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This paper examines the dynamic linkages among economic policy uncertainty (EPU), the green bond market, the carbon market, and the macroeconomy using the time-varying parameter vector autoregressive (TVP-VAR) model with monthly data spanning from January 2016 to December 2021. Additionally, it assesses the robustness and accuracy of the empirical results through the Bayesian vector autoregressive (BVAR) model. The findings indicate that EPU negatively affects the green bond market in the short term but has a positive impact in the medium and long term. Conversely, EPU has a positive impact on the carbon market in the short term but a negative impact in the medium and long term. Furthermore, the green bond market negatively influences the carbon market in both the short and medium to long term. These results suggest that emerging markets, such as the green bond and carbon markets, are influenced by EPU. The adverse impact of the green bond market on the carbon market, however, contributes to expediting China's attainment of its low-carbon objectives. Appropriate economic policies can play a vital role in accelerating the transition to a low-carbon economy. The study also reveals that the US-China trade war has expedited the development of green capital markets in China, despite its impact on the green economic transition in the country. These findings provide insights for the government and investors to formulate suitable strategies for risk mitigation. [ABSTRACT FROM AUTHOR]

The process of global integration has accelerated, and the international financial market has become increasingly closely linked. The financial risks that come with them are becoming more complex and difficult to guard against. Multimedia modeling in Health Cloud biometric authentication and data management systems can be applied to the analysis of financial markets. Most of the current financial risk analysis models are based on a single time, and the models are relatively simple and cannot adapt to the current complex multidimensional mixed financial risk environment. Therefore, this paper aimed to analyze the spatial spillover effect of financial risk contagion based on the directed asymmetric (DAI) spatial econometric model. This paper proposed to transfer entropy information weight information and introduce the GARCH (generalized auto-regressive conditional heteroskedasticity) model to improve the traditional econometric model. By constructing a DAI measurement model, the spatial contagion of multidimensional mixed financial risks was analyzed, and on this basis, a generalized multidimensional economic space was established to analyze spatial spillover effects and analyze the specific path of spatial spillover effects. The model results in this paper showed that the degree of correlation between the stock and bond market varied greatly between countries. Among them, the change coefficient W s ⟶ b r s D of the event period was judged to have a large degree of negative change in the United Kingdom, Germany, and France in the European Union, which were −0.9885, −0.9876, and −0.9748, respectively. This showed that the model in this paper had a good and reliable ability to cope with the current multidimensional mixed complex financial risk environment and could be used as a reference for financial risk-related research. At the same time, it also proved that multimedia modeling in health cloud biometric authentication and data management system could provide a role in financial risk contagion analysis. [ABSTRACT FROM AUTHOR]

In China's rural credit system, the problem of credit constraints is prominent. Due to the imperfect credit market, a large number of rural residents have credit constraints. Rural credit constraint is a serious problem restricting China's rural economic development. Aimed at solving the rural credit constraints, this paper makes an optimization analysis on the rural credit system and loan decision-making. To more reasonably evaluate customers' borrowing ability, the credit risk based on farmers' data on the big data platform is evaluated in this paper. The stacked denoising autoencoder network is improved by adopting the deep learning framework to improve the accuracy of credit evaluation. For improving the loan decision-making ability of rural credit system, a loan optimization strategy based on multiobjective particle swarm optimization algorithm is proposed. The simulation results show that the optimization ability, speed, and stability of the proposed algorithm have achieved good results in dealing with the loan portfolio decision-making problem. [ABSTRACT FROM AUTHOR]

Because of its creditor's rights, equity, and options, convertible bonds have been developed rapidly since its emergence and have become one of the main tools in the financial market. One of the core problems of convertible bonds is pricing. The research on the pricing model of convertible bonds in China is relatively late. Most of them use foreign technologies, but they are quite different from the actual situation in China, and most of the models are characterized by a single factor. Therefore, this paper puts forward the nonarbitrage pricing model in the stock market and the application of the finite element numerical solution in the value of convertible bonds. The biggest innovation of this paper is to design a combined pricing model by using the model of nonarbitrage pricing theory and finite element numerical solution. The model combines the advantages of the nonarbitrage pricing theory and finite element numerical solution, and through the design of this paper, the model effectively improves the calculation accuracy and is suitable for most of the current market environment. While improving the comprehensive performance of the pricing model, it also simplifies the calculation methods and steps. In order to further verify the actual effect of the pricing model in this paper, the traditional binary tree model is taken as the experimental contrast object, including the comparative analysis of the market price and the theoretical price of the convertible bond, the comparative experiment of the prediction effect between the model and the binary tree model, and the analysis of the relative price error of the convertible bond. The results show that the comprehensive performance of the pricing model in this paper is significantly better than the traditional binary tree model. This study has achieved ideal results and can be widely recommended. [ABSTRACT FROM AUTHOR]

