# Risk Evaluation on China Government Bonds with EWMAVaR and SVM Methods

Authors: Tianhao Ouyang; Xiaoyong Lu
Journal: Security and Communication Networks
 (2021)

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## Abstract

This research studies the strategy of risk evaluation of China government bonds with the latest data. The angle of evaluation focuses on the interest rate and the stability risk, employing the EWMAVaR and SVM methods. The weights of each risk indicator are determined by the entropy method. Experimental results show that the risk of government bonds is stable in recent years. However, the impact of COVID-19 cannot be ignored because the risk level increased in the year 2020. The issuing of one trillion special antipandemic bonds could explain the fluctuation of the market because the fiscal incomes of Chinese government decreased in 2020 and could not be recovered in a short time. The experimental results show that the method proposed in this paper has a better performance than the existing methods, and it can help well in realizing the risk assessment of government bonds.


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## Body

## 1. Introduction

With the development of China’s economy and finance, its bond market has become the second-largest market in the world. Meanwhile, the share of the government bonds increases rapidly. The most important reason is that Chinese (central and local) government largely relies on government investment to boost economic growth, making government debts pile up fast. China started to allow its local governments to issue government bonds in 2015, and the existing local government debts will be identified and transferred in the form of bonds. This explains the fast growth of government bonds in China market.Government debt is one of the crucial issues of governance. Most countries run with a fiscal deficit, making them rely on government debt to support fiscal expenditure. This affects a nations’ defense, administration, education, etc. When a nation’s fiscal situation is in trouble, it may have to cut down the spending as mentioned above, which results in severe social unrest, if not revolts. Statistics of history show it is quite common of government defaults [1]. The debt problem causes many regime changes in the world. Like Latin America, for instance, many countries suffered economic slowdown, which triggered the debt problem (because of shrinking taxation income). This enforced the government to regulate the government expenditures, which is not welcomed by the people. This usually results in the left-wing taking power. However, the new government’s new policies put more emphasis on welfare (as they promised in the election), which further affected economic growth. Extreme measures are used to compensate for losses and fill gaps in the welfare system, such as in Venezuela. Eventually, the recession will end with an economically conscious new government. Political instability is directly related to the price of government bonds [2]. This chaos occurs not only in Latin America but also all over the world. For example, the lessons of the European sovereign debt crisis require sound fiscal policy [3]. In short, the problem of untreated debt has serious consequences.A country can borrow from other countries, but it can only delay the default because interest rates cannot be very low in an unhealthy fiscal situation. The defaults would worsen the credibility of the nation, which leads to the collapse of the fiscal system and the financial system. Because the debt of a nation exists in the form of government bonds, the fluctuation of government bonds also influences the financial market. Since government bonds are considered as risk-free assets, markets believe the government bonds will not default. This results in the products of the financial market being priced with the interest rate of a national bond plus premium. Thus, if the government bonds were at risk, other financial products will be affected. For instance, the fluctuations of the stock market and bond market are related [4]. Furthermore, bonds take a large share in some types of banks’ assets, making them sensitive to the risk of bonds [5]. It is true that the nation debt situation can be eased by buy-in from the domestic banks, but it weakens banks’ ability to counterattack risks [6]. In addition, unexpected fluctuations in