# Risk Spillover: A New Perspective on the Study of Financing Difficulties for SMEs—Evidence from China

Authors: Jinghong Xu; Dong Lian; Daguang Yang
Journal: Discrete Dynamics in Nature and Society
 (2021)

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## Abstract

Existing studies on the financing difficulties of middle- and small-sized enterprises (SMEs) have neglected the quantitative analysis of SMEs’ risk spillovers to banks. Therefore, taking China as an example, we have analyzed the financing difficulties of SMEs from the perspective of risk spillover. The GARCH time-varying copula-CoVaR model based on the skewed-t distribution was used to measure the risk spillover effects of SMEs on banks. Furthermore, the heterogeneous impacts of risk spillovers on different scale banks were analyzed, including state-owned banks, joint-stock banks, and city commercial banks. The study found that SMEs always have obvious risk spillover effects on banks; it is particularly difficult for SMEs to obtain loans from the largest state-owned banks because in extreme cases, SMEs have the highest risk spillover effects on state-owned banks. The changes in risk spillover effects are attributed to two reasons. One is that the degree of association between SMEs and various banks is different, and the other is that there are varying degrees of risk spillover effects among various banks.


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## Body

## 1. Introduction

SMEs play a positive role in alleviating employment pressure, promoting economic growth, and maintaining social stability. The economy relies to a large extent on small and medium enterprises, mainly due to the small size of the market, limited resources, consumer patterns, and their evolution in the market, but also due to the prevailing business culture.Input-oriented technologies could integrate the internal systems of financial sectors that facilitate the processes of collecting, processing, storing, and circulating information and carrying out various tasks. On the other hand, technologies that are widely applied in the financial sector are more closely related to the products and services offered and mainly concern transactions with customers. It should be noted, however, that most of the aforementioned technologies, despite their primary orientation, are mixed in nature, affecting both financial inflows and outflows [1].Financial sectors, in the face of growing challenges and pressures in an ever-changing environment, are redefining their goals and strategies by adopting new standards of conduct and operation. Mergers and acquisitions, expansion into new markets with higher risk, large-scale investments in information technology, and changes in products and delivery channels are the main ways in which financial sectors react. The survival and development of SMEs are inseparable from financial support, but the problem of financing difficulties for SMEs has not been resolved for a long time. In addition to the financial market [2], credit rationing system [3, 4], financial products and services [5], financial and fiscal policies [6], and other factors, the risk spillover of SMEs to banks is also an important reason for the financing difficulty of SMEs [7, 8].During the economic downturn, SMEs have difficulties in operating; for example, the rate of return has fallen, the speed of capital turnover has slowed, and the default by SMEs on bank loans has increased [9]. At this time, the risk of SMEs on lending banks has increased. Under the new normal, China’s economic growth has gradually slowed down and the bank loan interest rates are at a high level. The divergence between the two factors has increased the financing cost burden of SMEs and aggravated the risk spillover of SMEs to banks. In order to make up for the losses that SMEs may bring to banks, banks increase the interest rate of SME loans, which means that SMEs have to bear higher costs in order to obtain bank loans, which undoubtedly increases the financing difficulty for SMEs.What is a scale of the risk spillover effect of SMEs on commercial banks? Which method can we use to quantify that effect? How to alleviate the financing difficulties of SMEs under the premise of controlling risks? These problems need to be solved urgently. Therefore, this paper attempts to describe the risk spillover effects and changes of SMEs to commercial banks and explain the reasons for the financing difficulty for SMEs from the perspective of risk spillover, which is of great significance for exploring the problem of financing difficulties for SMEs.


## 2. Literature Review

There are a lot of research results on the causes of financing difficulties for SMEs. In summary, there are three main points of view. The first view is that the cause of financing difficulties for SMEs lies in the enterprises themselves. Gompers et al. [10] believe that SMEs have their own shortcomings, such as short-term corporate behavior, low diversification, weak profitability, and difficulty in providing necessary mortgage guarantees. This leads to partial market failure in the financial market, resulting in financing gaps. The second view is that the financing difficulties of SMEs are caused by external factors. Under the psychology of loss aversion, banks are unwilling to provide loans to SMEs, the essence of which is the banks’ overreaction to risks [11]. There is a negative relationship between the financing difficulty of SMEs and bank scale. With the expansion of the scale of banks and the increase in concentration, it will be more and more difficult for SMEs to obtain loans from banks [12]. In developing countries, capital markets started late, the marketization degree is low, and the listing and issuance of bonds by SMEs are strictly regulated, which means the structural defects of the basic financial market have led to a single financing method for SMEs [13]. The third view is that the financing difficulty for SMEs is the result of a combination of internal and external factors. SMEs have weak profitability and low credit levels, and banks raise risk premiums. The combined effect of the two factors makes it difficult and expensive for SMEs to receive financing [14]. The scales of SMEs are different and financial institutions are unwilling to take loan risks, which are two important reasons for SMEs’ financing difficulties [15]. SMEs are highly dependent on external financing, especially banks, and a good bank-enterprise relationship can help alleviate SMEs’ financing difficulties [16]. At the same time, if an enterprise chooses a new bank for every loan, it will reduce the enterprise’s reliance on a single bank and intensify bank competition [17], but SMEs have a typical asymmetric reliance on banks, and the logic behind the asymmetric reliance is power [18, 19].The measurement of risk spillover effects mostly uses market data to establish financial risk models for analysis, including CCA [20], EVT [21], MES [22], and CoVaR [23]. At present, the main business between SMEs and banks in various countries is the lending business. The business derivative chain of lending business is simple, so the applicability of CCA based on option pricing is not strong. However, the probability of bank bankruptcy caused by credit default of SMEs is extremely low, so the EVT based on the probability of default and bankruptcy does not have a high application value. Similarly, MES reflects the capital that enterprises need to replenish under the crisis but ignores the influence of factors such as the size of financial institutions, leverage ratios, and capital adequacy ratios, and the results may be biased [24]. CoVaR measures the risk spillover effect corresponding to a single financial institution; that is, when financial institution j experiences extreme losses, other financial institutions face the loss value under a certain confidence level. This method is closer to the reality of SMEs and banking businesses, and the data are easy to collect. Adrian and Brunnermeier [25] expanded the definition of CoVaR and relaxed the conditions to be the case where the loss of financial institution j is less than the extreme loss. ΔCoVaR [26] is considered to not consider the normal risk level of financial institution j and simply measures the risk spillover effect of financial institution j to financial institution i when financial institution j is in an extreme situation. Heinen and Valdesogo used ΔCoVaR to test common market factors that trigger systemic financial risks [27].To sum up, on the one hand, the problem of financing difficulties for SMEs has been around for a long time, and the academic community has also obtained many achievements on the reasons for such financing difficulties. However, there is no study analyzing from the perspective of risk spillover as far as we know. On the other hand, the CoVaR model can fully measure the risk spillover of SMEs to banks, but it has not been applied to the research on financing difficulties of SMEs. Therefore, this paper explains the reasons for the financing difficulties for SMEs from the perspective of risk spillover. Consider that, in the selection of the CoVaR calculation method, the copula function can make up for the defect that the traditional quantile regression cannot measure the complex risk spillover. Therefore, according to the data characteristics of SMEs and banks, the GARCH time-varying copula-CoVaR under the skewed-t distribution is adopted for the research. The model measures the risk spillover effects of SMEs on banks and further explores the heterogeneous effects of SMEs on the risk spillovers of different banks.


