In this paper, we investigate alternative one-factor and two-factor continuous-time models with both affine and non-affine variance dynamics for the Chinese options market. Through extensive empirical analysis of the option panel fit and diagnostics, we find that it is necessary to include both the non-affine feature and the multi-factor structure. For performance evaluation, we examine various measures from both aggregate and dynamic perspectives. Our results are statistically significant.

冯静仪

This study applies large language models (LLMs) to measure technological links and examines its predictive power in the Chinese stock market. Using the BAAI General Embedding (BGE) model, we extract semantic information from patent textual data to construct the technological momentum measure. As a comparison, the measure based on traditional International Patent Classification (IPC) is also considered. Empirical analysis shows that both measures significantly predict stock returns and they capture complementary dimensions of technological links. Further investigation through stratified analysis reveals the critical role of investor inattention in explaining their differential performance: in stocks with low investor inattention, IPC-based measure loses its predictive power while BGE-based measure remains significant, indicating that straightforward information is fully priced in while complex semantic relationships require greater cognitive processing; in stocks with high investor inattention, both measures exhibit predictability, with BGE-based measure showing stronger effects. These findings support behavioral finance theories suggesting that complex information diffuses more slowly in markets, especially under significant cognitive constraints, and demonstrate LLMs’ advantage in uncovering subtle technological connections that traditional methods overlook.

To address the challenges posed by renewable energy integration in power systems, China is advancing the development of Pumped Hydro Storage (PHS). However, the rapid growth of PHS installations, coupled with strict regulations and a high reliance on capacity compensation, has led to increasing financial burdens on other utilities. One solution is to reallocate the capacity compensation through market-based approaches to implement the “beneficiary-pays” principle. To achieve this goal, an operational policy named ’partial-regulated dispatch’ is proposed in this study. The analysis of this policy encompasses two crucial dimensions: the dispatch mechanism and business models. The dispatch mechanism evaluates PHS’s capacity contribution to grid stability, while the business models focus on enhancing PHS profitability to reduce dependency on capacity compensation while ensuring long-term economic sustainability. Furthermore, the flexibility of PHS is introduced as a criterion for assessing system security contributions, considering both individual unit vibration characteristics and multi-unit commitment strategies. The case study shows that through partial-regulated dispatch, PHS can reduce its reliance on capacity compensation by nearly 50% while ensuring its regulation service via flexibility compensation. This policy effectively balances economic viability with system support capabilities. Moreover, flexibility compensation provides PHS operators with a risk mitigation strategy in the complex power market environment. Under an appropriate operational strategy and policy incentives, the flexibility can be enhanced by nearly 30% in a fully marketized scenario, contributing to both system stability and operational efficiency.

The high-penetration integration of renewable energy requires huge demand for reliable capacity resources, and the coal-fired units are the main providers of the reliable capacity in China. This study proposes a future-oriented approach to facilitate coal-fired power’ transition through capacity market development. Focusing on China’s power market reform context, we propose a two-stage capacity market mechanism integrating annual capacity auctions and monthly capacity bidding, and design the procedural and transactional framework for coal-fired power participation. We further outline three market strategies including energy market trading, centralized capacity market trading, and renewable energy alliance leasing. Environmental costs are incorporated to construct revenue models and derive boundary conditions for coal-fired units’ decision-making. Research results reveal that current capacity prices fail to cover costs, requiring substantial market-driven price increases to achieve profitability. While stable capacity revenue can reduce medium-to-long-term and spot market prices, fostering competition between coal-fired power and renewable energy resources. However, coal-fired power remains highly sensitive to price volatility, demanding robust resilience to fluctuations. Carbon prices significantly influence capacity prices, yet excessive free carbon quota allocations weaken carbon price transmission effects, necessitating optimized quota ratios to enhance market responsiveness. Finally, policy implications are proposed according to the research results.

Prefabricated buildings(PBs) have significant advantages in improving construction efficiency, saving resources, and reducing environmental pollution. They have become an important direction for transforming and upgrading the global construction industry. However, the high construction costs have severely restricted their large-scale adoption. To systematically explore the key influencing factors and the mechanism of the construction cost of PBs, this study uses the method of combining interpretative structural model (ISM) and structural equation model (SEM), identifies the main influencing factors by synthesizing literature and data analysis, analyze hierarchical relationships between these factors via ISM, and quantifies the influence intensity and mechanism of the construction cost by SEM method. The results show that the driving factors of the construction cost of PBs can be divided into several levels. The core factors, such as the assembly rate, the production scale of prefabricated components, the integration of design management, the technical level of designers, and the specialization of prefabricated components in the factory, play a crucial role in cost optimization. In conclusion, this study deeply reveals the impact mechanism of the construction cost of PBs, offers practical guidance for reducing construction costs and optimizing resource allocation, and provides a scientific basis for government policy-making and enterprise strategic decision-making.

Climate change presents a global threat, prompting nations to adopt low-carbon development pathways to mitigate its potential impacts. However, current research lacks a comprehensive framework capable of integrating multiple variables and providing dynamic optimization capabilities. This article focuses on designing pathways for developing a low-carbon economy to tackle climate challenges. Specifically, we construct a low-carbon economy model that incorporates economic, environmental, social, energy, and policy factors to analyze the drivers of economic growth and carbon emissions. We utilize economic model predictive control and tracking model predictive control to optimize development pathways aligned with various low-carbon targets, creating and validating a comprehensive framework for low-carbon policy design using historical data from China. This study highlights significant advantages in analyzing low-carbon pathways through advanced techniques like hierarchical regression and model predictive control, providing a robust framework that enhances our understanding of causal relationships within the LCE system, captures system feedback, dynamically optimizes pathways, and accommodates diverse policies within a comprehensive low-carbon economy system.

We revisit cross-asset time-series momentum (XTSM) and single-asset time-series momentum (TSM) in China's stock and bond markets. With a fixed-effects model, we find a positive momentum from bonds to stocks and a negative momentum from stocks to bonds, with both momentum persisting for no more than six months. By employing a cross-grouping method, we find that the choice of lookback periods and asset signals impacts the performance of XTSM and TSM. A comparison between XTSM, TSM, and time-series historical (TSH) portfolios reveals that XTSM outperforms in small/midcap stocks and government bonds, while its performance is weak in large-cap stocks and corporate bonds. A spanning test confirms that XTSM generates excess returns that other pricing factors can not explain. XTSM is more prone to momentum crashes. Increased market stress has similarly adverse effects on XTSM and TSM. Furthermore, Market illiquidity, IPO counts, new investor accounts, and consumer confidence index positively correlate with the returns of XTSM and TSM portfolios, while IPO first-day return and turnover rate correlate negatively. The effects of these sentiment indicators exhibit heterogeneity.

Exploring the relationship between government subsidies, market competition, and the digital transformation of cultural and tourism enterprises (CTEs) will provide inspiration for upgrading and promoting the digitization development of China’s cultural and tourism industry. This paper theoretically and empirically evaluates the effect of government subsidies on digital transformation of CTEs by developing an evolutionary game combined with Hotelling model, and meanwhile using the econometric model. The theoretical model demonstrates that different subsidy scale will affect the evolutionary efficiency of the system under different market competition intensities. And the empirical model shows that the relationship between government subsidies and the digital transformation of CTEs is a U-shaped curve and the market competition exerts the flattening moderation on the U-shaped relationship. The findings provide guidance to both policymakers and managers.

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.