This study investigates return and volatility spillovers between green financial assets—green bonds, clean energy, and ESG equities—and a range of innovation-driven technology sectors, including AI, robotics, digital platforms, and decentralized finance. Using a DECO-GARCH and quantile connectedness framework, the analysis s both cyclical and crisis-driven dynamics. The results reveal that connectedness intensifies shows under extreme market conditions, with volatility spillovers persistently exceeding return spillovers across all regimes. Volatility also exhibits strong persistence, particularly during and after crisis periods, indicating prolonged market sensitivity and risk clustering. Connectedness is asymmetric and state-dependent, peaking during downturns and reducing diversification opportunities when they are most needed. Green bonds, clean energy, and the space sector consistently act as net receivers of return and volatility spillovers, suggesting they may serve as safe havens and portfolio diversifiers. Conversely, digital and innovation-led sectors—such as decentralized banking, AI, and smart factories—alongside the S&P ESG index, generally function as net transmitters of shocks, especially during turbulent phases. These findings underscore the importance of tail- sensitive and volatility-aware investment strategies. For policymakers, monitoring volatility- based spillovers and systemic nodes is crucial for safeguarding stability. Societally, the defensive behavior of green assets highlights their dual value: supporting environmentalobjectives while enhancing financial system resilience during times of heightened uncertainty. JEL Codes: Keywords: Digital technology, green finance, volatility, return, quantile spillover.

This study examines the extreme frequency connectedness among Gulf Cooperation Council (GCC) Islamic stock markets, Sukuk, global stock markets, bond markets, and global green assets (Sustainability Index, Green Bond Index, and S&P ESG Index). We use the quantile-frequency connectedness approach by Chatziantoniou et al. (2022) to rely on different market modes (tranquil, bear, and bull market statuses). The results show stronger market interconnection during extreme market conditions compared to stable periods. Spillover effects are more pronounced in bearish market phases than in bullish ones. Short-term connectedness dominates under normal and bearish conditions, while long-term connectedness becomes more significant during bullish phases, especially in times of crisis. GCC equities and bonds contribute to market volatility, while Omani, Qatari, and Saudi Islamic indices, along with green bonds and Sukuk indices, are net volatility recipients, acting as portfolio diversifiers and potential safe havens in bear market states.

Purpose This paper investigates the dynamic connectedness between US sectoral equity markets and key green financial segments – namely green bonds, clean energy and ESG equities. It aims to assess how investor sentiment shapes intermarket linkages and systemic risk transmission during periods of economic and geopolitical stress. Design/methodology/approach The study employs the generalized R2 connectedness framework and the quantile-on-quantile regression approach to capture asymmetric and nonlinear interactions across markets. The analysis covers the period from February 2015 to November 2024, encompassing major global events such as the USA–China trade war, the COVID-19 pandemic and the Russia–Ukraine conflict. Findings Results indicate strong systemic interdependence among US sectors and green financial assets, with contemporaneous connectedness dominating lagged spillovers, suggesting rapid shock transmission. Clear sectoral asymmetries emerge: Materials, Industrials and the S&P ESG index act as major shock transmitters, whereas Healthcare, Utilities, Communication Services, green bonds and clean energy serve as net volatility absorbers. Furthermore, investor sentiment exerts nonlinear effects – neutral and bullish moods amplify connectedness, while bearish sentiment dampens it. Practical implications These findings reinforce financial contagion and risk aversion theories, highlighting sustainable finance's evolving role in systemic stability. They provide valuable guidance for portfolio diversification, risk management and policy design aimed at fostering financial resilience during the ecological and energy transition. Originality/value This study is the first to jointly apply the generalized R2 connectedness and quantile-on-quantile frameworks to explore sentiment-driven linkages between US sectors and green financial assets. By integrating sustainability dynamics with behavioral factors, it offers a novel perspective on how ecological finance interacts with traditional markets under varying sentiment and crisis conditions.

In an increasingly interconnected financial system shaped by the global energy transition and heightened uncertainty, understanding how shocks spread across traditional and green financial markets become critically important. This study examines the spillover dynamics among G7 stock markets, oil, clean energy, and green bonds assets, while assessing how different forms of uncertainty—global economic policy, climate policy, and ESG-related—shape these linkages. Using a novel quantile-frequency connectedness, wavelet quantile correlation, non-parametric quantile causality and quantile-on-quantile regression, the analysis provides several key findings. The dataset covers the period from August 3, 2015, to August 20, 2025. The findings reveal that connectedness remains elevated even under normal market conditions and intensifies markedly during extreme bearish and bullish scenarios, peaking during major financial crises. Importantly, connectedness is highly asymmetric and predominantly concentrated in the short term. European and Japanese equities often acting as net transmitters, while U.S. and Canadian equities together with oil, clean energy and green bonds tend to be net receivers. These patterns reverse sharply during turmoil, underscoring the regime-dependent nature of risk spillovers. WQC results show stronger long-term than short-term dependencies. Quantile causality highlights median-quantile dominance, with WilderHill and Canada acting as key bidirectional hubs. Uncertainty amplifies these dynamics, with climate and ESG-related uncertainty exerting stronger effects than global economic policy uncertainty. The findings highlight that static diversification is insufficient, emphasizing the need for adaptive, regime-aware strategies incorporating hedging, liquidity management, and stress testing. Policymakers must address systemic risk through clear transition roadmaps, harmonized disclosures, and forward-looking climate stress tests.