## 3. Model Design

Compared with the Pearson correlation coefficient method and Granger causality test, copula function essentially decomposes the joint cumulative distribution, making the construction of edge distribution more flexible. Also, the consistency and correlation measure of copula function remain unchanged when strict monotone increment transformation is performed on variables. This is a significant advantage of the copula function over the usual linear correlation. The commonly used linear correlation measure does not change when the variable changes linearly. However, the conditions requiring the variable to change linearly are too strict in real life. In this context, the copula function is chosen to describe the interdependence structure between variables and then the CoVaR model is used to measure the risk spillover effects of SMEs on banks.


### 3.1. Fitting of Marginal Distribution: GARCH Model

In addition to the phenomenon of volatility agglomeration, financial time series also have asymmetry and leptokurtosis (sharp peak and heavy tail). In practical applications, GARCH(1, 1) can accurately simulate most financial time series. In order to better fit the actual data, this study uses the GARCH(1, 1)-skewed-t model to fit the marginal distribution.(1)rit=μi+εit,εit=ηitσit,ηit∼Skewed−tηit|νi,λi,σit2=ωi+αiεit−12+βiσit−12i=1,2,…,where ωi>0,αi≥0,βi≥0, and αi+βi<1. ηit is a skewed-t distribution subject to the degree of freedom parameter νi and the skewness parameter λi, and the density function of the skewed-t distribution is as follows:(2)Skewed−tηit|νi,λi=bc1+1νi−2bηit+a1−λi2−νi+1/2,ηit<−abbc1+1νi−2bηit+a1+λi2−νi+1/2,ηit≥−ab,where a = 4 λicνi−2/νi−1, b = 1 + 3 λi2−a2, c = Γνi+1/2/πνi−2Γνi/2, 3 < νi < ∞, and −1 < λi < 1.


### 3.2. The Selection of Dependent Structure as Time-Varying Copula Function

The Dynamic Conditional Correlation (DCC)-copula proposed by Girardi and Tolga Ergün [24] brings the DCC model into the time-varying copula process. The specific process is as follows.The DCC-copula model assumes that the conditional correlation coefficient matrixRt=1ρtρt1 obeys the dynamic DCC (1, 1) process:(3)Qt=1−α−βQ¯+αηt−1ηt−1'+βQt−1,Rt=Q˜t−1QtQ˜t−1,where ηt−1=ηi,t−1,ηj,t−1, in which ηi,t−1 is the subsequence transformed by probability integral on the residual error obtained by performing marginal distribution fitting on the return sequence and obeys the uniform distribution of (0, 1); Q¯ is the sample covariance matrix of ηt; Qt is a 2 ∗ 2 order matrix with the main diagonal element being the square root of Qt and the subdiagonal element being 0; and α, β ∈ (0, 1), α + β < 1. DCC-copula can be used to describe the dependency of high-level variables.


### 3.3. Risk Spillover Measurement: CoVaR Model

According to the concept of CoVaR proposed by Adrian and Brunnermeier,CoVaRqi|j means that when the significance level is q and institution j is at a specific risk level, the risk level of institution i is measured by VaR value. The mathematical formula is(4)PXi≤CoVaRi|j|Xj≤VaRj=q.Through further calculations, we can calculate the risk spillover value of institutionj to institution i by(5)ΔCoVaRqi|j=CoVaRqi|j−CoVaR0.5i|j,where ΔCoVaRqi|j represents the risk spillover faced by institution i that when institution j is in an extreme risk situation, after the risk level faced by institution i subtracts the risk spillover level of institution i when institution j is at the mean value.


### 3.4. Calculation of ΔCoVaR

According to Sklar theorem, the density function of copula function C is denoted asc and then the joint density function of random variables XiandXj is expressed as(6)fijXi,Xj=∂2FijXi,Xj∂Xi∂Xj=cFixi,Fjxj∗fixi∗fjxj,where fixi and fjxj are the edge density functions of Xi and Xj, respectively.From equation (6), we have fi|jXi|Xj=cFixi,Fjxj∗fixi, and then the conditional distribution function of the rate of return sequence Xi can be expressed as(7)Fi|jXi|Xj=∫−∞XicFixi,Fjxj∗fixidxi.According to the invariance of the monotonic increase and change of the copula function, equation (7) can be transformed into the calculation of residuals:(8)Ti|jηi|ηj=∫−∞ηicTiηi,Tjηj∗tiηidηi.When the significance levelq is given,(9)q=Ti|jηi|ηj=∫−∞ηicTiηi,Tjηj∗tiηidηi.In the residual calculation formula,ηj takes the q quantile value and solves the ηi value and CoVaRqi|j=μi+ηitσit, and then we can obtain ΔCoVaRqi|j.


