North American Journal of Economics and Finance最新文献

英文中文

Cross-asset contagion and risk transmission in global financial networks 全球金融网络中的跨资产传染与风险传导

IF 3.8 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-07-24 DOI: 10.1016/j.najef.2025.102511

Baoxiu Wu , Qing Wang

This research examines cross-asset contagion and risk transmission by modeling global financial markets as a dynamic network, integrating equities, currencies, commodities, and cryptocurrencies. Using extreme value theory and tail-dependent copulas, we develop novel measures of contagion centrality and risk pathways, uncovering a persistent core-periphery structure where central assets exhibit shock-absorber properties during crises, while peripheral nodes amplify systemic fragility. Our findings reveal that financial contagion intensifies under stress, with enduring post-crisis interconnectedness, challenging traditional diversification strategies. Crucially, network topology-not just asset class-determines vulnerability: central assets demonstrate resilience to tail risks, whereas peripheral nodes face heightened susceptibility. These insights have profound implications for systemic risk monitoring, suggesting regulators prioritize real-time tracking of core-periphery linkages, while investors adjust hedging strategies to account for nonlinear contagion channels. The study advances financial network theory by unifying cross-asset spillovers within a topological framework and offers actionable tools for crisis mitigation in interconnected markets.

本研究通过将全球金融市场建模为一个动态网络，整合股票、货币、商品和加密货币，研究了跨资产传染和风险传导。利用极值理论和尾依赖联结，我们开发了传染中心性和风险路径的新措施，揭示了一个持续的核心-外围结构，其中中心资产在危机期间表现出减震器特性，而外围节点则放大了系统脆弱性。我们的研究结果表明，金融传染在压力下加剧，危机后的相互关联性持续存在，挑战了传统的多元化战略。至关重要的是，网络拓扑——不仅仅是资产类别——决定了脆弱性：中心资产表现出对尾部风险的弹性，而外围节点则面临更高的敏感性。这些见解对系统风险监测具有深远的影响，建议监管机构优先考虑核心与外围联系的实时跟踪，而投资者则调整对冲策略以考虑非线性传染渠道。该研究通过在拓扑框架内统一跨资产溢出来推进金融网络理论，并为相互关联的市场中的危机缓解提供了可行的工具。

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引用次数: 0

Inflation shocks and the New Keynesian model: When should central banks fear inflation expectations? 通胀冲击与新凯恩斯主义模型：央行何时应该担心通胀预期？

IF 3.8 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-07-23 DOI: 10.1016/j.najef.2025.102508

Lucio Gobbi , Ronny Mazzocchi , Roberto Tamborini

When inflation picks up, central banks fear that de-anchored expectations trigger ever increasing inflation, but this scenario does not materialize in the standard New Keynesian (NK) blueprint for central banks. Divergent inflation processes may result introducing boundedly rational beliefs about future inflation that de-anchor endogenously, together with indexed wages and persistent shocks. However, by means of simulations of the model, we find that the relevant parameters should be far beyond their consensus empirical values. Either the concern with the de-anchoring of inflation expectations is overrated or it should be given different theoretical underpinnings than the NK ones.

当通胀上升时，央行担心去锚定的预期会引发不断上升的通胀，但这种情况并没有在标准的新凯恩斯主义（NK）央行蓝图中实现。不同的通胀过程可能导致引入对未来通胀的有限理性信念，这些信念会与指数化的工资和持续的冲击一起，内在地去锚定。然而，通过对模型的模拟，我们发现相关参数应该远远超出了它们的共识经验值。要么对通胀预期去锚化的担忧被高估了，要么应该给予与朝鲜不同的理论基础。

引用次数: 0

Price dynamics in artificial stock market with realistic order book mechanism 具有现实订单机制的人工股票市场的价格动态

IF 3.8 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-07-23 DOI: 10.1016/j.najef.2025.102504

Uzay Çetin , Şükrü C. Demirtaş , Senem Çakmak Şahin

We analyzed the effect of the daily price margin on artificial stock markets. In our study, we have two distinct market scenarios: One designed to imitate a market akin to that of Türkiye, characterized by the presence of a daily price margin regulation, and the other reflecting a market resembling the United States, where orders are not subject to daily price margin constraints. With daily price margin regulations stock prices become more accessible, positively impacting market volume. We incorporated a realistic order book mechanism for keeping track of the bid and ask orders. Traders are classified as either fundamental or noise, according to their strategies. We have also established a dynamic risk level for each stock, based on its weekly transaction volumes. Only fundamentals are risk-aware. That is, they tend to order stocks with low risk and avoid high risk stocks. We have detected emerging patterns of price fluctuations within the market scenario governed by the daily price margin regulations. Risk-aware herd behavior, despite not being explicitly modeled as an input, emerges also spontaneously within the system. These patterns emerge because of the complex relationship among dynamic risk levels of stocks, risk-aware traders and the daily price margin regulation.

