1、 Introduction
In 2007, MC Culley proposed a parallel banking system and called it "shadow banking" to refer to all non bank investment channels, tools and organizations with leverage. It is a financial organization that transforms the traditional bank credit relationship into the credit relationship in asset securitization. After the 2008 financial crisis, the central bank responded to this environmental change through a series of policy reforms. After 2011, the central bank provided liquidity for the banking system through mortgage supplementary loans and targeted RRR reduction, but the measures to ease the tight credit supply failed repeatedly. During the three years from 2012 to 2015, the central bank adopted interest rate reduction policies for many times, but the interest rate reduction did not reduce the market interest rate, but increased it. After that, the central bank also publicly acknowledged that the transmission of monetary policy was not smooth, and the role of credit channel and interest rate channel did not play its due role.
The important reason for the poor transmission of monetary policy and even the failure of regulation may be the change in the transmission efficiency of monetary policy caused by China's financial liberalization (especially the rapid development of financial innovation represented by shadow banking). While the scale of shadow banking is expanding, the influence of shadow banking is also spreading. Therefore, it is necessary to study the impact of the development of shadow banking on the transmission of monetary policy.
2、 Literature review
Through literature review, the research mainly focuses on three aspects. Firstly, the definition of shadow banking by scholars from various countries includes two aspects. One is the credit function standard (Qiu Xiang, 2014; Zeng Gang, 2013). The most representative is that the Financial Stability Board (FSB) will define shadow banking as a credit intermediary system of institutions and businesses outside the banking system that cause systemic risks due to three transformations and can seek interest margin by avoiding supervision. The second is the standard of financing methods (Ba Shusong, 2013; Huang Yiping, 2012). The most representative is the definition of the people's Bank of China, which includes financial institutions' businesses such as off balance sheet wealth management and trust, and financing businesses of non-financial institutions such as small loans, pawnbrokers and private loans. Since the definition of FSB is highly recognized, this paper defines shadow banking as a financial intermediary that relies on the credit provided by banks and engages in banking related businesses other than traditional bank balance sheet loans and bond investments but is not supervised by financial regulators.
Secondly, the existing literature also accounts for the financing scale of shadow banks from two aspects: on the one hand, it accounts from a business perspective, that is, the financing scale of each sub business of shadow banks is added to get the scale of shadow banks (ehlersetal.2018; Wang Zhen and Zeng Hui, 2014). On the other hand, from the perspective of institutions, the sum of the shadow of banks and other non bank shadows is taken as the scale of shadow banks (sunguofeng and jiajunyi, 2015). The former is prone to repeated statistics or missing data, while the latter can only account for 60% of the shadow banking scale announced by Moody's. Therefore, this paper refers to the "liability accounting method" of liwenzhe (2019) to calculate the total assets of shadow banks from the liability side of the balance sheet. This not only avoids double counting of data, but also is closer to the shadow bank financing scale value announced by Moody's.
Finally, scholars mainly study the impact of shadow banking on monetary policy from two aspects: one is that some scholars study the impact of shadow banking financing scale on specific channels of monetary policy (Wang Zhen and Zeng Hui, 2014; Qiu Xiang and Zhou Qianglong, 2014); the other is that some scholars study the impact of shadow banking on monetary policy regulation (zhaoshengmin and heyujie, 2018; Li Bo and Wu Ge, 2012). However, it is too narrow to study the impact of shadow banking on monetary policy from the perspective of credit. Therefore, starting from the transmission mechanism of monetary policy, this paper uses partial derivative separation technology to analyze the impact of shadow banking on specific channels.