government bonds will have an impact on the credibility of these bonds. This will not help to improve a country’s financial situation.As for China, a few things need to be introduced. China has restarted to issue national bonds (NBs) since 1981, which greatly improves the fiscal situation. However, with the reform and open policy progress, the national taxation cannot follow the newly developed economic situation (that private corporation thrives). The tax system went through reforms in 1994 that divides the tax into national tax and local tax (J. T.) [7]. The new system improves the national fiscal situation, yet the local government has to deal with more responsibility with less tax income. Meanwhile, the local government was not granted the right of issuing bonds. Local governments resort to other means to support their fiscal situation, mainly from the real estate industry, which pushes up the real estate price. With the revision of the budget law of China in 2014 [8], the local government was able to issue local government bonds (LBs). Also, local governments’ other forms of debt are organized into the form of bonds with the order. That brings the debt of China governments to the unity of form. Thus, analyzing the government bonds of China alone will provide overall insights to evaluate the Chinese fiscal situation.Since the market views the bond with different perspectives [9], a better approach would be focused on analyzing the risk based on the data of the bonds themselves. It is a common way to study the bonds, such as by predicting the price change by historical data [10] or technical indicators [11]. This research analyzes two types of risks. The first is the stability risk. The price of financial products fluctuates as other commodities. However, due to the nature of their large trade volume, any price change would cause a huge amount of profit and/or loss. With the tools of financial derivatives, the effect of price change is amplified by few folds if not more. Thus, the predictability price change constitutes stability risk. If the price of a financial product was predictable, then the product behaves as the historical data. However, when the accuracy of the forecast is not as good as before, the price model of the product will change, indicating that the operation scheme will also change. In addition, it will bring instability to the market and greater stability risk.For interest rate risk, it evaluates the impact of market interest-rate risk on the bonds. Generally, when the interest rate rises, the price of securities would drop (including stocks and bonds), and vice versa because the rising interest rate will attract the liquidity from investment to saving. Also, the cost of borrowing will increase, which affects the monetary credit situation. The interest rate risk has a larger influence on government bonds because the term/maturity of government bonds is longer than corporate bonds. Also, there is no need to mention stock/security does not even have maturity.Some researchers use other tools to investigate government bonds, such as the KMV model [12]. This does not breach the boundary of bond analysis, considering a government bond is still a kind of bond, yet the issuer is different. However, the KMV method is based on the data of corporate bond default records to predict the default possibility. In itself, the KMV method is an innovative method to evaluate the bond risk. But, for government bonds, it is not suitable due to the lacking default data. Also, the Chinese government bonds do not have a default history while the interest rates are low (indicating the market generally values them as a low-risk product). So, it may not be the best approach to analyze the risk of Chinese government bonds with the KMV method. Following, the research methods of this paper are introduced and elaborated. To deal with these problems, this paper proposes a new method based on EWMAVaR and SVM.The main contributions of this paper can be described as follows:(1)
We have studied the risk evaluation on Chinese government bonds which is now an important topic, and it is very useful(2)
We combine EWMAVaR and SVM methods that can help to speed up the risk evaluation on Chinese government bondsThe structure of this paper is as follows: Section2 is the introduction of the proposed method. Section 3 gives the experimental results. Section 4 gives the discussion.


