This paper studies the strategic interaction between shareholders and managers in an insurance company under model uncertainty using a two-player non-cooperative differential game. The firm's surplus serves as the state variable, with the shareholder choosing dividend and capital-injection policies to maximise the expected discounted value of dividends net of financing costs, while the manager independently selects an excess-of-loss reinsurance strategy to maximise expected utility. To capture heterogeneous ambiguity attitudes, we examine three scenarios: (i) a benchmark case in which both parties fully trust the reference model; (ii) a setting where only the shareholder is ambiguity-averse; and (iii) a case where both agents face model uncertainty, potentially with different beliefs about surplus dynamics. The analysis characterises robust Nash equilibria across these settings and highlights how ambiguity aversion influences risk retention, dividend distribution, and capital support. Numerical results reveal nonlinear and asymmetric effects of managerial risk aversion and capital-injection costs, offering new insights into the role of heterogeneous beliefs in corporate governance and insurance risk management.

This paper addresses a class of value-maximization problems in storage-type optimal control involving operational scale and withdrawals with absorption. Moving beyond the drifted Brownian motion model, we develop a unified methodological approach under a general linear diffusion framework. We demonstrate the solvability of a broad class of problems by deriving semi-explicit optimal strategies. We further establish conditions for the existence of fully explicit solutions and illustrate solution methods with examples based on models for a variety of scenarios, both where explicit solutions exist and where only semi-explicit solutions are available.

This paper investigates the optimal dividend and business scale strategies aimed at maximizing the value of an insurance company. While prior studies typically assume that insurers can only adjust their business scale through reinsurance, this study extends the framework by allowing the insurer to control the premium rate. Under more realistic market assumptions, we examine the joint optimization problem for two common types of reinsurance - proportional and excess-of-loss - across both arbitrage and non-arbitrage scenarios. We derive the optimal strategies for dividends and premium pricing, along with their corresponding value functions. The results show that the insurer should decrease the premium rate and reduce reinsurance coverage as the surplus increases. The optimal dividend policy follows a barrier strategy. Economic interpretations and numerical examples are provided to illustrate the findings.

This study explores the synergy between the development of financial technology (fintech) and green finance (GF) by evaluating city-level carbon emissions (CE), is explored against the backdrop of advancing China's "dual" carbon peak (2030) and neutrality (2060) goals. Panel data spanning 2011-2019 from 249 Chinese cities were used, and panel fixed-effects regressions with year and city controls, clustered standard errors, and a series of robustness checks were performed. Results indicate an uneven distribution of effects, with the impact of fintech varying based on city size and geographic factors. Mechanism analysis shows that fintech improves GF initiatives and financial efficiency and lowers CE. The findings offer empirical evidence of fintech's environmental benefits, suggest policies to promote fintech-enabled GF, tailor local support, and highlight environmental criteria in fintech design.

Green finance and inclusive finance are critical to advancing the "dual-carbon" strategy. However, green finance lacks enough inclusivity, and inclusive finance falls short on enough sustainability. Integrating the two is essential to jointly enhance carbon emission efficiency. Using sample data from 30 provinces in China between 2010 and 2021, this study employs the Super-SBM model to construct carbon emission efficiency indicators and the entropy weight-TOPSIS method to calculate the development level of green inclusive finance. A panel fixed-effects model examines the impact of green inclusive finance on carbon emission efficiency. The findings show that the development of green inclusive finance significantly improves carbon emission efficiency. This conclusion holds robust through tests such as lagging the explained variable by one period, changing the measurement of the explanatory variable, excluding the effects of epidemics, and eliminating extreme values. Mechanism tests reveal that green inclusive finance achieves this by optimizing the energy consumption structure and reducing mismatches in innovation resources. Heterogeneity analysis indicates that its impact is most pronounced in regions with lower environmental regulation, higher R&D expenditures, and greater financial autonomy. To maximize its potential, we recommend improving the green inclusive finance policy system, increasing support for green energy projects, optimizing the allocation of resources, and formulating region-specific policies. These measures are aimed toward achieving a win-win outcome, fostering economic growth and ecological sustainability.