Investing in green bonds has been recognized as a strategy that not only promotes environmental performance but also offers attractive investment returns. However, macroeconomic factors, such as geopolitical risk (GPR), climate policy uncertainty (CPU), and global economic policy uncertainty (GEPU), play a substantial role in shaping green bond returns. Using the returns of US green bonds as a proxy for the returns of green bonds, we argue in this paper that these heterogenous uncertainty measures might have asymmetric impacts on the returns of green bonds (GB). By employing monthly data from January 2016 to August 2022, we apply the nonlinear autoregressive distributed lags model (NARDL) to examine the asymmetric impact of these heterogenous uncertainty measures on the returns of GB. The NARDL findings reveal evidence of short-run asymmetric impacts of GPR, GEPU, and CPU on the returns of green bonds. In the long run, there is an asymmetric impact of GPR and CPU on the returns of GB. However, there is a symmetric impact of CPU on the returns of GB in the long run. Specifically, in the short run, we found that a positive CPU shock causes a decline in the returns of green bonds. A similar magnitude of negative CPU shock causes an increase in the returns of green bonds. Moreover, a positive shock to GPR increases the return of green bonds, while a comparable negative shock to GPR reduces the return of GB. Furthermore, only negative shocks to GEPU have an impact on GB returns in the short run. Specifically, a negative GEPU shock reduces GB returns. In the long term, the returns of GB are positively impacted by negative shocks in GPR and both positive and negative shocks in GEPU, whereas positive shocks in GPR affect the returns of GB negatively. [ABSTRACT FROM AUTHOR]

In recent years, the new agricultural business entities have grown rapidly in all parts of the country and have become an effective carrier to achieve the basic stability of China's rural family operation system and moderate-scale agricultural operation. However, the systemic defects of rural financial market and the credit rationing of formal financial institutions make the capital needs of rural areas unsatisfied for a long time. Financial demand determines the direction of supply reform. Therefore, in order to improve rural financial supply and promote the transformation and development of agricultural economy under the current situation, it is necessary to effectively understand the formal credit demand and the impact factors of accessibility of the new agricultural business entities, which represents the direction of China's agricultural development in the future. Based on back propagation (BP) neural network, this paper constructs the farmers' formal credit availability prediction model and studies the formal credit demand and the impact factors of the availability of new agricultural business entities. The experimental results show that the farmers' formal credit availability prediction model has good performance in prediction accuracy, prediction time, and mean squared error. [ABSTRACT FROM AUTHOR]

From the perspective of financial constraint, this paper constructs a mathematical model to analyze the impact of digital financial development on firm exit probability. The relationship between digital finance and firm exit was tested empirically based on the industrial firm data in 2011–2013. The results show that digital financial development significantly suppresses firm exit probability. Mechanism analysis suggests that digital financial development can ease the information asymmetry of the credit market, facilitate the credit acquisition of firms, and alleviate the constraint on corporate financing, thereby reducing the probability of firm exit. This paper provides the theoretical basis and empirical evidence for controlling firm exit from the angle of digital finance development. [ABSTRACT FROM AUTHOR]

Public policy and public opinion directly affect the image of the government, but due to the lack of appropriate monitoring and early warning tools, the government's handling of credit changes is seriously lagging behind. In response to this problem, this paper integrates the internet, public information, market credit information, and other data, uses hidden Markov models and natural language processing technology, and establishes a modern government public policy and public opinion monitoring and early warning model to evaluate government credit in real time; the government can formulate relevant policies based on the evaluation results to improve the government's governance capabilities. Empirical analysis shows that, based on the dynamic scoring framework and Markov model, the government credit monitoring and early warning models established, respectively, have 90% of the reference value, and the analysis results have the same reference. This method can effectively predict the trend of hot online public opinion. The subsequent establishment of an online public opinion early warning system and an online public opinion guidance mechanism provided theoretical support. [ABSTRACT FROM AUTHOR]

The Chinese bond market has achieved rapid development over the years. However, since the "rigid payment" in China was broken in 2014, the number and size of bond defaults have climbed up promptly and caused huge volatility in bond and even stock markets. To better manage and control the risk of the Chinese corporate bonds market, deep learning can be used as a helpful tool to predict the corporate default risk. This paper constructs a security warning model based on deep neural networks after a reasonable selection of characteristic indicators. By comparing Multi-Layer Perceptron (MLP) with the logistic regression method, it is found that MLP is more suitable in the security warning model to predict the default risk. And, hyper-parameter analyses and ablation study are conducted to explore the performance and accuracy of the best MLP settings. The experimental results show that the deep learning method accommodating the widely chosen factors in the security warning model is effective in predicting corporate bond defaults in China. [ABSTRACT FROM AUTHOR]

