North American Journal of Economics and Finance最新文献

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Modeling and forecasting commodity price volatility using a common leverage factor 建模和预测商品价格波动使用一个共同的杠杆因素

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-12-17 DOI: 10.1016/j.najef.2025.102570

László Kamocsai , Mihály Ormos

We propose a new variant of the heterogeneous autoregressive model, the pseudo leverage HAR model, which exploits the well-known leverage effect to improve forecasting performance. Built on the fact there is an interconnectedness among commodities we employ a common leverage factor in forecasting exercises which is derived by principal component regression. Including this common leverage variable in HAR framework leads to significant improvements in both in-sample estimates and out-of-sample forecasts, suggesting that the factor structure is a valid assumption not just for return and volatility, but for volatility asymmetry too. The robustness tests confirm the usefulness of the common leverage factor, since the model incorporating this variable consistently remains in the model confidence set, implying that the model’s performance independent of the choice of the leverage structure or volatility proxy. Moreover, the portfolio evaluation exercise and the cumulative sum of forecast errors revealed the incremental gain of using the common leverage variable at all forecasting horizons, especially in turbulent periods.

本文提出了一种异质自回归模型的新变体——伪杠杆HAR模型，该模型利用众所周知的杠杆效应来提高预测性能。基于商品之间存在相互联系的事实，我们在预测练习中采用了一个由主成分回归得出的共同杠杆因子。在HAR框架中包含这个常见的杠杆变量会导致样本内估计和样本外预测的显着改善，这表明因素结构不仅是回报和波动性的有效假设，也是波动性不对称的有效假设。稳健性检验证实了共同杠杆因子的有用性，因为纳入该变量的模型始终保持在模型置信集中，这意味着模型的性能与杠杆结构或波动率代理的选择无关。此外，投资组合评估工作和预测误差的累积总和揭示了在所有预测范围内使用共同杠杆变量的增量收益，特别是在动荡时期。

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引用次数: 0

On the non-neutrality of socially responsible investing in the presence of a greenium 论社会责任投资的非中立性

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-12-15 DOI: 10.1016/j.najef.2025.102567

Fabian Alex

The neutrality of SRI in the AD-GE model by Arnold (2023) ceases to hold once the law of one price is violated for an asset that sufficiently many individuals (a single one may suffice) are not indifferent towards. The introduction of a green bond priced at a premium leads to an illusory gain, that is, a pure utility gain accompanied by a reduction of consumption, of green investors. Their financial losses are allocated to those that were sufficiently un-green to not buy too many green bonds themselves. To profit financially this way, an individual needs to start out as (partial) owner of a firm that “turns out” to be a green bond issuer. Impact investing still does not generate environmental impact in this model.

在Arnold（2023）的AD-GE模型中，一旦有足够多的个人（一个人可能就足够了）对一项资产并不漠不关心，那么一价定律就被违反了，SRI的中立性就不再成立了。引入溢价定价的绿色债券会导致绿色投资者获得虚幻的收益，即纯粹的效用收益伴随着消费的减少。他们的经济损失被分配给了那些不够环保的人，他们自己也不会购买太多的绿色债券。要想以这种方式在经济上获利，个人需要从一家公司（部分）的所有者开始，而这家公司“最终”成为了一家绿色债券发行人。在这个模型中，影响力投资仍然不会产生环境影响。

引用次数: 0

Short-Term market impact of 2024 US President elections and Trump-Zelensky meeting in defence industry 2024年美国总统大选和特朗普-泽伦斯基会晤对国防工业的短期市场影响

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-12-15 DOI: 10.1016/j.najef.2025.102569

António Miguel Martins , Bruno Albuquerque , Luís Sardinha , Nuno Moutinho

This study examines the short-term market effect of the US and European largest defence firms on the 2024 US presidential election (November 5, 2024) and the Trump-Zelensky meeting (February 28, 2025). By employing an event study methodology, our results show a positive and statistically significant stock price impact for both events. The results for the 2024 US presidential election are consistent with political business cycle theory. National elections in the arms-producing country drive a growth in sales revenues for defence firms, which tend to be higher when the Republican Party candidate wins the US elections. Our results also show the presence of heterogeneous abnormal returns between US and European defence firms around the Trump-Zelensky meeting, with European firms showing high and statistically significant positive returns while US firms show non-significant returns. This result is explained by the failure of security guarantees given by the US to the European countries and the awareness of the need for a rapid increase in military spending for self-defence purposes in Europe. This meeting reinforced the application of the principle of “Europe preference” in the acquisition of weapons. Finally, we conclude that stock market responses are reinforced or mitigated by firm-specific characteristics.

