Adverse selection remains a significant challenge in the insurance industry, often resulting in substantial financial losses for insurers. The primary hurdle in addressing the issue lies in accurately identifying and quantifying adverse selection. Traditional methods often fail to adequately account for the heterogeneity of insurance purchasers and the endogenous nature of their insurance decisions. This study introduces an innovative approach that integrates the Gaussian Mixture Model and the regression-based model from Dionne et al. (2001) to assess adverse selection, addressing the limitations of previous methods. Through comprehensive simulations, we demonstrate that our method yields unbiased estimates, outperforming existing approaches. Applied to China’s automobile insurance market, leveraging IoT devices to track telematics data, this method captures risk heterogeneity among the insured. The results offer robust evidence of adverse selection, in contrast to conventional methods that fail to detect this phenomenon due to their inability to capture the underlying relationship between customer risk and claim behavior. Our approach offers insurers a robust framework for identifying information asymmetries in the market, thereby enabling the development of more targeted policy interventions and risk management strategies.

This paper proposed an innovative WTC-LSTM-ASSA-NSTformer framework for gold price forecasting. The model integrates Wavelet Transform Convolution, Long Short-Term Memory networks (LSTM), and an improved Nyström Spatial-Temporal Transformer (NSTformer) based on Adaptive Sparse Self-Attention (ASSA), effectively capturing the multi-scale features and long- and short-term dependencies of gold prices. Additionally, for the first time, various financial and economic uncertainty indices (including VIX, GPR, EPU, and T10Y3M) are innovatively incorporated into the forecasting model, enhancing its adaptability to complex market environments. An empirical analysis based on a large-scale daily dataset from 1990 to 2024 shows that the model significantly outperforms traditional methods and standalone deep learning models in terms of MSE and MAE metrics. The model’s superiority and stability are further validated through multiple robustness tests, including varying sliding window sizes, adjusting dataset proportions, and experiments with different forecasting horizons. This study not only provides a highly accurate tool for gold price forecasting but also offers a novel methodological pattern to financial time series analysis, with important practical implications for investment decision-making, risk management, and policy formulation.

This study investigates the role of contextual cues in memory-based decision-making within high-stakes trading environments. Using trade records from a large Chinese brokerage firm and a novel dataset on COVID-19 quarantines, we find that quarantine periods trigger the recall of previously traded stocks, increasing the likelihood of subsequent orders for those stocks. The observed patterns align more closely with similarity-based recall than with alternative channels. Welfare analysis reveals that these memory-induced trades lead to an annualized loss of approximately 70 percentage points for the representative investor's portfolio. We also find evidence at the market level: when the geographical distribution of quarantine risks is recalled, the probability of recalling the cross-sectional stock return-volume distribution from the same day increases by 1.6 percentage points. This study provides causal evidence from a real-world setting for memory-based theories, particularly similarity-based recall, and highlights a novel channel through which COVID-19 policies affect financial markets.

We study tail-risk contagion in Belt and Road (BRI) stock markets by conditioning on shocks from China and global commodities. We construct time-varying contagion indices from conditional higher co-moments (CoHCM) estimated within a DCC-GARCH model with generalized hyperbolic innovations, and apply them to daily data for 32 BRI markets. The higher-moment index isolates two channels: a China-driven financial-institutional channel and a WTI-driven commodity-real-economy channel, whereas a covariance benchmark fails to recover this separation. Furthermore, the system-GMM estimates link the China-conditional channel to institutional quality and financial depth, and the WTI-conditional channel to real activity. In out-of-sample portfolio tests, the WTI-conditional signal improves risk-adjusted performance relative to equally weighted and mean-variance benchmarks, while the China-conditional signal does not. Tail-based measurement thus sharpens identification of contagion paths and yields information that is economically relevant for risk management in interconnected emerging markets.

