North American Journal of Economics and Finance最新文献

MRN-based connectedness: A nonlinear approach for capturing systemic risk dynamics in financial systems 基于核磁共振的连通性：捕捉金融系统系统性风险动态的非线性方法

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2026-01-08 DOI: 10.1016/j.najef.2025.102572

Shijia Song , Handong Li

Measuring connectedness among financial institutions is critical for monitoring systemic risk, understanding its formation and transmission, identifying key institutions, and formulating effective regulatory policies. Traditional methods, often based on parametric models, typically represent financial relationships using linear correlations or rely on idealized nonlinear mappings, limiting their ability to capture the inherent nonlinear dynamics and complex interdependencies in financial systems. To address this limitation, this study constructs connectedness indicators using multiplex recurrence networks (MRNs). The MRN-based approach embeds time series into phase space to capture their temporal structures and leverages mutual information to quantify nonlinear dependencies among institutions. Additionally, it requires minimal preprocessing, avoids strong assumptions, and reduces reliance on precise parameter estimation. Simulation experiments demonstrate that the MRN-based approach effectively captures changes in tail dependencies across multidimensional returns, closely reflecting systemic risk dynamics. Empirical analyses of China’s publicly listed banks further illustrate its ability to track the evolution of systemic risk, identify systemically important banks, and highlight the increasing role of state-owned banks in economic adjustments. These results suggest that the MRN-based method offers advantages over VAR-based approaches, providing a more nuanced and timely reflection of systemic risk. By emphasizing the nonlinear characteristics of financial variables, this study complements prudential regulatory tools and enhances the understanding of systemic risk evolution in complex financial systems.

衡量金融机构之间的连通性对于监测系统性风险、了解其形成和传播、确定关键机构以及制定有效的监管政策至关重要。传统方法通常基于参数模型，通常使用线性相关性或依赖于理想化的非线性映射来表示金融关系，这限制了它们捕捉金融系统中固有的非线性动态和复杂的相互依赖性的能力。为了解决这一限制，本研究使用多重递归网络（mrn）构建了连通性指标。基于核磁共振的方法将时间序列嵌入相空间以捕获其时间结构，并利用互信息量化机构之间的非线性依赖关系。此外，它需要最少的预处理，避免强假设，并减少对精确参数估计的依赖。模拟实验表明，基于核磁共振的方法有效地捕获了多维回报中尾部依赖关系的变化，密切反映了系统风险动态。对中国上市银行的实证分析进一步说明了其跟踪系统性风险演变、识别系统重要性银行的能力，并突出了国有银行在经济调整中的日益重要的作用。这些结果表明，基于核磁共振的方法比基于var的方法更有优势，可以更细致、更及时地反映系统风险。通过强调金融变量的非线性特征，本研究补充了审慎监管工具，增强了对复杂金融系统中系统性风险演变的理解。

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引用次数: 0

Sustainability disclosure and bank liquidity risk: evidence from global banking sector 可持续性信息披露与银行流动性风险：来自全球银行业的证据

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2026-01-03 DOI: 10.1016/j.najef.2026.102582

Jianjin Huang , Song-Lin(Sony) Hsieh , Jia Wang

We examine whether sustainability disclosure mitigates banks’ liquidity risk using an international panel of 640 listed banks from 52 countries over 2008–2023. Liquidity risk is a core yet understudied stability dimension in the ESG–banking literature, despite its critical role in financial resilience. Employing a dynamic difference GMM estimator, propensity score matching, and a multi-period difference-in-differences design exploiting staggered ESG disclosure regulations, we find that higher sustainability disclosure significantly reduces banks’ liquidity risk. This effect is economically meaningful and robust across alternative liquidity measures and extensive sensitivity tests. Decomposing ESG into its components, we show that environmental and social disclosures drive the reduction in liquidity risk, whereas governance disclosure has no discernible effect. The impact is stronger for larger banks and in jurisdictions with voluntary rather than mandatory disclosure regimes, consistent with signaling and credibility theories of voluntary reporting. Our results highlight a novel risk channel through which ESG disclosure influences bank stability, offering actionable insights for bank managers and regulators seeking to enhance liquidity resilience through disclosure policy.

