Pub Date : 2025-07-22DOI: 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.
{"title":"An analytical approximation for European options under a Heston-type model with regime switching","authors":"Wenting Chen , Xin-Jiang He","doi":"10.1016/j.najef.2025.102500","DOIUrl":"10.1016/j.najef.2025.102500","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"80 ","pages":"Article 102500"},"PeriodicalIF":3.8,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702749","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}
Pub Date : 2025-07-20DOI: 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.
{"title":"Cascading failure, financial network and systemic risk","authors":"Chuangxia Huang , Hualu Miao , Xiaoguang Yang , Jie Cao , Huirui Yang","doi":"10.1016/j.najef.2025.102505","DOIUrl":"10.1016/j.najef.2025.102505","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"80 ","pages":"Article 102505"},"PeriodicalIF":3.8,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696696","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}
Pub Date : 2025-07-17DOI: 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.
{"title":"Enhanced index tracking: A relative downside risk approach","authors":"Ronghua Luo , Zeyu Huang , Yangyi Liu","doi":"10.1016/j.najef.2025.102501","DOIUrl":"10.1016/j.najef.2025.102501","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"80 ","pages":"Article 102501"},"PeriodicalIF":3.8,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672275","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}
Pub Date : 2025-07-16DOI: 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.
{"title":"Understanding the connectedness between US traditional assets and green cryptocurrencies during crises","authors":"Nikolaos Kyriazis , Shaen Corbet","doi":"10.1016/j.najef.2025.102474","DOIUrl":"10.1016/j.najef.2025.102474","url":null,"abstract":"<div><div>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 <span><span>Cunado et al. (2023)</span></span>. 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.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"80 ","pages":"Article 102474"},"PeriodicalIF":3.8,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672276","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}
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.
{"title":"Oil price shocks and green investments: Upside risks, hedging, and safe-haven properties","authors":"Nedal Al-Fayoumi , Bana Abuzayed , Elie Bouri , Nadia Arfaoui","doi":"10.1016/j.najef.2025.102502","DOIUrl":"10.1016/j.najef.2025.102502","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"80 ","pages":"Article 102502"},"PeriodicalIF":3.9,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144827341","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}
Pub Date : 2025-07-11DOI: 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.
{"title":"Quantile on quantile connectedness between safe-haven assets and stock markets: a portfolio risk perspective","authors":"Walid Mensi , Mohamed Amine Nabli , Mouna Guesmi , Houssem Eddine Belghouthi , Sang Hoon Kang","doi":"10.1016/j.najef.2025.102496","DOIUrl":"10.1016/j.najef.2025.102496","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"80 ","pages":"Article 102496"},"PeriodicalIF":3.8,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614195","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}
Pub Date : 2025-07-08DOI: 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.
{"title":"Who A(m) I? exploring quantile frequency connectedness in emerging AI and IoT token markets","authors":"David Y. Aharon , Shoaib Ali , Muhammad Naveed","doi":"10.1016/j.najef.2025.102497","DOIUrl":"10.1016/j.najef.2025.102497","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"80 ","pages":"Article 102497"},"PeriodicalIF":3.8,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604797","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}
This study provides a thorough analysis of the market power within the banking sector of the Gulf Cooperation Council (GCC) countries over the period from 2000 to 2023, by employing robust econometric models and indices. Utilizing a translog cost function model, the study estimates marginal costs and calculates initially the Lerner Index, thereby revealing substantial market power among the GCC banks. To address the issues of high collinearity and heteroscedasticity, the model is refined by highlighting the significant roles of the total assets and labor costs in determining the overall costs. Further, the Adjusted Lerner Index and the Boone Indicator confirm the prevalence of market power in the region’s banking sector. The Panzar-Rosse H-statistic analysis suggests that the GCC banking sector operates under an oligopolistic framework, where the competitive pressures are insufficient to diminish the substantial market power held by the GCC banks. Dynamic GMM panel data models also confirm the persistence of market power, showing that past market power strongly predicts the current levels with minimal fluctuations over time. The study also investigates the relationship between market power and bank stability, revealing that while higher market power is generally associated with lower bank risk, excessive market power may paradoxically increase risk, indicating a non-linear relationship. These findings highlight the importance of regulatory reforms aimed at enhancing competition and fostering a more resilient banking sector. By providing a comprehensive understanding of market power dynamics, this study offers valuable insights for policymakers and financial regulators in the GCC, thus, guiding the efforts to improve competition, reduce systemic risk, and strengthen financial stability.
