Turkey, Brazil, India, South Africa, and Indonesia are referred as the fragile five countries in 2013. Since then, however, the macro-economic environment of those countries has improved a lot. The objective of the study is to investigate whether the stock market of those countries is still vulnerable to US monetary policy using a novel quantile coherency methodology. The vulnerability is based on the general dependency structure at the quantile of joint distribution across frequencies. Besides, the pre and post 2013 dependency is compared to examine the effectiveness of macro-economic factors in controlling the impacts of the US monetary policy. Positive and negative dependencies were observed during conventional and unconventional quantitative easing and tightening respectively. Largely, it persists in the long-to-medium term across the state of the market. Domestic macroeconomic fundamentals seem to be relatively less effective in controlling the impact of US monetary policy. Thus, additional institutional reforms are required to make these markets resilient to global monetary policy shocks.
{"title":"Stock market vulnerability to US monetary policy: Evidenced from quantile coherency analysis","authors":"Sangram Keshari Jena , Amine Lahiani , Ashutosh Dash , Sougata Ray","doi":"10.1016/j.najef.2025.102536","DOIUrl":"10.1016/j.najef.2025.102536","url":null,"abstract":"<div><div>Turkey, Brazil, India, South Africa, and Indonesia are referred as the fragile five countries in 2013. Since then, however, the macro-economic environment of those countries has improved a lot. The objective of the study is to investigate whether the stock market of those countries is still vulnerable to US monetary policy using a novel quantile coherency methodology. The vulnerability is based on the general dependency structure at the quantile of joint distribution across frequencies. Besides, the pre and post 2013 dependency is compared to examine the effectiveness of macro-economic factors in controlling the impacts of the US monetary policy. Positive and negative dependencies were observed during conventional and unconventional quantitative easing and tightening respectively. Largely, it persists in the long-to-medium term across the state of the market. Domestic macroeconomic fundamentals seem to be relatively less effective in controlling the impact of US monetary policy. Thus, additional institutional reforms are required to make these markets resilient to global monetary policy shocks.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102536"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144996613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-08-18DOI: 10.1016/j.najef.2025.102521
Kei-Ichiro Inaba
By conducting international panel-data regressions to investigate the determinants of listed companies’ average qs in 18 countries’ representative stock market indices over the period 2009–2019 in consideration of the companies’ market capitalization differences, I find that better social and governance management levels were associated with higher qs, and that corporate cash value was positively priced across the countries. This positive pricing of corporate cash was strengthened in countries with better environment, social, and governance management levels, and in those with higher R&D investments. Pricing was more positive in the United Kingdom than in the United States (U.S.) or Japan. It was weakened as national indices with greater market capitalization were downplayed more in the regression analysis. It was strengthened in the U.S. index in response to increasing passive index funds.
{"title":"Corporate cash value and ESG management: Panel data analyses of stock indices across countries","authors":"Kei-Ichiro Inaba","doi":"10.1016/j.najef.2025.102521","DOIUrl":"10.1016/j.najef.2025.102521","url":null,"abstract":"<div><div>By conducting international panel-data regressions to investigate the determinants of listed companies’ average <em>q</em>s in 18 countries’ representative stock market indices over the period 2009–2019 in consideration of the companies’ market capitalization differences, I find that better social and governance management levels were associated with higher <em>q</em>s, and that corporate cash value was positively priced across the countries. This positive pricing of corporate cash was strengthened in countries with better environment, social, and governance management levels, and in those with higher R&D investments. Pricing was more positive in the United Kingdom than in the United States (U.S.) or Japan. It was weakened as national indices with greater market capitalization were downplayed more in the regression analysis. It was strengthened in the U.S. index in response to increasing passive index funds.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102521"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903954","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 : 2026-01-01Epub Date: 2025-12-11DOI: 10.1016/j.najef.2025.102565
Yinghua Ren , Xin Chen , Han Chen , Huiming Zhu
This study proposes a novel binary-classification model for systemic risk warning, utilizing inter-industry tail-risk spillover networks as input. These networks are constructed using the Tail-Event driven network (TENET) model, which captures high-dimensional and non-linear characteristics of risk contagion. The model leverages the Gated Graph Neural Network (GGNN) framework to uncover the ambiguous specification of crisis prediction. Applied to 11 key U.S. industry indices, the empirical results demonstrate that: (i) the topology of the risk spillover network is strongly correlated with financial crises during critical periods; and (ii) the GGNN model based on the TENET network provides superior reliability in early warning compared to traditional machine learning and other graph-based models.
