As China's bond market expands rapidly, the complexity of financial guarantee networks and their implications for credit risk have become critical issues in both academic research and financial practice. Utilizing micro-level data from China's credit bond market spanning 2014 to 2024, this study constructs a multi-layer network incorporating bonds, guarantors, and issuing firms to empirically examine the impact of guarantor network centrality on bond credit spreads. The results reveal a significant U-shaped relationship: moderate centrality reduces spreads by bolstering market confidence, whereas excessive centrality increases them due to heightened systemic risk. Mechanism analyses identify systemic risk and information asymmetry as key mediating channels through which centrality affects credit risk premiums. Heterogeneity tests indicate that this U-shaped pattern is more pronounced among state-owned guarantors, real estate firms, and high-risk clusters within the network. Furthermore, both cross-layer connectivity within the multi-layer structure and regional financial development levels significantly moderate the centrality-spread relationship. These findings offer a structural perspective on credit risk pricing in emerging markets and provide valuable policy insights for credit rating system design, guarantee regulation, and systemic risk prevention. International investors could also leverage these findings to better assess systemic risk in interconnected financial markets across emerging economies.

城镇居民收入持续正增长，非必需消费增速却从高位大幅回落，2022年起更是逼近零增长。这一“收入稳、消费停”的矛盾格局，暴露出现行消费理论对中国居民行为的结构性盲区。本文从抵押品约束视角切入，构建“抵押品单向派生安全垫与杠杆率——双指标双向反馈——消费非对称突变”的完整因果链条。研究发现：抵押品是安全垫与杠杆率的单向底层驱动因素，反向不存在长期因果关系；双指标无固定线性相关，上行周期同向变动，下行周期反向变动，突破阈值后形成共振放大。最核心的发现是：下行阶段对非必需消费的抑制力度是上行提振的3.09倍，其中2.25倍源于损失厌恶，0.84倍来自信贷刚性收缩与宏观负螺旋。安全垫指数跌破100、居民杠杆率突破45%两条阈值击穿后，消费抑制效应非线性跳升，叠加下跌加速度、持续时长、跌幅深度、外部冲击与偿债压力五层因素，冲击强度进一步逐级放大。

We investigate the stock market reaction in China to Trump’s tariff policy announcement on April 2, 2025. We find that the tariff policy reduced stock prices of Chinese firms except those in the agricultural sector. Large-cap stocks, value stocks, stocks of high profitability firms, and stocks of state-owned enterprises experienced smaller negative impacts. Stocks with higher institutional holdings by mutual funds and Social Security Funds exhibited higher resilience, possibly due to these investors' superior capability in selecting stocks and forecasting trade war risks. In contrast, stocks held by Qualified Foreign Institutional Investors (QFII) did not exhibit such resilience.

This study examines the mean-reversion behavior of trading volume in China’s A-share market, with a focus on the speed at which abnormal surges dissipate. We compare two competing hypotheses: the stealth-trading hypothesis, where persistent volume reflects order-splitting by informed traders, and the informational-efficiency hypothesis, which interprets faster reversion as a sign of efficient information absorption. Using the Ornstein–Uhlenbeck (OU) model, we estimate the reversion speed for over 3,000 stocks and link it to firm- and industry-level characteristics. We find that trading volume is strongly mean-reverting, with over 98% of stocks classified as stationary. The OU model forecasts reversion speed with less than 7% error. Faster reversion is associated with larger size, higher analyst coverage, lower volatility, and greater liquidity. Notably, reversion speed increased after the 2006 IFRS reform but declined following Stock Connect, suggesting that stock market policies can influence informational efficiency. Our OU-based methodology offers a simple, observable proxy for monitoring how quickly markets process information. These results position trading volume as a core variable in market microstructure research and policy evaluation.

We investigate how the loss of informative salient signals in financial markets influences stock return volatility, using the 2024 intraday disclosure reform of the mainland China-Hong Kong Stock Connect program as a natural experiment. The reform eliminated the real-time disclosure of northbound capital (NC) flows on trading platforms, rendering NC trading information invisible to Chinese investors during market hours. We find that the removal of NC signals induces increased investor belief dispersion and intensifies informed trading, thereby amplifying intraday volatility in NC-eligible stocks. Moreover, this effect is more pronounced for stocks with higher investor attention, indicating that attentive investors suffer stronger anchor loss when NC signals disappear. In contrast, lottery-type stocks and stocks with alternative NC trading clues exhibit weaker volatility responses, since the presence of strong alternative signals reduces the effect of NC signal loss. These findings highlight the informational role of insightful salient signals in stabilizing stock returns.

Straddle Option is a financial trading tool that explores volatility premiums in high-volatility markets without predicting price direction. Although deep reinforcement learning has emerged as a powerful approach to trading automation in financial markets, existing work mostly focused on predicting price trends and making trading decisions by combining multidimensional datasets like blogs and videos, which led to high computational costs and unstable performance in high-volatility markets. To tackle this challenge, we develop automated straddle option trading based on reinforcement learning and attention mechanisms to handle unpredictability in high-volatility markets. Firstly, we leverage the attention mechanisms in Transformer DDQN through both self-attention with time series data and channel attention with multi-cycle information. Secondly, a novel reward function considering excess earnings is designed to focus on long-term profits and neglect short-term losses over a stop line. Thirdly, we identify the resistance levels to provide reference information when great uncertainty in price movements occurs with intensified battle between the buyers and sellers. Through extensive experiments on the Chinese stock, Brent crude oil, and Bitcoin markets, our attention-based Transformer-DDQN model exhibits the lowest maximum drawdown across all markets, and outperforms other models by 92.5% in terms of the average return excluding the crude oil market due to relatively low fluctuation.

