North American Journal of Economics and Finance最新文献_第5页

Cybersecurity risk and firm growth: Empirical evidence based on text analysis 网络安全风险与企业成长：基于文本分析的经验证据

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-09-26 DOI: 10.1016/j.najef.2025.102542

Gengxi Xu, Yugang Li, Shanshan Liu, Zhuhong Ye

Despite cybersecurity risk emerging as a critical firm threat, research on effective prevention and response strategies remains limited. Using a sample of A-share listed companies in Shanghai and Shenzhen from 2010 to 2022, this study adopts text analysis to construct indicators that portray the cybersecurity risk of Chinese listed companies and systematically examines the impact of cybersecurity risk on firm growth. The findings reveal that cybersecurity risk significantly inhibits firm growth. Mechanism analysis indicates that cybersecurity risk adversely impacts growth by increasing firms’ excessive cash holdings, amplifying operational risks, and exacerbating financing constraints. Further analysis shows that the growth-inhibiting effect is more pronounced among firms in technology-intensive industries, larger scale, higher media attention, and higher analyst attention. This study provides empirical evidence to guide firms in developing preemptive cybersecurity strategies, supports regulators in implementing differentiated governance, and helps governments refine cybersecurity incentives. These measures help firms strike a balance between growth and risk while supporting effective cybersecurity governance.

尽管网络安全风险已成为重要的企业威胁，但对有效预防和应对策略的研究仍然有限。本研究以2010 - 2022年沪深两市a股上市公司为样本，采用文本分析法构建中国上市公司网络安全风险指标，系统考察网络安全风险对企业成长的影响。研究结果显示，网络安全风险显著抑制企业成长。机制分析表明，网络安全风险通过增加企业的过度现金持有量、放大经营风险、加剧融资约束等方式对经济增长产生负面影响。进一步分析表明，在技术密集型行业、规模较大、媒体关注度较高、分析师关注度较高的企业中，增长抑制效应更为明显。本研究为指导企业制定先发制人的网络安全战略、支持监管机构实施差异化治理、帮助政府完善网络安全激励机制提供了实证依据。这些措施有助于企业在增长与风险之间取得平衡，同时支持有效的网络安全治理。

{"title":"Cybersecurity risk and firm growth: Empirical evidence based on text analysis","authors":"Gengxi Xu, Yugang Li, Shanshan Liu, Zhuhong Ye","doi":"10.1016/j.najef.2025.102542","DOIUrl":"10.1016/j.najef.2025.102542","url":null,"abstract":"<div><div>Despite cybersecurity risk emerging as a critical firm threat, research on effective prevention and response strategies remains limited. Using a sample of A-share listed companies in Shanghai and Shenzhen from 2010 to 2022, this study adopts text analysis to construct indicators that portray the cybersecurity risk of Chinese listed companies and systematically examines the impact of cybersecurity risk on firm growth. The findings reveal that cybersecurity risk significantly inhibits firm growth. Mechanism analysis indicates that cybersecurity risk adversely impacts growth by increasing firms’ excessive cash holdings, amplifying operational risks, and exacerbating financing constraints. Further analysis shows that the growth-inhibiting effect is more pronounced among firms in technology-intensive industries, larger scale, higher media attention, and higher analyst attention. This study provides empirical evidence to guide firms in developing preemptive cybersecurity strategies, supports regulators in implementing differentiated governance, and helps governments refine cybersecurity incentives. These measures help firms strike a balance between growth and risk while supporting effective cybersecurity governance.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102542"},"PeriodicalIF":3.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Early warning systems for cryptocurrency markets: Predicting ‘zombie’ assets using machine learning 加密货币市场的预警系统：使用机器学习预测“僵尸”资产

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-09-26 DOI: 10.1016/j.najef.2025.102543

Barbara Będowska-Sójka , Piotr Wójcik , Daniel Traian Pele

The cryptocurrency market harbours a hidden risk: assets that silently disappear from trading, leaving investors stranded. These ‘zombie’ cryptocurrencies technically exist but become temporarily untradable on exchanges, ranging from weeks to permanent delisting. This study predicts which cryptocurrencies are at risk of becoming zombies using predictors derived from return statistics, trading volume, market capitalisation, and asset-specific features. Our sample includes cryptocurrencies listed for at least 210 days between January 2015 and December 2022. We employ various machine learning algorithms and novel explainable AI (XAI) tools, including permutation-based feature importance and partial dependence plots (PDPs), to identify and analyse key factors influencing zombification risk. Our machine learning models achieve 84% out-of-time balanced accuracy in predicting whether a cryptocurrency will become a zombie within the next 28 days. Tree-based approaches, particularly random forests, significantly outperform traditional econometric methods. Trading volumes and past returns emerge as the most influential predictors.