{"title":"Does inter-industry risk spillover network predict financial crisis? Evidence from a gated graph neural networks approach","authors":"Yinghua Ren , Xin Chen , Han Chen , Huiming Zhu","doi":"10.1016/j.najef.2025.102565","DOIUrl":"10.1016/j.najef.2025.102565","url":null,"abstract":"<div><div>This study proposes a novel binary-classification model for systemic risk warning, utilizing inter-industry tail-risk spillover networks as input. These networks are constructed using the Tail-Event driven network (TENET) model, which captures high-dimensional and non-linear characteristics of risk contagion. The model leverages the Gated Graph Neural Network (GGNN) framework to uncover the ambiguous specification of crisis prediction. Applied to 11 key U.S. industry indices, the empirical results demonstrate that: (i) the topology of the risk spillover network is strongly correlated with financial crises during critical periods; and (ii) the GGNN model based on the TENET network provides superior reliability in early warning compared to traditional machine learning and other graph-based models.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"82 ","pages":"Article 102565"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791082","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 : 2026-01-01Epub Date: 2025-07-29DOI: 10.1016/j.najef.2025.102513
Qiang Liu , Ting Liu , Chen Xu
Climate change is one of the greatest challenges of the 21st century, with its uncertainty significantly impacting financial stability. This study examines the spillover effects of China’s climate policy uncertainty on the stock, money, bond, foreign exchange and futures markets, using data from October 2006 to August 2024 and applying the QVAR-DY spillover index method. The findings reveal: (1) Extreme conditions amplify the spillover effects of China’s climate policy uncertainty on financial markets, especially during market booms. (2) The static analysis shows that under normal conditions, the largest spillovers are seen in the bond and futures markets. Under extreme conditions, the bond market is the most affected. Dynamic analysis shows that spillovers increase significantly during climate events (Copenhagen Summit, Carbon Peaking and Carbon Neutrality Goals). During market downturns, the bond market is impacted most; during market booms, the money market is more susceptible. (3) Net spillover analysis finds significant positive net spillovers to financial sub-markets during market booms. The findings guide efforts to manage climate policy uncertainty and reduce systemic financial risks.