Climate change is increasing the risks of weather-related disasters in many regions around the world. This has an adverse socio-economic impact on households, farmers and small businesses. Some strategies for effectively managing climate related disasters include index based insurance products, which are increasingly offered as alternatives to traditional insurance, particularly in low-income countries. However, the uptake of index insurance remains low, which can be partially attributed to the inherent problem of basis risk. This review assesses the problem of

Copper resources are widely used in power networks and clean - energy tech like PV panels, wind turbines, and NEVs. Restricted by domestic resources, China's copper supply chain is vulnerable with risks. Based on six supply - chain stages, this paper builds an assessment system for China's copper supply - chain risks. By adopting an improved Benefit of Doubt (BOD) model, this paper has systematically evaluated the risks in the whole copper supply chain, revealing the trends and deep-rooted causes of these risks. The findings of this study reveal that: (1) The supply chain risk of China's copper resources presents a significant upward trend over the past 15 years; (2) The current supply chain risks in copper are mainly concentrated at the stages of import, production, and application; and the recycling risk has a great potential for reducing the copper supply chain risks in the future. Based on these findings, this paper proposes two policy recommendations: (1) Develop diversified channels for importing copper resources and optimize overseas investment patterns and; (2) Improve the domestic supply capacity of secondary copper resources and reduce the risks at the recycling stage.

With the increasing penetration of renewable energy (RE) in power systems, the electricity spot market has become increasingly uncertain, presenting significant challenges for generation companies (GenCos) in formulating effective bidding strategies. Most existing studies assume that GenCos act as perfectly rational decision makers, overlooking the impact of irrational bidding behaviors in uncertain market environments. To address this limitation, we incorporate prospect theory to model the decision-making process of bounded rational GenCos operating under risk. A bilevel stochastic model is developed to simulate strategic bidding in the spot market. In addition, a distributional re-inforcement learning algorithm is proposed to tackle the decision-making challenges faced by bounded rational GenCos with risk considerations. The proposed model and algorithm are validated through simulations using a 27-bus system from a region in eastern China. The results demonstrate that the algorithm effectively captures market uncertainties and learns the distribution of GenCo’s profits. Furthermore, simulated bidding strategies for various types of GenCos highlight the applicability of prospect theory to describe bounded rational decision-making behavior in electricity markets.

The 16th annual China International Conference on Insurance and Risk Management (CICIRM 2026) will be held on July 8-11, 2026 at the Yunnan Lianyun Hotel in Kunming, Yunnan, China. The conference is organized by the China Center for Insurance and Risk Management, School of Economics and Management, Tsinghua University, and co-organized by the School of Finance, Yunnan University of Finance and Economics.

We construct a new geospatial measure using high-resolution river vector data from National Geomatics Center of China (NGCC) to study how urban riparian exposure shapes local government debt financing costs. Our base-line results show that cities with higher riparian exposures have significantly lower credit spreads, with a one-standard-deviation increase in riparian exposure reducing credit spreads by approximately 12 basis points. By comparing cities crossed by natural rivers with those intersected by artificial canals, we disentangle the dual role of riparian zones as sources of natural capital benefits (e.g., enhanced transportation capacity) versus climate risks (e.g., flood vulnerability). We find that climate change has amplified the impact of natural disasters, such as floods and droughts, particularly in riparian zones, thus weakening the cost-reducing effect of riparian exposure on bond financing. In contrast, improved water infrastructure and flood-control facilities strengthen the cost-reduction effect. Our findings contribute to the literature on natural capital and government financing, offering valuable implications for public finance and risk management.

The frequent occurrence of extreme weather events not only poses serious challenges to global economic growth and financial stability but also affects firms negatively across multiple dimensions. Using a sample of Chinese A-share listed firms from 2006-2016, this study aims to explore the effect of climate risk on firm export sophistication. The findings show that climate risk inhibits firm export sophistication, with the results varying depending on firm and industry types. Specifically, climate risk (i) inhibits export sophistication for firms with low government subsidies more than for firms with high government subsidies; (ii) restraints export sophistication for firms in high-tech industries rather than for low-and medium-tech industries; and (iii) reduces export sophistication for firms in low-marketization regions more than for firms in high-marketization regions. In addition, channel analysis shows that climate risk inhibits firm export sophistication by increasing financial constraints and reducing human capital.