加密货币市场隐藏着一个风险：资产在交易中悄然消失，让投资者陷入困境。这些“僵尸”加密货币在技术上是存在的，但在交易所暂时无法交易，从几周到永久退市不等。这项研究使用来自回报统计、交易量、市值和资产特定特征的预测因子来预测哪些加密货币有成为僵尸的风险。我们的样本包括2015年1月至2022年12月期间上市至少210天的加密货币。我们采用各种机器学习算法和新颖的可解释人工智能（XAI）工具，包括基于排列的特征重要性和部分依赖图（pdp），以识别和分析影响僵尸化风险的关键因素。我们的机器学习模型在预测加密货币是否会在未来28天内成为僵尸方面达到了84%的超时平衡准确率。基于树的方法，特别是随机森林，明显优于传统的计量经济学方法。交易量和过去的回报率成为最具影响力的预测指标。

{"title":"Early warning systems for cryptocurrency markets: Predicting ‘zombie’ assets using machine learning","authors":"Barbara Będowska-Sójka , Piotr Wójcik , Daniel Traian Pele","doi":"10.1016/j.najef.2025.102543","DOIUrl":"10.1016/j.najef.2025.102543","url":null,"abstract":"<div><div>The cryptocurrency market harbours a hidden risk: assets that silently disappear from trading, leaving investors stranded. These ‘zombie’ cryptocurrencies technically exist but become temporarily untradable on exchanges, ranging from weeks to permanent delisting. This study predicts which cryptocurrencies are at risk of becoming zombies using predictors derived from return statistics, trading volume, market capitalisation, and asset-specific features. Our sample includes cryptocurrencies listed for at least 210 days between January 2015 and December 2022. We employ various machine learning algorithms and novel explainable AI (XAI) tools, including permutation-based feature importance and partial dependence plots (PDPs), to identify and analyse key factors influencing zombification risk. Our machine learning models achieve 84% out-of-time balanced accuracy in predicting whether a cryptocurrency will become a zombie within the next 28 days. Tree-based approaches, particularly random forests, significantly outperform traditional econometric methods. Trading volumes and past returns emerge as the most influential predictors.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102543"},"PeriodicalIF":3.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

International main precious metals futures price forecasting based on decomposition-combinatorial time series model 基于分解-组合时间序列模型的国际主要贵金属期货价格预测

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-09-23 DOI: 10.1016/j.najef.2025.102541

Zihan Zhang , Xiaojuan Dong , Haigang An , Hai Qi , Sufang An , Zhiliang Dong

In the complex and volatile macroeconomic environment, precious metals play an important role in investment risk management because of their value preservation, value-added, and hedging functions. If investors can effectively predict price fluctuations in the precious metals market and thus optimize their investment portfolio strategies in time, they may be able to avoid market risks. In this paper, the futures prices of three international precious metals on the New York Mercantile Exchange of the Wind Database from 2014 to 2024 are taken as examples. First of all, the time-varying characteristics of non-pervasive, non-Gaussian, aging and delay are obtained for precious metals. Then the trend term, seasonal term, and residual term of the price series are modeled with the Autoregressive Integrated Moving Average (ARIMA) model, the Exponen Tial Smoothing (ETS) model, and the Long-Short Term Memory (LSTM) model, respectively, and the results are summarized to form a forecast of the futures prices of precious metals for the next 100 days. The results show that the error of the combination model for the three precious metal price predictions is less than 0.03, and the model fit is more than 0.98, indicating that the decomposition-combination model is suitable for predicting the precious metal futures prices. According to the results of the study, gold and silver have investment value in a short period, while the investment value of platinum is not obvious. Corresponding investment advice for investors is also given.

在复杂多变的宏观经济环境下，贵金属因其保值增值和套期保值功能，在投资风险管理中发挥着重要作用。如果投资者能够有效预测贵金属市场的价格波动，从而及时优化投资组合策略，就有可能规避市场风险。本文以纽约商品交易所Wind数据库2014 - 2024年三种国际贵金属期货价格为例。首先，得到了贵金属的非普适、非高斯、时效和延迟等时变特性。然后分别采用自回归综合移动平均（ARIMA）模型、指数平滑（ETS）模型和长短期记忆（LSTM）模型对价格序列的趋势期、季节期和剩余期进行建模，并对结果进行汇总，形成未来100天贵金属期货价格的预测。结果表明，组合模型对三种贵金属价格预测的误差小于0.03，模型拟合大于0.98，表明分解组合模型适用于贵金属期货价格的预测。研究结果表明，黄金和白银在短期内具有投资价值，而铂金的投资价值不明显。并对投资者提出了相应的投资建议。