我们使用来自52个国家的640家上市银行在2008-2023年的国际面板来研究可持续性披露是否减轻了银行的流动性风险。尽管流动性风险在金融弹性中起着关键作用，但在esg银行文献中，流动性风险是一个核心但尚未得到充分研究的稳定性维度。采用动态差分GMM估计、倾向得分匹配和利用交错ESG披露规则的多期差异中差异设计，我们发现较高的可持续性披露显著降低了银行的流动性风险。这种效应在替代流动性措施和广泛的敏感性测试中具有经济意义和稳健性。将ESG分解为其组成部分，我们发现环境和社会披露推动了流动性风险的降低，而治理披露则没有明显的影响。对于大型银行和实行自愿而非强制性披露制度的司法管辖区，这种影响更大，这与自愿报告的信号和可信度理论是一致的。我们的研究结果突出了ESG披露影响银行稳定性的一个新的风险渠道，为寻求通过披露政策增强流动性弹性的银行经理和监管机构提供了可操作的见解。

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引用次数: 0

Entropy-Based portfolio optimization under Varma–Tsallis Statistics: Evidence from stock markets Varma-Tsallis统计下基于熵的投资组合优化：来自股票市场的证据

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2026-01-03 DOI: 10.1016/j.najef.2026.102581

Muhammad Sheraz , Mihăiță Drăgan , Vasile Preda

In this paper, we propose a novel entropic portfolio model inspired by Cover’s universal portfolio framework, incorporating Tsallis statistics to generalize the traditional approach. Utilizing an

(a, b)

-deformed logarithmic function derived from Tsallis entropy, we introduce the concept of

(a, b)

-growth rate for stock market portfolios and extend it to the Varma–Tsallis entropic framework. Within this setting, we define the optimal

(a, b)

-growth rate and derive the growth-optimal portfolio that maximizes terminal

(a, b)

-wealth over

n

-trading periods. We further establish the asymptotic optimality of our approach, proving that the generalized logarithmic utility portfolio achieves expected returns at least as high as any other strategy under this entropy-based paradigm, ensuring long-run performance dominance. By introducing parameters that govern tail sensitivity and non-extensive entropy effects, our model provides a flexible alternative to conventional strategies. Empirical analyses demonstrate that the Varma–Tsallis portfolio not only adapts more effectively to complex market dynamics but also delivers competitive and often superior performance relative to benchmark Cover’s portfolio strategies, particularly during periods of financial turbulence.

本文在Cover的通用投资组合框架的启发下，提出了一种新的熵投资组合模型，并结合Tsallis统计对传统方法进行了推广。利用由Tsallis熵导出的（a,b）变形对数函数，我们引入了股票市场投资组合的(a,b)-增长率的概念，并将其推广到Varma-Tsallis熵框架。在这种情况下，我们定义了最优（a,b）增长率，并推导出在n个交易周期内使终端（a,b）财富最大化的增长最优投资组合。我们进一步建立了我们的方法的渐近最优性，证明了广义对数效用组合在这种基于熵的范式下实现的预期收益至少与任何其他策略一样高，确保了长期绩效优势。通过引入控制尾部灵敏度和非广泛熵效应的参数，我们的模型为传统策略提供了一个灵活的替代方案。实证分析表明，Varma-Tsallis投资组合不仅能更有效地适应复杂的市场动态，而且相对于基准的Cover投资组合策略，尤其是在金融动荡时期，还能提供有竞争力的、往往更优的表现。

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引用次数: 0

Bank systemic risk prediction based on text mining and explainable machine learning 基于文本挖掘和可解释机器学习的银行系统风险预测