3、 Empirical research
According to Gao Tiemei (2005), this paper uses the p-order structural vector autoregressive model SVAR (P) with K variables. The specific forms are as follows:
P-order structured vector autoregressive model SVAR (P) with K variables
Since the AB type of SVAR model can represent the current relationship between endogenous variables, and can visually analyze the impact of random disturbance on the system, this paper uses AB model to identify it. The A and B matrices taking the three variable model as an example are as follows:
The AB model is used to identify it, and the A and B matrix form of the three variable model is taken as an example
In this paper, the value (sb) obtained by calculating the year-on-year growth rate with reference to the shadow bank scale value calculated by the shadow bank financing scale accounting method (liwenzhe, 2019) is used as the proxy variable of the shadow bank. The year-on-year growth rate of the generalized money supply (M2) is used as the proxy variable of monetary policy instrument variables; Using the year-on-year growth rate (loan) of RMB loans of financial institutions as the proxy variable of the credit transmission channel of monetary policy; The weighted monthly data (CNI) of interbank offered rate is used as the proxy variable of interest rate transmission channel; Referring to (Zhan Minghua, 2018), the year-on-year growth rate (HI) of Shanghai Composite Index (SSE) and national housing prosperity index is used as the proxy variable of asset price channel. Use the two variables of gross domestic product (GDP) and consumer price index (CPI). All variable data in this paper are from wind database and people's Bank of China. Table 3-1 is the statistical description of variables. Because it is necessary to ensure that all the data are stable when building the SVAR model. When the data are unstable, the results are prone to pseudo regression. The ADF unit root test is to test the stability of the data. Table 3-2 shows the stability test results of the variables. Therefore, it can be seen that the series exists in the first-order stability.
Table 3-1 statistical description of variables
Statistical description of variables
Table 3-2 unit root test results of variables
Unit root test results of variables
Since the model selected in this paper is structured vector autoregression (SVAR), it will involve the problem of variable ordering. Since the implementation of monetary policy by the central bank will affect the current monetary policy instrument variables, but will not affect the current output and price level, the money supply is placed in front of the output and price. According to kimandroubini (2000), the output and price assumptions will not affect other variables in the current period, so the output and price are placed at the end of the model. Secondly, according to the incomplete market and price stickiness (zhanminghua, 2018), the impact of prices on monetary policy has a certain time lag, so prices are put at the back here. Since monetary policy affects the final goal through the channel of instrumental variables, the channel variables are placed after the instrumental variables and before the final goal. Finally, as a regulatory evasion institution, shadow banking will be affected by all other variables, so it is placed at the end of the model.
（1） The impact of shadow banking on credit transmission channels
Establish SVAR system S21 without shadow banking, the variable combination of the model is (DM2, dcni, dgdp, dcpi) and SVAR system S22 with shadow banking, the variable combination of the model is (DM2, dcni, dgdp, drcpi, DSB). Due to the limited space, all models passed the AR unit root test and other relevant tests. Here, only the impulse response results are briefly analyzed. From the impulse response results of M2 to GDP and CPI in S11 and S12 models, it can be seen that given a positive impact on m2, a positive change in GDP will be caused, and this change is a short-term change. In order to better observe the impact of shadow banking on credit channels, this paper refers to (zhanminghua, 2018) and selects the impulse partial derivative separation method, that is, the impulse response values of the two models are extracted and then obtained by corresponding difference.
Difference between impulse response results of GDP
Figure 3-1 difference between impulse response results of GDP
Difference between impulse response results of CPI
Figure 3-2 difference between impulse response results of CPI
As shown in figures 3-1 and 3-2, the difference between impulse response results of model S11 and S12 is shown. Therefore, under the credit transmission channel, shadow banking weakens the impact of money supply on output and prices. This is because when the central bank implements loose monetary policy, the impact of increasing money supply on output will be weakened with the role of shadow banking. From the perspective of banks, off balance sheet businesses of banks provide loans to enterprises, which makes the central bank unable to achieve the expected purpose of regulating available loans through monetary policy. From the perspective of enterprises, as banks reduce the scale of loanable funds, it is difficult for enterprises to obtain financing from banks. At this time, the emergence of shadow banking solves the problem of financing for enterprises.