{"title":"International main precious metals futures price forecasting based on decomposition-combinatorial time series model","authors":"Zihan Zhang , Xiaojuan Dong , Haigang An , Hai Qi , Sufang An , Zhiliang Dong","doi":"10.1016/j.najef.2025.102541","DOIUrl":"10.1016/j.najef.2025.102541","url":null,"abstract":"<div><div>In the complex and volatile macroeconomic environment, precious metals play an important role in investment risk management because of their value preservation, value-added, and hedging functions. If investors can effectively predict price fluctuations in the precious metals market and thus optimize their investment portfolio strategies in time, they may be able to avoid market risks. In this paper, the futures prices of three international precious metals on the New York Mercantile Exchange of the Wind Database from 2014 to 2024 are taken as examples. First of all, the time-varying characteristics of non-pervasive, non-Gaussian, aging and delay are obtained for precious metals. Then the trend term, seasonal term, and residual term of the price series are modeled with the Autoregressive Integrated Moving Average (ARIMA) model, the Exponen Tial Smoothing (ETS) model, and the Long-Short Term Memory (LSTM) model, respectively, and the results are summarized to form a forecast of the futures prices of precious metals for the next 100 days. The results show that the error of the combination model for the three precious metal price predictions is less than 0.03, and the model fit is more than 0.98, indicating that the decomposition-combination model is suitable for predicting the precious metal futures prices. According to the results of the study, gold and silver have investment value in a short period, while the investment value of platinum is not obvious. Corresponding investment advice for investors is also given.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102541"},"PeriodicalIF":3.9,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigating the impact of the Covid-19 pandemic on stock markets volatility in USA and Europe 调查新冠肺炎疫情对美国和欧洲股市波动的影响

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-09-20 DOI: 10.1016/j.najef.2025.102540

Mohamed Chikhi , François Benhmad

Financial data exhibit distinctive characteristics known as stylized facts including volatility clustering, long memory, the leverage effect, and risk premium.

In this paper, we introduce a innovative volatility model (ARFIMA-HYAPGARCH-M) designed to effectively capture these features in both the S&P 500 and the European STOXX600 indices, before and during the Covid-19 pandemic.

Empirical findings reveal a significant surge in return volatility across both U.S. and European stock markets during the pandemic. Moreover, the data exhibit dual long memory properties in both the mean and variance of returns, along with an evidence of asymmetry and the leverage effect. Furthermore, the results show that risk premiums increased during the Covid period, confirming that investors demand higher compensation during periods of “bad” volatility compared to periods of “good” volatility.

As such, the ARFIMA-HYAPGARCH-M volatility model provides a valuable tool for improved risk assessment, enabling investors and portfolio managers to make more informed decisions. Additionally, the model can enhance the performance of hedging strategies by accurately capturing volatility dynamics.

金融数据表现出被称为程式化事实的独特特征，包括波动性聚类、长记忆、杠杆效应和风险溢价。在本文中，我们引入了一个创新的波动率模型（ARFIMA-HYAPGARCH-M），旨在有效地捕捉标普500指数和欧洲STOXX600指数在Covid-19大流行之前和期间的这些特征。实证研究结果显示，在疫情期间，美国和欧洲股市的回报率波动性大幅上升。此外，数据在收益的均值和方差中都表现出双重长记忆特性，同时也有不对称和杠杆效应的证据。此外，结果显示，风险溢价在新冠肺炎期间有所增加，这证实了投资者在“糟糕”波动期间比“良好”波动期间要求更高的补偿。因此，ARFIMA-HYAPGARCH-M波动率模型为改进风险评估提供了一个有价值的工具，使投资者和投资组合经理能够做出更明智的决策。此外，该模型可以通过准确捕捉波动动态来提高对冲策略的性能。

{"title":"Investigating the impact of the Covid-19 pandemic on stock markets volatility in USA and Europe","authors":"Mohamed Chikhi , François Benhmad","doi":"10.1016/j.najef.2025.102540","DOIUrl":"10.1016/j.najef.2025.102540","url":null,"abstract":"<div><div>Financial data exhibit distinctive characteristics known as stylized facts including volatility clustering, long memory, the leverage effect, and risk premium.</div><div>In this paper, we introduce a innovative volatility model (ARFIMA-HYAPGARCH-M) designed to effectively capture these features in both the S&P 500 and the European STOXX600 indices, before and during the Covid-19 pandemic.</div><div>Empirical findings reveal a significant surge in return volatility across both U.S. and European stock markets during the pandemic. Moreover, the data exhibit dual long memory properties in both the mean and variance of returns, along with an evidence of asymmetry and the leverage effect. Furthermore, the results show that risk premiums increased during the Covid period, confirming that investors demand higher compensation during periods of “bad” volatility compared to periods of “good” volatility.</div><div>As such, the ARFIMA-HYAPGARCH-M volatility model provides a valuable tool for improved risk assessment, enabling investors and portfolio managers to make more informed decisions. Additionally, the model can enhance the performance of hedging strategies by accurately capturing volatility dynamics.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102540"},"PeriodicalIF":3.9,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing financial stability through prospective resilience: Insights from the EN-VAR-DY-PR framework in international stock market networks 通过预期弹性增强金融稳定：来自国际股票市场网络EN-VAR-DY-PR框架的见解