In this paper, we design two chapters to discuss trade dynamics with heterogeneous fluctuations, contributing new insights to macroeconomic issues related to international trade. In the first chapter, we model general exchange rate fluctuations through stochastic processes and analyze the impact of heterogeneous price shocks on export competitiveness. We find that monetary policy and innovation both show positive effects on export trade, while monetary policy stabilizes exchange rate fluctuations to comprehensively boost provincial export competitiveness, innovation reduces its reliance on exchange rate mechanisms. The optimal policy according to exchange rate fluctuations aims to solve the wealth distribution of exporters, and it suggests that optimal policy should promote dynamic transitions in trade patterns rather than maintain existing comparative advantages in heterogeneous trade structures. In the second chapter, we model labor market fluctuations and the ability to utilize production factors through stochastic processes, and we analyze the impact of heterogeneous aggregate production shocks on general international trade. We find that labor market fluctuations only benefit international trade under the cooperation policy. Moreover, for both sanction and cooperation policy scenarios, positive shocks (i.e., shocks where average wage growth in the labor market exceeds unemployment) strengthen their impact on import trade while weakening their impact on export trade, and vice versa. Regarding the theories proposed in these two chapters, we prove them through empirical analyses using the provincial data of China.

基于资本“金融生息与生产增值”的二重属性，本文构建二维资本汇率定价理论（Capital-Pricing Exchange Rate Theorem,CPERT）。从无套利公理出发证明：汇率由利差与资本边际产出（MPK）差值共同决定，两大因子近似独立且定价权重对等，无摩擦环境下对冲系数精确等于√2。传统无抛补利率平价仅为该框架在MPK差值为零时的特殊形式。三维数据集（1998—2025年，中美长时序+省级面板+27国跨国面板）的实证结果验证了这一理论预言：双因子模型调整后R²为81.27%，较传统单一利差模型提升约17个百分点；24个市场化经济体的对冲系数均值为1.450，与√2理论基准的相对偏差约为2.54%，统计上无法拒绝二者相等的原假设。研究进一步识别出跨境资本的三层异质性，以及利差与MPK的双重门槛效应，据此划分四类汇率风险状态。由此形成的√2双重稳健性标准、金融结构杠杆乘数与三元悖论弹性系数三类工具，可应用于跨境收支统计校验与宏观政策评估。二维框架弱化了传统三元悖论的刚性约束，为开放经济体协同实现货币政策独立与汇率稳定提供了量化依据。

新质生产力实体资产技术迭代速度快、有效运营周期偏短，传统永续现金流折现估值易产生系统性偏差，导致该类资产难以被商业银行认定为合格抵质押物，融资约束问题突出。结合股份制银行实地调研与公开行业数据测算，国内新质生产力实体资产抵押融资存在显著规模缺口，形成“资产具备稳定收益、却难以实现抵押融资”的结构性矛盾。本文构建达叔（DASU,Debt Asset Settlement Unit,偿债资产清算单元）估值框架，以历史经营性现金流为核心锚点，设定五年估值截断规则与五维度审慎折价体系，依托三方均衡模型完成理论推演，并选取 49 只公募 REITs 开展实证检验。结果显示，框架估值与市场成交均价平均偏差仅 1.50%，估值波动较传统永续模型下降 42.3%；五维模型整体解释力达 89.2%，市场投机因子边际影响不足 0.5%，对新质生产力资产、粤港澳大湾区标的适配性更强。研究提炼现金流锚定、期限匹配两大理论命题，形成可审计的银行抵质押物估值标准，为商业银行估值逻辑转向有限期审慎评估提供实证支撑。

全球美元流动性的周期性涨跌被学界称作 “美元潮汐”，传统研究过度强调美联储政策、跨境资本等外生因素，不仅存在因果逻辑倒置问题，也难以精准研判周期演化趋势。本文以 1900—2025 年美国长时序数据为样本，提取十年期美债收益率的中长期周期成分作为观测变量，结合非线性框架下的临界逃逸速度与结构相变理论展开实证研究。结果显示，人口代际滞后特征与住房按揭存续周期构成了美元潮汐的内生底层约束；人口结构、住房偿债负担率、资本边际产出三类核心变量，对美元潮汐外生波动的解释力度达到 51.7%。1983 年是美元运行体系发生永久性结构突变的关键节点，适龄购房人口占比突破 22.0% 临界阈值后，市场波动模式发生本质转变，形成稳定的周期性运行态势。外部扰动仅具备短期效应，无法左右周期的中长期运行轨迹。本文厘清了美元潮汐的内生形成机理，实证发现 26 年人口代际周期与美元潮汐周 期存在固定嵌套关系，一个完整人口代际周期恰好对应 1.5 轮美元潮汐。