{"title":"Asymmetric spillovers of climate policy uncertainty on financial markets – Evidence from China","authors":"Qiang Liu , Ting Liu , Chen Xu","doi":"10.1016/j.najef.2025.102513","DOIUrl":"10.1016/j.najef.2025.102513","url":null,"abstract":"<div><div>Climate change is one of the greatest challenges of the 21st century, with its uncertainty significantly impacting financial stability. This study examines the spillover effects of China’s climate policy uncertainty on the stock, money, bond, foreign exchange and futures markets, using data from October 2006 to August 2024 and applying the QVAR-DY spillover index method. The findings reveal: (1) Extreme conditions amplify the spillover effects of China’s climate policy uncertainty on financial markets, especially during market booms. (2) The static analysis shows that under normal conditions, the largest spillovers are seen in the bond and futures markets. Under extreme conditions, the bond market is the most affected. Dynamic analysis shows that spillovers increase significantly during climate events (Copenhagen Summit, Carbon Peaking and Carbon Neutrality Goals). During market downturns, the bond market is impacted most; during market booms, the money market is more susceptible. (3) Net spillover analysis finds significant positive net spillovers to financial sub-markets during market booms. The findings guide efforts to manage climate policy uncertainty and reduce systemic financial risks.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"81 ","pages":"Article 102513"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144888731","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 : 2026-01-01Epub Date: 2025-12-27DOI: 10.1016/j.najef.2025.102577
Pucong Wang, Sumuya Borjigin
This study utilizes textual data from The Wall Street Journal, employing 12 machine learning models to forecast systemic risk in the US banking sector. Then, this paper applies the SHAP method to interpret the prediction results. The empirical conclusions are as follows: Firstly, in terms of time series forecasting, deep learning models exhibit the best performance, tree models demonstrate moderate predictive efficacy, while linear models perform poorly in predictions. Secondly, there is a positive correlation between SHAP values and banking systemic risk, this conclusion fills the previous research gap. Further research reveals that Topic_29 consistently ranks at the top in feature importance across various time windows. Its keywords (interest rate, bank, stock, company, inflation, rate cut, China) suggest that interest rate policies, corporate operations, inflation control, and geoeconomic factors play pivotal roles in systemic risk. Additionally, the study observes a negative correlation between news sentiment and SHAP values; negative sentiment has a stronger impact and a longer duration. Finally, this study links the topic keywords back to the original news texts to elucidate the impact of news on systemic risk across different sliding window periods.
{"title":"Bank systemic risk prediction based on text mining and explainable machine learning","authors":"Pucong Wang, Sumuya Borjigin","doi":"10.1016/j.najef.2025.102577","DOIUrl":"10.1016/j.najef.2025.102577","url":null,"abstract":"<div><div>This study utilizes textual data from The Wall Street Journal, employing 12 machine learning models to forecast systemic risk in the US banking sector. Then, this paper applies the SHAP method to interpret the prediction results. The empirical conclusions are as follows: Firstly, in terms of time series forecasting, deep learning models exhibit the best performance, tree models demonstrate moderate predictive efficacy, while linear models perform poorly in predictions. Secondly, there is a positive correlation between SHAP values and banking systemic risk, this conclusion fills the previous research gap. Further research reveals that Topic_29 consistently ranks at the top in feature importance across various time windows. Its keywords (interest rate, bank, stock, company, inflation, rate cut, China) suggest that interest rate policies, corporate operations, inflation control, and geoeconomic factors play pivotal roles in systemic risk. Additionally, the study observes a negative correlation between news sentiment and SHAP values; negative sentiment has a stronger impact and a longer duration. Finally, this study links the topic keywords back to the original news texts to elucidate the impact of news on systemic risk across different sliding window periods.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"82 ","pages":"Article 102577"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884902","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 : 2026-01-01Epub 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":"2026-01-01","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 : 2026-01-01Epub Date: 2025-12-15DOI: 10.1016/j.najef.2025.102569
António Miguel Martins , Bruno Albuquerque , Luís Sardinha , Nuno Moutinho
This study examines the short-term market effect of the US and European largest defence firms on the 2024 US presidential election (November 5, 2024) and the Trump-Zelensky meeting (February 28, 2025). By employing an event study methodology, our results show a positive and statistically significant stock price impact for both events. The results for the 2024 US presidential election are consistent with political business cycle theory. National elections in the arms-producing country drive a growth in sales revenues for defence firms, which tend to be higher when the Republican Party candidate wins the US elections. Our results also show the presence of heterogeneous abnormal returns between US and European defence firms around the Trump-Zelensky meeting, with European firms showing high and statistically significant positive returns while US firms show non-significant returns. This result is explained by the failure of security guarantees given by the US to the European countries and the awareness of the need for a rapid increase in military spending for self-defence purposes in Europe. This meeting reinforced the application of the principle of “Europe preference” in the acquisition of weapons. Finally, we conclude that stock market responses are reinforced or mitigated by firm-specific characteristics.