## 2. Methods

### 2.1. Stability Risk Evaluation

The government bond risks can be divided into two categories, stability risk and interest rate risk. The stability risk is evaluated by the SVM algorithm, which can capture the pattern of financial time series. Thus, predictions on the future course can be generated from the historical data. The error of the prediction can be considered as the stability risk which is the deviation from the historical pattern. The calculation of the stability risk is described below [13].For samplexi,yi, where i=1,2,3,…,t, t is the sample size., x is the input vector, and y is the output vector, what is the data we aim to predict? For each time epoch t,(1)y=w0+w1x1+w2x2+w3x3+…wnxn,wherexn represents the dimensions of vector x. Parameter wn and w0 define the hyperplane of (2), which can be noted as follows:(2)y=b+∑αiyixi·x.Here,xi is the volume of the input sample for training, when yi is the output of the sample. xi is the support vector of x, when x is the input sample. According to (2), the equation can be solved by b and αi, which is similar to a linear problem. The core idea of SVM is projecting a support vector into higher dimensions when facing a linearly nonseparable problem, whereas in the higher dimension, the linear classification is possible. Thus, pattern capturing and identification can be conducted.(3)y=b+∑αiyiKxi·x.The equation can be transformed into (3), in which Kxi·x is the kernel function that would influence the result’s performance. Here, RBF (Radial Basis Function) is applied, noted in (4). In the experiments, σ is usually noted in the form of γ, by letting γ=−1/2σ2.(4)Kx,y=exp−x−y22σ2,Before the calculation, input data need to be standardized intox∗ by (5), where μ is the mean of the time series and σ is the standard deviation.(5)x∗=x−μσ.By examining the error of prediction, the stability risk value can be measured by (6), where x and xp are the actual data and its prediction.(6)esrv=x−xpx.Ifesrv increases in a certain period, then the pattern of time series is changed because the prediction error based on historical data has increased.The evaluations on government bonds’ stability risks are given in the following. The Shanghai Stock Exchange T-Bond Index (000012) is applied as raw data for NB, while the SCI Aggregate Bond Index (H11001), local bonds 5 years (930865), and local bonds 10 years (930866) in the SCI index are evaluated for LB.For the NB evaluation, the input vectors are high, low, open, close, amplitude, weekday, and change. Because the T-bond index is organized by SSE, the dimension of the data is rich, which is good for prediction algorithms. Also, the calculation parameters are as follows: training window is 100 market days,C=135, gamma=0.001, and the output/prediction xp is the close price of the next market day.The stability risk of LB is calculated by two sets of data. The first set of data input vectors is the close of SCI Aggregate Bond Index and close of local bond 5 years and weekday, and the output vector is the prediction on the close of local 5 years. The second set of data input is almost identical to the first, only the local 5 years index is replaced by the local 10 years index. The final results are the averageesrv of the two sets of data. One thing about the data that needs to be mentioned is that currently, only 5 and 10 years of LB index data are available. Also, there are no LB’s term structure data. Considering most LBs are issued in the form of 10-year, the average term reached 15 years just in 2020. Thus, this research takes the average esrv of the two sets of data mentioned above to reach an approximation of the LB market.Following, the government bond interest rate risk is introduced. It represents the changes in bond returns influenced by interest rate fluctuations. For instance, when the money supply increases, the fixed return bonds are more in need, and in the hike cycle of interest rate, bonds are less favored. Those changes are shown in the change of bond return.


### 2.2. Interest Rate Risk Evaluation

As a form of credit commitment, the bond is influenced by the interest rate. Thus, the market interest rate can alter a bond’s value directly. For government bonds, their credit risk can be ignored because there is no LB default so far. Also, the NB works well for decades. The main risk of government bonds is interest rate risk. This research applies a modified VaR method, Exponentially Weighted Moving Average VaR (EWMAVaR). Research shows EWMAVaR is a more accurate method [14].EWMAVaR is calculated by (7), where Rt is the return of a certain financial product. λ is the decay factor range in 0 to 1. λ is set to 0.94 as industry standard suggested. z is the standard normal cumulative distribution corresponding to a specified confidence level (0.95 in this research).(7)VaRt=z×∑t=1n1−λ×λt−1×Rt21/2.1−λ×λt−1 in (7) is the weight on time t. For data points in time series, the closer to the current time, the bigger its weight. The embedding decay factor is based on the assumption that the far and distant information could only slimly affect the current situation. The value VaRt generated by (7) finishes the evaluation on bonds’ interest rate risk.


### 2.3. Indicators’ Weight Structuring

The entropy method is employed to construct a straightforward and instructive government bond risk evaluation result. The method is an objective analysis, which does not require prior judgment and initial setting. All indicators’ weights are decided by the information they carry. What is mentioned below is how the weights are constructed.Consider there arem indicators and n sets of data. The data can be arranged in a matrix B by (8), where x2j is the value (in the second data set) of indicator j.(8)B=x11x12…x1j…x1mx21x22…x2j…x2m…………xn1xn1…xnj…xnm.What follows is data normalization by (9), where xij is the element in row i and column j. For simplicity, data after normalization will be still noted as xij.(9)xij′=xij−minxjmaxxj−minxj.Equation (10) calculates the ratio of the i-th data of the j indicator. Also, the entropy of j is defined by (11). Here, k=1/lnm.(10)Pij=xij∑1nxij,j=1,2,…,m,(11)ej=−k∑1nPij∗logPij.The final weight of indicatorj is carried out by(12)Wj=1−ej∑1m1−ej.All the risk indicators are calculated by the entropy method introduced above. Due to the LB being launched a few years ago (especially the long-term bond), some indicators’ records started from 2018. The weights of indicators are organized in Table1.Table 1
Weights of government bond risk indicators.
Risk typeIndicatorWeightStability riskNB stability risk0.271LB stability risk0.186Interest rate riskNB interest rate risk0.355LB interest rate risk0.187