本研究考察了美国和欧洲最大的国防公司对2024年美国总统选举（2024年11月5日）和特朗普-泽伦斯基会议（2025年2月28日）的短期市场影响。通过采用事件研究方法，我们的结果显示两个事件对股价都有积极的统计显著影响。2024年美国总统大选的结果符合政治经济周期理论。军火生产国的全国大选推动了国防公司销售收入的增长，当共和党候选人赢得美国大选时，销售收入往往会更高。我们的研究结果还显示，在特朗普-泽伦斯基会晤前后，美国和欧洲防务公司之间存在异质异常回报，欧洲公司显示出高且统计上显著的正回报，而美国公司显示出不显著的回报。造成这一结果的原因是，美国没有向欧洲国家提供安全保证，而且意识到有必要迅速增加欧洲用于自卫目的的军事开支。这次会议加强了在购买武器方面适用“欧洲优先”原则。最后，我们得出结论，股票市场的反应会因公司的特定特征而增强或减弱。

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引用次数: 0

Constrained portfolio optimization via Artificial Gorilla Troops: Benchmarking against swarm-intelligence metaheuristic algorithms 通过人工大猩猩部队的约束投资组合优化：对群体智能元启发式算法的基准测试

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-12-15 DOI: 10.1016/j.najef.2025.102568

Vasileios Gkonis , Ioannis Tsakalos , Ilias Kampouris

Over the years, the challenge of portfolio optimization has gained increasing attention from scientists and experienced investors, where maximizing returns with the least possible risk is the major goal. In recent times, there has been a significant surge in interest in metaheuristic algorithms across various industries. In this study, we propose the use of the Artificial Gorilla Troops Optimizer (AGTO) for portfolio optimization. We evaluate its effectiveness and compare it against sixteen other swarm intelligence-based metaheuristic algorithms under varying population and epoch sizes. Our evaluation is based on the Sharpe ratio, utilizing a portfolio composed of stocks from the Dow Jones Industrial Average. The results indicate that AGTO demonstrates strong potential as an effective method for optimal portfolio selection. In addition, this work provides valuable insights into metaheuristic algorithms that have seen relatively limited application in the existing portfolio optimization literature.

多年来，投资组合优化的挑战越来越受到科学家和经验丰富的投资者的关注，以最小的风险最大化回报是主要目标。近年来，各个行业对元启发式算法的兴趣激增。在本研究中，我们提出使用人工大猩猩部队优化器（AGTO）进行投资组合优化。我们评估了其有效性，并将其与其他16种基于群体智能的元启发式算法在不同种群和epoch大小下进行了比较。我们的评估是基于夏普比率，利用由道琼斯工业平均指数股票组成的投资组合。结果表明，AGTO作为最优投资组合选择的有效方法具有很强的潜力。此外，这项工作为在现有投资组合优化文献中应用相对有限的元启发式算法提供了有价值的见解。

引用次数: 0

Dynamic distortions of the security market line: Evidence from asymmetric volatility and regime-switching models 证券市场线的动态扭曲：来自不对称波动和制度转换模型的证据

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-12-14 DOI: 10.1016/j.najef.2025.102566

Hatem Brik

This study re-examines the empirical validity of the Capital Asset Pricing Model (CAPM) by asking whether the Security Market Line (SML) remains stable when markets experience asymmetric volatility and regime transitions. Using high-frequency data from 2015 to 2024 for four major U.S. large-cap firms, we implement a unified empirical framework that integrates quantile regression, rolling-window CAPM estimation, and a two-state Markov-Switching AR(1) model.

The results reveal that the beta–return relationship is non-linear, time-varying, and state-dependent. Beta significance concentrates around the median of the return distribution but vanishes in the tails, confirming distributional asymmetry. Rolling estimations indicate persistent yet gradual beta drift, while the Markov-Switching model uncovers regime-contingent SML slopes—positive in stable phases and inverted during stress periods. These findings demonstrate that systematic risk is not uniformly priced and that the classical CAPM’s assumptions of linearity, stationarity, and rational expectations fail under dynamic market conditions.

Beyond U.S. equities, the framework offers a diagnostic tool for risk monitoring and portfolio management in environments characterized by volatility shocks and behavioral frictions—conditions typical of many emerging markets. By integrating dynamic, distributional, and regime dimensions, the paper contributes to a more adaptive understanding of asset pricing under uncertainty.

