Forecasting the value at risk (VaR) of crude oil futures can be a challenging task for investors due to the high volatility of these prices. It is crucial to describe the return in the tail distribution, as extreme values can trigger larger price fluctuations and market risks. In this study, we proposed a hybrid model, ARIMA-SVR-POT, which uses a combination of the autoregressive integrated moving average (ARIMA), support vector regression (SVR), and peak over threshold (POT) method from the extreme value theory. We compared the performance of our hybrid model with three other models, namely ARIMA-EGARCH, ARIMA-SVR, and ARIMA-EGARCH-POT. We demonstrated the effectiveness of our model using crude oil WTI Futures as a sample from June 23, 2016, to September 30, 2022. Our findings show that the ARIMA-SVR-POT hybrid model provides accurate predictions of the returns and volatility. Furthermore, the model performs exceptionally well in capturing the extreme tail of returns and outperforms the other models. We also conducted back-testing in the proposed model and the results show that the ARIMA-SVR-POT model passed the Kupiec test at confidence levels of 95 %, 99 %, 99.5 %, and 99.9 %. Our proposed model provides a more precise reflection of potential losses when estimating VaR. The predicted loss probability is closer to the actual loss occurrence probability, indicating superior performance compared to traditional statistical models, which enhanced crude oil risk management tools and suggested effective measures to manage market risks.

Green finance is a sustainable force in promoting green development. China’s social financing structure determines the key role that green credit plays in sustainable development. Under the dual pressure of future economic downturn and huge capital gaps, it is worth exploring whether to continue promoting green credit that conforms to the long-term market mechanism. From the perspective of Chinese commercial banks, this paper analyzes whether promoting green credit is compatible with the incentives and their profit maximization goals. To this end, the research in this paper is based on the following three aspects: (1) Based on financial analysis, this paper reveals the different pricing of green industries in the capital market and credit market and explains the mechanism through which green credit policies improve the operating conditions of commercial banks; (2) combined with the conclusions from the literature and financial analysis, the influence of different index types on the modeling results is analyzed, and it is determined that the main reasons causing a decline in the return on assets are the excessive expansion of capital and the decline in internal resource-use efficiency; (3) a data envelopment model (more accurately, SBM-DDF) with undesirable outputs is established to dynamically analyze the operating efficiency of Chinese commercial banks, and the role of green credit in improving efficiency is studied. The main conclusions of this paper are as follows: if Chinese commercial banks increase their proportion of green credit, they can not only increase their profit scale but also improve and optimize the allocation of their internal resources, thus improving their operating efficiency. The main sample of this study comprises 43 commercial banks in China from 2007 to 2020.

As a mixed-methods research in economics and psychology, this study aimed to analyze the influence from the intergenerational succession on the financialization level including asset financialization and revenue financialization, and further test the moderating effect of the heirs’ typical growing experience according to The Imprinting Theory, based on the 2009–2020 annual data of listed family enterprises of China. There were two key findings. First, the effect of Chinese family enterprises’ intergenerational succession on asset financialization was positively significant while the effect on revenue financialization was not significant, indicating that the financialization behavior has not brought about effective financial profits. Second, among the heirs’ typical growing experiences, their parents’ entrepreneurial experience during their childhood, oversea study experience, and MBA education experience had the significantly positive moderating effects on the influence from intergeneration succession to asset financialization level of Chinese family enterprises, which was an important internal mechanism for the heirs to promote the financialization process of family enterprises.

Supply chain finance plays a crucial role as a financing channel for small‐ and medium‐sized enterprises (SMEs). However, issues such as financial problems and credit defaults have led to disruptions in this channel. To address credit risk control in SME financing within the field of supply chain finance, this paper focuses on a sample of 506 equipment manufacturing companies listed on the SME board of the Shenzhen Stock Exchange from 2016 to 2020. Taking into consideration, the overall risks faced by these enterprises, the study establishes seven first‐level indicators and identifies 84 candidate second‐level indicators. Partial correlation and variance analysis are then used for the first round of indicator screening, followed by the use of a BP neural network for the second round of selection. As a result, a system of 26 indicators for supply chain financial risk is constructed. The XGBoost model is employed to evaluate the constructed risk index system, while SVM and random forest models are used as comparison models. Bayesian optimization is utilized for parameter tuning of the three models. Empirical results demonstrate that the BO‐XGBoost model reduces prediction errors in comparison to the control models. Furthermore, statistical tests reveal that the predicted values of the BO‐XGBoost model significantly differ from those of the other control models. Compared to other individual models, the BO‐XGBoost model exhibits increased accuracy in credit risk prediction and a significant discriminative effect. These findings highlight the effectiveness of constructing an efficient risk indicator system and utilizing Bayesian optimization for parameter tuning in XGBoost to better differentiate between risky and normal enterprises, thereby minimizing default losses. The research results underscore the advantages of employing Bayesian optimization in XGBoost, which can be applied in credit default prediction for SMEs and serves as a valuable tool in financial risk management and control.

This article focuses the research sample on Chinese State-Owned Enterprises (SOEs), using the Beyond-logarithmic Stochastic Frontier Production Function and the ordinary fixed effect model to examine, and evaluates the influence of mixed ownership reform on the innovation of SOEs from the perspective of incentive and constraint mechanisms. Our study investigates the impact of the corporate governance, policy burden, and environmental regulation. The main conclusions are as follows. First, mixed ownership reform can enhance the innovation capability of SOEs. Second, mixed ownership reform stimulates innovation of SOEs by optimizing corporate governance and reducing policy constraints on enterprises which regulate enterprise decision-making behavior. Third, the environmental regulation also affects SOEs’ innovation. This study can provide a reference for the government to further deepen the reform, optimize the living environment of SOEs, improve environmental regulations and promote the sustainable development of economy.

It is of great significance to build an efficient and cooperative innovation system and support system under the background of China persisting in leading the development with innovation and speeding up the construction of an innovation-oriented country. The collaborative relationship and profit distribution of each innovation subject will be analyzed through the study of the innovation activity in the innovation chain. With consideration of the characteristics of the innovation chain such as high risk, financing difficulty, cooperative partnership and so on and using the internal financing mode of supply chain for reference, the game model of internal financing between members of the innovation chain will be established. The game equilibriums will be solved and analyzed, and the optimal equilibriums will be achieved by designing the contracts to establish the internal financing mechanisms in the innovative chain. For the risk asymmetry at each stage of the innovation chain, the contracts will be optimized to construct the risk sharing mechanism to control the risk of the internal financing in the innovation chain. In order to make innovations, this paper creates the financial support mode of the innovation chain, improves the innovation support system and increases the efficiency of the innovation chain.