North American Journal of Economics and Finance最新文献

This study investigates the dynamic risk nexus among climate risks, oil shocks and China’s energy futures market from a time–frequency-quantile perspective. We first explore the dynamic connectedness of “climate risks – oil shocks – energy futures” and examine the risk transmission channels through mediation effects model. The Quantile-on-Quantile regression is used to study the time–frequency impact of climate risks and oil shocks on energy futures across different market conditions and investment horizons. Our empirical results are as follows: First, climate transition risks, oil demand and risk shocks play mediating roles in risk transmission channels. Second, the impact of climate risks and oil shocks on energy futures is heterogeneous and asymmetric under extreme conditions. Notably, global warming, oil supply shock and international climate summits are the greatest shocks to China’s energy market. Finally, climate risks and oil shocks are more pronounced in the short term. Overall, these findings offer valuable insights for shaping risk management strategies and implementing effective hedging practices within the energy market.

This study examines the frequency domain connectedness and synchronization between the exchange rates of Association of Southeast Asian Nations (ASEAN) member countries and those of China, Japan, and South Korea across quantile levels. We propose a quantile cross-spectrum of exchange rates to establish the coherency of connectedness and synchronization measurements. Our empirical results are as follows: First, the return connectedness between the exchange rates is heterogeneous, being stronger in the long run than in the short run and more pronounced under normal market conditions than under extreme market conditions. Second, the dynamic return connectedness among the exchange rates follows a similar trend in the monthly and yearly cycles. Third, exchange rate returns and volatility exhibit long-term synchronization. However, short-term heterogeneity persists across market conditions and investment horizons. Overall, these findings offer valuable insights for monetary authorities in their efforts to maintain exchange rate stability and for investors in making informed portfolio decisions.

Security analysts play a vital role as an information intermediary in the stock market. Their stock recommendations are important references for investors. The efficiency of investment decision-making could be improved by judging the reliability of stock recommendations based on analyst characteristics and fusing the recommendations. We propose an information fusion method for security analysts’ stock recommendations based on two-dimensional Dempster-Shafer (D-S) evidence theory, which comprehensively considers the external and internal characteristics of analysts. The characteristics of analysts are used to measure the reliability of the stock recommendations and modify the evidence, then the D-S fusion rule is used for evidence fusion. Compared with the forecast results of statistical methods and machine learning methods, the two-dimensional D-S evidence theory model we proposed has a higher forecast accuracy, which effectively improves the information efficiency of the stock market and helps investors to make decisions efficiently and scientifically.

We investigate the economic value of adding Bitcoin, instead of Gold, to a traditional portfolio from the perspective of a volatility timing framework. Using futures data, we find that Bitcoin adds more value than Gold does to the portfolio during periods of dovish monetary policy. However, during periods of rapid rate hikes, Bitcoin destroys value while Gold offers safe haven and diversification benefits. Rebalancing strategies matter when considering adding alternative assets to a stock–bond portfolio in the presence of transaction costs. This study is timely given the macroeconomic environment of rate hikes and the downturn of cryptocurrencies.

Research on predicting financial crises has produced various composite early warning indicators (EWIs) using macroeconomic and financial time-series. Much of the focus has been on identifying the best leading indicators for financial crises (e.g., credit-to-GDP ratios, financial asset prices, etc.). This paper instead focuses on how to optimally extract and combine signals from multiple cyclical indicators. We find that when combining multiple indicators into a composite EWI, jointly optimizing the indicators improves performance relative to optimizing individually and combining their signals. The performance of our jointly optimized EWIs is robust to the key modelling choices inherent in their design including the trend-cycle decomposition method and the preference for false positives over false negatives.

From a global and dynamic perspective, this paper conducts the network measurement of risk contagion among global stock markets by employing time-varying spillover index and complex network method. Furthermore, this paper investigates the influence mechanism of dynamic risk contagion, combining multiple factors such as financial opening, international trade, and cross-border capital flow. The results show that: (1) There exists a strong risk contagion effect among global stock markets, especially for developed countries, which have obvious time-varying characteristics in both direction and intensity. (2) The risk contagion effect is also highly event-dependent, which shows a rapid upward trend during extreme risk events such as the financial crisis and the COVID-19 epidemic. (3) Different economic and financial development situations lead to different risk contagion effects, and the ranking of countries with stronger risk effects remains at a stable level, which can prompt important risk events. (4) International trade, cross-border capital flow, financial market volatility, investor sentiment, and the US monetary policy are key influence mechanisms of dynamic risk contagion. However, financial opening and economic fundamentals are not statistically significant, which is contrary to our intuition.

