North American Journal of Economics and Finance最新文献

{"title":"Asymmetric drivers of inflation: new evidence from machine learning and quantile method","authors":"Kingsley Imandojemu ,&nbsp;Adetutu Omotola Habib ,&nbsp;Omozele Lynda Showunmi ,&nbsp;Loveth Oribhabor Agboola","doi":"10.1016/j.najef.2025.102551","DOIUrl":"10.1016/j.najef.2025.102551","url":null,"abstract":"<div><div>This paper investigates the complex, nonlinear forces behind price movements in Nigeria by applying quantile econometric techniques. Using monthly data from December 2012 to August 2024, the analysis applies Elastic Net Regression for variable selection and employs Quantile-on-Quantile Kernel Regularized Least Squares (QQKRLS) alongside Quantile-on-Quantile Granger Causality (QQGC) tests. The results show that while money supply consistently drives inflation, the effects of other variables are regime-dependent; for instance, private sector credit fuels inflation in moderate-to-high periods, while bank reserves can dampen it in moderate ones. Furthermore, the analysis confirms a directional causality from these variables of interest to inflation, with the strength of the relationship varying significantly across quantiles. The results reveal that uniform policies are inadequate. Policymakers should, therefore, adopt quantile-specific and context-sensitive fiscal and monetary strategies to ensure durable price stability in Nigeria.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102551"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519584","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}

{"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":"2026-01-01","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}

{"title":"Spillover and return connectedness between uncertainties, digital assets, green bond, green and traditional energy markets: Evidence from quantile VAR","authors":"Rana Muhammad Nasir ,&nbsp;Feng He ,&nbsp;Mehrad Asadi ,&nbsp;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":"2026-01-01","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}

{"title":"Modeling and forecasting commodity price volatility using a common leverage factor","authors":"László Kamocsai ,&nbsp;Mihály Ormos","doi":"10.1016/j.najef.2025.102570","DOIUrl":"10.1016/j.najef.2025.102570","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"82 ","pages":"Article 102570"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840950","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}

{"title":"Early warning systems for cryptocurrency markets: Predicting ‘zombie’ assets using machine learning","authors":"Barbara Będowska-Sójka ,&nbsp;Piotr Wójcik ,&nbsp;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":"2026-01-01","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}

{"title":"The impact of green cryptocurrency and nongreen cryptocurrency on energy markets: Evidence from geopolitical risk and higher-order moment connectedness","authors":"Wan-Lin Yan ,&nbsp;Adrian (Wai Kong) Cheung ,&nbsp;Jiawei Yuan","doi":"10.1016/j.najef.2025.102527","DOIUrl":"10.1016/j.najef.2025.102527","url":null,"abstract":"<div><div>Cryptocurrency market has a significant impact on energy markets due to the intensive usage of energy in the mining process. This study analyzes the impact of green and nongreen cryptocurrency markets on traditional and clean energy markets by using a TVP-VAR connectedness approach. Moreover, the higher-order moment connectedness is investigated. The empirical results show that there is a time varying connectedness between cryptocurrency and energy markets and extreme events can intensify the connectedness. The transmission of volatility spillover and return asymmetry is more obvious between nongreen cryptocurrency and energy markets, while the probability of occurring extreme events is higher between green cryptocurrency and energy markets. Energy markets act as the net shock receiver, while cryptocurrencies are mainly the net shock transmitters in each order moment connectedness. The impact of geopolitical acts is mostly negative and the moderating impact of geopolitical threats on skewness is different between green and nongreen cryptocurrencies. This study significantly contributes to a deeper understanding of the impacts of green and non-green cryptocurrencies on energy markets, which has significant implications for investors and policymakers.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102527"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893666","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}

{"title":"Dynamic interrelations and the potential of global industrial sectors to function as a refuge for the global transition towards a low-carbon economy","authors":"Murad A. Bein","doi":"10.1016/j.najef.2025.102545","DOIUrl":"10.1016/j.najef.2025.102545","url":null,"abstract":"<div><div>The article analyzes the interconnections among ten global industrial sectors and the returns associated with low-carbon investments across a spectrum of investment horizons. The findings derived from a time-varying parameter and quantile connectedness reveal that volatility primarily stems from transient economic and financial events rather than lasting structural changes within the market. The global low-carbon returns exhibit a remarkable resilience against the volatility inherent in the global industrial sectors across diverse market conditions and within various temporal frameworks. The findings from cross-quantilograms indicate that during periods of reduced low-carbon emissions, the utilities, consumer staples, energy, materials, financial, and communication sectors act to hedge against losses, thus providing potential stability to investors seeking refuge during economic downturns. Additionally, the estimation results reveal a significant influence of monetary policy and bitcoin valuation on connectedness. A tightening monetary policy is inversely linked, and this effect is more pronounced in a declining market. Similarly, the increase in bitcoin valuations diminishes interconnectedness, indicating that cryptocurrencies may serve as alternative investment vehicles during episodes characterized by market turbulence. Overall, the outcome highlights the importance of integrating financial strategies that align with environmental sustainability.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102545"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519583","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}

{"title":"Credit information sharing and corporate debt maturity structure: Evidence from a quasi-natural experiment in China","authors":"Zhiliang Zhu ,&nbsp;Wuqi Song","doi":"10.1016/j.najef.2025.102549","DOIUrl":"10.1016/j.najef.2025.102549","url":null,"abstract":"<div><div>Credit information sharing allows creditors to access borrowers’ credit histories, serving as an effective tool to monitor and discipline firms. Using China’s Social Credit System (CSCS) as an exogenous shock to credit information sharing, this study employs a difference-in-difference analysis and demonstrates that such sharing extends corporate debt maturity. This increase in debt maturity is attributable to improved information transparency and lowered debt agency costs. We further find that the effect is more pronounced among firms with state ownership and firms with higher leverage ratio. Additional tests show that shared credit files help alleviate firms’ investment and financing maturity mismatch issues. Collectively, this study provides new insights into the economic consequences of credit information sharing through the lens of debt maturity structure.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102549"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465928","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}

{"title":"Cryptocurrencies and economic sanctions","authors":"José Almeida ,&nbsp;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":"2026-01-01","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}

{"title":"Financial and business cycles in the US: A non-parametric time–frequency investigation","authors":"Marco Gallegati","doi":"10.1016/j.najef.2025.102547","DOIUrl":"10.1016/j.najef.2025.102547","url":null,"abstract":"<div><div>In this study, we contrast U.S. financial and business cycles using turning point and wavelet analysis. These non-parametric methods enable us to identify the key characteristics of financial cycles and assess their relationship with business cycles without imposing assumptions about their cyclical or secular components. Contrary to the conventional view in the literature, we find that financial and business cycles are more similar than generally assumed. Wavelet analysis reveals that: i) since the 1990s, the dominant frequency range of both cycles has shifted towards lower frequencies; and ii) the observed increase in their average length is linked to a change in the relationship between financial and business cycles − from shorter business cycle frequencies (4–8 years) to higher medium-term frequencies (8–16 years). Focusing on the post-1990s period and using a measure of the financial cycle that includes equity prices, we find that the average lengths of business and financial cycles have become more aligned, at approximately 9 and 10 years, respectively. From a policy perspective, these findings cast doubt on the need for macroprudential policy as a distinct tool separate from traditional macroeconomic stabilization policy.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102547"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415996","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}