This paper investigates how the geopolitical risk (GPRD), economic policy uncertainty (EPU) index, and Twitter economic uncertainty (TEU) related to the Russo-Ukrainian conflict can affect cryptocurrency returns (Bitcoin [BTC], Ethereum [ETH], Ripple [XRP], Dogecoin [DOGE], Litecoin [LTC], Cardano [ADA], BNB, and TRON [TRX]) over the period ranging from January 1, 2020, to April 24, 2023. Using the Spectral Breitung Candelon causality and wavelet coherence methods, interesting findings are reported. This study reports noteworthy findings. First, we observe that during the armed battle, ADA, BNB, DOGE, LTC, TRX, and XRP appear as hedges against GPRD. However, we found a negative impact on BTC and ETH. Second, the results show that EPU and TEU have no effect on cryptocurrency, respectively. These findings provide a comprehensive overview of cryptocurrency fluctuations during the ongoing conflicts in Ukraine. Finally, findings show that only ADA, BNB, DOGE, LTC, TRX, and XRP could be used as hedging tools during times of uncertainty. These results have practical implications for cryptocurrency investors and elements influencing its returns, especially during uncertain times.

In this study, we examine the relevance of the coexistence of structural change and long memory to model and forecast the volatility of Tunisian stock returns and to deliver a more accurate measure of risk along the lines of VaR and expected shortfall. To this end, we propose three time-series models that incorporate long-term dependence on the level and volatility of returns. In addition, we introduce structural change points using the iterated cumulative sums of squares (ICSS) and the modified ICSS algorithms, synonymous with stock market turbulence, into the conditional variance equations of the models studied. We choose a conditional innovation density function other than the normal distribution, that is, a Student distribution, to account for excess kurtosis. The empirical results show that the inclusion of structural breakpoints in the conditional variance equation and Dual LM provides better short- and long-term predictability. Within such a framework, the ICSS-ARFIMA-HYGARCH model with Student’s t distribution was able to account for the long-term dependence in the level and volatility of TUNINDEX index returns, excess kurtosis, and structural changes, delivering an accurate estimator of VaR and expected shortfall.

This study examines whether and how political uncertainty affects the returns of the TUNINDEX index. The impact of the main political events is supported by parametric and non-parametric tests with an event-driven approach as well as regression analysis. These events are then classified into a typology at different levels to later conduct separate analyses on increasingly homogeneous types. To our knowledge, this work constitutes the first work that tries to tackle the impact of political uncertainty on the Tunisian stock market from this perspective. Our empirical results show that the market response varies according to event type. Thus, the popular uprising has a destructive effect on stock market returns. Democratic transition positively affects the market. The announcement of the election results leads, on average, to a positive reaction and stipulates, among other things, that the market prefers secularists rather than Islamists. Partisan conflicts are destructive to stock market returns. Tunisia offers us a real and rare field of experimentation for a battery of major political events, with the persistence of the state and its institutions. The results of our study are of direct interest to financial authorities and decision-makers who wish to assess the role of political uncertainty in triggering or exacerbating stock price movements and contribute to a better understanding of investor behavior.

Purpose — This research aims to analyze the determinants of firm profitability in the Tunisian Stock Exchange. Design/methodology/approach—This research used a panel static model on a sample of 30 firms listed on the Tunisian stock exchange from 2016 to 2021. Findings — The results show that capital, size, liquidity, and economic growth positively affect firm profitability, but inflation and financial autonomy negatively affect firm profitability. Practical implications—This scholarly article's practical implications are that organizations can improve their profitability by focusing on capital, size, liquidity, and economic growth while also being cautious about inflation and financial autonomy. Originality/value — This scholarly article's original value lies in examining the determinants of firm profitability in the context of Tunisian stock exchange-listed firms. It provides insights into the specific factors that influence profitability and their effects. Paper type — Case research

The purpose of this paper was to comprehend what are the characteristics that allow companies to be more resilient to cope with the crisis caused by the COVID-19 pandemic. More specifically, we explore the relationship between families’ involvement in corporate ownership and leadership and financial performance. Using a sample of 226 French-listed firms during the period from January 24 to April 27, 2020, we found that firms controlled by family shareholders showed higher stock market performance than their non-family in the pandemic period. This finding is stronger in the case of the first family firms’ generation where the founder still holds the position of executive chef, president or general manager. Contrary to our expectations, family firms perform better when led by a professional chief executive officer (CEO). Overall, our results add to previous research by illustrating how family ties influence a firm’s response to external shocks.