This paper develops a depth-gated recurrent neural network (DGRNN) with self-attention mechanism (SAM) based on long-short-term memory (LSTM)\gated recurrent unit (GRU) \Just Another NETwork (JANET) neural network to improve the accuracy of credit spread prediction. The empirical results of the U.S. bond market indicate that the DGRNN model is more effective than traditional machine learning methods. Besides, we discovered that the Depth-JANET model with one gated unit performs better than Depth-GRU and Depth-LSTM models with more gated units. Furthermore, comparative analyses reveal that SAM significantly improves DGRNN's prediction performance. The results show that Depth-JANET neural network with SAM outperforms most other methods in credit spread prediction. [ABSTRACT FROM AUTHOR]

The purpose of this interdisciplinary study is to investigate the applications of modern computational technologies in the field of credit investigation and discuss the related legal governance measurements in order to unblock the bottlenecks in the future development of credit investigation market. By analyzing the computational technologies and algorithms most commonly used in credit data collection and data storage, data transmission and data access, data analysis and processing, data calculation, result output and effect evaluation, this paper summarizes and proposes a unified general process of modern credit investigation, pointing out that in this general process, low data quality, privacy violation, algorithmic bias are the main challenges in the big data era, and countermeasures like data quality control, privacy protection, and algorithm governance need to be to be taken seriously into account in order to further explore the great potential of the credit investigation under the legal framework. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of multidimension liquidity, credit risk, and the interaction between liquidity and credit risk on corporate bond spreads based on a large transaction data set from July, 2006 to June, 2016, including the monthly data of 3716 bonds in China. Our main findings reveal that liquidity premiums are the main parts of corporate bond spreads. The interaction between liquidity and credit risk plays a significant role in determining corporate bond spreads. In addition, the differences between the interbank market and the exchange market have a significant impact on corporate bond spreads in normal period, and the interaction between liquidity and credit risk has an enhanced impact on corporate bond spreads during financial crisis. We also find that the interaction between liquidity and credit risk will increase with the increase of liquidity risk and credit risk and it is a time-varying dynamic process. [ABSTRACT FROM AUTHOR]

The bond market is an important part of China's capital market. However, defaults have become frequent in the bond market in recent years, and consequently, the default risk of Chinese credit bonds has become increasingly prominent. Therefore, the assessment of default risk is particularly important. In this paper, we utilize 31 indicators at the macroeconomic level and the corporate microlevel for the prediction of bond defaults, and we conduct principal component analysis to extract 10 principal components from them. We use the XGBoost algorithm to analyze the importance of variables and assess the credit debt default risk based on the XGBoost prediction model through the calculation of evaluation indicators such as the area under the ROC curve (AUC), accuracy, precision, recall, and F1-score, in order to evaluate the classification prediction effect of the model. Finally, the grid search algorithm and k -fold cross-validation are used to optimize the parameters of the XGBoost model and determine the final classification prediction model. Existing research has focused on the selection of bond default risk prediction indicators and the application of XGBoost algorithm in default risk prediction. After optimization of the parameters, the optimized XGBoost algorithm is found to be more accurate than the original algorithm. The grid search and k -fold cross-validation algorithms are used to optimize the XGBoost model for predicting the default risk of credit bonds, resulting in higher accuracy of the proposed model. Our research results demonstrate that the optimized XGBoost model has a significantly improved prediction accuracy, compared to the original model, which is beneficial to improving the prediction effect for practical applications. [ABSTRACT FROM AUTHOR]

With the improvement of the intelligence level of the power network and the gradual improvement of the information and communication of the power system, the application of power big data to the corporate credit market is not only an important supplement to the national social credit system but also an important strategic goal for the State Grid to expand profitability. This paper constructs a benefit evaluation model for power data credit investigation products based on the utility of the products that financial institutions experience when they purchase and use financial risk control products. Based on the theory of customer value, comprehensively considering the characteristics of power credit reporting products, and taking profit maximization as the corporate goal, the pricing of a single power credit reporting product is studied, and the method is verified using example products. This article gives the pricing strategy of power credit investigation, which can be used as a reference for enterprises to further develop market strategies. [ABSTRACT FROM AUTHOR]

This paper proposes a generalized bond pricing model, accounting for all the effects of credit risk, liquidity risk, and their correlation. We use an informed trading model to specify the bond liquidity payoff and analyze the sources of liquidity risk. We show that liquidity risk arises from reduced information accuracy and market risk tolerance, and it is market risk tolerance that links credit and liquidity. Then, we extend the traditional bond pricing model with only credit risk by incorporating liquidity risk into the framework in which the probabilities of the two risk events are estimated by a joint distribution. Using numerical examples, we analyze the role of the correlation between credit and liquidity in bond pricing, especially during a financial crisis. We document that the varying correlation between default and illiquidity explains the phenomenon of bond death spiral observed in a financial crisis. Finally, we take the US corporate bond market as an example to demonstrate our conclusions. [ABSTRACT FROM AUTHOR]