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2026-01-01 DOI: 10.1016/j.najef.2025.102577

Pucong Wang, Sumuya Borjigin

This study utilizes textual data from The Wall Street Journal, employing 12 machine learning models to forecast systemic risk in the US banking sector. Then, this paper applies the SHAP method to interpret the prediction results. The empirical conclusions are as follows: Firstly, in terms of time series forecasting, deep learning models exhibit the best performance, tree models demonstrate moderate predictive efficacy, while linear models perform poorly in predictions. Secondly, there is a positive correlation between SHAP values and banking systemic risk, this conclusion fills the previous research gap. Further research reveals that Topic_29 consistently ranks at the top in feature importance across various time windows. Its keywords (interest rate, bank, stock, company, inflation, rate cut, China) suggest that interest rate policies, corporate operations, inflation control, and geoeconomic factors play pivotal roles in systemic risk. Additionally, the study observes a negative correlation between news sentiment and SHAP values; negative sentiment has a stronger impact and a longer duration. Finally, this study links the topic keywords back to the original news texts to elucidate the impact of news on systemic risk across different sliding window periods.

本研究利用《华尔街日报》的文本数据，采用12个机器学习模型来预测美国银行业的系统性风险。然后，应用SHAP方法对预测结果进行解释。实证结论如下：首先，在时间序列预测中，深度学习模型的预测效果最好，树模型的预测效果中等，线性模型的预测效果较差。其次，SHAP值与银行系统性风险之间存在正相关关系，这一结论填补了以往研究的空白。进一步的研究表明，Topic_29在不同时间窗口的特征重要性上始终名列前茅。它的关键词（利率、银行、股票、公司、通货膨胀、降息、中国）表明，利率政策、企业运营、通货膨胀控制和地缘经济因素在系统性风险中起着关键作用。此外，研究发现新闻情绪与SHAP值呈负相关；负面情绪的影响更强，持续时间更长。最后，本研究将主题关键词与原始新闻文本联系起来，以阐明新闻对不同滑动窗口期系统性风险的影响。

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引用次数: 0

Systemic spillovers in high-growth private market sectors: determinants and portfolio implications 高增长私人市场部门的系统性溢出效应：决定因素和投资组合影响

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2026-01-01 DOI: 10.1016/j.najef.2025.102579

Adnan Aslam , Rayenda Khresna Brahmana

This study investigates the systematic spillover dynamics across high-growth private market sectors and their key drivers, with particular emphasis on portfolio diversification implications. Using a time-varying parameter vector autoregression framework, we document substantial and persistent return spillovers, with AI, HealthTech, FinTech, and Mobility Tech acting as dominant transmitters, and AgTech, BioPharma, ClimateTech, and Cybersecurity serving primarily as receivers. Spillover intensity peaks during post-pandemic capital inflows and green policy expansions, and declines during monetary tightening and geopolitical shocks. Employing robust regression and eXplainable AI approaches, we identify short-term interest rates, trade policy uncertainty, and geopolitical risk as the most influential determinants of connectedness. Portfolio tests show that minimum correlation and connectedness strategies outperform minimum variance portfolios, achieving higher risk-adjusted returns and better tail-risk protection. Our results provide new insights into the structural dynamics of high-growth private markets and offer a practical framework for spillover-aware asset allocation.