## 3.1. Fitting of Marginal Distribution: GARCH Model

In addition to the phenomenon of volatility agglomeration, financial time series also have asymmetry and leptokurtosis (sharp peak and heavy tail). In practical applications, GARCH(1, 1) can accurately simulate most financial time series. In order to better fit the actual data, this study uses the GARCH(1, 1)-skewed-t model to fit the marginal distribution.(1)rit=μi+εit,εit=ηitσit,ηit∼Skewed−tηit|νi,λi,σit2=ωi+αiεit−12+βiσit−12i=1,2,…,where ωi>0,αi≥0,βi≥0, and αi+βi<1. ηit is a skewed-t distribution subject to the degree of freedom parameter νi and the skewness parameter λi, and the density function of the skewed-t distribution is as follows:(2)Skewed−tηit|νi,λi=bc1+1νi−2bηit+a1−λi2−νi+1/2,ηit<−abbc1+1νi−2bηit+a1+λi2−νi+1/2,ηit≥−ab,where a = 4 λicνi−2/νi−1, b = 1 + 3 λi2−a2, c = Γνi+1/2/πνi−2Γνi/2, 3 < νi < ∞, and −1 < λi < 1.


## 3.2. The Selection of Dependent Structure as Time-Varying Copula Function

The Dynamic Conditional Correlation (DCC)-copula proposed by Girardi and Tolga Ergün [24] brings the DCC model into the time-varying copula process. The specific process is as follows.The DCC-copula model assumes that the conditional correlation coefficient matrixRt=1ρtρt1 obeys the dynamic DCC (1, 1) process:(3)Qt=1−α−βQ¯+αηt−1ηt−1'+βQt−1,Rt=Q˜t−1QtQ˜t−1,where ηt−1=ηi,t−1,ηj,t−1, in which ηi,t−1 is the subsequence transformed by probability integral on the residual error obtained by performing marginal distribution fitting on the return sequence and obeys the uniform distribution of (0, 1); Q¯ is the sample covariance matrix of ηt; Qt is a 2 ∗ 2 order matrix with the main diagonal element being the square root of Qt and the subdiagonal element being 0; and α, β ∈ (0, 1), α + β < 1. DCC-copula can be used to describe the dependency of high-level variables.


## 3.3. Risk Spillover Measurement: CoVaR Model

According to the concept of CoVaR proposed by Adrian and Brunnermeier,CoVaRqi|j means that when the significance level is q and institution j is at a specific risk level, the risk level of institution i is measured by VaR value. The mathematical formula is(4)PXi≤CoVaRi|j|Xj≤VaRj=q.Through further calculations, we can calculate the risk spillover value of institutionj to institution i by(5)ΔCoVaRqi|j=CoVaRqi|j−CoVaR0.5i|j,where ΔCoVaRqi|j represents the risk spillover faced by institution i that when institution j is in an extreme risk situation, after the risk level faced by institution i subtracts the risk spillover level of institution i when institution j is at the mean value.


## 3.4. Calculation of ΔCoVaR

According to Sklar theorem, the density function of copula function C is denoted asc and then the joint density function of random variables XiandXj is expressed as(6)fijXi,Xj=∂2FijXi,Xj∂Xi∂Xj=cFixi,Fjxj∗fixi∗fjxj,where fixi and fjxj are the edge density functions of Xi and Xj, respectively.From equation (6), we have fi|jXi|Xj=cFixi,Fjxj∗fixi, and then the conditional distribution function of the rate of return sequence Xi can be expressed as(7)Fi|jXi|Xj=∫−∞XicFixi,Fjxj∗fixidxi.According to the invariance of the monotonic increase and change of the copula function, equation (7) can be transformed into the calculation of residuals:(8)Ti|jηi|ηj=∫−∞ηicTiηi,Tjηj∗tiηidηi.When the significance levelq is given,(9)q=Ti|jηi|ηj=∫−∞ηicTiηi,Tjηj∗tiηidηi.In the residual calculation formula,ηj takes the q quantile value and solves the ηi value and CoVaRqi|j=μi+ηitσit, and then we can obtain ΔCoVaRqi|j.


## 4. Data Selection and Empirical Analysis

### 4.1. Data Selection

This paper aims to use the ΔCoVaR to quantify the risk spillover effect of SMEs on banks and to study the financing difficulties of SMEs from the perspective of risk spillover. Taking China as an example, we first measured the risk spillover effects of SMEs on all banks and then analyzed the heterogeneous impact of SMEs on the risk spillovers of state-owned banks, joint-stock banks, and city commercial banks. State-owned banks have the largest scale, followed by joint-stock banks and city commercial banks successively. This study selected the small and medium board, banking, state-owned banking, joint-stock banking, and urban commercial banking indexes as the empirical analysis objects. For small- and medium-sized enterprises, the daily closing price of the small and medium board index was used; for banks, the daily closing price of the CITIC Securities industry index was used. The daily data of banks were collected from June 1, 2010, to May 31, 2020, excluding holidays, and a total of 2429 observations were obtained. In the time dimension, the selected data included a number of major events such as China’s bank money shortage, the end of quantitative easing in the United States, China’s stock market crash, China’s circuit breaker mechanism, China’s supply-side reforms, the Sino-US trade war, and the COVID-19 pandemic. All market data came from the Wind database. The daily return rate of each market was obtained by the first-order logarithmic difference of the index closing price given byrt=100∗lnyt/yt−1, where yt is the index closing price on day t.


### 4.2. Descriptive Statistics

It can be seen from Table1 that the skewness of each return sequence was not 0 and the kurtosis was greater than 3, indicating the phenomenon of leptokurtosis. The J–B test (Jarque–Bera test) was used to test whether the data followed the normal distribution. The J–B test showed that all the P values were 0, indicating that each sequence did not follow the normal distribution. The ADF test (augmented Dickey–Fuller test) tests whether there is unit root in the sequence because the existence of unit root is nonstationary time series. The ADF test shows that each return series is stable at the significance level of 1%. The ARCH-LM test (autoregressive conditional heteroskedasticity-to-Lagrange multiplier test) examines whether the sequences have a high ARCH effect. The ARCH-LM test showed that there was significant ARCH effect between sequences. Therefore, this study used the ARMA(0, 0)-GARCH(1, 1)-skewed-t model to fit the marginal distribution of each return rate series.Table 1
Descriptive statistics.
ItemsMeanStandard deviationSkewnessKurtosisJ–B testQ (10)ARCHADFSMEs0.0091.692−0.7176.2601263 (0.00)24.90 (0.00)274.50 (0.00)−46.23 (0.00)Banks0.0311.5180.0629.2983952 (0.00)29.01 (0.00)227.72 (0.00)−49.68 (0.00)State-owned banks0.0221.355−0.05113.3011000 (0.00)24.9 (0.00)274.53 (0.00)−46.20 (0.00)Joint-stock banks0.3621.6450.1558.3022809 (0.00)23.22 (0.00)182.06 (0.00)−49.53 (0.00)City commercial banks0.3031.7310.2129.4664181 (0.00)37.50 (0.00)223.55 (0.00)−50.42 (0.00)Source: Wind database.P values are in parentheses; ARCH means the LM test lagged with 10 items.


