As the origin of modern commercial insurance, ship insurance underpins global maritime supply chain stability. Yet shipping modernization and AI advances expose three flaws in traditional risk profiling: misalignment with frequency‐severity pricing, inadequate for accommodating to complex risk factor system, and lack of data stream adjustment mechanisms. To address these, we propose POM principles ( Personalized risk portrait , Omnispective risk factors , and Maneuverable calibration ) and an AI framework with three cores: (1) extensible “retrospective + prospective” risk factors; (2) independent AI modules for premium rate/insurance amount prediction; (3) data steam calibration on historical data. Validated via 15,007 records (15% of China's 2016–2021 registered ships) using random forest regression, it outperforms traditional generalized linear models and mainstream machine learning models in accuracy and risk differentiation. This pioneers intelligent ship insurance profiling, fills gaps in individualized pricing, and offers insights for sectors like aviation insurance, sharing its “premium rate × insurance amount” logic.

Against deglobalization and intensifying geopolitical conflicts, maritime bulk commodity supply chain vulnerability and resilience governance are strategic priorities for 75% of countries. To tackle rising global uncertainty, this study proposes the country-level risk identification, monitoring, and extrapolation (RIME) framework for such supply chains, which aligns with the theoretical demand for macro, end-to-end risk integration beyond the traditional firm-level focus. Based on the “supplier country–shipping route–importing country” spatiotemporal linkage, we construct the first standardized country-level vulnerability index. It overcomes the limitations of existing static and localized assessments by integrating spatiotemporal, multi-source risks across the full physical chain, thereby enabling dynamic, macro-level monitoring and supporting systematic diagnostics and trend tracking of national supply chain security. We also develop an emergent risk simulation technique to quantify the direction and intensity of compound disturbances as well as the system’s dynamic responses. Empirical validation with China’s iron ore imports shows that the index effectively captures risk evolution, while the simulations confirm that sudden disruptions amplify systemic risk. This framework fills national strategic security theoretical gaps and provides governments with dynamic monitoring, quantitative assessment, and policy forecasting tools.

Rising fragility in bulk commodity seaborne supply chains has amplified price volatility and spawned notable spatiotemporal arbitrage opportunities. Traditional cost-of-carry arbitrage theories ignore floating storage capacity, a key variable that significantly affects the basis (spot-futures spread) and distorts pricing in extreme market conditions. This paper addresses the gap by building a contango arbitrage index integrating spot and floating storage arbitrage, utilizing ships' dual transport-storage role. The index quantifies seaborne bulk arbitrage profits, reveals how maritime dynamics shape pricing efficiency, and extends the 'Theory of Storage' from land to floating storage. Applying the index to two transatlantic LNG routes (U.S. Henry Hub to UK Cardiff/Dutch Rotterdam) and using the Diebold-Yilmaz spillover model (2017-2023), our results indicate that global LNG vessel capacity functions as a latent spillover transmitter, repeatedly amplifying volatility transmission during COVID-19, Europe's energy crisis, Russia-Ukraine war, and Nord Stream explosion. Short-term peaks in the index are driven by geopolitical shocks and capacity crunches, whereas mid-term peaks arise from floating storage dynamics, uncovering storage's nonlinear role in transmitting macroeconomic shocks to volatility. The study advances commodity pricing theory, guides LNG arbitrage strategy optimization, and gives policymakers tools to reduce distortions, boosting market efficiency.

To better understand the complex financial dynamics under increasing global uncertainty, we propose an innovative empirical framework based on multifractal detrended cross-correlation analysis and wavelet coherence. We examine the dynamic cross-correlation relationships among the stock markets of the US, UK, Japan, China, and Australia, and explore the impact of US economic policy uncertainty on these relationships. The results reveal significant intermarket cross-correlations, particularly during influential macroeconomic events such as COVID-19 and the regional capital market turbulence of 2015–2016. We also find that the impact of US economic policy uncertainty on stock market correlations varies over time and is more pronounced in the medium to long term. Specifically, the impact is the largest on the US-UK stock market linkage and the smallest on the US-Japanese stock market linkage. During periods of global crises, such as COVID-19, intermarket correlations tend to be strengthened, while in regional crises, intermarket correlations become weakened. Moreover, the lead-lag relationships between economic uncertainty and intermarket correlations differ depending on the type of crisis. Our findings shed light on how economic uncertainty affects global financial market linkages, offering valuable insights for policymakers and investors.