The purpose of this paper is to shed light on the impact of the rising terrorist threat on the performance of a small capitalization market - the Tunisian stock market-. Using an event study methodology as well as conditional volatility, we investigate the impact of recent terrorist attacks in Tunisia on the general index TUNINDEX and sector indices. Our main findings are as follows. First, we find that terrorist attacks negatively affect the Tunisian stock market. However, the decline - considerable in certain cases- is short-lived: the market recovers from terrorist shocks in one day. Second, Oil and Gas, Insurance and Telecommunications, are the most affected sectors. Third, different terrorist tactics have varied effects on the stock market that leads us to conclude that attack type, weapon type, target type, and severity of the attack may determine the market's reactions.

This paper analyses the impact of political uncertainty on the volatility of the Tunisian stock market from November 2010 to February 2016. In particular, it examines structural breaks in the variance by using the Iterated Cumulative Sums of Squares (ICSS) and modified ICSS algorithms. Asymmetric GARCH models are then extended by taking account regime shifts. Our results suggest that Tunisian stock market volatility is sensitive to local and political events. Large shifts coincide with civil uprisings and periods of political turbulence during the democratic transition and argue that the relationship between volatility and returns reflects the common effects of political factors. Diagnostic tests emphasize the asymmetric volatility response to news. However, there is no evidence that taking into account regime shifts reduces the volatility persistence which leads to think that the Tunisian stock market is well controlled and supervised.

Finance 3.0 is still in its infancy, although big data represents an unprecedented opportunity for finance. The large increase in data generated by individuals every day on the Internet offers researchers the opportunity to approach the question of financial market movement prediction from a new perspective. In this paper, we study the relationship between a well-known Twitter micro-blogging platform and the Tunisian financial market. In particular, we consider, over a 12-month period, Twitter volume and sentiment of 22 companies listed on the Tunindex. We found a relatively low Pearson correlation and Granger causality estimates between the corresponding time series over the entire period.

The purpose of this paper is to estimate the functions impulsions-response of liquidity on the Tunisian Stock Exchange (TSE). We will use the methodology proposed by Abrigo and Love (2016). Our study is done on an order-driven market. The data is composed of high frequency data of orders listed on the TSE for the period April 2014 to June 2014. Inspired of the study of Jarnecic and Snape (2014), we apply a panel VAR model to stocks traded in continuous in order to examine the dynamic interactions between spread, volatility, size and frequency of transactions. Then we study the liquidity of the TSE through the impulse response function of the Panel VAR model. Our findings show dynamic relationships between spread, volatility, size and frequency of trading. Some differences exist in the dynamics of liquidity when we take into account the trading intensity of the stock. Furthermore, we note that shocks are absorbed after three gaps of 45minutes.

The capital adequacy ratio measures the ability of a financial institutions to meet its liabilities by comparing its capital with assets. This article studied the relationship between bank capital and bank profitability measured by (Return on assets; return on equity; net interest margin). We used a method of static panel for a sample of 11 banks in Tunisia between (2000…2018). We found that bank capital has a significant impact on ROA. But capital has a non significant effect on bank return on equity and not significant impact on bank net interest margin.

Finance 3.0 is still in its infancy. Yet big data represents an unprecedented opportunity for finance. The massive increase in the volume of data generated by individuals every day on the Internet offers researchers the opportunity to approach the question of financial market predictability from a new perspective. In this article, we study the relationship between a well-known Twitter micro-blogging platform and the Tunisian financial market. In particular, we consider, over a 12-month period, Twitter volume and sentiment across the 22 stock companies that make up the Tunindex index. We find a relatively weak Pearson correlation and Granger causality between the corresponding time series over the entire period.

Contrary to the trade-off theory, pecking order theory is based on the information asymmetry that exists between internal stakeholders (owners, managers) and external stakeholders (donors) to the company. We study firms’ financing behaviour over life cycle stages in the context of the pecking order theory. This paper is interested in testing the relation between ownership structure, the life cycle and the funding classification in French companies in the period 2005-2014. The hypotheses tested were derived from the pecking order models and analysis was conducted on data panel with econometric software Stata. The results show that the pecking order explains the debt in French companies that are in growth phase, maturity or decline.

This paper investigates the impact of internal governance structure on firm-level stock return volatility in Paris Stock Exchange based on our study of a sample of 65 firms for the daily period from January 2010 to December 2012.The research has sixth hypotheses. To test each hypothesis; a model was defined based on dependent variables employed to measure the share price volatility. Our findings reveal different results by using different models of multivariate regression. The empirical results show no statistically significant relationship to any components of ownership structure. However, the results also show that the components for the board structure reduce volatility. Indeed, we document a statistically significant negative relationship between the board independence, the CEO Duality, the board size and the share price volatility. Hence, the board structure is not expected to cause severe volatility in the stock prices, which in turn, is consistent with the results of this study.