我们分析了日差价对人为股票市场的影响。在我们的研究中，我们有两种不同的市场情景：一种是模仿类似于 rkiye的市场，其特点是存在每日价格保证金监管，另一种反映了类似于美国的市场，其中订单不受每日价格保证金约束。随着每日保证金规定的实施，股票价格变得更容易接近，这对市场交易量产生了积极影响。我们加入了一个现实的订单簿机制，以保持跟踪出价和要价订单。根据交易者的策略，他们可以分为基本面交易者和噪音交易者。我们还根据每只股票的周交易量为其设定了动态风险水平。只有基本面才是有风险意识的。也就是说，他们倾向于购买低风险的股票，而避开高风险的股票。我们发现，在每日保证金规定的市场情况下，出现了价格波动的新模式。有风险意识的群体行为，尽管没有被明确地建模为输入，但也会在系统中自发地出现。这些模式的出现是由于股票的动态风险水平、风险意识强的交易员和每日价格保证金监管之间的复杂关系。

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引用次数: 0

An analytical approximation for European options under a Heston-type model with regime switching 带制度切换的heston型模型下欧式期权的解析逼近

IF 3.8 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-07-22 DOI: 10.1016/j.najef.2025.102500

Wenting Chen , Xin-Jiang He

In this paper, we consider the pricing of European options under a generalized regime-switching Heston model. By “generalized”, it means that all parameters of the original Heston model are expected to vary among various economic states. This broad assumption regarding regime switching has impeded the application of existing analytical techniques used to calculate European option prices under Heston-type regime-switching models. Albeit difficult, we have managed to derive an analytical approximation for the price of European options with the use of frozen coefficient technique. Remarkably, an error estimation for the approximation has been established theoretically and verified quantitatively through numerical experiments. Finally, through a preliminary empirical study, the current model is shown to be superior to a class of generally used Heston-type models, implying that the present model, together with the newly derived formula, can be safely used in actual financial market for pricing European options expiring in no more than three months.

本文研究了在广义制度切换赫斯顿模型下欧式期权的定价问题。所谓“普遍化”，是指原赫斯顿模型的所有参数在不同的经济状态下都是不同的。这种关于制度转换的广泛假设阻碍了现有分析技术在赫斯顿型制度转换模型下计算欧洲期权价格的应用。尽管困难重重，我们还是成功地利用冻结系数技术推导出了欧洲期权价格的解析近似。值得注意的是，在理论上建立了近似的误差估计，并通过数值实验进行了定量验证。最后，通过初步的实证研究，本文模型优于一类常用的heston型模型，这意味着本文模型与新导出的公式可以安全地用于实际金融市场中到期不超过3个月的欧式期权定价。

引用次数: 0

Cascading failure, financial network and systemic risk 级联失效、金融网络与系统性风险

IF 3.8 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-07-20 DOI: 10.1016/j.najef.2025.102505

Chuangxia Huang , Hualu Miao , Xiaoguang Yang , Jie Cao , Huirui Yang

How to accurately measure the systemic risk is one of the fundamental and challenging problems in the field of risk management. Most previous studies do not fully consider the cascading failure mechanism caused by risk co-contagion and network effects, leading to misestimation of systemic risk. We construct financial institution tail risk networks by LASSO technique and then simulating the cascading process of risk contagion by ΔCoES on the networks. By developing a general cascading failure model, this paper proposes a novel indicator, ESRank, to measure systemic risk. We apply ESRank to analyze Chinese financial institutions and the empirical results suggest that: (i) during the crisis periods, especially the 2015–2016 stock crash period, the Chinese financial system manifests a higher ESRank in comparison to normal periods; (ii) the securities sector is the largest risk contributor before the stock crash, while the diversified financial institutions have displayed increasing risk contributions afterwards; (iii) compared with the traditional systemic risk indicators such as VaR, CoVaR and SRISK, the proposed ESRank demonstrates the outstanding characteristics of better predictive and explanatory capabilities regarding institutional profitability.