{"title":"Dynamics of market power and stability in GCC banking: econometric analysis and policy implications","authors":"Idries Mohammad Wanas Al-Jarrah , Khalid Al-Abdulqader , Yazan Idries Al-Jarrah , Shawkat Hammoudeh","doi":"10.1016/j.najef.2025.102499","DOIUrl":"10.1016/j.najef.2025.102499","url":null,"abstract":"<div><div>This study provides a thorough analysis of the market power within the banking sector of the Gulf Cooperation Council (GCC) countries over the period from 2000 to 2023, by employing robust econometric models and indices. Utilizing a translog cost function model, the study estimates marginal costs and calculates initially the Lerner Index, thereby revealing substantial market power among the GCC banks. To address the issues of high collinearity and heteroscedasticity, the model is refined by highlighting the significant roles of the total assets and labor costs in determining the overall costs. Further, the Adjusted Lerner Index and the Boone Indicator confirm the prevalence of market power in the region’s banking sector. The Panzar-Rosse H-statistic analysis suggests that the GCC banking sector operates under an oligopolistic framework, where the competitive pressures are insufficient to diminish the substantial market power held by the GCC banks. Dynamic GMM panel data models also confirm the persistence of market power, showing that past market power strongly predicts the current levels with minimal fluctuations over time. The study also investigates the relationship between market power and bank stability, revealing that while higher market power is generally associated with lower bank risk, excessive market power may paradoxically increase risk, indicating a non-linear relationship. These findings highlight the importance of regulatory reforms aimed at enhancing competition and fostering a more resilient banking sector. By providing a comprehensive understanding of market power dynamics, this study offers valuable insights for policymakers and financial regulators in the GCC, thus, guiding the efforts to improve competition, reduce systemic risk, and strengthen financial stability.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"80 ","pages":"Article 102499"},"PeriodicalIF":3.9,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144772432","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}
Pub Date : 2025-07-06DOI: 10.1016/j.najef.2025.102498
Marina Murdock , Thanh Ngo , Nivine Richie
We explore the relationship between vertical integration of the firm and customer satisfaction, contributing to the emerging literature on the interaction of finance and marketing. Using the American Customer Satisfaction Index (ACSI) to measure customer satisfaction from 1995 to 2021 and the vertical integration scores gathered from Frésard, Hoberg, & Phillips (2020), we find that customer satisfaction is higher for vertically integrated firms. We examine two channels—trade credit and inventory management—through which vertical integration may affect customer satisfaction. We find that increased trade credit is associated with higher levels of customer satisfaction, and this relationship is intensified for firms that are more highly integrated along the supply chain. Additionally, customer satisfaction is inversely related to inventory turnover, and this inverse relationship is mitigated by increased relatedness along the integrated supply chain. The findings suggest that control over various stages of production and distribution enables firms to better anticipate and manage inventory levels, reducing the risk of stockouts and overstocking.
{"title":"Customer satisfaction and vertical integration","authors":"Marina Murdock , Thanh Ngo , Nivine Richie","doi":"10.1016/j.najef.2025.102498","DOIUrl":"10.1016/j.najef.2025.102498","url":null,"abstract":"<div><div>We explore the relationship between vertical integration of the firm and customer satisfaction, contributing to the emerging literature on the interaction of finance and marketing. Using the American Customer Satisfaction Index (ACSI) to measure customer satisfaction from 1995 to 2021 and the vertical integration scores gathered from <span><span>Frésard, Hoberg, & Phillips (2020)</span></span>, we find that customer satisfaction is higher for vertically integrated firms. We examine two channels—trade credit and inventory management—through which vertical integration may affect customer satisfaction. We find that increased trade credit is associated with higher levels of customer satisfaction, and this relationship is intensified for firms that are more highly integrated along the supply chain. Additionally, customer satisfaction is inversely related to inventory turnover, and this inverse relationship is mitigated by increased relatedness along the integrated supply chain. The findings suggest that control over various stages of production and distribution enables firms to better anticipate and manage inventory levels, reducing the risk of stockouts and overstocking.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"80 ","pages":"Article 102498"},"PeriodicalIF":3.8,"publicationDate":"2025-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595900","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}
Using data on hourly frequency observed temperature and daily forecasted temperatures across major U.S. metropolitan areas over a 30-year period, we analyze the relationship between the daily returns of the NYMEX Henry Hub Natural Gas futures and U.S. weather fluctuations. We propose the existence of a novel risk premium linked to extreme weather forecasts for U.S. Natural Gas futures, which outperforms the S&P500 index on an absolute and risk-adjusted basis over a 30-year period. Our findings contribute to opening a new perspective on the non-linear interplay between weather and financial markets emphasizing the importance of these factors in financial risk management and in the context of climate change.
{"title":"Can extreme weather forecasts lead to a risk premium? Evidence of a non-linear response in U.S. natural gas futures","authors":"Manou Monteux , Maria Cristina Arcuri , Gino Gandolfi , Stefano Caselli","doi":"10.1016/j.najef.2025.102494","DOIUrl":"10.1016/j.najef.2025.102494","url":null,"abstract":"<div><div>Using data on hourly frequency observed temperature and daily forecasted temperatures across major U.S. metropolitan areas over a 30-year period, we analyze the relationship between the daily returns of the NYMEX Henry Hub Natural Gas futures and U.S. weather fluctuations. We propose the existence of a novel risk premium linked to extreme weather forecasts for U.S. Natural Gas futures, which outperforms the S&P500 index on an absolute and risk-adjusted basis over a 30-year period. Our findings contribute to opening a new perspective on the non-linear interplay between weather and financial markets emphasizing the importance of these factors in financial risk management and in the context of climate change.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"80 ","pages":"Article 102494"},"PeriodicalIF":3.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144779949","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}
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