To address the limitations of traditional systemic risk indices in measuring nonlinearity and network interdependence, we introduce ESRISK, a novel systemic risk measure that incorporates ensemble learning and risk spillover networks. Our approach can effectively analyze the complex nonlinearity in high-dimensional data, enabling more accurate quantification of systemic risk in China's financial system. Comprehensive evaluations reveal that ESRISK outperforms prevailing systemic risk measures, particularly in predictability, accuracy in measuring systemic risk, and effectiveness in early warning detection of systemic events. Moreover, ESRISK demonstrates superior predictive power for macroeconomic downturns. Our findings highlight the importance of applying machine learning methods and considering inter-institutional spillovers when measuring systemic risk in China's financial ecosystem.

Financial uncertainty shocks are emerging as potential drivers for the spillovers of risk originating from the oil market into the stock market, with the increasing financialization of the oil market. This paper explores this phenomenon and provides compelling findings. First, the oil market generates substantial risk spillovers to the stock market, reaching a peak amid the COVID-19 crisis. Second, according to the backtesting results, the Delta CoVaR values derived from the Student-t Copula model reflect the true level of such risk spillovers. Third, shocks to financial uncertainty increase systemic risk by causing risk to spill over from the oil to the stock market, with larger spillovers occurring during periods of increased economic vulnerability. Finally, financial uncertainty shocks are the fundamental drivers of variance changes in risk spillovers, making a greater contribution than macroeconomic uncertainty shocks, according to the time-varying forecast error variance decomposition.

The relationships between the oil market and other financial markets remain poorly understood. In this paper, we first construct a set of spillover indices that measure the return spillovers from the oil market to other financial markets in the short, medium, and long terms, and then we examine the drivers of spillover intensity by focusing on the effect of quantitative easing in the U. S. The main empirical results are as follows. First, the return spillovers from the oil market to other markets are driven by various frequencies (short-term to long-term), and intensified during the global financial crisis of 2007-2009. Second, quantitative easing has different effects on the spillover intensity at different frequencies, with the effect on short-term spillovers being less significant. Third, our research provides the first empirical evidence for a double-edged sword effect of quantitative easing on the systemic risk from the frequency perspective.

There is a growing interest in the influence of blockholder exit threats on corporate governance. When perceive managerial underperformance, blockholders have strong incentives to reduce their shareholding, which in turn requires managers to align their actions with the interests of shareholders. In this paper, we further develop the theory of blockholder exit threats by scrutinizing the influence of blockholder exit threats on excess executive perks. Empirical results suggest that blockholder exit threats have a significant reducing effect on excess executive perks, which is further confirmed via robustness checks. It is also found that the reducing effect is enhanced when managers’ wealth is closely related to stock prices, blockholders are long-term strategic blockholders, or short selling is allowed, under the condition of a deteriorating information environment or corporate governance, or before the Blockholders Reduction Restrictions.

This study examines the impact of blockholder exit threats on excess cash holdings following China's split-share structural reform. Previous studies have confirmed the governance role of blockholder exit threats, but their effectiveness is limited to companies with greater private benefits. Using a sample of 2340 firm-year observations of Chinese listed firms between 2002 and 2009, we find that the exit threat of blockholders increases the level of excess cash holdings. These results suggest that blockholders view exit threats as a means of collaborating with controlling shareholders to boost excess cash holdings and diversify corporate resources for private benefits. The collusion effect is more pronounced in firms with poorer investor protections, lower shareholding concentrations, and more severe agency conflicts. Additionally, in terms of economic consequences, blockholder exit threats decrease buy-and-hold abnormal returns and increase the occurrence of corporate scandals. Overall, this study provides empirical evidence of collusion among large shareholders, which harms small shareholders' interests from the perspective of excess cash holdings.

Despite the rich literature on the relationship between trading volumes and stock prices, few studies explore the underlying linkage arising from multifractality. Here, we propose a Hurst-based market-trend index measuring the dynamic cross-correlation between volumes and prices, and illustrate its usefulness with applications on the Chinese stock market. We show that the new index can reflect the volume-price relationship's underlying changes with varying market conditions across different sectors. We also show how the new index dominates the change in the Granger causality. Further economic analyses demonstrate how the new index can be used for improving trading strategies. The insights on the underlying relationship between stock volumes and prices are important for investment decisions and have insightful policy implications.