## 2.1. Stability Risk Evaluation

The government bond risks can be divided into two categories, stability risk and interest rate risk. The stability risk is evaluated by the SVM algorithm, which can capture the pattern of financial time series. Thus, predictions on the future course can be generated from the historical data. The error of the prediction can be considered as the stability risk which is the deviation from the historical pattern. The calculation of the stability risk is described below [13].For samplexi,yi, where i=1,2,3,…,t, t is the sample size., x is the input vector, and y is the output vector, what is the data we aim to predict? For each time epoch t,(1)y=w0+w1x1+w2x2+w3x3+…wnxn,wherexn represents the dimensions of vector x. Parameter wn and w0 define the hyperplane of (2), which can be noted as follows:(2)y=b+∑αiyixi·x.Here,xi is the volume of the input sample for training, when yi is the output of the sample. xi is the support vector of x, when x is the input sample. According to (2), the equation can be solved by b and αi, which is similar to a linear problem. The core idea of SVM is projecting a support vector into higher dimensions when facing a linearly nonseparable problem, whereas in the higher dimension, the linear classification is possible. Thus, pattern capturing and identification can be conducted.(3)y=b+∑αiyiKxi·x.The equation can be transformed into (3), in which Kxi·x is the kernel function that would influence the result’s performance. Here, RBF (Radial Basis Function) is applied, noted in (4). In the experiments, σ is usually noted in the form of γ, by letting γ=−1/2σ2.(4)Kx,y=exp−x−y22σ2,Before the calculation, input data need to be standardized intox∗ by (5), where μ is the mean of the time series and σ is the standard deviation.(5)x∗=x−μσ.By examining the error of prediction, the stability risk value can be measured by (6), where x and xp are the actual data and its prediction.(6)esrv=x−xpx.Ifesrv increases in a certain period, then the pattern of time series is changed because the prediction error based on historical data has increased.The evaluations on government bonds’ stability risks are given in the following. The Shanghai Stock Exchange T-Bond Index (000012) is applied as raw data for NB, while the SCI Aggregate Bond Index (H11001), local bonds 5 years (930865), and local bonds 10 years (930866) in the SCI index are evaluated for LB.For the NB evaluation, the input vectors are high, low, open, close, amplitude, weekday, and change. Because the T-bond index is organized by SSE, the dimension of the data is rich, which is good for prediction algorithms. Also, the calculation parameters are as follows: training window is 100 market days,C=135, gamma=0.001, and the output/prediction xp is the close price of the next market day.The stability risk of LB is calculated by two sets of data. The first set of data input vectors is the close of SCI Aggregate Bond Index and close of local bond 5 years and weekday, and the output vector is the prediction on the close of local 5 years. The second set of data input is almost identical to the first, only the local 5 years index is replaced by the local 10 years index. The final results are the averageesrv of the two sets of data. One thing about the data that needs to be mentioned is that currently, only 5 and 10 years of LB index data are available. Also, there are no LB’s term structure data. Considering most LBs are issued in the form of 10-year, the average term reached 15 years just in 2020. Thus, this research takes the average esrv of the two sets of data mentioned above to reach an approximation of the LB market.Following, the government bond interest rate risk is introduced. It represents the changes in bond returns influenced by interest rate fluctuations. For instance, when the money supply increases, the fixed return bonds are more in need, and in the hike cycle of interest rate, bonds are less favored. Those changes are shown in the change of bond return.