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-09-19 DOI: 10.1016/j.najef.2025.102539

Jiang-Cheng Li , Yi-Zhen Xu , Chen Tao , Guang-Yan Zhong

The increasing interconnectedness and systemic vulnerabilities of financial networks underscore the necessity of enhancing their resilience to shocks and ensuring the stability of the global financial system. This paper proposes the EN-VAR-DY-PR framework, which integrates Elastic Net (EN) regularization, Vector Autoregression (VAR), and the Diebold–Yilmaz (DY) index. This novel approach enables the dynamic assessment of prospective resilience (PR) in complex financial networks, capturing both temporal and structural dimensions of risk. Focusing on three scenarios – economic blockade, financial liberalization, and random behavior – this research examines the dynamic evolution of network prospective resilience across three distinct periods marked by major market crises. Empirical analysis of 40 countries reveals that while economic blockade temporarily enhances network resilience, it undermines long-term shock absorption. Conversely, financial liberalization consistently improves network stability, and an optimal level of randomness significantly improve the resilience of financial networks and strengthen overall financial stability. Additionally, over the three periods, the clustering of the network decreases and the network becomes more homogeneous, suggesting heightened risk concentration and intensified interconnectedness. The significant growth in both the prospective resilience and volatility of network modularity underscores an escalating systemic vulnerability and a weakening of overall network stability. This study provides a novel perspective on financial stability, demonstrating how network science can effectively identify systemic vulnerabilities and inform strategies to mitigate systemic risks.

金融网络的互联性和系统性脆弱性日益增强，凸显了增强金融网络抵御冲击能力和确保全球金融体系稳定的必要性。本文提出了EN-VAR-DY- pr框架，该框架集成了弹性网络（EN）正则化、向量自回归（VAR）和Diebold-Yilmaz （DY）指数。这种新颖的方法能够动态评估复杂金融网络中的预期弹性（PR），同时捕获风险的时间和结构维度。本研究着眼于经济封锁、金融自由化和随机行为三种情景，考察了网络预期弹性在三个以重大市场危机为标志的不同时期的动态演变。对40个国家的实证分析表明，虽然经济封锁暂时增强了网络弹性，但它破坏了长期的冲击吸收。相反，金融自由化持续提高网络稳定性，最优随机性水平显著提高金融网络的弹性，增强整体金融稳定性。此外，在这三个时期，网络的聚类性降低，网络变得更加均匀，表明风险集中度提高，互联性增强。网络模块化的预期弹性和波动性的显著增长强调了系统脆弱性的升级和整体网络稳定性的减弱。这项研究为金融稳定提供了一个新的视角，展示了网络科学如何有效地识别系统脆弱性，并为减轻系统风险的策略提供信息。

{"title":"Enhancing financial stability through prospective resilience: Insights from the EN-VAR-DY-PR framework in international stock market networks","authors":"Jiang-Cheng Li , Yi-Zhen Xu , Chen Tao , Guang-Yan Zhong","doi":"10.1016/j.najef.2025.102539","DOIUrl":"10.1016/j.najef.2025.102539","url":null,"abstract":"<div><div>The increasing interconnectedness and systemic vulnerabilities of financial networks underscore the necessity of enhancing their resilience to shocks and ensuring the stability of the global financial system. This paper proposes the EN-VAR-DY-PR framework, which integrates Elastic Net (EN) regularization, Vector Autoregression (VAR), and the Diebold–Yilmaz (DY) index. This novel approach enables the dynamic assessment of prospective resilience (PR) in complex financial networks, capturing both temporal and structural dimensions of risk. Focusing on three scenarios – economic blockade, financial liberalization, and random behavior – this research examines the dynamic evolution of network prospective resilience across three distinct periods marked by major market crises. Empirical analysis of 40 countries reveals that while economic blockade temporarily enhances network resilience, it undermines long-term shock absorption. Conversely, financial liberalization consistently improves network stability, and an optimal level of randomness significantly improve the resilience of financial networks and strengthen overall financial stability. Additionally, over the three periods, the clustering of the network decreases and the network becomes more homogeneous, suggesting heightened risk concentration and intensified interconnectedness. The significant growth in both the prospective resilience and volatility of network modularity underscores an escalating systemic vulnerability and a weakening of overall network stability. This study provides a novel perspective on financial stability, demonstrating how network science can effectively identify systemic vulnerabilities and inform strategies to mitigate systemic risks.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102539"},"PeriodicalIF":3.9,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spillover and return connectedness between uncertainties, digital assets, green bond, green and traditional energy markets: Evidence from quantile VAR 不确定性、数字资产、绿色债券、绿色和传统能源市场之间的溢出和回报连通性：来自分位数VAR的证据