The impact of seasonal shocks on China's agricultural supply chain is not well understood, as they pertain to the capital efficiency of small- and medium-sized enterprises. Because they are crucial to national strategy and social progress, we thoroughly investigate upstream and downstream enterprises in this context in terms of their financial indicators using a detailed multilayer double-market nested game. Focusing on receivables recovery pressure and average real rate of return, our model simulates three conditions: no financing, supply chain financing, and supply chain factor financing. The results highlight the potential effects of shocks on enterprise capital utilization rates, revealing that factoring can effectively reduce this impact and help maintain high capital utilization efficiency.

By taking all A-share listed companies as samples from 2011 to 2021, the paper empirically studies the impact of the coupling and coordinated development of green finance and fintech on the level of corporate risk-taking. The study finds that, first, the coupling and coordinated development of green finance and fintech can improve the level of corporate risk-taking. Second, the mechanisms are easing financing constraints, improving the quality of information disclosure and promoting green technology innovation. Third, in high-tech industries and state-owned enterprises, the coupling and coordinated development of the two improves the level of corporate risk-taking more significantly.

Science and technology-driven small and medium-sized enterprises (SMEs) have emerged as vital players in China’s economic landscape, with unlisted SMEs assuming a significant role. However, these unlisted SMEs face formidable financing challenges, exacerbated by information asymmetry and limited access to diverse funding sources. This research introduces a comprehensive Evaluation Indicator System tailored for unlisted science and technology-based SMEs in Province A, China. Grounded in the Catastrophe Progression Method, this system provides an objective and multidimensional assessment of financing capacity, accounting for factors like innovation, growth, solvency, and regional dynamics. Empirical validation demonstrates the system’s practical relevance, reaffirming its potential as a reliable tool for evaluating financing capacity. The study sheds light on the financing stages of these SMEs, emphasizing the importance of external investment channels, especially for growth-stage enterprises. Bank loans remain the primary financing avenue, underscoring the need for enhanced bank evaluation systems. Policymakers should consider tailored approaches based on SME growth stages to optimize resource allocation and support. By addressing the financing challenges faced by unlisted SMEs and fostering a more supportive environment, this research contributes to unlocking their potential for technological innovation, economic growth, and job creation. It underscores the significance of diverse financing options, the role of external factors, and the need for improved bank evaluation processes to facilitate the growth of these enterprises in the dynamic landscape of knowledge-driven economies.

We investigated the optimal risk management strategies of an insurance company within a two-player non-cooperative differential game framework. The key feature of this model is considering the company’s surplus as a strategic variable to make the insurance company more attractive to shareholders. In this setup, shareholders are participants who demand a share of the surplus, while managers are participants concerned with the risk management of the surplus. The objective of the shareholders is to maximize the expected discounted dividends. We address this asymmetric game under two different assumptions about the managers’ objectives: in the first scenario, the managers aim to minimize the probability of bankruptcy; in the second scenario, the managers aim to maximize the expected discounted utility derived from the surplus.

We investigate the optimal reinsurance problem considering a more realistic two-layer design involving excess-of-loss reinsurance and multiple reinsurers. Unlike the commonly assumed single-layer design in the existing literature, our approach involves a top layer consisting of excess-of-loss reinsurance and the involvement of multiple reinsurers. Our focus is on the insurer's management of risk exposure and costly capital reinvestment strategies to maximize the company value. We formulate this problem as an optimization problem with constrained multivariate reinsurance exposure variables, and capital injection control. Our findings show that, from a profitability standpoint, when the surplus is low and the excess-of-loss reinsurance is not too expensive, the insurer should obtain a two-layer reinsurance with an excess-of-loss reinsurance at the top layer and a combination of proportional reinsurance from multiple reinsurers at the bottom layer. In contrast, when the surplus is relatively large or the excess-of-loss reinsurance is too expensive, the insurer should purchase a single-layer proportional reinsurance from multiple reinsurers. When the surplus is large enough, the insurer should not acquire any reinsurance. Our results suggest that stand-alone excess-of-loss reinsurance is never optimal when there is also proportional reinsurance with a variance premium principle available for losses below the excess threshold, and we theoretically confirm that a multi-layer reinsurance design is preferable over a single-layer design. Furthermore, our results demonstrate that in order to maximize the company's value, dividends should be paid according to the lump-sum barrier strategy, and capital injections should be considered only if the surplus is null and the transaction costs on capital injections are not too high.