本研究探讨了高增长私人市场部门的系统性溢出动态及其关键驱动因素，特别强调了投资组合多元化的影响。使用时变参数向量自回归框架，我们记录了大量和持续的回报溢出效应，其中人工智能、医疗科技、金融科技和移动科技是主要的发射器，农业科技、生物制药、气候科技和网络安全主要是接收器。溢出强度在大流行后资本流入和绿色政策扩张期间达到峰值，在货币紧缩和地缘政治冲击期间下降。采用稳健回归和可解释的人工智能方法，我们确定短期利率、贸易政策不确定性和地缘政治风险是连通性最具影响力的决定因素。投资组合测试表明，最小相关性和连通性策略优于最小方差投资组合，获得更高的风险调整收益和更好的尾部风险保护。我们的研究结果为高增长私人市场的结构动态提供了新的见解，并为考虑溢出效应的资产配置提供了一个实用的框架。

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引用次数: 0

Expected versus unexpected Inflation:The role of Trade Policy 预期通货膨胀与意外通货膨胀：贸易政策的作用

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2026-01-01 DOI: 10.1016/j.najef.2025.102578

Hakan Yilmazkuday

This paper investigates how trade policy shocks, specifically tariffs, distinctly affect the 1-year expected versus unexpected components of U.S. inflation. Using a Bayesian structural vector autoregression model and monthly data from 2007 to 2025, we decompose headline inflation to uncover a powerful and nuanced role for trade policy. We find that tariffs have a statistically significant and positive long-run impact on unexpected inflation. In contrast, while a tariff shock does not produce a statistically significant response in the level of expected inflation, our variance decomposition reveals it is the single largest long-run contributor to its volatility. These effects are distinct from short-run dynamics, where oil prices primarily drive inflation surprises. We also identify a formal structural break in May 2018, after which the effects of monetary policy grew to overshadow those of trade policy. A counterfactual analysis confirms that in the absence of tariff shocks, the inflation path would have been different. A key implication is that trade policy, through tariffs, creates significant long-term uncertainty for inflation expectations, complicating the task of maintaining price stability.

本文研究了贸易政策冲击，特别是关税，如何显著影响美国1年预期通胀率和非预期通胀率。利用贝叶斯结构向量自回归模型和2007年至2025年的月度数据，我们对总体通胀进行了分解，揭示了贸易政策的强大而微妙的作用。我们发现，关税对非预期通胀具有显著的长期积极影响。相比之下，虽然关税冲击在预期通胀水平上不会产生统计上显著的反应，但我们的方差分解显示，它是其波动性的单一最大长期贡献者。这些影响与短期动态不同，在短期动态中，油价主要推动通胀意外。我们还发现，2018年5月出现了正式的结构性断裂，此后货币政策的影响逐渐超过了贸易政策的影响。一项反事实分析证实，如果没有关税冲击，通胀路径将会不同。一个关键的含义是，通过关税，贸易政策给通胀预期带来了重大的长期不确定性，使维持价格稳定的任务复杂化。

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引用次数: 0

Credit ratings and top executives’ political ideology 信用评级和高管的政治意识形态

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2026-01-01 DOI: 10.1016/j.najef.2025.102573

Abdulaziz A. Alshamrani , David Rakowski , Salil Sarkar

We examine whether credit ratings reflect the political ideology of the broader top management team rather than that of the CEO alone. Using political donation data for top executives from 1992 to 2017, we show that firms with more conservative executive teams receive higher credit ratings and are more likely to be investment grade. While CEO conservatism is positively associated with ratings, the ideology of non-CEO executives has comparable and often greater explanatory power. In firms where CEO and executive team ideologies diverge, ratings align more closely with the ideology of non-CEO managers. Additional analyses exploiting CEO turnover, firm fixed effects, and matched samples largely rule out alternative explanations based on firm culture or selection. Overall, the results suggest that credit rating agencies condition on the risk preferences of senior leadership teams rather than solely on CEOs.

