Pub Date : 2025-10-28DOI: 10.1016/j.najef.2025.102548
Yingnan Cong , Yufei Hou , Yuan Ji , Xiaojing Cai
Restructuring energy consumption is essential for promoting green, low-carbon economic and societal development. Innovation-driven policies, particularly those implemented in pilot cities, play a crucial role in this transformation. This study conducts a theoretical analysis to examine how such policies influence urban energy-consumption structures. Using a multitime-point difference-in-differences model, it treats China’s national innovation-driven city pilot policies as a quasi-natural experiment. The results indicate that these policies significantly improve urban energy structures. Mechanism analyses reveal that the improvements occur mainly through green innovation and industrial upgrading. Heterogeneity analysis further indicates that the effects are more pronounced in cities with lower administrative tiers, more challenging geographical conditions, and stronger environmental priorities. These findings provide valuable policy insights for refining innovation-driven strategies, enhancing urban energy-consumption structures, and promoting sustainable economic development in China.
{"title":"Does innovation-driven policy optimize urban energy consumption? Evidence from China’s innovation-driven city pilot policies","authors":"Yingnan Cong , Yufei Hou , Yuan Ji , Xiaojing Cai","doi":"10.1016/j.najef.2025.102548","DOIUrl":"10.1016/j.najef.2025.102548","url":null,"abstract":"<div><div>Restructuring energy consumption is essential for promoting green, low-carbon economic and societal development. Innovation-driven policies, particularly those implemented in pilot cities, play a crucial role in this transformation. This study conducts a theoretical analysis to examine how such policies influence urban energy-consumption structures. Using a multitime-point difference-in-differences model, it treats China’s national innovation-driven city pilot policies as a quasi-natural experiment. The results indicate that these policies significantly improve urban energy structures. Mechanism analyses reveal that the improvements occur mainly through green innovation and industrial upgrading. Heterogeneity analysis further indicates that the effects are more pronounced in cities with lower administrative tiers, more challenging geographical conditions, and stronger environmental priorities. These findings provide valuable policy insights for refining innovation-driven strategies, enhancing urban energy-consumption structures, and promoting sustainable economic development in China.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102548"},"PeriodicalIF":3.9,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465929","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 : 2025-10-28DOI: 10.1016/j.najef.2025.102549
Zhiliang Zhu , Wuqi Song
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.
{"title":"Credit information sharing and corporate debt maturity structure: Evidence from a quasi-natural experiment in China","authors":"Zhiliang Zhu , 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":"2025-10-28","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}
Pub Date : 2025-10-26DOI: 10.1016/j.najef.2025.102547
Marco Gallegati
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.
{"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":"2025-10-26","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}
Pub Date : 2025-10-25DOI: 10.1016/j.najef.2025.102545
Murad A. Bein
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.
{"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":"2025-10-25","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}
Pub Date : 2025-10-24DOI: 10.1016/j.najef.2025.102546
Xiaorui Xue , Shaofang Li , Xiaonan Wang , Tingting Ren
Predicting stock trends is vital in financial systems, providing insights for strategies aimed at generating excess returns. The market’s intrinsically chaotic, nonlinear, and multivariate characteristics hinder the efficacy of traditional deep learning models, especially in recognizing dynamic interdependencies and temporal non-stationarity. This study introduces an innovative hybrid framework (MVMD-NT-TiF) that integrates multivariate signal decomposition, non-stationary sequence modeling, and dual-attention-based feature selection into a cohesive architecture. By concurrently tackling noise, temporal adaptability, and feature redundancy, the approach facilitates precise and resilient forecasting in intricate financial contexts. Empirical findings regarding key stock indices illustrate its enhanced accuracy and universality relative to leading baselines, underscoring its use in real-world scenarios such as quantitative investing, risk management, and trend analysis.
{"title":"Enhancing stock market predictions with multivariate signal decomposition and dynamic feature optimization","authors":"Xiaorui Xue , Shaofang Li , Xiaonan Wang , Tingting Ren","doi":"10.1016/j.najef.2025.102546","DOIUrl":"10.1016/j.najef.2025.102546","url":null,"abstract":"<div><div>Predicting stock trends is vital in financial systems, providing insights for strategies aimed at generating excess returns. The market’s intrinsically chaotic, nonlinear, and multivariate characteristics hinder the efficacy of traditional deep learning models, especially in recognizing dynamic interdependencies and temporal non-stationarity. This study introduces an innovative hybrid framework (MVMD-NT-TiF) that integrates multivariate signal decomposition, non-stationary sequence modeling, and dual-attention-based feature selection into a cohesive architecture. By concurrently tackling noise, temporal adaptability, and feature redundancy, the approach facilitates precise and resilient forecasting in intricate financial contexts. Empirical findings regarding key stock indices illustrate its enhanced accuracy and universality relative to leading baselines, underscoring its use in real-world scenarios such as quantitative investing, risk management, and trend analysis.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102546"},"PeriodicalIF":3.9,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415995","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 : 2025-09-28DOI: 10.1016/j.najef.2025.102544
Lili Zhao , Yutong Lin , Zhenhao Liu , Guozheng Yang
Climate change has profound effects on society and the global economy. This study investigates the impact of climate risk attention (CRA) on China’s overall and sectoral stock markets by constructing a CRA index and applying the Quantile-on-Quantile regression approach. We find asymmetric and heterogeneous effects of CRA on the overall stock market, with the strongest positive effects concentrated in the upper quantiles. The results also reveal a saturation point beyond which further increases in CRA exert diminishing influence. At the sectoral level, high CRA is positively associated with non-distressed market states in Public Utilities, Information Technology, Optional Consumption, Materials, and Industrials. By contrast, its significant effects appear only during extremely prosperous conditions in Real Estate and Source Energy. Both low and high CRA are positively linked to upside volatility in the Medical Care and Daily Consumption sectors. The Financials sector responds mainly on the downside, with reduced CRA showing a positive association. Our findings underscore the importance of integrating climate risk considerations into financial strategies to support sustainable market development.
{"title":"Examining climate risk attention in stock markets: insights from quantile-on-quantile regression","authors":"Lili Zhao , Yutong Lin , Zhenhao Liu , Guozheng Yang","doi":"10.1016/j.najef.2025.102544","DOIUrl":"10.1016/j.najef.2025.102544","url":null,"abstract":"<div><div>Climate change has profound effects on society and the global economy. This study investigates the impact of climate risk attention (CRA) on China’s overall and sectoral stock markets by constructing a CRA index and applying the Quantile-on-Quantile regression approach. We find asymmetric and heterogeneous effects of CRA on the overall stock market, with the strongest positive effects concentrated in the upper quantiles. The results also reveal a saturation point beyond which further increases in CRA exert diminishing influence. At the sectoral level, high CRA is positively associated with non-distressed market states in Public Utilities, Information Technology, Optional Consumption, Materials, and Industrials. By contrast, its significant effects appear only during extremely prosperous conditions in Real Estate and Source Energy. Both low and high CRA are positively linked to upside volatility in the Medical Care and Daily Consumption sectors. The Financials sector responds mainly on the downside, with reduced CRA showing a positive association. Our findings underscore the importance of integrating climate risk considerations into financial strategies to support sustainable market development.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102544"},"PeriodicalIF":3.9,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219715","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 : 2025-09-26DOI: 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.
{"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}
Pub Date : 2025-09-26DOI: 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.
{"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}
Pub Date : 2025-09-23DOI: 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.
{"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}
Pub Date : 2025-09-20DOI: 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.
{"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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号