This research article explores whether the climate transition risk (CTR) and climate physical risk (CPR) transmit greater shocks towards the sustainable, gold-backed, energy-related and Sharia-compliant cryptocurrencies during bullish market conditions as compared with the normal and bearish market conditions. We employ the novel quantile vector auto-regression (QVAR)-based connectivity framework. Overall findings suggested that CPR and CTR transmitted greater shocks towards cryptocurrency classes during extremely high and lower quantiles as compared with the median quantile. This U-shaped and non-linear climate risks shock transmission indicates that Sharia-compliant, energy-related and gold-backed cryptocurrencies become more vulnerable during extreme market conditions (higher and lower quantiles) and may not consistently serve as reliable hedging or diversification instruments, particularly during periods of heightened climate uncertainty. Overall findings suggested that both the CPR and CTR transmitted greater shocks towards energy-related, gold-backed, and Sharia-compliant cryptocurrencies as compared with the sustainable cryptocurrencies, across all the quantiles. Therefore, sustainable cryptocurrencies, particularly those with energy-efficient consensus mechanisms such as Stellar, Cardano and Ripple, exhibited resilience to climate risks and can therefore function as stabilizing core holdings in diversified portfolios. Fund managers should incorporate a rebalancing strategy that increases allocation to these climate-resilient, sustainable digital assets during periods of elevated climate risk. Fund managers should integrate CPR and CTR into the quantile-domain forecasting frameworks for predicting digital asset market returns to enhance financial stability. Portfolio managers should undertake dynamic and quantile-contingent climate risk hedging strategies that account for tail-risk exposure rather than relying on average market behavior.

Abstract The unprecedented rise of artificial intelligence (AI, hereafter) equity and the increasing popularity of clean energy investments have raised concerns among numerous researchers and market participants who are seeking to assess market risks and dependencies. Thus, we explore the connectedness and spillover effects across AI stocks, clean energy, and traditional asset classes under different market conditions (bearish, normal, and bullish). For the empirical analysis, we employ quantile vector autoregression (QVAR, hereafter) and the quantile connectedness approach on daily data spanning from January 2012 to December 2025. The findings of this study demonstrate that within the network, AI stocks, particularly Alphabet Inc Class C (GOOG1), Microsoft Corporation (MSFT), and NVIDIA Corporation (NVDA1), consistently serve as dominant transmitters of return spillovers. On the other hand, findings suggest that clean energy stocks are more susceptible to downside risks and their responses to market environments are asymmetric. Furthermore, the findings demonstrate that the short-term spillovers prevail when the market is bearish and normal, and the long-term spillovers are more prominent during bullish market conditions. Findings further demonstrate that portfolio strategies provide effective hedging benefits, whereas dynamic portfolio approaches may amplify risk in volatile market environments. Additionally, the findings provide significant implications for investors, portfolio managers, and policymakers by identifying the various effects of AI-based stocks and clean energy assets and the necessity to consider market conditions and investment horizons.

Type of the article: Research ArticleAbstractThis study aims to examine whether herding behavior intensifies during shock events and whether such crisis-induced herding leads to abnormal returns across nine IDX sectors over the 1997–2025 period. The study employs a quantitative event study approach using daily stock return data from 13 major global and domestic shock events representing nine industrial sectors listed on the Indonesia Stock Exchange. Herding behavior is measured using the cross-sectional absolute deviation model, while its effect on abnormal returns is analyzed through time series regressions incorporating interaction terms between herding indicators and shock event dummies, controlling for firm size, SMB, and HML factors. The results show strong asymmetry across market conditions and sectors. During bearish market phases, herding intensifies significantly in the agriculture, finance, and property real estate construction sectors and is associated with positive short-term abnormal returns, indicating crisis-driven market inefficiency. In contrast, during bullish market phases, most sectors exhibit anti-herding behavior, reflected in greater return dispersion and more selective investor decision making. The interaction between herding behavior and shock events is positive and statistically significant in most sectors, with the strongest effects observed during the 1997–1998 Asian Financial Crisis, the 2008 Global Financial Crisis, and the 2020 COVID-19 pandemic. Overall, the findings indicate that the Indonesian capital market remains in a transitional stage toward full efficiency, where psychological factors and information asymmetry continue to influence price formation during periods of extreme uncertainty.