本研究通过询问证券市场线（SML）在市场经历不对称波动和制度转变时是否保持稳定，重新检验了资本资产定价模型（CAPM）的实证有效性。利用2015年至2024年美国四家大型公司的高频数据，我们实现了一个统一的经验框架，该框架集成了分位数回归、滚动窗口CAPM估计和两状态马尔可夫切换AR(1)模型。结果表明，β -回归关系是非线性的、时变的和状态相关的。贝塔显著性集中在收益分布的中位数附近，但在尾部消失，证实了分布的不对称性。滚动估计表明持续而渐进的β漂移，而马尔可夫切换模型揭示了状态随状态变化的SML斜率-在稳定阶段为正，在应力期间为反转。这些发现表明，系统风险不是统一定价的，经典CAPM的线性、平稳性和理性预期假设在动态市场条件下失效。除了美国股市之外，该框架还为以波动冲击和行为摩擦为特征的环境中的风险监测和投资组合管理提供了一个诊断工具，这些环境是许多新兴市场的典型条件。通过整合动态、分配和制度维度，本文有助于对不确定性下的资产定价有更适应性的理解。

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引用次数: 0

Does inter-industry risk spillover network predict financial crisis? Evidence from a gated graph neural networks approach 行业间风险溢出网络能否预测金融危机？来自门控图神经网络方法的证据

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-12-11 DOI: 10.1016/j.najef.2025.102565

Yinghua Ren , Xin Chen , Han Chen , Huiming Zhu

This study proposes a novel binary-classification model for systemic risk warning, utilizing inter-industry tail-risk spillover networks as input. These networks are constructed using the Tail-Event driven network (TENET) model, which captures high-dimensional and non-linear characteristics of risk contagion. The model leverages the Gated Graph Neural Network (GGNN) framework to uncover the ambiguous specification of crisis prediction. Applied to 11 key U.S. industry indices, the empirical results demonstrate that: (i) the topology of the risk spillover network is strongly correlated with financial crises during critical periods; and (ii) the GGNN model based on the TENET network provides superior reliability in early warning compared to traditional machine learning and other graph-based models.

本文以行业间尾部风险溢出网络为输入，提出了一种新的系统性风险预警二元分类模型。这些网络使用尾事件驱动网络（TENET）模型构建，该模型捕获了风险传染的高维和非线性特征。该模型利用门控图神经网络（GGNN）框架揭示了危机预测的模糊规范。运用美国11个关键行业指数，实证结果表明：(1)风险溢出网络的拓扑结构与关键时期金融危机具有较强的相关性；（ii）与传统机器学习和其他基于图的模型相比，基于TENET网络的GGNN模型在预警方面具有更高的可靠性。

引用次数: 0

Simultaneous inference in testing conditional alphas of momentum portfolios 同时推理在动量组合条件阿尔法检验中的应用

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-12-04 DOI: 10.1016/j.najef.2025.102557

Jinyong Kim , Yongsik Kim , Seunghyun Lee

We evaluate the conditional performance of intermediate and recent past momentum portfolios using nonparametric estimation and simultaneous inference. The outperformance of intermediate past momentum is driven by the relative performance of winners. The largest contributors to the outperformance are negative and positive exposures of the intermediate and recent past momentum to the value factor, respectively, which are strengthened when the market risk premium is high and coincide with the significant outperformance of the intermediate momentum. These contrasting signs of exposures are robust to a sub-period analysis and controls for the January effect. The outperformance disappears when prior Months 12 and 2 are excluded from constructing the momentum portfolios, due to changes in losers’ returns. However, the opposing exposures to the value factor remain consistent.

我们使用非参数估计和同步推理来评估中间和最近的过去动量组合的条件性能。中间过去势头的优异表现是由赢家的相对表现驱动的。表现优异的最大贡献者分别是中期和最近的过去势头对价值因素的负和正风险敞口，当市场风险溢价较高并与中期势头的显著表现相吻合时，这种风险敞口得到加强。这些对比鲜明的暴露迹象对于1月效应的子周期分析和控制是稳健的。当之前的第12个月和第2个月被排除在动量投资组合之外时，由于输家回报的变化，优异的表现就会消失。然而，对价值因素的相反敞口保持一致。

引用次数: 0

Dynamic Agency, financial hedging and optimal investment 动态代理、金融对冲与最优投资

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-11-19 DOI: 10.1016/j.najef.2025.102556

Yehong Yang, Xun You, Yuqian Sun

Based on the continuous-time contract framework (DeMarzo et al., 2012), hereinafter referred to as the DFHW model), we develop an enhanced continuous-time contract framework incorporating an optimal financial hedging strategy to effectively mitigate firm-specific risks. This paper confirms the presence of a lagged effect of the hedging strategy on a firm’s investment. Compared with the DFHW model, our findings reveal that the optimal hedging strategy has a significant influence on dynamic contracts, including investors’ value, dynamic investment, dividend payments, and so on. Especially in an equilibrium state, the investors tend to distribute dividends at lower levels and earlier than those predicted by the DFHW model, thereby mitigating the risk burden on entrepreneurs. Finally, the hedging strategy is significantly influenced by hedging costs, which in turn have an impact on firms’ investment and dividend boundary. This paper further validates the rationality and feasibility of our devised financial hedging strategy.