Forward Freight Agreements (FFAs) are exchange-traded futures and the main means of risk management for shipping spot freight. The relationship between the FFA market and the shipping spot market has attracted much attention, but few studies focus on the dynamic FFA term structure and its correlation with the spot price. In this study, we establish the dynamic term structure of the FFA market by quantifying the level factor, the slope factor and the curvature factor of the FFA term structure to reveal the underlying information in the shipping derivatives market, and further scrutinize the impact of the FFA term-structure factors on the shipping spot market. The empirical results indicate that the term-structure factors have significant time-varying effects on the BDI and BPI: The effect of the term-structure factors on BDI and BPI changes with shipping market conditions and economic environment. The level factor has a positive effect, while the slope factor and the curvature factor both have negative effects, with the impacts on the BPI being about twice as large as the impacts on the BDI. The three factors all have their strongest effect in the short term and the weakest effect in the long term.

PurposeBased on the textual-analyzed data covering 2148 IPO firms in China’s stock market during the 2007–2018 period, the authors’ purpose is to examine the influence of anti-takeover provision (ATP) adoption on initial public offerings (IPO) underpricing and identify the reducing effect of the former.Design/methodology/approachThe authors examine the sample consisting of Chinese A-share listed IPO firms between 2007 and 2018 from China Stock Market Accounting Research and Chinese Research Data Services, with ATP data collected from the IPO firm chapters. Specifically, the authors use text analysis to identify whether there are ATPs in the IPO firm chapters, as well as the number of ATPs. H1: IPO underpricing is less severe for firms adopting ATPs. H2: The effect of ATP adoption on IPO underpricing is more salient for firms in worse information environments.FindingsThe authors examine the influence of ATP adoption on IPO underpricing and identify the reducing effect of the former. This effect can be explained by the fact that adopting ATPs in IPO firm chapters can reduce information asymmetry to a large extent by helping external investors obtain more private information, which alleviates IPO underpricing. The authors also find that the reducing effect is more significant in the worsened information environment. Furthermore, the authors explore the influence of adopting ATPs on other IPO characteristics and find positive effects on IPO over-subscription, funds raised and trading activity and negative effects on listing fees.Originality/valueThis study mainly contributes to the literature from the following two aspects. First, the study enriches the literature about the influencing factors of IPO underpricing. Second, the study also enriches the literature about the economic consequences of ATP adoption. This study also has important policy implications. With the coming of the era of decentralized ownership in China’s capital market, ATP adoption has become more important and attracted more attention. Also, investors focus more on pricing efficiency. The findings in this paper provide a more comprehensive understanding of the relationship between ATP adoption and IPO underpricing.

When forecasting the value-at-risk (VaR) of the crude oil market, traditional models often fail to capture the information embedded in low-frequency macro-variables and tend to underestimate the high quantiles caused by adopting commonly used distributions. To address these problems, this paper proposes a new approach, which combines the generalized autoregressive condition heteroskedasticity (GARCH)-mixed data sampling (MIDAS) models with extreme value theory (EVT). Our empirical results show that first, the GARCH-MIDAS models outperform the benchmark models when they incorporate suitable low-frequency macroeconomic variables. Second, the VaR forecasting accuracy of some GARCH-MIDAS models can be further improved when combined with EVT. Third, the EVT-based GARCH-MIDAS model that contains the demand-side information of the oil market achieves the best performance among all the models. Fourth, the historical simulation (HS) method that is widely used by financial institutions is extremely inaccurate.

With gold being one of the most important precious metals that play irreplaceable roles in the global market, understanding the future movement of the gold price is of significant importance for investment and risk management worldwide. However, gold prices are subject to volatility and can experience significant fluctuations over time due to economic uncertainty shocks such as the China-US Trade War, Russia-Ukraine war, and COVID-19, which make the forecasting of gold price a challenging task. In this paper, we propose a hybrid forecasting model for gold prices based on the Hurst-oriented reconfiguration and machine learning approach and illustrate its usefulness by analyzing the gold prices of three major markets. We conduct a multifractal analysis of the decomposed series and scrutinize the predictability of each sub-series and its relationship with the Hurst exponent. Empirical results show that there are negative relationships between forecasting error and the Hurst exponent and between the number of embedding dimensions and the Hurst exponent. Our Hurst-based hybrid model outperforms other conventional prediction models in terms of prediction errors and accuracy of direction prediction. The findings of this study shed light on a better understanding of the temporal features of the gold market and provide references for improving investment and hedging strategies.

To quantify the impacts of risk shocks on time-domain and frequency-domain spillovers, we propose a new empirical framework based on TVP-VAR and wavelet coherence analysis. We illustrate the methodology by analysing the spillovers among the gold, oil, emerging, and developed markets from 10 February 2011 to 2 April 2024 and obtain intriguing findings. First, the dynamic spillovers among markets rise significantly during turbulent periods. The dynamic net spillover results show that the gold and emerging markets are mainly the spillover receivers, developed markets are spillover emitters, and the oil market plays a switching role over time. Second, risk shocks have frequency-dependent impacts on the spillovers among markets. The effects are concentrated in the medium- to long-term ranges of 2015, 2018, and 2020-2021, and the relationship between risk volatility and dynamic total connectedness is positive. The impact of risk volatility on net market dynamic spillovers is heterogeneous in the time and frequency domains and the lead-lag relationship. Our findings have important implications for policymakers and investors.