Our research addresses the increasing concern of interdependency in the global financial markets, especially the stock market of G20 nations (Australia, Argentina, China, France, Japan, and the USA) and the WTI (West Texas Intermediate) oil market between January 2017 and September 2025. The dynamic connectedness approach and quantile VAR are a strong check as we use them to determine the spillover effect in various market conditions. We find that the spillover effect in bullish and bearish market conditions is strong with the Total Connectedness Index (TCI) of 71.98% and 70.25%, respectively. In the normal case, the highest connectedness effect is achieved at tau=0.5, and this result is consistent with the dynamic TCI of 39.12%. Based on the varying market conditions, USA, Australia, and France are strong net transmitters, but WTI is a net receiver in a bullish and bearish market situation, then Argentina, China and Japan, respectively depending on the return spillover effects. The USA, France, and Australia can strengthen their leading position as a net transmitter in the volatility spillover. Japan is a net transmitter in bearish market conditions only in the volatility series, and it is a net transmitter in bullish market conditions only in the return series. This became particularly clear during the global epidemic, the war between Russia and Ukraine, and the ongoing tariff war. In general, our study points to the increased role of the USA stock market in world markets concerning WTI. These findings can be used by investors and policymakers to maximize returns and ensure market stability.

ABSTRACT This paper explores the relationship between the Cryptocurrency Environment Attention Index (ICEA) and the level of uncertainty in the cryptocurrency market, including cryptocurrency price uncertainty and cryptocurrency policy uncertainty. We apply the wavelet coherence method, the novel quantile coherency technique introduced by Baruník and Kley, and a quantile‐on‐quantile regression analysis to a sample of data ranging from 2 January 2014 to 30 December 2022. Findings indicate that, in the near term, ICEA can exacerbate uncertainty in the cryptocurrency market due to news events and social media discourse concerning environmental issues associated with cryptocurrencies. In the medium run, pricing and policy uncertainties in the cryptocurrency market may attract public scrutiny about environmental concerns, resulting in regulatory discussions and modifications. The findings indicate that the correlation between environmental concerns over cryptocurrencies and uncertainties in the crypto market demonstrates a more consistent and enduring trend over the long run. Our analysis uncovers dynamic and asymmetric bidirectional effects between ICEA and cryptocurrency uncertainty indices, particularly pronounced during bullish market regimes, a dimension not previously explored in the literature. Methodologically, our choice to employ wavelet coherence, quantile coherency and quantile‐on‐quantile regression is deliberate. Unlike traditional cointegration or linear approaches, which can only reveal average or long‐term equilibrium relationships, these frequency‐ and quantile‐based tools capture the asymmetric, time‐varying and tail‐dependent co‐movements between ICEA and cryptocurrency uncertainty. This methodological design is thus directly aligned with the volatile and non‐linear nature of cryptocurrency markets, where extreme events and regime shifts are central features.

The quest for the optimal balance between risk and return remains a pivotal area of research within modern portfolio theory. As two traditional approaches, the Markowitz model and the single-index model embody distinct philosophies: precise calculation versus simplified approximation. The Markowitz model delves deeply into the covariance relationships between assets, laying the foundation for modern portfolio theory; the single-index model enhances practical applicability by streamlining the estimation process of the covariance matrix. This study constructs the efficient frontier, global minimum variance portfolio, and optimal risky portfolio under both the Markowitz model and single index model using data from 21 selected sample stocks. Empirical testing and comparisons are conducted across various scenarios using metrics such as the Sharpe ratio and Sortino ratio. Empirical results indicate that the single-index model demonstrates superior risk-adjusted returns and robustness across most market conditions. Whilst the Markowitz model theoretically achieves greater diversification, cumulative estimation errors may lead to comparatively weaker performance in empirical outcomes. These findings not only assist investors in model selection across dynamic market environments but also inform future research on expanding constraints and incorporating factor models.

Aim: This study explores the interaction between clean energy, sustainability, green bonds, and six blockchain indices (e.g., CBDCAI, ICEA, Bitcoin, Ethereum) from October 2017 to June 2023 to highlight sustainability issues related to recent financial innovations. The study aims to examine the extent to which sustainable equity prices are impacted by the stochastic features of Bitcoin and CBDC news across bull, bear, and normal market phases. Method: This study utilizes the Quantile Autoregressive Distributed Lag (QARDL) method to examine the short- and long-run impacts. Results: The QARDL results reveal a significant long-term equilibrium between blockchain variables and the green industry, with varying reactions across quantiles. Bitcoin, Ethereum, and green bonds positively impact the sustainable market index over time, especially in higher quantiles, supporting Sustainable Development Goals (SDGs). Conclusion: The study advocates using blockchain for SDG policies and renewable energy to reduce blockchain’s environmental impact and this research extends the literature by offering a deeper insight into the financial and environmental influences shaping sustainability.