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-09-13 DOI: 10.1016/j.najef.2025.102538

Rana Muhammad Nasir , Feng He , Mehrad Asadi , David Roubaud

This study investigates the extreme connectedness and spillover transmission between cryptocurrencies, digital assets, green bonds, traditional and green energy markets against different uncertainties from July 2, 2018, to February 3, 2023. First, we employ Quantile VAR to unveil extreme connectedness among markets. Further, Baruník and Křehlík (BK) framework is used to understand time frequency spillover transmission across our chosen markets. Our results indicate that spillover magnitude under bullish market conditions is higher than normal and bearish market conditions. In addition, the equity market volatility, geopolitical risk, Twitter-based economic risk, and oil markets are the major receiver of spillover across all market conditions. In contrast, NFTs and Defis are the significant transmitters of spillover across all quantiles. Similarly, natural gas and green bonds act as spillover transmitters under extreme quantiles. While, green energy and cryptocurrencies are net transmitters in only bearish market conditions. Based on these findings, this study proposed several important implications for investors, financial markets participants, portfolio managers and market regulators in terms of diversifying their risk and design effective market regulation policies.

本研究调查了2018年7月2日至2023年2月3日不同不确定性下加密货币、数字资产、绿色债券、传统和绿色能源市场之间的极端连通性和溢出传导。首先，我们使用分位VAR来揭示市场之间的极端联系。此外，Baruník和Křehlík （BK）框架用于理解我们所选市场的时频溢出传输。我们的研究结果表明，牛市条件下的溢出幅度高于正常和看跌市场条件。此外，股票市场波动、地缘政治风险、基于twitter的经济风险和石油市场是所有市场条件下溢出效应的主要接受者。相反，nft和赤字是所有分位数溢出效应的重要传导因素。同样，天然气和绿色债券在极端分位数下充当溢出效应的传导器。而绿色能源和加密货币只有在看跌的市场条件下才是净发射器。基于这些发现，本研究对投资者、金融市场参与者、投资组合经理和市场监管机构在分散风险和设计有效的市场监管政策方面提出了几点重要启示。

{"title":"Spillover and return connectedness between uncertainties, digital assets, green bond, green and traditional energy markets: Evidence from quantile VAR","authors":"Rana Muhammad Nasir , Feng He , Mehrad Asadi , David Roubaud","doi":"10.1016/j.najef.2025.102538","DOIUrl":"10.1016/j.najef.2025.102538","url":null,"abstract":"<div><div>This study investigates the extreme connectedness and spillover transmission between cryptocurrencies, digital assets, green bonds, traditional and green energy markets against different uncertainties from July 2, 2018, to February 3, 2023. First, we employ Quantile VAR to unveil extreme connectedness among markets. Further, Baruník and Křehlík (BK) framework is used to understand time frequency spillover transmission across our chosen markets. Our results indicate that spillover magnitude under bullish market conditions is higher than normal and bearish market conditions. In addition, the equity market volatility, geopolitical risk, Twitter-based economic risk, and oil markets are the major receiver of spillover across all market conditions. In contrast, NFTs and Defis are the significant transmitters of spillover across all quantiles. Similarly, natural gas and green bonds act as spillover transmitters under extreme quantiles. While, green energy and cryptocurrencies are net transmitters in only bearish market conditions. Based on these findings, this study proposed several important implications for investors, financial markets participants, portfolio managers and market regulators in terms of diversifying their risk and design effective market regulation policies.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102538"},"PeriodicalIF":3.9,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cryptocurrencies and economic sanctions 加密货币和经济制裁

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-09-05 DOI: 10.1016/j.najef.2025.102537

José Almeida , Tiago Cruz Gonçalves

This study examines the role of cryptocurrencies in modern War, specifically during the Russia-Ukraine conflict. Utilizing a Time-Varying Parameter Vector Autoregression (TVP-VAR) model, the research assesses the dynamic financial behaviors of cryptocurrencies, focusing on changes in liquidity, safe haven status, and their use in circumventing economic sanctions. The analysis distinguishes financial behaviors across three distinct phases: Pre-Conflict, Conflict, and financial sanctions periods, highlighting the interaction between cryptocurrencies and traditional financial markets. The findings indicate shifts in the role of cryptocurrencies from net transmitters to net receivers of spillovers in both returns and volatility, particularly during the financial sanctions phase. This study provides insights into the integration of cryptocurrencies with traditional financial assets and their potential impact on local economies during military conflicts. The results document the increased liquidity and interconnectedness of cryptocurrencies during military conflict periods and explore their potential use in evading sanctions and supporting War efforts.