### 4.3. Marginal Distribution Fitting

Table2 shows the marginal distribution parameters of each return rate series, and all series parameters were significant. After fitting the original sequence, the model residuals were transformed into probability integrals, and the standardized residuals were subjected to a 10-order ARCH-LM test. The results showed that there was no heteroscedasticity. Therefore, this study adopted the GARCH(1, 1) model in which the residual items obeyed the skewed-t distribution to model the SMEs and banks’ yield series.Table 2
Parameter estimation results of the GARCH(1, 1) model and skewed-t distribution.
ItemsωiαiβiviλiARCH (10)SMEs0.021∗∗(2.24)0.059∗∗∗(6.43)0.934∗∗∗(89.10)7.631∗∗∗(7.09)−0.143∗∗∗(−5.30)12.73 (0.24)Banks0.024∗∗(2.21)0.071∗∗∗(3.86)0.929∗∗∗(53.80)3.51∗∗∗(12.71)0.09∗∗∗(4.52)14.53 (0.15)State-owned banks0.03∗∗∗(2.75)0.10∗∗∗(4.27)0.89∗∗∗(34.74)3.63∗∗∗(13.07)0.06∗∗∗(2.71)11.13 (0.35)Joint-stock banks0.02∗(1.81)0.06∗∗∗(3.34)0.94∗∗∗(48.60)3.67∗∗∗(12.91)0.10∗∗∗(4.52)10.70 (0.38)City commercial banks0.32∗∗∗(3.14)0.07∗∗∗(5.17)0.93∗∗∗(77.49)3.84∗∗∗(12.41)0.07∗∗∗(3.03)15.94 (0.11)The numbers inQ (10) parentheses are P values, and the other numbers in parentheses are t statistics; ∗∗∗, ∗∗, and ∗ indicate being significant at the level of 1%, 5%, and 10%, respectively.


### 4.4. Parameter Estimation of Time-Varyingt-Copula Function

Table3 shows the parameter estimation results of the copula model of logarithmic return rate of SMEs and different banks. All parameters rejected the null hypothesis that the parameter was 0 at the significance level of 5%, and the parameter results were valid.Table 3
Parameter estimation results of the time-varyingt-copula model by SMEs on different banks.
ItemsυαβAICBanks5.05∗∗∗(7.69)0.029∗∗∗(3.22)0.953∗∗∗(56.74)−713.5State-owned banks5.88∗∗∗(6.21)0.029∗∗(2.52)0.954∗∗∗(43.54)−809.9Joint-stock banks5.62∗∗∗(7.66)0.028∗∗∗(3.48)0.952∗∗∗(62.10)−669.1City commercial banks5.61∗∗∗(7.69)0.028∗∗∗(3.97)0.959∗∗∗(85.61)−820.2


### 4.5. Calculation of Risk Spillover Effects

According to Table4, the CoVaR value of SMEs to various banks was calculated, and the risk spillover value △CoVaR of SMEs to various banks was further obtained.Table 4
Risk spillover effects of SMEs on various banks (α = 5%).
YearCoVaR△CoVaRBanksState-owned banksJoint-stock banksCity commercial banksBanksState-owned banksJoint-stock banksCity commercial banks2010−8.01−6.28−8.10−8.91−7.12−5.57−7.21−7.672011−6.63−4.71−6.99−6.86−5.90−4.17−6.25−5.902012−5.50−4.09−5.70−6.14−4.89−3.63−5.09−5.282013−9.15−5.67−10.06−8.36−8.14−5.02−9.04−7.192014−6.83−5.80−6.89−6.83−6.07−5.14−6.13−5.882015−12.21−11.37−11.81−13.07−10.85−10.08−10.46−11.252016−5.34−4.65−5.30−6.11−4.75−4.12−4.71−5.262017−4.65−4.32−4.79−5.01−4.14−3.83−4.28−4.312018−6.62−6.26−6.53−5.92−5.88−5.55−5.79−5.092019−5.80−4.40−6.18−5.67−5.16−3.90−5.53−4.882020−6.51−4.62−6.99−6.23−5.91−4.12−6.78−5.38Mean−6.74−5.64−7.09−7.26−6.13−5.04−7.17−6.17As shown in Table4, the mean CoVaR of SMEs to banks was −6.74 and the mean △CoVaR was −6.13. Therefore, SMEs had obvious risk spillover effects on banks. Specifically, SMEs had the highest risk spillover effect on joint-stock banks, with a mean △CoVaR of −7.17, followed by city commercial banks, with a mean △CoVaR of −6.17. The risk spillover effects of SMEs on joint-stock banks and city commercial banks were higher than those on the banks as a whole. Besides, the risk spillover effect of SMEs on state-owned banks was the lowest, with the mean △CoVaR of −5.04.