我们考察信用评级是否反映了更广泛的高层管理团队的政治意识形态，而不仅仅是首席执行官的政治意识形态。利用1992年至2017年高管的政治捐款数据，我们发现，高管团队越保守的公司获得的信用评级越高，而且更有可能达到投资级。虽然首席执行官的保守主义与评级呈正相关，但非首席执行官的意识形态具有类似且往往更大的解释力。在首席执行官和高管团队意识形态存在分歧的公司中，评级与非首席执行官经理的意识形态更为一致。利用CEO离职、公司固定效应和匹配样本的其他分析在很大程度上排除了基于企业文化或选择的其他解释。总体而言，研究结果表明，信用评级机构以高管团队的风险偏好为条件，而不仅仅是ceo。

引用次数: 0

Inflation targeting and stock market liquidity: a difference-in-difference and doubly robust analysis of emerging markets 通胀目标制与股市流动性：对新兴市场的双重稳健分析

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2026-01-01 DOI: 10.1016/j.najef.2025.102580

Ichrak Dridi , Mohamed Malek Belhoula , Adel Boughrara

This study examines the impact of full-fledged inflation targeting (IT) regime adoption on stock market liquidity in emerging markets, addressing a critical yet underexplored dimension of monetary policy’s financial market effects. Understanding how IT influences financial market stability is crucial, particularly for emerging economies where liquidity constraints exacerbate financial fragility. Analyzing 35 emerging countries, of which 15 are inflation targeters, over the period 1990–2023, we employ Difference-in-Differences and Doubly Robust methods to assess the influence of IT on stock market liquidity, utilizing several proxies for liquidity. Our findings indicate that IT has a significant impact on liquidity, particularly during crises such as the Global Financial Crisis (GFC) and the COVID-19 pandemic. The positive impact of IT adoption on stock market liquidity emerges after a three-year delay and becomes statistically significant once key economic and financial variables are controlled for. Robust across multiple checks, our study extends prior literature by offering a broad multi-country perspective, isolating IT’s unique role, and using advanced methods to address selection bias. It highlights IT as a key policy tool for financial stability, equipping central bankers with strategies to prevent liquidity dry-ups and strengthen economic resilience in turbulent times.

本研究考察了成熟的通货膨胀目标制（IT）制度对新兴市场股票市场流动性的影响，解决了货币政策对金融市场影响的一个关键但尚未得到充分探索的维度。了解信息技术如何影响金融市场稳定至关重要，特别是对流动性限制加剧金融脆弱性的新兴经济体而言。分析了1990年至2023年期间35个新兴国家，其中15个是通胀目标国家，我们采用差异中的差异和双重稳健方法来评估IT对股票市场流动性的影响，利用几个流动性代理。我们的研究结果表明，IT对流动性有重大影响，特别是在全球金融危机（GFC）和COVID-19大流行等危机期间。信息技术采用对股票市场流动性的积极影响在三年的延迟后出现，一旦控制了关键的经济和金融变量，就会变得具有统计学意义。通过多次检查，我们的研究扩展了先前的文献，提供了广泛的多国视角，孤立了IT的独特作用，并使用先进的方法来解决选择偏差。报告强调，信息技术是维持金融稳定的关键政策工具，为央行行长提供了防止流动性枯竭和在动荡时期增强经济韧性的策略。

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引用次数: 0

Regime-Switching volatility and risk quantification in South Asian and developed stock Markets: A Comparative perspective using Markov-Switching GARCH with MLE and MCMC estimations 南亚和发达国家股票市场的制度转换波动率和风险量化：使用马尔可夫转换GARCH与MLE和MCMC估计的比较视角

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2026-01-01 DOI: 10.1016/j.najef.2025.102576

Hina Mushtaq , Muhammad Ishtiaq , Surayya Jamal , Syed Maisam Raza Rizvi , Hamad Raza

This study investigates volatility regime dynamics and risk quantification across the developed stock markets of the NYSE and SSEC, and the emerging markets of South Asia, using the Markov-Switching GARCH framework. By employing both Maximum-Likelihood Estimation (MLE) and Bayesian Markov Chain Monte Carlo (MCMC) methods, the study captured volatility clustering that depends on regimes, their persistence, and transition probabilities. The findings of the MLE have revealed significant regime shifts in the markets of South Asia and have displayed frequent transitions, high volatility clustering, especially during low-volatility regimes, and a higher level of instability than in developed equity markets. Moreover, the MCMC findings further substantiate these findings by providing robust parameter estimates and revealing stronger volatility persistence during the calm regime and greater volatility persistence during turbulent periods in the developing South Asian stock markets.