本研究探讨了加密货币在现代战争中的作用，特别是在俄罗斯-乌克兰冲突期间。该研究利用时变参数向量自回归（TVP-VAR）模型，评估了加密货币的动态金融行为，重点关注流动性、避险状态的变化及其在规避经济制裁方面的应用。该分析区分了三个不同阶段的金融行为：冲突前、冲突和金融制裁时期，突出了加密货币与传统金融市场之间的相互作用。研究结果表明，加密货币的角色从回报和波动性溢出效应的净发送者转变为净接收者，尤其是在金融制裁阶段。这项研究为加密货币与传统金融资产的整合及其在军事冲突期间对当地经济的潜在影响提供了见解。研究结果记录了军事冲突期间加密货币的流动性和互联性的增加，并探讨了它们在逃避制裁和支持战争努力方面的潜在用途。

{"title":"Cryptocurrencies and economic sanctions","authors":"José Almeida , Tiago Cruz Gonçalves","doi":"10.1016/j.najef.2025.102537","DOIUrl":"10.1016/j.najef.2025.102537","url":null,"abstract":"<div><div>This study examines the role of cryptocurrencies in modern War, specifically during the Russia-Ukraine conflict. Utilizing a Time-Varying Parameter Vector Autoregression (TVP-VAR) model, the research assesses the dynamic financial behaviors of cryptocurrencies, focusing on changes in liquidity, safe haven status, and their use in circumventing economic sanctions. The analysis distinguishes financial behaviors across three distinct phases: Pre-Conflict, Conflict, and financial sanctions periods, highlighting the interaction between cryptocurrencies and traditional financial markets. The findings indicate shifts in the role of cryptocurrencies from net transmitters to net receivers of spillovers in both returns and volatility, particularly during the financial sanctions phase. This study provides insights into the integration of cryptocurrencies with traditional financial assets and their potential impact on local economies during military conflicts. The results document the increased liquidity and interconnectedness of cryptocurrencies during military conflict periods and explore their potential use in evading sanctions and supporting War efforts.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102537"},"PeriodicalIF":3.9,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stock market vulnerability to US monetary policy: Evidenced from quantile coherency analysis 股市对美国货币政策的脆弱性：来自分位数一致性分析的证据

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-09-02 DOI: 10.1016/j.najef.2025.102536

Sangram Keshari Jena , Amine Lahiani , Ashutosh Dash , Sougata Ray

Turkey, Brazil, India, South Africa, and Indonesia are referred as the fragile five countries in 2013. Since then, however, the macro-economic environment of those countries has improved a lot. The objective of the study is to investigate whether the stock market of those countries is still vulnerable to US monetary policy using a novel quantile coherency methodology. The vulnerability is based on the general dependency structure at the quantile of joint distribution across frequencies. Besides, the pre and post 2013 dependency is compared to examine the effectiveness of macro-economic factors in controlling the impacts of the US monetary policy. Positive and negative dependencies were observed during conventional and unconventional quantitative easing and tightening respectively. Largely, it persists in the long-to-medium term across the state of the market. Domestic macroeconomic fundamentals seem to be relatively less effective in controlling the impact of US monetary policy. Thus, additional institutional reforms are required to make these markets resilient to global monetary policy shocks.

2013年，土耳其、巴西、印度、南非和印度尼西亚被称为“脆弱五国”。然而，自那时以来，这些国家的宏观经济环境有了很大改善。本研究的目的是使用一种新颖的分位数一致性方法来调查这些国家的股市是否仍然容易受到美国货币政策的影响。该漏洞基于频率联合分布分位数处的一般依赖结构。此外，对比2013年前后的依赖关系，检验宏观经济因素在控制美国货币政策影响方面的有效性。在常规和非常规量化宽松和紧缩期间，分别观察到正相关性和负相关性。在很大程度上，在整个市场状态下，这种情况会持续到中长期。在控制美国货币政策影响方面，国内宏观经济基本面似乎相对不那么有效。因此，需要进一步的制度改革，使这些市场能够抵御全球货币政策冲击。

{"title":"Stock market vulnerability to US monetary policy: Evidenced from quantile coherency analysis","authors":"Sangram Keshari Jena , Amine Lahiani , Ashutosh Dash , Sougata Ray","doi":"10.1016/j.najef.2025.102536","DOIUrl":"10.1016/j.najef.2025.102536","url":null,"abstract":"<div><div>Turkey, Brazil, India, South Africa, and Indonesia are referred as the fragile five countries in 2013. Since then, however, the macro-economic environment of those countries has improved a lot. The objective of the study is to investigate whether the stock market of those countries is still vulnerable to US monetary policy using a novel quantile coherency methodology. The vulnerability is based on the general dependency structure at the quantile of joint distribution across frequencies. Besides, the pre and post 2013 dependency is compared to examine the effectiveness of macro-economic factors in controlling the impacts of the US monetary policy. Positive and negative dependencies were observed during conventional and unconventional quantitative easing and tightening respectively. Largely, it persists in the long-to-medium term across the state of the market. Domestic macroeconomic fundamentals seem to be relatively less effective in controlling the impact of US monetary policy. Thus, additional institutional reforms are required to make these markets resilient to global monetary policy shocks.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102536"},"PeriodicalIF":3.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144996613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic q-dependent cross-correlation test for investment classification and its application on green finance 投资分类的动态q相关检验及其在绿色金融中的应用