基于连续时间契约框架（DeMarzo et al., 2012），以下简称DFHW模型)，我们开发了一个增强的连续时间契约框架，其中包含了最优的财务对冲策略，以有效降低企业特定风险。本文证实了套期保值策略对企业投资存在滞后效应。与DFHW模型相比，我们的研究结果表明，最优对冲策略对动态合约有显著影响，包括投资者价值、动态投资、股息支付等。特别是在均衡状态下，投资者倾向于比DFHW模型预测的水平更低、更早地分配股息，从而减轻了企业家的风险负担。最后，套期保值策略受套期保值成本的显著影响，套期保值成本又对企业的投资和股息边界产生影响。本文进一步验证了所设计的财务套期保值策略的合理性和可行性。

引用次数: 0

Time-frequency quantile effect of global uncertainty on stock markets: evidence from wavelet decomposition 全球不确定性对股票市场的时频分位数效应：来自小波分解的证据

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-11-15 DOI: 10.1016/j.najef.2025.102554

Jiayuan Yuan , Weineng Zhu , Zishan Huang , Huiming Zhu

This study explores the time–frequency effect of the World Uncertainty Index (WUI) on the stock markets across quantile levels.We utilize wavelet decomposition and quantile regression to measure the time–frequency relationship between WUI and stock markets. Our empirical results are as follows. First, there exists a significant long-term Granger causality between WUI and stock markets. Second, the impacts of WUI on stock markets change with time-scales, the short term impacts being more intense than that of the other time scale. Third, there is significant asymmetric impacts of WUI on the stock markets. At extreme quantile levels, the absolute values of quantile regression coefficients become significantly larger relationship, indicating the relationship between the two becomes more pronounced. Overall, our findings provide a investors and policymakers with a more deeper perspective on dynamic market risk management.

本研究探讨了世界不确定性指数（WUI）对股票市场的时频效应。我们利用小波分解和分位数回归来衡量WUI与股票市场之间的时频关系。我们的实证结果如下：首先，WUI与股市之间存在显著的长期格兰杰因果关系。第二，WUI对股票市场的影响随时间尺度变化，短期影响强于其他时间尺度。第三，WUI对股票市场存在显著的不对称影响。在极端分位数水平上，分位数回归系数的绝对值关系变得显著变大，表明两者之间的关系变得更加明显。总体而言，我们的研究结果为投资者和政策制定者提供了更深入的动态市场风险管理视角。

引用次数: 0

Physical climate risk and banks’ credit risk: Worldwide evidence 物理气候风险与银行信用风险：全球证据

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-11-13 DOI: 10.1016/j.najef.2025.102550

Isabel Abinzano, Pilar Corredor, José Manuel Mansilla-Fernández

This paper examines the impact of physical climate risk on banks’ credit risk and, by extension, on global financial stability. We use the S&P Physical Risk Index as a proxy for banks’ exposure to physical climate risk. Credit risk is measured using the forward-looking probability of default from the Chan-Lau and Sy (2007) model, which adapts the structural Black–Scholes–Merton framework to the banking context. Based on a worldwide sample of 374 listed banks, our results show that physical climate risk increases banks’ credit risk. This effect is particularly pronounced for banks with greater informativeness, intrinsically vulnerable, and operating in more fragile countries. The findings provide empirical support for the financial regulators’ proposal to incorporate climate risks into capital requirements to more effectively manage climate-related financial risks. Importantly, the results indicate that such regulatory adjustments should be calibrated to each bank’s specific characteristics and operating environment.

本文考察了物理气候风险对银行信用风险的影响，进而对全球金融稳定的影响。我们使用s&p；P物理风险指数作为银行对物理气候风险敞口的代理。信用风险是使用Chan-Lau和Sy（2007）模型的前瞻性违约概率来衡量的，该模型将结构性布莱克-斯科尔斯-默顿框架适用于银行环境。基于全球374家上市银行的样本，我们的研究结果表明，物理气候风险增加了银行的信用风险。这种影响对于那些信息量更大、本质上脆弱、在更脆弱的国家经营的银行尤为明显。研究结果为金融监管机构将气候风险纳入资本要求以更有效地管理气候相关金融风险的建议提供了实证支持。重要的是，结果表明，这种监管调整应根据每家银行的具体特点和经营环境进行校准。

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全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

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North American Journal of Economics and Finance

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