#### 4.5.1. Overall Analysis of SMEs’ Risk Spillover Effects on Banks

Note. Event 1: in December 2012, China’s bank bad assets continued to increase; Event 2: in June 2013, China’s historical bank money shortage; Event 3: in November 2014, the United States officially ended quantitative easing policy; Event 4: in January 2015, China’s A-share stampede; Event 5: in June 2015, China’s A-share 1,000 shares falling limit; Event 6: in January 2016, China’s stock circuit breaker mechanism; Event 7: the Sino-US trade war that began in February 2018; Event 8: COVID-19 pandemic.As shown in Figure1, SMEs had long-term and significant risk spillover effects on banks, and the risk spillover effects were easily affected by external factors. In November 2010, the survival and development of China’s SMEs were challenged by the government’s tightening regulation, the intensive issuance of new stocks, and the slowdown in economic growth. The risk spillover effect of SMEs on banks appeared at a local low point at the end of 2010. Subsequently, the risk spillover effect of China’s SMEs on banks appeared in two periods of low value. One was the result of the continuous increase of bank bad assets at the end of 2012; the other was the impact of the historic bank money shortage event in June 2013. The money shortage of SMEs greatly threatened the survival of SMEs, and the risk spillover of SMEs to banks increased significantly. At the end of 2014, the United States completely ended the quantitative easing policy, the 2015 A-share stampede occurred, and the stock market encountered the major turmoil of 1000 shares falling limit, which had a great impact on Chinese SMEs. SMEs were adjusted twice in-depth, which continuously has a risk spillover effect on banks. The risk spillover of SMEs to banks entered an extremely dangerous period. The circuit breaker mechanism and related events in 2016 also affected the risk spillover effects of Chinese SMEs on banks. From 2016 to 2018, China’s supply-side reforms promoted industrial upgrading and achieved a soft economic landing. The survival and development environment of SMEs was significantly improved, and the risk spillover of SMEs to banks was relatively stable. In the Sino-US trade war that began in 2018, the economic and trade negotiations went up and down and the positive and the negative alternated, resulting in extremely unstable risk spillover effects of Chinese SMEs on banks. At the beginning of 2020, the COVID-19 pandemic caused the suspension of China’s real economy, the interruption of the industrial chain, and the hindrance of foreign trade, in which time SMEs were struggling, and the risk spillover effect of SMEs on banks deepened again.Figure 1
Risk spillover effects of SMEs on banks (△CoVaR,α = 5%).Since SMEs had long-term and obvious risk spillover effects on banks and the risk spillover effects could easily be further aggravated by the influence of unfavorable external factors, commercial banks strictly controlled the lending and loan approvals for SMEs and improved the interest rate of loans for SMEs as a result from risk control, which led to the situation where it was difficult for SMEs to obtain financing.


#### 4.5.2. Specific Analysis of Risk Spillover Effects of SMEs on Various Banks

It can be seen from Figure2 that, in the past ten years, the trend of risk spillovers of SMEs to banks of different sizes was similar, but the level of risk spillovers was different.Figure 2
Risk spillover effects of SMEs on various banks (△CoVaR,α = 5%).In general, SMEs had the lowest risk spillover effect on the largest state-owned banks, slightly higher risk spillover effects on the smallest city commercial banks, and the highest risk spillover effects on joint-stock banks. The different risk spillover effects of SMEs on various banks were mainly due to the different degrees of association between Chinese SMEs and these banks. The most important factor was the proportion of SME loans in bank loans. In 2017, China’s SME loans by state-owned banks accounted for no more than 18% of all bank loans, with a mean value of 14.70%; SME loans by joint-stock banks accounted for 12%–41% of all bank loans, concentrated in 17%–27%; SME loans by city commercial banks were highly dispersed, with the highest proportion being 75% and the lowest proportion being 12%. Therefore, under normal circumstances, since SME loans by joint-stock banks had the highest proportion, SMEs had the largest risk spillover effect on joint-stock banks. In order to compensate for possible loss risks, joint-stock banks raised the loan interest rates to SMEs, which made financing difficult for SMEs.In extreme cases (the extreme points of risk spillover in 2014 and 2015), the risk spillover effect of SMEs on the largest state-owned banks was significantly greater than that on city commercial banks and joint-stock banks. This was because the spillover risks of SMEs with various banks would be spillover again through the business transactions between banks. As shown in Figures3 and 4, the GARCH time-varying copula-CoVaR model was used to measure the risk spillover effects between China’s state-owned banks, joint-stock banks, and city commercial banks. It can be seen that, under normal circumstances, state-owned banks had greater risk spillover effects on city commercial banks and joint-stock banks; at the extreme value of risk spillovers, city commercial banks and joint-stock banks had greater risk spillovers on state-owned banks. Therefore, in extreme cases, risk spillover accumulated to state-owned banks, coupled with the deterioration of the extreme economic environment, the operational risks of state-owned banks further intensified, and the safety of state-owned banks’ funds was greatly threatened. Therefore, the largest state-owned banks provided higher loan interest rates to Chinese SMEs.Figure 3
Risk spillover effects between city commercial banks and state-owned banks (△CoVaR,α = 5%).Figure 4
Risk spillover effects between joint-stock banks and state-owned banks (△CoVaR,α = 5%).


## 4.1. Data Selection

This paper aims to use the ΔCoVaR to quantify the risk spillover effect of SMEs on banks and to study the financing difficulties of SMEs from the perspective of risk spillover. Taking China as an example, we first measured the risk spillover effects of SMEs on all banks and then analyzed the heterogeneous impact of SMEs on the risk spillovers of state-owned banks, joint-stock banks, and city commercial banks. State-owned banks have the largest scale, followed by joint-stock banks and city commercial banks successively. This study selected the small and medium board, banking, state-owned banking, joint-stock banking, and urban commercial banking indexes as the empirical analysis objects. For small- and medium-sized enterprises, the daily closing price of the small and medium board index was used; for banks, the daily closing price of the CITIC Securities industry index was used. The daily data of banks were collected from June 1, 2010, to May 31, 2020, excluding holidays, and a total of 2429 observations were obtained. In the time dimension, the selected data included a number of major events such as China’s bank money shortage, the end of quantitative easing in the United States, China’s stock market crash, China’s circuit breaker mechanism, China’s supply-side reforms, the Sino-US trade war, and the COVID-19 pandemic. All market data came from the Wind database. The daily return rate of each market was obtained by the first-order logarithmic difference of the index closing price given byrt=100∗lnyt/yt−1, where yt is the index closing price on day t.