Abstract The purpose of this study was to assess the dependence structure and volatility connectedness among the COVID-19 crisis, the 2022 Russia–Ukraine war, and their influence on cryptocurrencies, crude oil, developed markets, and the equity markets of China and ASEAN countries under varying market conditions. The analysis segmented the sample into three distinct periods: pre-COVID-19, during COVID-19, and the 2022 Russia–Ukraine conflict. To assess the dependence structure and risk spillover patterns across the markets for each period, we employed the generalized autoregressive conditional heteroskedasticity (GARCH)-extreme value theory (EVT)-vine copula and quantile vector autoregression (QVAR) connectedness methodologies. Findings from our GARCH-EVT-Vine-Copula model indicated that subsequent to the outbreak of COVID-19, market portfolios associated with the MSCI-developed markets index demonstrated significantly lower tail connectedness. However, the impact of the 2022 Russia–Ukraine war on the stock markets of China and ASEAN countries was found to be overestimated. Furthermore, the QVAR connectedness analysis revealed that connectedness was greater in bullish market conditions than in normal and extreme downside periods. Additionally, the portfolio analysis results suggested that the equity markets of China and ASEAN countries, along with the crude oil markets, cryptocurrency indices, and the MSCI developed markets index, were unable to achieve high levels of hedging effectiveness. Concurrently, it was recommended that investments be directed toward Chinese and ASEAN equities as safe-haven assets.

Global warming remains one of the greatest threats to the sustainability of the planet. The literature is replete with studies investigating the economic effects of global warming with very little attention being paid to the financial ramifications. To this end, the present study attempts to ascertain the predictive power of global warming for green financial assets (Clean Energy Index, Green Bond Index, World ESG Index, and Sustainability World Index) under bearish, normal and bullish market conditions. Employing a novel rolling windows wavelet quantile Granger causality testing procedure, which controls for time, frequency, and quantile asymmetries, findings reveal that the predictive power of global warming for green financial assets is more (less) stable across time at lower (higher) frequencies when markets are normal. In bearish and bullish markets, however, the predictability of global warming for green financial assets is observed to be more stable at higher frequencies and less stable at relatively lower frequencies. These results imply that global warming encourages low-frequency trading in normal markets, but induces relatively more speculative trading in bearish and bullish markets. Based on these findings, policy commendations are offered. • Modelling the predictive content of global warming for Green Financial Assets (GFAs). • Employ a novel Rolling Windows Wavelet Quantile Granger Causality test. • Predictive content of global warming has an asymmetric pattern across the distribution of GFAs. • Predictive content of global warming for GFAs is more stable at lower frequencies when markets are normal. • At bearish and bullish markets, the predictability is more stable at higher frequencies.

MACRO AND MICRO FACTORS DETERMINING FLUCTUATIONS IN GOLD AND SILVER PRICES AND THEIR IMPACT ON THE ECONOMY

Who Stays Calm in Chaos ? The Financial Instability of Crypto-Assets

Research background: The majority of research on equity crowdfunding concerns its evolution in developed countries. There are still relatively few works devoted to equity crowdfunding in developing regions, including Poland. Taking this into account and the lack of research on the effectiveness of ECF-based IPOs, there is a research gap that this article is trying to fill. This paper also contributes to the extensive literature dealing with the occurrence of the IPO underpricing phenomenon and focuses on a regional study on IPO underpricing in the still niche ECF-based IPOs. Purpose of the article: The article aims to show the development of equity crowdfunding in Poland. The article also aims to (i) evaluate the effectiveness of debuts of companies that raised funds (and thus carried out their IPOs) using ECF platforms, and (ii) find the determinants of ECF-based IPOs performance. Methods: The model for testing the potential determinants of ECF-based IPO performance is based on univariate linear regressions measuring the relationship between a dependent variable which stands for ECF-based IPO underpricing and one independent variable (chosen from a set of potential explanatory variables. Findings & value added: The article shows that since 2020 ECF has become an important source of financing for listed SMEs in Poland. Based on the stylized fact on the risk-return tradeoff, it is assumed that ECF-based IPOs are more underpriced than IPOs of other companies to attract investors. The paper revealed that the effectiveness of ECF-based IPOs has mainly the cyclical nature and it depends on the stock price cycle. ECF-based IPOs are more underpriced then other IPOs only in the bull market while in the bear market they are more overpriced. To the best of my knowledge, this is the first study to explore the performance of ECF-based IPOs. Although this paper focuses on Poland, it opens the potential for broader global research in this area. The article can also hold significant practical value for all participants of the ECF market.

