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The burgeoning literature on climate-related finance suggests that climate change influences financial markets. Building on this foundation, the present study aims to investigate the time-varying predictive power of news related to physical and transition climate risks for financial instability across the financial systems of the US, EU, and the ASEAN+3 countries (comprising the Association of Southeast Asian Nations plus China, Japan, and South Korea), from January 2003 to August 2022, on a monthly basis.

In this study, we use the VAR-based Granger-causality test in the presence of instabilities introduced by Rossi and Wang (2019), and combine it with the innovative rolling and recursive bootstrap time-varying Granger-causality approach of Shi et al. (2020). These methods were chosen for their capacity to effectively capture the dynamic influence of climate risk-related news on financial instability over time, offering an advantage over traditional constant parameter regressions and standard Granger causality methods. Additionally, we make use of the Media Climate Change Concerns indices recently developed by Ardia et al. (2022), coupled with regional financial stress indices.

Our findings indicate that the predictive power of climate change news for financial instability is substantial but varies over time. This influence becomes especially pronounced during periods that align with specific local and global events. In the US and EU, the predictive power is influenced by a combination of global and local macroeconomic, political, health, and climate-related factors. In contrast, ASEAN+3 financial systems show a stronger response to regional and local events, with comparatively less sensitivity to global events.

The results of this study are noteworthy for investors, highlighting increased market instability during periods with prevalent climate change news. Investors can adjust their strategies to mitigate risks and respond to macro-events that trigger climate news-related market instability, while considering regional sensitivities. Similarly, these findings are significant for policymakers, emphasizing the need to consider the influence of climate news on financial markets when designing regulatory frameworks. This could involve enacting measures to stabilize the financial system during periods of significant climate news. Policymakers might consider developing macroprudential regulations to bolster financial institutions’ resilience against climate change news effects.

This study pioneers the exploration of how climate change news affects financial system stability at the macro level. It extends beyond traditional research, typically focusing on direct effects of climate change in banking and asset markets, by examining broader implications of climate risk-related news for financial system instability. Furthermore, this study enhances our understanding of the predictors of global financial stability by examining the financial systems of the US, the EU, and ASEAN+3. It specifically investigates the impact of climate change news, a topic not extensively explored in previous research focusing mainly on macro-factors such as financial liberalization and business cycles.

The purpose of this paper is to analyse the dynamic and evolving relationship between Bitcoin mining (BTC) and climate policy uncertainty. By using the newly developed U.S. Climate Policy Uncertainty (CPU) indicator by Gavriilidis (2021) as a proxy for global climate-related transition risk, this study aims to explore the complex bidirectional causality between these two critical phenomena in climate-related finance. Further, we explore how economic and market factors influence the cryptocurrency market, focusing on the relationship between CPU and Bitcoin mining.

We employ a linear and non-linear rolling window sub-sample Granger causality approach combined with a probit model to examine the time-varying causalities between Bitcoin mining and the U.S. Climate Policy Uncertainty (CPU) indicator. This method captures asymmetric effects and dynamic interactions that are often missed by linear and static models. It also allows for the endogenous determination of key drivers in the BTC–CPU nexus, ensuring that the results are not influenced by ad-hoc assumptions but are instead grounded in the data’s inherent properties.

The findings indicate that Bitcoin mining is negatively impacted by climate policy uncertainty during periods of increased environmental concern, while its energy-intensive nature contributes to increasing climate policy uncertainty. In addition to market factors, such as Bitcoin halving, and alternative assets, such as green equity, five main macroeconomic factors influence these relationships: financial instability, economic policy uncertainty, rising oil prices and increasing industrial production. Furthermore, two non-linear dynamics in the relationship between climate policy uncertainty and Bitcoin (CPU-BTC nexus) are identified: the “anticipatory regulatory decline effect”, when miners boost activity ahead of expected regulatory changes, but this increase is unsustainable due to stricter regulations, compliance costs, investor scrutiny and reputational risks linked to high energy use.

This study is the first in the literature to examine the time-varying and asymmetric relationships between Bitcoin mining and climate policy uncertainty, aspects often overlooked by static causality and average-based coefficient models used in previous research. It uncovers two previously unidentified non-linear effects in the BTC-CPU nexus: the “anticipatory regulatory decline effect” and the “mining-driven regulatory surge”, and identifies major market factors macro-determinants of this nexus. The implications are substantial, aiding policymakers in formulating effective regulatory frameworks, helping investors develop more sustainable investment strategies and enabling industry stakeholders to better manage the environmental challenges facing the Bitcoin mining sector.

In recent years, the topic of women's entrepreneurship has gained increasing attention from researchers and policymakers. Its role in economic growth and development has been widely recognized in several studies. However, the relationship between gender in entrepreneurship and innovation is an underexplored aspect in particular at a country-level perspective. This paper aims to answer the following question: Does female entrepreneurship impact innovation at a national level?