The paper reviews and integrates the definitions, measurement techniques of climate risk, and its economic, social, and financial impacts. It systematizes the various dimensions of climate risk, including climate transition risks and physical risks, and how these risks are defined and understood in the current literature. The paper compiles the methodologies used to measure climate risks, highlighting innovative approaches such as big data analytics, machine learning, and complex network models. Additionally, it assesses the societal impacts of climate change, examining how different sectors, including agriculture, energy, transportation, and finance, are affected. By synthesizing findings from a broad range of studies, this review provides a detailed understanding of the multifaceted nature of climate risk and offers insights into effective risk management strategies. Significantly, this research serves as a crucial theoretical foundation for policymakers in formulating policies to combat climate change and for industry practitioners in devising investment strategies, thus providing practical implications that are essential for mitigating the adverse effects of climate change on society. The paper aims to be a valuable resource for those working in this area.

This paper studies optimal investment and proportional reinsurance policies for an insurer with Markov regime-switching model and random time solvency regulation. The goal of the insurer is to maximise the probability that its wealth exceeds a predetermined level l before the regulatory time arrives. By constructing an auxiliary control problem without regime switching, together with the classical results on Hamilton-Jacobi-Bellman (HJB) equation and fixed-point method, we prove the regularity of the value function. When the current state of the Markov chain is given, we found that the optimal policies for an insurer in a multiple-regime market is the same as those in a single-regime market. Explicit optimal policies can be derived when the premium is calculated by the expectation principle. For more general cases, numerical schemes for value functions and feedback optimal policies are given by the Markov chain approximating method.

This article focuses on the classic optimal dividend and reinsurance problems. Different from the existing literature, it assumes that the insurance company has two lines of business with a common shock dependence. It can purchase proportional reinsurance to reduce business risk and pay dividends to stay competitive. The goal is to find out the optimal dividend and reinsurance strategies for maximizing the company's value. Under the diffusion approximation model, we decomposed the problem into several situations and gave the corresponding solutions by using the stochastic control method. Some numerical examples and economic explanations are presented to illustrate the results.

Stock index forecasting is a hot research topic in the financial field. The traditional forecasting methods mostly use ARMA, ARIMA and GARCH to forecast the stock index. In recent years, many scholars have introduced machine learning such as SVM and RNN into the stock index forecasting model, but the accuracy of these forecasting results still needs to be improved. In this paper, by constructing DLWR-LSTM model, the trend of three indexes in Shanghai Stock Exchange is separated and predicted by layers to improve the accuracy of stock market index prediction, and the final prediction MAPE (average absolute percentage error) is close to 1%. In this paper, different samples with different volatility but similar overall trend are replaced for experiments. The results show that the prediction accuracy of DLWR-LSTM model is not affected by the fluctuation of sample time series, and its final prediction result is maintained at around 1% regardless of the variance of time series.

Suppose that insurer can control dividend, refinancing and reinsurance strategies dynamically. Different from the past, there are multiple reinsurers rather than sole reinsurer in the market. The insurer aim at finding the optimal strategies for maximizing the company's value. illustrates that refinancing can be considered iff the company has strong profitability; It should reduce reinsurance purchase when the surplus increases. The amount of risk ceded to the reinsurer depends on its risk attitude. The optimal dividend policy is of barrier type when the dividend rate is unbounded and is of threshold type when the dividend rate is bounded.