{"title":"Short-Term market impact of 2024 US President elections and Trump-Zelensky meeting in defence industry","authors":"António Miguel Martins , Bruno Albuquerque , Luís Sardinha , Nuno Moutinho","doi":"10.1016/j.najef.2025.102569","DOIUrl":"10.1016/j.najef.2025.102569","url":null,"abstract":"<div><div>This study examines the short-term market effect of the US and European largest defence firms on the 2024 US presidential election (November 5, 2024) and the Trump-Zelensky meeting (February 28, 2025). By employing an event study methodology, our results show a positive and statistically significant stock price impact for both events. The results for the 2024 US presidential election are consistent with political business cycle theory. National elections in the arms-producing country drive a growth in sales revenues for defence firms, which tend to be higher when the Republican Party candidate wins the US elections. Our results also show the presence of heterogeneous abnormal returns between US and European defence firms around the Trump-Zelensky meeting, with European firms showing high and statistically significant positive returns while US firms show non-significant returns. This result is explained by the failure of security guarantees given by the US to the European countries and the awareness of the need for a rapid increase in military spending for self-defence purposes in Europe. This meeting reinforced the application of the principle of “Europe preference” in the acquisition of weapons. Finally, we conclude that stock market responses are reinforced or mitigated by firm-specific characteristics.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"82 ","pages":"Article 102569"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791084","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 : 2026-01-01Epub 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":"2026-01-01","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}
Pub Date : 2026-01-01Epub 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":"2026-01-01","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 : 2026-01-01Epub Date: 2025-12-30DOI: 10.1016/j.najef.2025.102579
Adnan Aslam , Rayenda Khresna Brahmana
This study investigates the systematic spillover dynamics across high-growth private market sectors and their key drivers, with particular emphasis on portfolio diversification implications. Using a time-varying parameter vector autoregression framework, we document substantial and persistent return spillovers, with AI, HealthTech, FinTech, and Mobility Tech acting as dominant transmitters, and AgTech, BioPharma, ClimateTech, and Cybersecurity serving primarily as receivers. Spillover intensity peaks during post-pandemic capital inflows and green policy expansions, and declines during monetary tightening and geopolitical shocks. Employing robust regression and eXplainable AI approaches, we identify short-term interest rates, trade policy uncertainty, and geopolitical risk as the most influential determinants of connectedness. Portfolio tests show that minimum correlation and connectedness strategies outperform minimum variance portfolios, achieving higher risk-adjusted returns and better tail-risk protection. Our results provide new insights into the structural dynamics of high-growth private markets and offer a practical framework for spillover-aware asset allocation.
{"title":"Systemic spillovers in high-growth private market sectors: determinants and portfolio implications","authors":"Adnan Aslam , Rayenda Khresna Brahmana","doi":"10.1016/j.najef.2025.102579","DOIUrl":"10.1016/j.najef.2025.102579","url":null,"abstract":"<div><div>This study investigates the systematic spillover dynamics across high-growth private market sectors and their key drivers, with particular emphasis on portfolio diversification implications. Using a time-varying parameter vector autoregression framework, we document substantial and persistent return spillovers, with AI, HealthTech, FinTech, and Mobility Tech acting as dominant transmitters, and AgTech, BioPharma, ClimateTech, and Cybersecurity serving primarily as receivers. Spillover intensity peaks during post-pandemic capital inflows and green policy expansions, and declines during monetary tightening and geopolitical shocks. Employing robust regression and eXplainable AI approaches, we identify short-term interest rates, trade policy uncertainty, and geopolitical risk as the most influential determinants of connectedness. Portfolio tests show that minimum correlation and connectedness strategies outperform minimum variance portfolios, achieving higher risk-adjusted returns and better tail-risk protection. Our results provide new insights into the structural dynamics of high-growth private markets and offer a practical framework for spillover-aware asset allocation.</div></div>","PeriodicalId":47831,"journal":{"name":"North American Journal of Economics and Finance","volume":"82 ","pages":"Article 102579"},"PeriodicalIF":3.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884901","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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