如何准确地度量系统性风险是风险管理领域的基础性和挑战性问题之一。以往的研究大多没有充分考虑风险共传染和网络效应导致的级联失效机制，导致对系统性风险的错误估计。利用LASSO技术构建金融机构尾部风险网络，并利用ΔCoES在网络上模拟风险传染的级联过程。通过建立一个通用的级联失效模型，本文提出了一个新的指标ESRank来衡量系统性风险。我们运用ESRank对中国金融机构进行分析，实证结果表明：(1)在危机时期，特别是2015-2016年股灾时期，中国金融体系的ESRank高于正常时期；(2)股灾前证券行业是最大的风险贡献者，股灾后多元化金融机构的风险贡献呈上升趋势；（iii）与传统的系统风险指标VaR、CoVaR和SRISK相比，所提出的ESRank对机构盈利能力具有更好的预测和解释能力。

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引用次数: 0

Enhanced index tracking: A relative downside risk approach 加强指数跟踪：一种相对下行风险的方法

IF 3.8 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-07-17 DOI: 10.1016/j.najef.2025.102501

Ronghua Luo , Zeyu Huang , Yangyi Liu

We introduce the Relative Downside Tracking Error (RDTE) model, a dynamic enhanced indexing method that adapts to the time-varying and mean-reverting nature of market volatility. The RDTE model dynamically adjusts the weights assigned to downside deviations based on market volatility, allowing for greater flexibility during high-volatility periods. This flexibility helps the model reduce the emphasis on short-term fluctuations, focusing instead on minimizing overall downside risk. By doing so, the model effectively controls portfolio distortion, leading to more stable long-term performance. Empirical analyses of U.S. and Chinese stock markets demonstrate that the RDTE model consistently outperforms traditional models, delivering higher returns, lower downside risk, and better risk-adjusted performance. This outperformance is driven by the RDTE model’s effective downside risk management during volatile periods, as confirmed by its superior long-term performance in both markets.

我们引入了相对下行跟踪误差（RDTE）模型，这是一种适应市场波动时变和均值回归性质的动态增强索引方法。RDTE模型根据市场波动动态调整分配给下行偏差的权重，从而在高波动时期具有更大的灵活性。这种灵活性有助于模型减少对短期波动的强调，而将重点放在最小化整体下行风险上。通过这样做，该模型有效地控制了投资组合的扭曲，从而导致更稳定的长期表现。对美国和中国股市的实证分析表明，RDTE模型始终优于传统模型，具有更高的回报、更低的下行风险和更好的风险调整绩效。这种优异的表现是由RDTE模型在波动时期有效的下行风险管理所驱动的，正如其在两个市场的卓越长期表现所证实的那样。

引用次数: 0

Understanding the connectedness between US traditional assets and green cryptocurrencies during crises 了解危机期间美国传统资产与绿色加密货币之间的联系

IF 3.8 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-07-16 DOI: 10.1016/j.najef.2025.102474

Nikolaos Kyriazis , Shaen Corbet

This research examines the dynamic interaction between conventional financial assets, namely the US dollar, the S&P 500 index, gold and crude oil, and ten major green cryptocurrencies, focusing on their spillover linkages and hedging capacities during major global economic and geopolitical shocks. The study analyses daily data to uncover spillover effects using the innovative Quantile-Vector Autoregressive methodology developed by Cunado et al. (2023). Results indicate that green cryptocurrencies significantly interact with other examined instruments. Algorand, Cardano, IOTA, TRON and Powerledger demonstrate the largest interactive effects, with the latter standing out as a consistent transmitter of influence across both crises, demonstrating that this sub-class of cryptocurrency is exhibiting elevated maturity. Traditional assets predominantly act as receivers of such risk dynamics from more speculative asset classes, with gold identified as an effective absorber of spillovers, especially in bear markets. Conversely, the US dollar and crude oil are identified as large transmitters of spillover impacts, a result found to be particularly influential in periods of geopolitical conflict. The study further reveals that green cryptocurrencies promoting trust, innovation, and renewable energy are more effectively connected with traditional investments than those focusing on financial services or business accessibility, presenting diversification opportunities during crises.

