries

Industries	Average Investment Horizon	Relative Investment Horizon	Industrial Loans to Aggregate Domestic Loans
Aggregate	1.57
Mining	1.48	0.9427	2.4757%
Manufacturing	1.53	0.9745	19.9100%
Energy	1.75	1.1146	10.7886%
Construction	1.55	0.9873	0.4014%
Transportation	1.65	1.0510	19.2886%
Retail	1.41	0.8981	1.1143%
Real Estate	1.66	1.0573	32.6643%
Commercial Service	1.72	1.0955	0.9329%
Public Facility	1.72	1.0955	7.9514%

Note: The original data is collected from National Bureau of Statistics of China.

Note: The original data is collected from Wind Financial Database.

Figure 1. Macroprudential Policy experiment with Technological Shock.

Notes: the simulation results are calculated by the authors using Dynare 4.5.7.

Figure 2. Macroprudential Policy experiment with Monetary Policy Shock.

Notes: the simulation results are calculated by the authors using Dynare 4.5.7.

Figure 3. Macroprudential Policy experiment with Bank Net Worth Shock.

Notes: the simulation results are calculated by the authors using Dynare 4.5.7.

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