Purpose This study aims to examine the quantile connectedness of Islamic cryptocurrencies compared to conventional cryptocurrencies with Shari’ah-based sustainability indices. Design/methodology/approach For baseline results, the authors use a novel econometric method, the method of moments quantile regression (MMQR), to estimate coefficients across heterogeneous quantiles under bearish and bullish market trends. The main empirical investigation uses daily data of two Islamic cryptocurrencies, two conventional cryptocurrencies and two Shari’ah-based sustainability indices from January 3, 2022, to September 30, 2024. For robustness tests, the authors extend the sample to five Islamic cryptocurrencies and the top five conventional and green cryptocurrencies, ranked by market capitalization, alongside four Shari’ah-based sustainability indices. The quantile VAR (QVAR) connectedness approach is used as an alternative method to validate the main findings. Findings Islamic cryptocurrencies show stronger quantile connectedness with Shari’ah-based sustainability indices compared to conventional cryptocurrencies. Positive connectedness dominates in bullish markets, supporting growth-oriented strategies, while negative connectedness emerges in bearish markets, indicating potential for portfolio diversification and hedging. Practical implications Overall, this study findings are significant for both diversification-seeking investors, who may prefer negative connectedness, and growth-oriented investors, who may benefit more from positive connectedness driven by market integration. Originality/value This study investigates the quantile connectedness of Islamic cryptocurrencies with Shari’ah-based sustainability indices, including a robustness check with an extended crypto sample and an alternative QVAR connectedness methodology.

This paper takes the abnormal volatility of China's stock market in 2015 as a case study, analyzing how overconfidence drives investors to trade frequently and affects market efficiency from the perspective of behavioral finance. Research points out the limitations of the traditional "rational people" hypothesis, and constructs with a psychological deviation analysis framework with a core of overconfidence. Through the analysis of high-frequency data such as financing balance and turnover rate, it is shown that overconfidence during the bull market phase boosts the influx of leveraged funds and the deviation of asset prices from fundamentals. However, after the market reversal, the psychological deviation reverses, triggering panic selling and a liquidity crisis, which damages the market's pricing function. Finally, systematic suggestions are put forward from three aspects: investor education, market mechanism, and policy supervision, the entire text revolves around overconfidence as the core psychological factor, providing theoretical references and practical paths for enhancing market stability.

Pathway to environmental resilience: Analyzing financial dimensions to curb energy security risks.

This study examines how Economic Policy Uncertainty (EPU) and Monetary Policy Uncertainty (MPU) affect the returns of ten different cryptoassets using Quantile Regression (QR) and Robust Least Squares (RLS) methods. Quantile regression allows a nuanced examination of how these uncertainties affect returns at different levels under market conditions. Using monthly data from January 1, 2018, to June 1, 2024, the analysis shows that MPU has a negative impact on cryptoasset returns under normal and bull market conditions. However, this effect diminishes during bear market periods. Conversely, EPU has a significant negative impact only during bull markets. These results suggest that market conditions critically shape the sensitivity of cryptoassets to uncertainty, with such effects amplified during bull market periods.

Using a panel of 3515 Chinese listed firms from 2011 to 2023, this study shows that the education level of the top management team (TMT) positively influences firm stock liquidity. The beneficial effect of TMT education on stock liquidity is stronger in settings with lower industry competition, higher information disclosure quality, and bull market periods. Mediation analysis indicates that analyst coverage provides a weak channel through which TMT education affects stock liquidity. Endogeneity concerns are alleviated by reverse causality tests, two-stage least squares regressions, propensity score matching, and generalized method of moments. The results are also robust to alternative liquidity measures and alternative definitions of TMT education. This study offers practical implications for investors, corporate executives, and policymakers seeking to promote market efficiency and liquidity.