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-09-02 DOI: 10.1016/j.najef.2025.102535

Turker Acikgoz

This study develops a novel methodological framework, the dynamic

q

-dependent cross-correlation (DQCC) test, to evaluate the diversification, hedging, and safe-haven properties of financial assets under conditions of fractality and nonlinear dependence. Traditional econometric approaches often fail to capture three critical aspects of financial markets: time-varying structures, heterogeneous investment horizons, and fluctuation-dependent dynamics. To address these limitations, the proposed framework integrates fractal theory and econophysics-based cross-correlation measures, enabling a simultaneous analysis of time, scale, and moment dimensions. Empirically, the model is applied to the nexus between green bonds and equity markets. Methodologically, both quantile-based and event-based specifications are employed to assess asset behavior under normal conditions and during systemic crises, including the COVID-19 pandemic, the Russia–Ukraine war, and the Hamas–Israel conflict. The results reveal strong evidence of multifractality and nonlinear dynamics across all return series, rejecting market efficiency. Green bonds are found to provide persistent diversification benefits against both advanced and emerging market equities under ordinary conditions, while their safe-haven properties emerge during extreme downturns, particularly at lower quantiles and longer horizons. Event-based results confirm the safe-haven role of green bonds during COVID-19, with more mixed evidence during geopolitical crises. No robust hedging capacity is observed. The study contributes to the literature by introducing a comprehensive testing framework for investment classification under fractal dynamics and by extending the understanding of the green bond–equity nexus across advanced and emerging markets. The findings carry significant implications for portfolio construction, risk management, and sustainable finance, and the model can be applied to other asset classes to evaluate their potential roles as diversifiers, hedges, or safe-haven.

本研究开发了一种新的方法框架，即动态q相关互相关（DQCC）检验，以评估分形和非线性依赖条件下金融资产的多样化、套期保值和避险特性。传统的计量经济学方法往往无法捕捉到金融市场的三个关键方面：时变结构、异质投资视野和依赖波动的动态。为了解决这些限制，提出的框架集成了分形理论和基于经济物理学的相互关联度量，能够同时分析时间、尺度和力矩维度。实证研究表明，该模型适用于绿色债券与股票市场之间的关系。在方法上，采用基于分位数和基于事件的规范来评估正常情况下和系统性危机期间的资产行为，包括COVID-19大流行、俄罗斯-乌克兰战争和哈马斯-以色列冲突。结果揭示了多重分形和非线性动力学在所有收益序列的有力证据，拒绝市场效率。研究发现，在一般情况下，绿色债券对发达市场和新兴市场股票都能提供持续的多元化收益，而在极端低迷时期，尤其是在较低的分位数和较长的时间跨度时，绿色债券的避险属性会显现出来。基于事件的结果证实了绿色债券在2019冠状病毒病期间的避险作用，在地缘政治危机期间的证据则更为复杂。没有观察到稳健的对冲能力。本研究通过引入分形动态下投资分类的综合测试框架，并通过扩展对发达市场和新兴市场绿色债券-股票关系的理解，为文献做出了贡献。研究结果对投资组合构建、风险管理和可持续金融具有重要意义，该模型可应用于其他资产类别，以评估其作为多元化、对冲或避险的潜在作用。