针对中小微企业的融资难问题,应对传统的信用风险评级方法进行改进.本文以我国2021 年新三板中小微企业为样本,加入管理层角度的新指标,使信息更加完整.另外改进了经典的黏菌算法(SMA),结合基础支持向量机模型(SVM)进行参数优化,建立了RF-LSMA-SVM模型,考察信用风险判定问题.结果表明,管理层各角度、企业偿债能力和企业盈利能力等方面的指标对于信用风险均具有一定的解释能力,而企业的客户结构和企业性质是冗余信息.在大数据背景下,银行和中小微企业可以利用所构建的RF-LSMA-SVM模型进行信用风险评级来增强分类能力.本文研究结果补充了信用风险评级指标,也完善了评级模型,对提高评级准确性具有启示意义.

数字金融可以缓解环境敏感型企业的融资约束进而促进企业的创新投入,但在环境敏感型企业的不同生命周期阶段,数字金融对环境敏感型企业的影响可能有所差异.基于2011-2020年865个环境敏感型上市公司数据,从企业的生命周期角度,利用动态面板模型探讨数字金融发展对环境敏感型企业创新投入的影响,以及融资约束的中介效应.研究表明,数字金融发展对环境敏感型企业的创新投入起到了积极的促进作用,这种影响在成长期和成熟期的环境敏感型企业中更为显著,在衰退期的环境敏感型企业中不显著;中介效应分析结果显示,融资约束是数字金融发展对环境敏感型企业创新投入影响的重要传导变量,融资约束的负向中介作用在成长期和成熟期的环境敏感型企业中更为明显.因此,针对不同生命周期的环境敏感型企业,政府和金融机构要结合企业发展阶段采取差异化政策.

We investigate how fintech development affects carbon emissions using the panel data of 253 prefecture-level cities in China from 2011 to 2019. We employ the city-level digital financial inclusion index to gauge the fintech development and identify the impact mechanisms through which fintech affects the city's carbon emissions. Our results show that fintech can significantly reduce carbon emissions, and this conclusion still holds when considering potential endogeneity, when considering the impact of resource endowment, when using alternative measures of carbon emissions, even after removing the impact of low-carbon pilot cities policy, and after winsorization treatment. We further find that the main mechanisms by which fintech affects carbon emissions are industrial structure, financing constraints, and green technology innovation. Our results provide powerful evidence that fintech positively impacts the real economy, offering more confidence and reason to stimulate fintech development.

Clarifying the impact of digital inclusive finance (DIF) on green technology innovation is of great practical significance to promote the high-quality development of the green economy in developing countries. In contrast to existing research, this paper conducts a comparative study from the perspectives of innovation enthusiasm and innovation quality and creatively measures the latter based on the annual number of citations of green patents. On this basis, this paper takes Chinese A-share listed companies and the DIF index from 2011 to 2020 as the research objects, finding that the development of DIF has a more significant impact on the quality of green technology innovation than enthusiasm. The characteristics of the external environment, including financial supervision and financing constraints, may have different impacts on the innovation-driven effect of DIF, so the moderating effect model and the threshold model are used to analyze them. In particular, when the government's financial supervision intensity value for DIF is between 0.0024 and 0.0025, financial security can be taken into account, and the high-quality development of green technology can be achieved. In terms of enterprise characteristics, state-owned enterprises and enterprises that do not have a combination of general managers and chairmen can significantly achieve the green innovation-driven effect of DIF. For pollution-intensive enterprises, government supervision is necessary to help them achieve high-quality development of green technology innovation. This study provides policy implications for developing countries around the world to achieve green development by promoting the DIF level.

随着我国人口老龄化程度不断加深,针对老年人的长期护理保险的定价方法成为保险精算方向的热点问题.本文利用中国老年健康影响因素跟踪调查(CLHLS)2014-2018年的数据,在传统的三、四状态马尔科夫模型的基础上,进一步将老年人的健康状况划分为六种状态,采用马尔科夫模型对各状态进行数值测算,利用Robinson幂函数综合考虑了性别和年龄两种因素求解健康状态的转移强度矩阵和转移概率矩阵,随后运用双随机Lee-Carter模型、随机游走模型和预期寿命公式估算了65、75和85岁的保费年限,以此为依据给出了长期护理保险的保费计算方法,为我国的长期护理保险定价提供理论参考.