## 4.2. Descriptive Statistics

It can be seen from Table1 that the skewness of each return sequence was not 0 and the kurtosis was greater than 3, indicating the phenomenon of leptokurtosis. The J–B test (Jarque–Bera test) was used to test whether the data followed the normal distribution. The J–B test showed that all the P values were 0, indicating that each sequence did not follow the normal distribution. The ADF test (augmented Dickey–Fuller test) tests whether there is unit root in the sequence because the existence of unit root is nonstationary time series. The ADF test shows that each return series is stable at the significance level of 1%. The ARCH-LM test (autoregressive conditional heteroskedasticity-to-Lagrange multiplier test) examines whether the sequences have a high ARCH effect. The ARCH-LM test showed that there was significant ARCH effect between sequences. Therefore, this study used the ARMA(0, 0)-GARCH(1, 1)-skewed-t model to fit the marginal distribution of each return rate series.Table 1
Descriptive statistics.
ItemsMeanStandard deviationSkewnessKurtosisJ–B testQ (10)ARCHADFSMEs0.0091.692−0.7176.2601263 (0.00)24.90 (0.00)274.50 (0.00)−46.23 (0.00)Banks0.0311.5180.0629.2983952 (0.00)29.01 (0.00)227.72 (0.00)−49.68 (0.00)State-owned banks0.0221.355−0.05113.3011000 (0.00)24.9 (0.00)274.53 (0.00)−46.20 (0.00)Joint-stock banks0.3621.6450.1558.3022809 (0.00)23.22 (0.00)182.06 (0.00)−49.53 (0.00)City commercial banks0.3031.7310.2129.4664181 (0.00)37.50 (0.00)223.55 (0.00)−50.42 (0.00)Source: Wind database.P values are in parentheses; ARCH means the LM test lagged with 10 items.


## 4.3. Marginal Distribution Fitting

Table2 shows the marginal distribution parameters of each return rate series, and all series parameters were significant. After fitting the original sequence, the model residuals were transformed into probability integrals, and the standardized residuals were subjected to a 10-order ARCH-LM test. The results showed that there was no heteroscedasticity. Therefore, this study adopted the GARCH(1, 1) model in which the residual items obeyed the skewed-t distribution to model the SMEs and banks’ yield series.Table 2
Parameter estimation results of the GARCH(1, 1) model and skewed-t distribution.
ItemsωiαiβiviλiARCH (10)SMEs0.021∗∗(2.24)0.059∗∗∗(6.43)0.934∗∗∗(89.10)7.631∗∗∗(7.09)−0.143∗∗∗(−5.30)12.73 (0.24)Banks0.024∗∗(2.21)0.071∗∗∗(3.86)0.929∗∗∗(53.80)3.51∗∗∗(12.71)0.09∗∗∗(4.52)14.53 (0.15)State-owned banks0.03∗∗∗(2.75)0.10∗∗∗(4.27)0.89∗∗∗(34.74)3.63∗∗∗(13.07)0.06∗∗∗(2.71)11.13 (0.35)Joint-stock banks0.02∗(1.81)0.06∗∗∗(3.34)0.94∗∗∗(48.60)3.67∗∗∗(12.91)0.10∗∗∗(4.52)10.70 (0.38)City commercial banks0.32∗∗∗(3.14)0.07∗∗∗(5.17)0.93∗∗∗(77.49)3.84∗∗∗(12.41)0.07∗∗∗(3.03)15.94 (0.11)The numbers inQ (10) parentheses are P values, and the other numbers in parentheses are t statistics; ∗∗∗, ∗∗, and ∗ indicate being significant at the level of 1%, 5%, and 10%, respectively.


## 4.4. Parameter Estimation of Time-Varyingt-Copula Function

Table3 shows the parameter estimation results of the copula model of logarithmic return rate of SMEs and different banks. All parameters rejected the null hypothesis that the parameter was 0 at the significance level of 5%, and the parameter results were valid.Table 3
Parameter estimation results of the time-varyingt-copula model by SMEs on different banks.
ItemsυαβAICBanks5.05∗∗∗(7.69)0.029∗∗∗(3.22)0.953∗∗∗(56.74)−713.5State-owned banks5.88∗∗∗(6.21)0.029∗∗(2.52)0.954∗∗∗(43.54)−809.9Joint-stock banks5.62∗∗∗(7.66)0.028∗∗∗(3.48)0.952∗∗∗(62.10)−669.1City commercial banks5.61∗∗∗(7.69)0.028∗∗∗(3.97)0.959∗∗∗(85.61)−820.2


## 4.5. Calculation of Risk Spillover Effects

According to Table4, the CoVaR value of SMEs to various banks was calculated, and the risk spillover value △CoVaR of SMEs to various banks was further obtained.Table 4
Risk spillover effects of SMEs on various banks (α = 5%).
YearCoVaR△CoVaRBanksState-owned banksJoint-stock banksCity commercial banksBanksState-owned banksJoint-stock banksCity commercial banks2010−8.01−6.28−8.10−8.91−7.12−5.57−7.21−7.672011−6.63−4.71−6.99−6.86−5.90−4.17−6.25−5.902012−5.50−4.09−5.70−6.14−4.89−3.63−5.09−5.282013−9.15−5.67−10.06−8.36−8.14−5.02−9.04−7.192014−6.83−5.80−6.89−6.83−6.07−5.14−6.13−5.882015−12.21−11.37−11.81−13.07−10.85−10.08−10.46−11.252016−5.34−4.65−5.30−6.11−4.75−4.12−4.71−5.262017−4.65−4.32−4.79−5.01−4.14−3.83−4.28−4.312018−6.62−6.26−6.53−5.92−5.88−5.55−5.79−5.092019−5.80−4.40−6.18−5.67−5.16−3.90−5.53−4.882020−6.51−4.62−6.99−6.23−5.91−4.12−6.78−5.38Mean−6.74−5.64−7.09−7.26−6.13−5.04−7.17−6.17As shown in Table4, the mean CoVaR of SMEs to banks was −6.74 and the mean △CoVaR was −6.13. Therefore, SMEs had obvious risk spillover effects on banks. Specifically, SMEs had the highest risk spillover effect on joint-stock banks, with a mean △CoVaR of −7.17, followed by city commercial banks, with a mean △CoVaR of −6.17. The risk spillover effects of SMEs on joint-stock banks and city commercial banks were higher than those on the banks as a whole. Besides, the risk spillover effect of SMEs on state-owned banks was the lowest, with the mean △CoVaR of −5.04.