（2） Influence of shadow banking on interest rate transmission channel
Establish SVAR system without shadow Bank (hereinafter referred to as S21) and SVAR system with shadow Bank (hereinafter referred to as S22), in which the variable combination of S21 model is (DM2, dcni, dgdp, dcpi) and S22 is (DM2, dcni, dgdp, drcpi, DSB). In the impulse response of M2 to GDP and CPI in the two models, given a positive impact of M2, it will cause a positive change in GDP and CPI. Figure 3-3 shows the difference between impulse response results of models S21 and S22. Under the interest rate transmission mechanism, shadow banking will weaken the impact of money supply on output, and its weakening degree will decline briefly in the second period. However, the impact on CPI will be strengthened in the first three phases, and then weakened, but the degree of weakening is relatively weak. This is because banks are unable to provide loans to enterprises, and enterprises will turn to shadow banking channels that can obtain loans. However, shadow banking provides loans to enterprises, which increases the liquidity of funds, so that the market interest rate will not rise as the central bank hopes. However, when the funds of the shadow bank can not meet the financing needs of the whole industry, it will change this lack of liquidity by raising the loan interest rate, thus reducing the efficiency of the interest rate transmission channel. The reason why shadow banks have strengthened the impact of money supply on price fluctuations in the early stage may be that under the loose monetary policy, the participation of shadow banks has increased the actual amount of loans available, so consumers' purchasing power will increase, thus exacerbating price fluctuations.
Difference between impulse response results of CPI and GDP
Figure 3-3 difference between impulse response results of CPI and GDP
（3） Influence of shadow banking on asset price transmission channel
Establish an SVAR system without shadow banking (S31) and an SVAR system with shadow banking (S32), in which the variable combinations of S31 model are (m2, SSE, hi, GDP, CPI) and S32 are (m2, SSE, hi, GDP, RCPI, sb). From the impulse response results of M2 to GDP and CPI in S31 and S32 models, we can see that a given M2 positive impact, whether shadow banking is excluded or not, will cause positive changes in GDP and CPI.
Figure 3-4 shows the difference between impulse responses of models S31 and S32. It can be seen that shadow banking weakens the impact of asset price channels on output and prices, because the development of shadow banking brings a large number of innovative financial products to the financial market. These products are more sensitive to corporate balance sheets and residents' wealth and interest rates. At the same time, most of these products have good liquidity and yield, Therefore, people will invest their funds to buy these financial products instead of depositing them in the bank to obtain higher returns, which will lead to an increase in investment and an increase in output.
On the other hand, when the central bank adopts the expansionary monetary policy, increasing the money supply will reduce the interest rate, which will encourage residents to invest money in the capital market to buy shadow banking products to obtain higher returns, thus weakening the effect of asset price channels.
Difference between impulse response results of CPI and GDP
Figure 3-4 difference between impulse response results of CPI and GDP
conclusion
This paper explains the impact of shadow banking through the method of impulse partial derivative separation, and draws the following conclusions: under the credit transmission mechanism, shadow banking will weaken the impact of money supply on output and CPI; Under the interest rate transmission mechanism, the impact of money supply on output will be weakened, and the degree of weakening will decline briefly in the second period; In the first three phases, the impact on CPI was strengthened and then weakened, but the degree of weakening was relatively weak; Under the asset price transmission mechanism, the impact of money supply on output and prices is weakened, but the degree of weakening on prices is lower than that of output. The financial regulatory authorities should treat shadow banks correctly. They should not only recognize the benefits of their financial innovation products to China's economic development, but also realize the potential risks of their secrecy to China's economic environment. They should reasonably guide the development of shadow banks, encourage them to carry out financial business and product innovation, but also establish and improve the risk hedging mechanism; Strengthen the supervision and risk prevention capability of financial institutions; Unblock the transmission channels of monetary policy, such as enhancing the lending capacity of banks, accelerating the completion of interest rate marketization reform, and vigorously developing multi-level capital markets.