本研究考察了传统金融资产（即美元、标准普尔500指数、黄金和原油）与十大绿色加密货币之间的动态相互作用，重点研究了它们在重大全球经济和地缘政治冲击期间的溢出联系和对冲能力。该研究使用Cunado等人（2023）开发的创新分位数向量自回归方法分析日常数据，以揭示溢出效应。结果表明，绿色加密货币与其他被检查的工具显着相互作用。Algorand、Cardano、IOTA、TRON和Powerledger表现出最大的互动效应，后者在两次危机中都表现出一致的影响力，表明这一子类的加密货币正表现出更高的成熟度。传统资产主要充当来自更具投机性的资产类别的风险动态的接受者，而黄金被认为是溢出效应的有效吸收器，尤其是在熊市中。相反，美元和原油被认为是外溢影响的大传播者，这一结果在地缘政治冲突时期尤为重要。该研究进一步表明，促进信任、创新和可再生能源的绿色加密货币比那些专注于金融服务或商业可及性的加密货币更有效地与传统投资联系在一起，在危机期间提供了多样化的机会。

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引用次数: 0

Oil price shocks and green investments: Upside risks, hedging, and safe-haven properties 油价冲击与绿色投资：上行风险、对冲和避险资产

IF 3.9 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-07-14 DOI: 10.1016/j.najef.2025.102502

Nedal Al-Fayoumi , Bana Abuzayed , Elie Bouri , Nadia Arfaoui

This study investigates the systemic risk spillover from various oil price shocks (demand, supply, and risk) to several green investments covering sustainable, ESG, clean technology, carbon market, clean energy, and green bonds and assesses the hedging and safe-haven roles of these green investments against oil shocks. Based on daily data from January 4, 2012 to September 20, 2022, oil prices are decomposed and a dynamic conditional correlation model is used to assess conditional value-at-risk (CoVaR) as a measure of upper risk spillover from each oil price shock to green investments. The hedging and safe-haven roles of the green investments are examined, especially during the COVID-19 pandemic and Russia-Ukraine conflict. The results show that all upper CoVaRs resulting from oil demand shocks exceed the investment’s upper tail VaRs during Phase 1 of COVID-19, indicating a significant oil demand shock risk spillover to all green investments. During Phase 2 of COVID-19 and the Russia-Ukraine conflict, only some investments are influenced by demand oil shocks. When oil supply and risk shocks rise, the upside risk of all green investments tends to be mitigated, suggesting that, during unstable periods, investors should seek green investments to mitigate the risk spillovers of these two oil shocks. Further analysis indicates that the majority of green investments serve as diversifiers for oil demand shocks, and act as hedges against oil supply and risk shocks. However, only a few of these green investments are strong safe havens.

本研究探讨了各种油价冲击（需求、供应和风险）对包括可持续、ESG、清洁技术、碳市场、清洁能源和绿色债券在内的绿色投资的系统性风险溢出，并评估了这些绿色投资对石油冲击的对冲和避险作用。基于2012年1月4日至2022年9月20日的每日数据，对油价进行了分解，并使用动态条件相关模型来评估条件风险价值（CoVaR），以衡量每次油价冲击对绿色投资的上限风险溢出。研究了绿色投资的对冲和避险作用，特别是在2019冠状病毒病大流行和俄罗斯-乌克兰冲突期间。结果表明，在COVID-19第一阶段，石油需求冲击导致的所有上尾价值都超过了投资的上尾价值，表明石油需求冲击对所有绿色投资的风险溢出显著。在2019冠状病毒病第二阶段和俄乌冲突期间，只有部分投资受到石油需求冲击的影响。当石油供应和风险冲击上升时，所有绿色投资的上行风险往往会被缓解，这表明，在不稳定时期，投资者应该寻求绿色投资来减轻这两种石油冲击的风险溢出。进一步分析表明，大多数绿色投资作为石油需求冲击的多元化，并作为对冲石油供应和风险冲击。然而，这些绿色投资中只有少数是强大的避风港。

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引用次数: 0

Quantile on quantile connectedness between safe-haven assets and stock markets: a portfolio risk perspective 避险资产与股市之间的分位数连通性：投资组合风险视角

IF 3.8 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-07-11 DOI: 10.1016/j.najef.2025.102496

Walid Mensi , Mohamed Amine Nabli , Mouna Guesmi , Houssem Eddine Belghouthi , Sang Hoon Kang