### 4.5.1. Overall Analysis of SMEs’ Risk Spillover Effects on Banks

Note. Event 1: in December 2012, China’s bank bad assets continued to increase; Event 2: in June 2013, China’s historical bank money shortage; Event 3: in November 2014, the United States officially ended quantitative easing policy; Event 4: in January 2015, China’s A-share stampede; Event 5: in June 2015, China’s A-share 1,000 shares falling limit; Event 6: in January 2016, China’s stock circuit breaker mechanism; Event 7: the Sino-US trade war that began in February 2018; Event 8: COVID-19 pandemic.As shown in Figure1, SMEs had long-term and significant risk spillover effects on banks, and the risk spillover effects were easily affected by external factors. In November 2010, the survival and development of China’s SMEs were challenged by the government’s tightening regulation, the intensive issuance of new stocks, and the slowdown in economic growth. The risk spillover effect of SMEs on banks appeared at a local low point at the end of 2010. Subsequently, the risk spillover effect of China’s SMEs on banks appeared in two periods of low value. One was the result of the continuous increase of bank bad assets at the end of 2012; the other was the impact of the historic bank money shortage event in June 2013. The money shortage of SMEs greatly threatened the survival of SMEs, and the risk spillover of SMEs to banks increased significantly. At the end of 2014, the United States completely ended the quantitative easing policy, the 2015 A-share stampede occurred, and the stock market encountered the major turmoil of 1000 shares falling limit, which had a great impact on Chinese SMEs. SMEs were adjusted twice in-depth, which continuously has a risk spillover effect on banks. The risk spillover of SMEs to banks entered an extremely dangerous period. The circuit breaker mechanism and related events in 2016 also affected the risk spillover effects of Chinese SMEs on banks. From 2016 to 2018, China’s supply-side reforms promoted industrial upgrading and achieved a soft economic landing. The survival and development environment of SMEs was significantly improved, and the risk spillover of SMEs to banks was relatively stable. In the Sino-US trade war that began in 2018, the economic and trade negotiations went up and down and the positive and the negative alternated, resulting in extremely unstable risk spillover effects of Chinese SMEs on banks. At the beginning of 2020, the COVID-19 pandemic caused the suspension of China’s real economy, the interruption of the industrial chain, and the hindrance of foreign trade, in which time SMEs were struggling, and the risk spillover effect of SMEs on banks deepened again.Figure 1
Risk spillover effects of SMEs on banks (△CoVaR,α = 5%).Since SMEs had long-term and obvious risk spillover effects on banks and the risk spillover effects could easily be further aggravated by the influence of unfavorable external factors, commercial banks strictly controlled the lending and loan approvals for SMEs and improved the interest rate of loans for SMEs as a result from risk control, which led to the situation where it was difficult for SMEs to obtain financing.


### 4.5.2. Specific Analysis of Risk Spillover Effects of SMEs on Various Banks

It can be seen from Figure2 that, in the past ten years, the trend of risk spillovers of SMEs to banks of different sizes was similar, but the level of risk spillovers was different.Figure 2
Risk spillover effects of SMEs on various banks (△CoVaR,α = 5%).In general, SMEs had the lowest risk spillover effect on the largest state-owned banks, slightly higher risk spillover effects on the smallest city commercial banks, and the highest risk spillover effects on joint-stock banks. The different risk spillover effects of SMEs on various banks were mainly due to the different degrees of association between Chinese SMEs and these banks. The most important factor was the proportion of SME loans in bank loans. In 2017, China’s SME loans by state-owned banks accounted for no more than 18% of all bank loans, with a mean value of 14.70%; SME loans by joint-stock banks accounted for 12%–41% of all bank loans, concentrated in 17%–27%; SME loans by city commercial banks were highly dispersed, with the highest proportion being 75% and the lowest proportion being 12%. Therefore, under normal circumstances, since SME loans by joint-stock banks had the highest proportion, SMEs had the largest risk spillover effect on joint-stock banks. In order to compensate for possible loss risks, joint-stock banks raised the loan interest rates to SMEs, which made financing difficult for SMEs.In extreme cases (the extreme points of risk spillover in 2014 and 2015), the risk spillover effect of SMEs on the largest state-owned banks was significantly greater than that on city commercial banks and joint-stock banks. This was because the spillover risks of SMEs with various banks would be spillover again through the business transactions between banks. As shown in Figures3 and 4, the GARCH time-varying copula-CoVaR model was used to measure the risk spillover effects between China’s state-owned banks, joint-stock banks, and city commercial banks. It can be seen that, under normal circumstances, state-owned banks had greater risk spillover effects on city commercial banks and joint-stock banks; at the extreme value of risk spillovers, city commercial banks and joint-stock banks had greater risk spillovers on state-owned banks. Therefore, in extreme cases, risk spillover accumulated to state-owned banks, coupled with the deterioration of the extreme economic environment, the operational risks of state-owned banks further intensified, and the safety of state-owned banks’ funds was greatly threatened. Therefore, the largest state-owned banks provided higher loan interest rates to Chinese SMEs.Figure 3
Risk spillover effects between city commercial banks and state-owned banks (△CoVaR,α = 5%).Figure 4
Risk spillover effects between joint-stock banks and state-owned banks (△CoVaR,α = 5%).