## 2.2. Interest Rate Risk Evaluation

As a form of credit commitment, the bond is influenced by the interest rate. Thus, the market interest rate can alter a bond’s value directly. For government bonds, their credit risk can be ignored because there is no LB default so far. Also, the NB works well for decades. The main risk of government bonds is interest rate risk. This research applies a modified VaR method, Exponentially Weighted Moving Average VaR (EWMAVaR). Research shows EWMAVaR is a more accurate method [14].EWMAVaR is calculated by (7), where Rt is the return of a certain financial product. λ is the decay factor range in 0 to 1. λ is set to 0.94 as industry standard suggested. z is the standard normal cumulative distribution corresponding to a specified confidence level (0.95 in this research).(7)VaRt=z×∑t=1n1−λ×λt−1×Rt21/2.1−λ×λt−1 in (7) is the weight on time t. For data points in time series, the closer to the current time, the bigger its weight. The embedding decay factor is based on the assumption that the far and distant information could only slimly affect the current situation. The value VaRt generated by (7) finishes the evaluation on bonds’ interest rate risk.


## 2.3. Indicators’ Weight Structuring

The entropy method is employed to construct a straightforward and instructive government bond risk evaluation result. The method is an objective analysis, which does not require prior judgment and initial setting. All indicators’ weights are decided by the information they carry. What is mentioned below is how the weights are constructed.Consider there arem indicators and n sets of data. The data can be arranged in a matrix B by (8), where x2j is the value (in the second data set) of indicator j.(8)B=x11x12…x1j…x1mx21x22…x2j…x2m…………xn1xn1…xnj…xnm.What follows is data normalization by (9), where xij is the element in row i and column j. For simplicity, data after normalization will be still noted as xij.(9)xij′=xij−minxjmaxxj−minxj.Equation (10) calculates the ratio of the i-th data of the j indicator. Also, the entropy of j is defined by (11). Here, k=1/lnm.(10)Pij=xij∑1nxij,j=1,2,…,m,(11)ej=−k∑1nPij∗logPij.The final weight of indicatorj is carried out by(12)Wj=1−ej∑1m1−ej.All the risk indicators are calculated by the entropy method introduced above. Due to the LB being launched a few years ago (especially the long-term bond), some indicators’ records started from 2018. The weights of indicators are organized in Table1.Table 1
Weights of government bond risk indicators.
Risk typeIndicatorWeightStability riskNB stability risk0.271LB stability risk0.186Interest rate riskNB interest rate risk0.355LB interest rate risk0.187