This study investigates quantile-on-quantile connectedness between the stock markets of China, Europe, Japan, the UK, and the US, and safe-haven assets including gold, Bitcoin, and green bonds, employing the methodology proposed in Gabauer and Stenfors (2024). Furthermore, we examine the optimal design of investment portfolios built with these assets using Minimum Variance Portfolio, Minimum Correlation Portfolio, and Minimum Connectedness Portfolio measures. Our key findings show that reversely related quantiles show significantly stronger total connectedness than directly related ones, highlighting the significance of tail risk in portfolio management. The connectedness among these stock markets and safe haven assets is asymmetric and fluctuates over time, especially during major economic events such as the oil surplus of 2014, the Chinese economic deceleration in 2015, the COVID-19 pandemic in 2020, the Russia–Ukraine war in 2022, and the war between Israel and Hamas that began in 2023. We find that gold, Bitcoin and green bonds can act as safe havens for international equities, especially in periods of market stress, but their status depends on market conditions. A portfolio analysis indicates that Bitcoin and the Nikkei 225 index serve as effective hedges against stock market volatility, and that Bitcoin is an important portfolio component with the highest optimal weight.

本研究采用Gabauer和Stenfors（2024）提出的方法，调查了中国、欧洲、日本、英国和美国股票市场与黄金、比特币和绿色债券等避险资产之间的分位数间连通性。此外，我们使用最小方差投资组合、最小相关投资组合和最小连通性投资组合度量来检验由这些资产构建的投资组合的最佳设计。我们的主要发现表明，反向相关的分位数比直接相关的分位数显示出更强的总连通性，突出了尾部风险在投资组合管理中的重要性。这些股票市场和避险资产之间的联系是不对称的，并且随着时间的推移而波动，特别是在重大经济事件期间，如2014年的石油过剩、2015年的中国经济减速、2020年的COVID-19大流行、2022年的俄罗斯-乌克兰战争以及始于2023年的以色列和哈马斯之间的战争。我们发现，黄金、比特币和绿色债券可以作为国际股票的避风港，尤其是在市场承压时期，但它们的地位取决于市场状况。投资组合分析表明，比特币和日经225指数可以有效对冲股市波动，比特币是投资组合中最优权重最高的重要组成部分。

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引用次数: 0

Who A(m) I? exploring quantile frequency connectedness in emerging AI and IoT token markets 我是谁？探索新兴人工智能和物联网代币市场的分位数频率连通性

IF 3.8 3区经济学 Q1 BUSINESS, FINANCE

North American Journal of Economics and Finance

Pub Date : 2025-07-08 DOI: 10.1016/j.najef.2025.102497

David Y. Aharon , Shoaib Ali , Muhammad Naveed

This paper investigates the return spillover and connectedness between Artificial Intelligence (AI) and Internet of Things (IoT) tokens using the Quantile Vector Autoregression (QVAR) and quantile frequency connectedness approach. Using daily data from February 2021 to March 2024 for ten leading AI and IoT tokens, we find that connectedness is both time-varying and asymmetric across quantiles. In the short term, the Total Connectedness Index (TCI) peaks at 69.58 % under extreme market conditions (τ = 0.05), compared to 64.16 % in bull markets (τ = 0.95) and 61.43 % under normal conditions (τ = 0.50). Connectedness is weaker in the medium and long terms, but asymmetry persists as the TCI reaches 10.98 % vs. 5.32 % (medium term) and 10.52 % vs. 2.64 % (long term) for extreme vs. normal quantiles. These findings confirm that return transmission intensifies during periods of elevated market uncertainty, particularly in the left tail of the distribution. Moreover, AI and IOT tokens offer both diversification and hedging benefits against each other. Our analysis provides insights for investors, portfolio managers, and policymakers in understanding systemic risk and optimizing digital asset portfolios.

本文使用分位数向量自回归（QVAR）和分位数频率连通性方法研究了人工智能（AI）和物联网（IoT）令牌之间的回报溢出和连通性。使用2021年2月至2024年3月10个领先的人工智能和物联网代币的日常数据，我们发现连通性在分位数上既随时间变化又不对称。在短期内，在极端市场条件下，总连通性指数（TCI）峰值为69.58% (τ = 0.05)，而在牛市（τ = 0.95）和正常条件下（τ = 0.50）， TCI峰值分别为64.16%和61.43%。中期和长期连通性较弱，但不对称仍然存在，因为极端分位数和正常分位数的TCI分别达到10.98%和5.32%（中期），10.52%和2.64%（长期）。这些发现证实，在市场不确定性升高期间，特别是在分布的左尾，回报传播加剧。此外，人工智能和物联网代币既可以提供多样化，又可以相互对冲。我们的分析为投资者、投资组合经理和政策制定者理解系统性风险和优化数字资产投资组合提供了见解。

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