This study explores the relationship between equity market performance and asset allocation behaviour among Tier-I subscribers of the National Pension System (NPS) in India. The purpose is to examine how fluctuations in market-linked returns influence subscriber decisions within the constrained yet flexible framework of the NPS. Utilizing secondary data from the Pension Fund Regulatory and Development Authority (PFRDA) and monthly Net Asset Value (NAV) data of NPS funds alongside leading market indices (Nifty 50 and BSE Sensex), the analysis covers over 1,000,000 active Tier-I accounts from 2017–2024. Panel data regression techniques with fixed-effects models are employed to assess the dynamic association between equity market movements and shifts in asset allocation, particularly towards equity (Scheme E), corporate bonds (Scheme C), and government securities (Scheme G). The findings indicate a significant procyclical behavior: subscribers tend to increase equity allocation during bullish market phases and reduce it during downturns, reflecting limited adherence to long-term strategic asset allocation principles. This behavior raises concerns regarding investor rationality and goal-oriented financial planning. The study emphasizes the need for enhanced investor education, improved default fund design, and behavioural nudges to ensure retirement adequacy. The results carry important policy implications for strengthening the sustainability and efficiency of India’s pension architecture.

Purpose This study aims to investigate the asymmetric connectedness and spillover effects of Economic Policy Uncertainty and Geopolitical Risks on Green Sukuk, traditional energy (represented by oil, coal and natural gas), renewable energy stocks (represented by clean energy, water, wind and First Solar) and carbon trading waters under bearish, normal and bullish market conditions. Design/methodology/approach To conduct this analysis, the authors innovative time-series methods, specifically the cross-quantilogram and quantile vector autoregression approaches. These methods allow us to explore contemporaneous linkages and asymmetries among Green Sukuk, green/dirty energy and various financial market uncertainties. Findings The cross-quantilogram approach reveals robust dependencies between Green Sukuk and market uncertainties. Rolling cross-quantilogram plots reveal significant positive spillovers from uncertainty indices to clean energy stocks (i.e. clean energy, water, wind and First Solar) across all market states in 2020, a year characterized by high market volatility. Moreover, uncertainty factors negatively impacted traditional energy commodities such as Brent crude oil and natural gas, while GPRs had a significantly positive effect on Brent crude. During bearish and bullish market positions, the uncertainty indices function as net transmitters (receivers) of spillovers in the green stock markets. Additionally, the uncertainty indices and Green Sukuk also function as net recipients. All energy stock indices exhibit strong linkages to Green Sukuk at the extreme upper and lower quantiles. Originality/value This study contributes to this field by improving our understanding of the effect of uncertainties on the time-varying nexus of green sukuk, clean and dirty energy markets under various market conditions. The findings provide essential perspectives for green project managers, investors and policymakers, underscoring the significance of green sukuk in advancing environmental objectives.

Market forces that have an impact on the economy are generally known as macroeconomic factors. Any number of external influences, some of which are natural and others of which are man-made, may alter the trajectory of an economy. Natural catastrophes such as earthquakes, famines, and droughts, as well as man-made events such as war, worldwide inflation, recession, and so on, are examples of such macroeconomic forces. The rate of increase of gross domestic product (GDP), stock market movements, important industries, and other fundamental economic indicators are all affected by these variables. As a result, knowing these elements and how they affect the economy overall is crucial. These macroeconomic variables influence stock markets by affecting corporate profitability, investor sentiment, and the overall economic outlook. Strong macroeconomic indicators can lead to a bullish market sentiment, while weak indicators can result in bearish sentiment. The relationship between stock prices and the global economy may not stay the same and can shift in line with various economic and market conditions. The present work is a descriptive review of the empirical evidences and presents the findings to establish the need for the research during post post-COVID-19 period. The reason behind, the post-pandemic period selection is the preferences, patterns, and practices of many corporates about investments have changed and thereby macroeconomic factor dynamics are also varied. In this parlance, the research can help to undergo a review of the situation and make policy changes to bring sustainability and growth into the mainstream of an economy. Hence, the paper's principal goal is to establish the need for research and identify the research gap in the area. The findings of the current paper reflects that the macroeconomic factors and stock price have a substantial correlation. movements with special reference to BRIC countries during the past decade and continue to be dynamic with corrections, suggest the role of regulatory and governments to be vigilant and dynamic in taking policy decisions from time to time based on the macroeconomic key variables, which can help to maintain the perpetual growth and sustainability of economies.