## 3. Results

Based on the stability risk evaluation model, results of NB and LB are concluded in Figures1 and 2. The figures show the same X-axis and Y-axis. It is shown that the predicted error of LB is greater than that of NB. The esrv of NB in 2018 is treated as the risk baseline. By doing so, the stability risk values of NB and LB are organized in Table 2. Since the data of LB is not as rich as NB, the table listed the risk values from 2018 to 2020.Figure 1
EWMAVaR results of different terms NB.Figure 2
EWMAVaR results of different terms LB.Table 2
Stability risk values of government bonds.
DateNB stability risk valueLB stability risk value20181.0000.86520191.1301.28620200.9571.301The reason of setting the 2018 NB as the baseline of risk signifier is because the bond default started to increase in that year. China’s bond market enjoyed a long boom in the last decade, yet the default incident increased from 24 to 125 just in one year (2017 to 2018). Also, the amount of default money increased more significantly from 31.25 to 120.9 billion (2017 to 2018). Although the default volume is still at a low level considering the bond market size, the frequent defaults disrupt the information in the market [15]. The risk values are all relative risks based on the NB of 2018. For the NB stability risk, the 2018 NB esrv equals the risk value of 1. Also, the NB stability of 2019 can be calculated by dividing the esrv2019 to esrv2018. Stability risk values of government bonds are shown in Table 2. The relative prediction error of NB is shown in Figure 3. The relative prediction error of LB is shown in Figure 4.Figure 3
Relative prediction error of NB.Figure 4
Relative prediction error of LB.Result suggests the stability risk of NB is stabilized at the level of 2018. However, the risk of LB has been increasing fast since 2018. Also, the change is big. In 2018, the stability risk of LB was lower than that of NB. But, in a short two years, LB stability risk increased by almost 50%, while NB risk maintains at the same level. In the following, the interest rate risk results are demonstrated [16].To evaluate the government bonds’ interest rate risks, NB and LB yield to maturity under different terms need to be achieved. Related data are obtained from China money.com, which is held by the National Interbank Funding Center and China Foreign Exchange Trade System. For NB, we chose the terms 1, 3, 5, 7, 10, and 15 years. Also, for LB (AAA), 1, 3, 5, 10, 15, and 20 years are chosen. What needs to be mentioned is that the LB data were recorded since July 2017. Also, the 10Y LB data were launched from July 2019 [17].We run the EWMAVaR calculation on all the 12 sets of data mentioned above. However, without the exact term structure of the government bonds, the interest rate risk of NB and LB cannot be achieved. According to “Issuing and market functioning of treasury bonds and local government bonds (August 2019)” from the Ministry of Finance, the average NB issuing term is 6.55 years (0.4 times larger than that of last year). This suggests that, in 2018, the average issuing term was around 6 years. The terms of LB are also increasing. According to “Statistical analysis report of the bond market in 2019” from the China Central Depository & Clearing Corporation, the bond issued in 2019 has an average term of 10.26, which increased 4.14 years from 2018. By assigning different terms of bonds with weights, an average term equal to the figures mentioned by the reports above can be achieved. Thus, the interest rate risk values of NB and LB can be evaluated. Figures3 and 4 show the NB and LB EWMAVaR results of different terms.It shows, since March 2020, for the short-term bonds of 5 years and below, their risk increased drastically due to the impact of COVID-19. After the panic, the LB risk drops to a low level (equivalent to Q4 of 19), while the NB risk is slightly higher (especially the 1-year and 3-year bonds). NB term weight structuring for EWMAVaR is shown in Table3. LB term weight structuring for EWMAVaR is shown in Table 4.Table 3
NB term weight structuring for EWMAVaR.
Date1Y3Y5Y7Y10Y15YAggregated20180.130.170.250.250.10.16.1420190.070.130.30.30.10.16.5620200.050.10.250.350.150.17.05Table 4
LB term weight structuring for EWMAVaR.
Date1Y3Y5Y10Y15Y20YAggregated20180.050.250.30.4006.3201900.10.150.50.150.110.32020000.050.150.550.2515The VaR values of different terms need to be weighted to conclude a straightforward interest rate risk result. Both the NBs and LBs are weighed and organized in Tables3 and 4, which form the term structures. About the LB, because the 15Y and 20Y bond returns started to release in 2019 and the average term of 2018 LB is 6.3Y, the structure of 2018 does not employ the data of 15Y and 20Y. Again, the risk value is based on the NB data, which label the 2018 NB interest rate EWMAVaR result as the risk value 1.The government bonds’ returns fluctuate as market interest rates float. The synthetic NB and LB term structures in Section2 undergo the EWMAVaR calculation. The results are demonstrated in Figure 5.Figure 5
EWMAVaR results of NB and LB.The VaR results and the risk value they represent are shown in Table5. It shows the interest rate risk of NB falls below 1 for a short year and then a shoot up to 1.7, which happened in 2020. The LB shows the same pattern, yet its risk value does not exceed 1. That suggests the interest rate risk of LB is still under control. Yet, observation with care and attention would still be required. Because the LB is still in the phase of massive and rapid issuing, considering the situation of only few provinces of China has a positive fiscal budget surplus. Furthermore, the economic impact from COVID-19 will continue to influence the world, and a worldwide economic recovery may not happen soon.Table 5
VaR results and risk values of NB and LB.
DateVaR on NB returnVaR on LB returnNB interest risk valueLB interest risk value20181.2261.1551.0000.94220191.1170.8130.9110.66320202.0971.1171.7110.911