Using panel data from 87 China’s banks from 2011 to 2022, this research investigates whether and how climate change affects bank profitability. It is discovered that the improvement of bank profitability is severely hampered by climate change. The main ways that climate change affects bank profitability are by causing financial losses to bank creditors, changing the likelihood of defaults and the quality of bank credit assets. Energy conservation and carbon reduction, the implementation of green financial policies, and ensuring that banks have enough capital are all factors that can help mitigate the negative effects of climate change on bank profitability. In addition, climate change has a greater negative effect on the profitability of small-sized banks, regional banks, and banks with lower levels of liquidity. In conclusion, this study offers forward-looking suggestions for banks to reduce risks from climate change, which is critical for encouraging low-carbon and green development and averting systemic financial risks. It also offers theoretical references for Chinese banks to develop customized policies and strategies to address these risks.

We examine the dynamics of credit risk connectedness by analyzing credit default spreads in four major sectors (banks, transportation, manufacturing, electricity) across three global regions (Asia, Europe, North America) using the TVP-VAR spillover methodology from 2007 to 2024. We have identified significant findings regarding credit risk spillovers among them. First, there are consistently high levels of credit risk spillovers between sectors, indicating underlying economic factors influencing the transmission of credit risk shocks. Second, notable regional findings include a substantial increase in credit risk connectedness for Asian banks during the global financial crisis (GFC). European manufacturing sectors also displayed significantly high connectedness levels during both the GFC and the COVID-19, while North American banks saw a notable surge due to the collapse of Silicon Valley Banks (SVB) in March 2023. In addition, during the RU-war, the electricity and manufacturing sectors in Europe had high CDS connectedness. Lastly, a distinct observation emerged concerning the Asian transportation sector. These findings have practical implications for policymakers and portfolio managers. For instance, they can help policymakers assess the effectiveness of their policies by revealing global industry credit risk interconnections. Additionally, the dynamic credit risk linkages provide strategies for hedging credit risk.

It is increasingly accepted that green innovation plays a crucial role in the new development pattern. Despite the government’s efforts to promote green innovation in China, the efficiency of such initiatives is still inadequate. Therefore, it is essential to investigate how political connection affect the green innovation process of businesses, so as to better guide the government’s role, stimulate green innovation in businesses, and ultimately support sustainable economic and social progress. Drawing on the data of A-share listed companies in China from 2010 to 2020, this article empirically examines the influence and mechanism of political connection on green innovation of enterprises. The findings indicate that political connection can reduce the financing constraints for businesses, however, it can also stimulate the motivation for rent-seeking, which means that resources are used to meet government expectations or satisfy management’s private desires, thus crowding out innovation resources and eventually having a negative effect on innovation. Evidence suggests that the influence of political connection on green innovation in companies has a limit based on resource investment and allocation decisions. When resource investment reaches a certain point, green innovation can be significantly enhanced; however, if the resources allocated for social responsibility reach a particular threshold, political connection can adversely affect green innovation. This article proposes that strengthening both internal and external governance can mitigate the agency problems of executives and reduce the negative impact of political connections on green innovation within businesses. By delving deeper into the relationship between political connections and green innovation, this article offers new insights and policy recommendations aimed at fostering green innovation in enterprises.

The sudden eruption of COVID-19 has inflicted tremendous damage to the worldwide economy, and stock markets have become violently volatile due to its negative impact. Therefore, accurate forecasting of stock price index has been playing an essential role in maintaining national economic security and formulating related policies. In this paper, a novel decomposition-ensemble model is proposed to predict the highly fluctuating stock price index. To begin with, the modified ensemble empirical mode decomposition (MEEMD) method is adopted to decompose the original stock price index into subsequences with different frequencies. Then, the last high-frequency subsequence and other subsequences are predicted through multilayer perceptron (MLP) and long short-term memory (LSTM), respectively. Finally, the prediction outcomes of different model subsequences are reconstructed into the ultimate prediction results by utilizing the integration method. Compared with the contrast models, the MEEMD-LSTM-MLP model proposed in our paper not only demonstrates significant advantages in multi-step forecasting for both emerging and developed markets, but also achieves excellent prediction performance amidst the severe market fluctuations triggered by COVID-19. Furthermore, the application of the MEEMD-LSTM-MLP model is extended to financial time series with different data characteristics and market types, which further proves its high applicability and reliability. Therefore, the conducted hybrid MEEMD-LSTM-MLP model is an effective and stable multi-step forecasting tool to provide valuable intelligent technical support for governments and enterprises in complex economic conditions.