## 4.5.1. Overall Analysis of SMEs’ Risk Spillover Effects on Banks

Note. Event 1: in December 2012, China’s bank bad assets continued to increase; Event 2: in June 2013, China’s historical bank money shortage; Event 3: in November 2014, the United States officially ended quantitative easing policy; Event 4: in January 2015, China’s A-share stampede; Event 5: in June 2015, China’s A-share 1,000 shares falling limit; Event 6: in January 2016, China’s stock circuit breaker mechanism; Event 7: the Sino-US trade war that began in February 2018; Event 8: COVID-19 pandemic.As shown in Figure1, SMEs had long-term and significant risk spillover effects on banks, and the risk spillover effects were easily affected by external factors. In November 2010, the survival and development of China’s SMEs were challenged by the government’s tightening regulation, the intensive issuance of new stocks, and the slowdown in economic growth. The risk spillover effect of SMEs on banks appeared at a local low point at the end of 2010. Subsequently, the risk spillover effect of China’s SMEs on banks appeared in two periods of low value. One was the result of the continuous increase of bank bad assets at the end of 2012; the other was the impact of the historic bank money shortage event in June 2013. The money shortage of SMEs greatly threatened the survival of SMEs, and the risk spillover of SMEs to banks increased significantly. At the end of 2014, the United States completely ended the quantitative easing policy, the 2015 A-share stampede occurred, and the stock market encountered the major turmoil of 1000 shares falling limit, which had a great impact on Chinese SMEs. SMEs were adjusted twice in-depth, which continuously has a risk spillover effect on banks. The risk spillover of SMEs to banks entered an extremely dangerous period. The circuit breaker mechanism and related events in 2016 also affected the risk spillover effects of Chinese SMEs on banks. From 2016 to 2018, China’s supply-side reforms promoted industrial upgrading and achieved a soft economic landing. The survival and development environment of SMEs was significantly improved, and the risk spillover of SMEs to banks was relatively stable. In the Sino-US trade war that began in 2018, the economic and trade negotiations went up and down and the positive and the negative alternated, resulting in extremely unstable risk spillover effects of Chinese SMEs on banks. At the beginning of 2020, the COVID-19 pandemic caused the suspension of China’s real economy, the interruption of the industrial chain, and the hindrance of foreign trade, in which time SMEs were struggling, and the risk spillover effect of SMEs on banks deepened again.Figure 1
Risk spillover effects of SMEs on banks (△CoVaR,α = 5%).Since SMEs had long-term and obvious risk spillover effects on banks and the risk spillover effects could easily be further aggravated by the influence of unfavorable external factors, commercial banks strictly controlled the lending and loan approvals for SMEs and improved the interest rate of loans for SMEs as a result from risk control, which led to the situation where it was difficult for SMEs to obtain financing.


## 4.5.2. Specific Analysis of Risk Spillover Effects of SMEs on Various Banks

It can be seen from Figure2 that, in the past ten years, the trend of risk spillovers of SMEs to banks of different sizes was similar, but the level of risk spillovers was different.Figure 2
Risk spillover effects of SMEs on various banks (△CoVaR,α = 5%).In general, SMEs had the lowest risk spillover effect on the largest state-owned banks, slightly higher risk spillover effects on the smallest city commercial banks, and the highest risk spillover effects on joint-stock banks. The different risk spillover effects of SMEs on various banks were mainly due to the different degrees of association between Chinese SMEs and these banks. The most important factor was the proportion of SME loans in bank loans. In 2017, China’s SME loans by state-owned banks accounted for no more than 18% of all bank loans, with a mean value of 14.70%; SME loans by joint-stock banks accounted for 12%–41% of all bank loans, concentrated in 17%–27%; SME loans by city commercial banks were highly dispersed, with the highest proportion being 75% and the lowest proportion being 12%. Therefore, under normal circumstances, since SME loans by joint-stock banks had the highest proportion, SMEs had the largest risk spillover effect on joint-stock banks. In order to compensate for possible loss risks, joint-stock banks raised the loan interest rates to SMEs, which made financing difficult for SMEs.In extreme cases (the extreme points of risk spillover in 2014 and 2015), the risk spillover effect of SMEs on the largest state-owned banks was significantly greater than that on city commercial banks and joint-stock banks. This was because the spillover risks of SMEs with various banks would be spillover again through the business transactions between banks. As shown in Figures3 and 4, the GARCH time-varying copula-CoVaR model was used to measure the risk spillover effects between China’s state-owned banks, joint-stock banks, and city commercial banks. It can be seen that, under normal circumstances, state-owned banks had greater risk spillover effects on city commercial banks and joint-stock banks; at the extreme value of risk spillovers, city commercial banks and joint-stock banks had greater risk spillovers on state-owned banks. Therefore, in extreme cases, risk spillover accumulated to state-owned banks, coupled with the deterioration of the extreme economic environment, the operational risks of state-owned banks further intensified, and the safety of state-owned banks’ funds was greatly threatened. Therefore, the largest state-owned banks provided higher loan interest rates to Chinese SMEs.Figure 3
Risk spillover effects between city commercial banks and state-owned banks (△CoVaR,α = 5%).Figure 4
Risk spillover effects between joint-stock banks and state-owned banks (△CoVaR,α = 5%).


## 5. Conclusions and Recommendations

Taking China as an example, based on the GARCH time-varying copula-CoVaR model under the skewed-t distribution, this paper measures the overall risk spillover effects of SMEs on banks and further analyzes the heterogeneity of risk spillovers to state-owned banks, joint-stock banks, and city commercial banks. From the perspective of risk spillover, this paper explains the financing difficulty for SMEs, and the following conclusions are obtained.First, SMEs always have a relatively strong risk spillover effect on banks, and this risk spillover effect can easily increase sharply due to the influence of unfavorable external factors. For risk compensation considerations, banks increase the loan interest rates for SMEs, resulting in the financing difficulty of SMEs.Second, since joint-stock banks account for the highest proportion of SME loans, under normal circumstances, SMEs have the largest risk spillover effect on joint-stock banks, which makes it difficult for SMEs to obtain financing from joint-stock banks.Third, in extreme cases, risk spillovers from SMEs to various banks accumulate to the largest state-owned banks, leading to the largest risk spillover effects of SMEs on state-owned banks at this time. Therefore, it is more difficult for SMEs to obtain financing from large state-owned banks.Based on the above conclusions, the following suggestions are made. It is suggested to strengthen financial risk supervision, establish a financial market risk early warning mechanism, improve the foresight and effectiveness of supervision, and minimize the negative impact of adverse external environmental factors on the development of SMEs. Besides, joint-stock banks and city commercial banks are encouraged to provide financing services for SMEs and make the provision for risk spillover bad debts to strengthen internal risk management of financial institutions. Finally, it is suggested to broaden financing channels for SMEs to reduce reliance on bank loans and diversify the risk spillover effect of financing for SMEs.



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*Source: 5914519-2021-11-01.xml*