## 4. Discussion

According to the results in Section3, aggregate government bond risk can be summarized with all the risk indicator values. Table 6 gives the results. It can be noticed that the overall risk value of 2018 is close to 1. That is because the benchmark of risk value is based on the NB risk of 2018. Also, the weight of NB risks takes more than 60% of the overall risk. That is why the result of 2018 is close to 1. However, the trend deserves a worry.Table 6
Government bond risk with values of weighted risk indicators.
Risk indicator202020192018Government bond risk1.2790.9930.963NB interest risk value0.6070.3230.355LB interest risk value0.1700.1240.176NB stability risk value0.2590.3060.271LB stability risk value0.2420.2390.161The government bond risk stays at an acceptable level in 2018-2019 with the risk value below 1, which is below the risk alert level. However, the figure breaches and rises to 1.279, which is about 30% larger than the risk level in 2018. Though both the NB and LB suffer risk surges, NB contributes more to risk rising.The interest rate risk divergence of NB and LB in 2020 can be explained by the one trillion yuan issuing of COVID-19 control special bonds. This creates a huge impact on the market. Meanwhile, with government bonds as an important risk-avoiding product, fluctuation of bonds could disrupt the financial market. This could be the reason why NB interest rate risk rises dramatically in 2020.Though the COVID-19 impact was severe, China achieved a 2.3% growth in 2020. It was not as good as the 6% average in previous years. China is still one of the few economies that reached positive growth. But, the situation cannot be estimated with overoptimism; according to the Ministry of Finance of China and local governments, the fiscal income of China in 2020 has negative growth, due to the slow GDP growth and the systematic pandemic impact on the economy. With a decreasing fiscal income (which could become slow growing if the economic recovery could happen nationally and world widely) and growing expenditure (for financial aiding and economic stimulation), the government bond risk is very likely to increase. Because the fiscal balance will be relying much more on bond issuing. Considering the results in Table6, the government bond risk will be above the alert line in the next few years. This brings challenges to resolving China’s debt problem.About the future of the Chinese debt course, though in 2020, China achieved positive growth and recent data that Q1 and Q2 of 2021 show promising, the fiscal income growth of 2020 is negative. At the fundamental level, the government debt is fragile to risk. For the government bond, it is more sensitive to monetary policy. In the late 2020, the money market was in the trend of increasing demand, which made the central bank of China start a tight monetary policy such as setting quotas for the real estate industry which is the most important source of credit creation. Because the international organizations started to emphasize on monetary policies with sense after the European debt crisis [18], the central bank of China lowered the reserve ratio in July, which may be a better way to deal with debt suppression [19]. Yet, it cannot be concluded as the end of the tight policy. Researchers suggest fiscal rules are not important anymore in difficult times [20]. Also, there are provokes claiming while the debt of a nation is high, cutting the debt level will improve the welfare of the citizens [21]. The action of the Chinese central bank indicates the economy is not ready for a strict tight money supply at this moment because the policy brings fluctuations to the currency market. Future measures to regulate the economy depend on the reactions of the market and the world economic recovery.


## 5. Conclusions

This paper surveys the risk of Chinese government bonds. With the introduction of the new amendment to China’s budget law, the local governments are able to issue bonds. Hence, the existing accumulated debts are regulated and formed into local government bonds. Thus, all the government debts of China are unified into government bonds, which opens a good channel to survey the fiscal risk situation by analyzing the risk of government bond. Two risk indicators are applied in the risk evaluation, stability risk and interest rate risk. The stability risk evaluates the risk caused by the bond’s fluctuation. The interest rate risk analyzes the potential loss of bond return change caused by market interest change. The SVM and EWMAVaR methods are employed to tackle the two risks mentioned above. Also, the entropy method takes part in the weight construction of risk indicators. For the national bond, its interest rate risk rose rapidly in 2018–2020, while its stability risk decreased slightly. The local bond behaves differently with a sharp rise of stability risk. In summary, the national bond contributes more risk. The synthetic result suggests that the Chinese government bond risk was close to the brink in 2018 and 2019.The experimental results in this paper verify the good performance of the proposed method. Even though the proposed method has good performance, the precision of this method should be improved. So, in the future, we will do our best to improve the precision of the risk evaluation on Chinese government bonds.



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*Source: 9617933-2021-12-21.xml*