Using a panel dataset of 35 Organization for Economic Co-operation and Development (OECD) member countries over the period 2002–2019, the authors carried out a comprehensive econometric analysis, based on the fixed-effect model, the random-effect model and the feasible generalized least squares estimator, as well as a battery of tests to prevent problems of multicollinearity, heteroscedasticity and autocorrelation of the error terms. In doing so, the authors found consistent and robust results on the linear and nonlinear relationship between women's entrepreneurship and innovation, using selected country indicators from the Global Entrepreneurship Monitor (GEM) consortium, the Worldwide Governance Indicators (WGI) and the World Development Indicators (WDI), including female self-employment, female nascent entrepreneurship and R&D investment and controlling for the same relationships in the case of men's entrepreneurship.

This study shows that the level of R&D investment, which according to the literature can be considered as a proxy of innovation, is higher when the level of women's entrepreneurship is low. However, exploring more in depth this relationship and the relationship between male entrepreneurship and innovation, the authors found two important and new results. The first one involves the different impact on R&D investment of female self-employment and female nascent entrepreneurship. In particular, female self-employment appears to have a linear negative impact on the R&D, while the impact of female nascent entrepreneurship is statistically nonsignificant. The second one affects the nonlinearity of the negative effect, suggesting that very different challenges are possible at different levels of women's entrepreneurship. In addition, analyzing the role of human capital in the relationship between R&D investment and women entrepreneurship, it emerges that higher education (as the main component of human capital) makes early-stage women's entrepreneurship more technologically consuming, which promotes R&D investment. A higher level of education lessens the significance of the negative relationship between the simplest type of women entrepreneurship (female self-employment) and R&D investment.

The originality of the study is that it provides new evidence regarding the link between women's entrepreneurship and innovation at the macro level, with a specific focus on self-employed women entrepreneurs and early-stage women entrepreneurship. In this sense, to the best of the authors' knowledge, this study is among the few showing a nonlinear relationship between women's entrepreneurship and country-level innovation and a negative impact only in the case of female self-employment. Moreover, this study has relevant implications from a policymaking perspective, in terms of promoting more productive women's entrepreneurship.

This study adopts a new macro-perspective to explore the complex and dynamic links between financial instability and the Euro-American green equity market. Its primary focus and novelty is to shed light on the non-linear and asymmetric characteristics of dependence, causality, and contagion within various time and frequency domains. Specifically, the authors scrutinize how financial instability in the U.S. and EU interacts with their respective green stock markets, while also examining the cross-impact on each other's green equity markets. The analysis is carried out over short-, medium- and long-term horizons and under different market conditions, ranging from bearish and normal to bullish.

This study breaks new ground by employing a model-free and non-parametric approach to examine the relationship between the instability of the global financial system and the green equity market performance in the U.S. and EU. This study's methodology offers new insights into the time- and frequency-varying relationship, using wavelet coherence supplemented with quantile causality and quantile-on-quantile regression analyses. This advanced approach unveils non-linear and asymmetric causal links and characterizes their signs, effectively distinguishing between bearish, normal, and bullish market conditions, as well as short-, medium- and long-term horizons.

This study's findings reveal that financial instability has a strong negative impact on the green stock market over the medium to long term, in bullish market conditions and in times of economic and extra-economic turbulence. This implies that green stocks cannot be an effective hedge against systemic financial risk during periods of turbulence and euphoria. Moreover, the authors demonstrate that U.S. financial instability not only affects the U.S. green equity market, but also has significant spillover effects on the EU market and vice versa, indicating the existence of a Euro-American contagion mechanism. Interestingly, this study's results also reveal a positive correlation between financial instability and green equity market performance under normal market conditions, suggesting a possible feedback loop effect.

This study represents pioneering work in exploring the non-linear and asymmetric connections between financial instability and the Euro-American stock markets. Notably, it discerns how these interactions vary over the short, medium, and long term and under different market conditions, including bearish, normal, and bullish states. Understanding these characteristics is instrumental in shaping effective policies to achieve the Sustainable Development Goals (SDGs), including access to clean, affordable energy (SDG 7), and to preserve the stability of the international financial system.

This study aims to investigate the possibilities of cryptocurrencies as hedges and diversifiers in the Indian stock market before and during financial crisis due to the pandemic and the Russia–Ukraine war.

Researchers have used daily data on cryptocurrencies and Indian stock prices from March 10, 2015 to August 26, 2022. The researchers have used the dynamic conditional correlations (DCC)-GARCH model to determine the volatility spillover and dynamic correlation between stocks and digital currencies. Further, researchers have explored hedge ratio, portfolio weight and hedging effectiveness using the estimates of the DCC-GARCH model.

The findings indicate a negative conditional correlation between equities and cryptocurrencies before the crisis and a positive conditional correlation except for Tether during the crisis. Which implies that cryptocurrencies serve as a hedging asset in the stock market before a crisis but are not more than a diversifier during the crisis, except for Tether. Notably, Tether serves as a safe haven during times of crisis. Finally, the study suggests that Bitcoin, Ethereum, Binance Coin and Ripple are the most effective diversifiers for Indian stocks during the crisis.

This study makes several contributions to the existing literature. First, it compares the hedge and diversification roles of cryptocurrencies in the Indian stock market before and during crisis. Second, the study findings provide insights on risk hedging and can serve as a guide for investors. Third, it may help rational investors avoid underestimating risk while constructing portfolios, particularly in times of financial turmoil.