{"title":"Dynamic q-dependent cross-correlation test for investment classification and its application on green finance","authors":"Turker Acikgoz","doi":"10.1016/j.najef.2025.102535","DOIUrl":"10.1016/j.najef.2025.102535","url":null,"abstract":"<div><div>This study develops a novel methodological framework, the dynamic <span><math><mi>q</mi></math></span>-dependent cross-correlation (DQCC) test, to evaluate the diversification, hedging, and safe-haven properties of financial assets under conditions of fractality and nonlinear dependence. Traditional econometric approaches often fail to capture three critical aspects of financial markets: time-varying structures, heterogeneous investment horizons, and fluctuation-dependent dynamics. To address these limitations, the proposed framework integrates fractal theory and econophysics-based cross-correlation measures, enabling a simultaneous analysis of time, scale, and moment dimensions. Empirically, the model is applied to the nexus between green bonds and equity markets. Methodologically, both quantile-based and event-based specifications are employed to assess asset behavior under normal conditions and during systemic crises, including the COVID-19 pandemic, the Russia–Ukraine war, and the Hamas–Israel conflict. The results reveal strong evidence of multifractality and nonlinear dynamics across all return series, rejecting market efficiency. Green bonds are found to provide persistent diversification benefits against both advanced and emerging market equities under ordinary conditions, while their safe-haven properties emerge during extreme downturns, particularly at lower quantiles and longer horizons. Event-based results confirm the safe-haven role of green bonds during COVID-19, with more mixed evidence during geopolitical crises. No robust hedging capacity is observed. The study contributes to the literature by introducing a comprehensive testing framework for investment classification under fractal dynamics and by extending the understanding of the green bond–equity nexus across advanced and emerging markets. The findings carry significant implications for portfolio construction, risk management, and sustainable finance, and the model can be applied to other asset classes to evaluate their potential roles as diversifiers, hedges, or safe-haven.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102535"},"PeriodicalIF":3.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On completing the connectedness analysis—A bootstrap-based DCC-GARCH approach 完成连通性分析——基于bootstrap的DCC-GARCH方法

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-08-29 DOI: 10.1016/j.najef.2025.102526

Jingliang Huai , Adrian (Wai Kong) Cheung , Bin Wang

By mapping high-dimensional systems to directed weighted networks, VAR-based Diebold-Yilmaz connectedness framework provides a novel and nuanced perspective on spillovers. Nonetheless, its reliance on rolling windows and the absence of formal statistical evidence for event-based analysis may limit its applicability. To overcome these two shortcomings, this study develops an alternative connectedness framework based on the dynamic conditional correlation—generalized autoregressive conditional heteroskedasticity (DCC-GARCH) model and a bootstrap technique, augmented by a probabilistic analysis of an increase in connectedness in response to major events. We apply our framework to four major currencies against the US dollar. In terms of event probability analysis, it is observed that 15 out of the 20 identified events correspond to a probability exceeding 90% for an increase in total connectedness, which predominantly pertain to geopolitical crises, financial market collapses, and global health emergencies. Therefore, although traditional event analysis frequently capture increases in connectedness, our methodology underscores that these associations may lack statistical rigor. Beside this, we find that the total connectedness also responds instantaneously to such events with rapid dissipation or manifests a delayed reaction.

通过将高维系统映射到有向加权网络，基于var的Diebold-Yilmaz连通性框架为溢出效应提供了一种新颖而细致的视角。尽管如此，它对滚动窗口的依赖以及对基于事件的分析缺乏正式的统计证据可能限制了它的适用性。为了克服这两个缺点，本研究开发了一个基于动态条件相关-广义自回归条件异方差（DCC-GARCH）模型和自举技术的替代连通性框架，并通过对重大事件响应的连通性增加的概率分析进行了增强。我们将我们的框架应用于四种主要货币对美元的汇率。在事件概率分析方面，可以观察到，在已确定的20个事件中，有15个事件对应于总连通性增加的概率超过90%，这主要涉及地缘政治危机、金融市场崩溃和全球突发卫生事件。因此，尽管传统的事件分析经常捕捉到连通性的增加，但我们的方法强调这些关联可能缺乏统计严谨性。除此之外，我们还发现，总连通性对此类事件也有瞬时响应，并具有快速消散或表现出延迟反应。

{"title":"On completing the connectedness analysis—A bootstrap-based DCC-GARCH approach","authors":"Jingliang Huai , Adrian (Wai Kong) Cheung , Bin Wang","doi":"10.1016/j.najef.2025.102526","DOIUrl":"10.1016/j.najef.2025.102526","url":null,"abstract":"<div><div>By mapping high-dimensional systems to directed weighted networks, VAR-based Diebold-Yilmaz connectedness framework provides a novel and nuanced perspective on spillovers. Nonetheless, its reliance on rolling windows and the absence of formal statistical evidence for event-based analysis may limit its applicability. To overcome these two shortcomings, this study develops an alternative connectedness framework based on the dynamic conditional correlation—generalized autoregressive conditional heteroskedasticity (DCC-GARCH) model and a bootstrap technique, augmented by a probabilistic analysis of an increase in connectedness in response to major events. We apply our framework to four major currencies against the US dollar. In terms of event probability analysis, it is observed that 15 out of the 20 identified events correspond to a probability exceeding 90% for an increase in total connectedness, which predominantly pertain to geopolitical crises, financial market collapses, and global health emergencies. Therefore, although traditional event analysis frequently capture increases in connectedness, our methodology underscores that these associations may lack statistical rigor. Beside this, we find that the total connectedness also responds instantaneously to such events with rapid dissipation or manifests a delayed reaction.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102526"},"PeriodicalIF":3.9,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0