With the impact of the COVID-19 pandemic, global container freights have increased dramatically since the second half of 2020, which has significantly hampered the booking activities of fragmented transportation space for small and medium-sized import and export enterprises (SMIEEs). To provide SMIEEs with an effective tool for controlling shipping costs, we propose the design principles of index microinsurance under fragmented scenarios and design the container freight index microinsurance (CFIM) based on a comprehensive analysis of the term, compensation and share structures. We further establish the pricing model for the CFIM and selection procedure for product optimization, and illustrate the framework with a case study based on the data of the China Containerized Freight Index Europe Service, which demonstrates the good performance of the designed product even under extreme market conditions. The design principles proposed can shed light on the innovation of index microinsurance product that meets fragmented needs and the newly designed CFIM, along with the pricing and optimization procedure, provides practitioners with useful tools for cost control.

In this paper, we establish a frequentist framework that incorporates all confidence sets with guaranteed frequentist coverage probability for the binomial proportion, where different confidence sets are completely characterized by their tail functions. Based on measures of precision in the form of interval length, probability of false coverage, and a new evaluation criterion utilizing prior information, we construct the optimal confidence set for the binomial proportion. The newly proposed methodology is applied to clinical studies. It is shown that confidence intervals obtained via the tail functions are often better than prevailing confidence intervals in view of precision.

Global decarbonization has significantly tightened the link between the shipping industry and the carbon market. Understanding the mechanism of the interdependence between shipping energy and the carbon market is of great importance for carbon pricing and decarbonization of shipping; however, one challenge is that, unlike traditional energy resources, the transmission channels of carbon emissions from shipping energy are built on a global network with distinct geographical heterogeneity. In view of this, we propose a global carbon-shipping market-level framework and scrutinize the intermarket spillover effect in the time and frequency domains. The main findings are as follows: First, the short-term spillover dominates the interaction, where shipping energy markets mainly act as the transmitters and carbon markets mainly play the role of the receivers. Second, the EU carbon market has the largest influence on shipping energy markets, and the spillovers of the marine gas oil and high‑sulfur fuel oil markets on carbon markets are more prominent. Third, the intensity of the spillover effect is negatively correlated with the distance of major events, while the volatility of the spillover effect is the largest in European ports and the smallest in Asian ports. Last, policy-oriented events have the most significant impact on the volatility spillovers between markets, followed by politically oriented events, with market-oriented events having the mildest impact. This study sheds light on the mechanism of the spillovers between the carbon and shipping markets accounting for geographical heterogeneity and provides valuable insights for policymakers to better understand both markets and improve policy efficiency.

We propose a novel framework to analyze the potentially heterogeneous roles played by different market participants in the fire-sale process during a market crash and illustrate the methodology with the 2015–16 Chinese stock market turbulence. Unlike conventional analysis focusing on one particular channel of fire sales, we establish a market-level measure of fire sales based on the decomposition of diffusion processes to quantitatively compare the contribution of various channels in driving stock prices to plummet. Empirical results identify mutual funds as the main fire-sale propagator, as well as the heterogeneities in response to the price crash among different market participants.

With the global consensus on carbon emission reduction, the relationships between the carbon market and conventional financial markets have been extensively studied, while the risk spillover between the carbon and shipping markets is merely addressed. In this paper, we propose a new framework for analyzing the frequency-dependent spillover effects based on the wavelet transformation and DECO-ARMA-GARCH-type modelling, and scrutinize the dynamic interdependence between carbon futures and the stock returns of the top ten linear shipping companies under the impact of the COVID-19 pandemic. We further analyze dynamic portfolio management and hedging efficiency under time-varying market conditions with external shocks. The empirical results indicate that short-term spillovers dominate the spillover effect between the carbon and liner shipping markets and the interdependence is at relatively low levels satisfying the conditions for portfolio hedging. The COVID-19 pandemic has enhanced the correlation between the carbon and liner shipping markets, and hence led to reduced hedging efficiency of carbon futures. Also, due to the impact of the pandemic, the holding of shipping assets should be reduced in return for more carbon assets. This study provides shipping companies with a better understanding of carbon trading for shipping emission reduction and investors with applicable dynamic portfolio management strategies.