Since 2012, green bond markets have boomed worldwide, particularly in the European Union, the United States, and China. Under this background, the researchers use the methods of literature research, qualitative analysis, and descriptive research to compare the structure of Chinese and American green bond markets and analyze their differences from the perspective of historical evolution, issuance standards, and market operation characteristics. The researchers believe that China's bank-oriented financial structure and America's market-oriented financial structure are the main reasons for the difference between the two countries. The researchers then discuss the strengths and weaknesses of China's green bond market and conclude that the advantages of China's green bond market structure lie in low risk and close relationships between banks and enterprises, while the disadvantages lie in low financial efficiency and give relevant suggestions. This article makes up for the lack of cross-country comparison in the existing research on the green bond market and provides a qualitative research perspective. The suggestions put forward have specific policy significance for developing the green bond market in China and other developing countries. [ABSTRACT FROM AUTHOR]

Although China's bond market is expanding quickly and on a bigger scale than the stock market, it still faces a number of issues. One issue that restricts its long-term development is the relatively small scale of credit bonds. For the growth of credit bonds, it is necessary to boost the credit rating market first. In this study, it is assumed that the higher the credit rating of the bonds, the lower the issue cost will be. To prove this hypothesis, an empirical analysis has been made herein on the influence of credit ratings on the bond floatation market. [ABSTRACT FROM AUTHOR]

To improve the ability of market to avoid and prevent credit risk and strengthen the awareness of market risk early warning, SMOTE is used to process the unbalanced sample, and fruit fly optimization algorithm (FOA) is utilized to optimize the parameters of support vector machine (SVM), and thus an improved SVM market risk early warning model is proposed. The simulation results show that the proposed model has excellent stability and generalization ability, and it can predict market credit risk accurately. Compared with the prediction model based on FOA-SMOTE-BP and FOA-SMOTE-Logit, the proposed model performs better on the indicators of G value, F value, and AUC value, which provides a reference for market credit risk prediction. [ABSTRACT FROM AUTHOR]

This article attempts to fill important knowledge gaps to explore the spatial spillover effects of financial markets on regional financial efficiency in eight economic zones using three-stage superefficiency data envelopment analysis (DEA) and Durbin's spatial econometric model. The average financial efficiencies in the North coast, East coast, and South coast economic zones reach the superefficiency DEA relatively efficient level, while the average financial efficiencies in the Northeast, Middle Yellow River, Middle Yangtze River, and large West-south and West-north economic zones reach the superefficiency DEA relatively inefficient level. Except for the North coast economic zone, seven equity markets have significant impacts on regional financial efficiency, and local equity markets in the Northeast, South coast, Middle Yellow River, and Middle Yangtze River economic zones generate significant spatial spillover effects on neighboring regions' financial efficiency. Local credit markets only in the Northeast and South coast economic zones have significant spatial spillover influences on neighboring regions' financial efficiency. Debt markets in the North coast, East coast, South coast, Middle Yangtze River, and large West-south economic zones have significant influences on regional financial efficiency, and local debt markets in the East coast and Middle Yangtze River economic zones generate significant spatial spillover effects on neighboring regions' financial efficiency. [ABSTRACT FROM AUTHOR]

The objective of this paper is to test for nonlinear dependence in the GARCH residuals of a number of asset classes using nonlinear dynamic tools. The equity and bond market samples appear to be independent once GARCH has been applied, but evidence of nonlinear dependence in the CDS GARCH residuals is found. The sensitivity of this result is analysed by changing the specifications of the GARCH model, and the robustness of the result is verified by applying additional tests of nonlinearity. Evidence of nonlinear dependence in the GARCH residuals of CDS contracts has implications for the accurate modeling of the marginal distribution of the CDS market, for pricing of CDS contracts, for estimating risk neutral default probabilities in the bond market, as well as for bond market hedging strategies. [ABSTRACT FROM AUTHOR]

This study investigates the factors impacting the price difference between the interbank market and the exchange market for the same bond using a large transaction dataset from July 2006 to June 2016 in China. We find that market liquidity and macrofactors mainly affect the price difference between the two markets for the same bond. And individual bond liquidity explains only a small part of the price difference. We also find that the interaction between liquidity and credit risk is an important factor affecting the price difference, and the effect is greater during financial crisis. [ABSTRACT FROM AUTHOR]