Then, volatility forecasting shows sustained market uncertainty, with emerging South Asian stock markets exhibiting higher volatility than developed markets. Moreover, the findings on Value-at-Risk (VaR) and Expected Shortfall (ES) have confirmed the elevated tail risk in the developing South Asian market, especially in Nepal and the Dhaka Stock Exchange. These findings contribute to the literature by providing an empirical comparison of risk and volatility across developed and developing markets, validating the efficiency of regime-switching models when combined with Bayesian estimation techniques for capturing the complex behaviour of financial markets.

本研究使用马尔可夫转换GARCH框架，研究了纽约证券交易所和上海证券交易所的发达股票市场以及南亚新兴市场的波动机制动态和风险量化。通过采用最大似然估计（MLE）和贝叶斯马尔可夫链蒙特卡罗（MCMC）方法，该研究捕获了依赖于制度、其持久性和转移概率的波动性聚类。MLE的研究结果揭示了南亚市场的重大制度转变，并表现出频繁的转变、高波动性聚类，特别是在低波动性制度期间，以及比发达股票市场更高的不稳定性。此外，MCMC的研究结果通过提供稳健的参数估计进一步证实了这些发现，并揭示了发展中南亚股票市场在平静时期更强的波动性持久性和在动荡时期更大的波动性持久性。然后，波动率预测显示持续的市场不确定性，新兴南亚股市的波动率高于发达市场。此外，关于风险价值（VaR）和预期缺口（ES）的研究结果证实了南亚发展中市场尾部风险的升高，特别是在尼泊尔和达卡证券交易所。这些发现通过提供发达市场和发展中市场的风险和波动性的经验比较，验证了与贝叶斯估计技术相结合以捕获金融市场复杂行为的制度转换模型的效率，从而为文献做出了贡献。

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引用次数: 0

The debt-growth nexus in Canada: evidence from an open-economy ARDL model 加拿大的债务增长关系：来自开放经济的ARDL模型的证据

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-12-29 DOI: 10.1016/j.najef.2025.102574

George K. Zestos , Yixiao Jiang , Robert C. Winder , Charles Matzen

This study investigates the long-run relationship between public debt and economic growth in Canada from 1960 to 2022 using an Autoregressive Distributed Lag (ARDL) model. By incorporating key macroeconomic variables such as world GDP, the current account balance, and long-term interest rates, the analysis captures the macroeconomic dynamics of Canada’s small open economy. The findings reveal a negative relationship between public debt and economic growth in Canada, suggesting that fiscal prudence is crucial for sustained economic performance. Specifically, a 1% annual increase in public debt results in a 0.6–0.7% reduction in real GDP. Moreover, external factors such as global economic conditions and interest rates significantly influence Canada’s economic trajectory. These insights offer valuable policy implications not only for Canada, but also for similar open economies grappling with rising public debt levels.

本研究使用自回归分布滞后（ARDL）模型研究了1960年至2022年加拿大公共债务与经济增长之间的长期关系。通过纳入关键的宏观经济变量，如世界GDP、经常账户余额和长期利率，该分析捕捉到了加拿大小型开放经济的宏观经济动态。研究结果揭示了加拿大公共债务与经济增长之间的负相关关系，表明财政审慎对持续的经济表现至关重要。具体来说，公共债务每增加1%，实际GDP就会减少0.6-0.7%。此外，全球经济状况和利率等外部因素显著影响加拿大的经济轨迹。这些见解不仅为加拿大提供了宝贵的政策启示，也为正在努力应对公共债务水平上升的类似开放经济体提供了宝贵的政策启示。

引用次数: 0