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This study examines the integration of nine Asian stock markets using the new methodology of wavelet multiple correlation and multiple cross-correlation proposed by Fernandez (2012). This novel approach eliminates several limitations which are encountered when conventional pairwise wavelet correlation and cross-correlation are used to assess the comovement of a set of stock indices. Our results show that Asian stock markets are highly integrated at lower frequencies and comparatively less integrated at higher frequencies. From the perspective of international investors, the Asian stock markets therefore offer little potential gains from international portfolio diversification especially for monthly, quarterly, and bi-annual time horizon investors, whereas, higher potential gains are expected at intraweek, weekly, and fortnightly time horizons.

This work provides an in-depth investigation of the dynamic interaction patterns between water stocks and renewable energy markets through the application of continuous wavelet analysis, dynamic correlation analysis, and time-varying Granger causality analysis. In addition, this study utilizes daily pricing indices, namely the S&P Global Water Index, Solactive Global Wind Energy Index, and Solactive Global Solar Energy Index, spanning from 18 May 2011 to 23 June 2022. The results show significant correlation patterns between the indices, ranging from moderate to high. Notably, robust correlations have been detected starting from 2015. The research also discovered a varied and inconsistent relationship between frequency and causation throughout different time periods. Moreover, the results reveal an asymmetry in the causal effects and a symmetry correlation at tail quantile ranges. Policymakers and market participants must consider these insights to make wise financial and strategic decisions.

In the context where \"sustainable energy\" promotes \"environmental sustainability,\" and as private investment in clean energy gains traction as a vital measure for climate change mitigation, evaluating its impact on ecology-focused firms is essential. This study examines the \"clean energy-ecology nexus,\" investigating whether the rapid growth of clean energy technologies driving sustainable energy transitions positively influences ecology through effective water and waste management practices and optimized industrial systems. Understanding this interrelationship within the frequency domain allows for informed conclusions. We apply a recently developed frequency-domain Granger-causality inferential framework to analyze the clean energy-ecology nexus in both unconditional and conditional contexts. The results suggest that clean energy technologies benefit ecology, reinforcing the connection between clean energy and ecological health.

Based on market integration theory, we investigate the static and dynamic connectedness between nonfungible tokens (NFTs) and the Association of Southeast Asian Nations (ASEAN) equity markets using the Quantile Vector Auto Regressive model. We also compute optimal weights and hedge ratios for our variable of interest to establish their diversification and hedging potential. Our analysis infers a moderate level of return transmission at the median quantile, where equity markets evolved as the net recipients of return spillover from the system, while NFTs emerge as key transmitters. In extreme market conditions, transmission between variables is amplified, but the increase is symmetrical across extreme quantiles, suggesting a similar impact. However, the interlinkage among assets is symmetric across conditional quantiles. The dynamic analysis demonstrates that the system integration amplifies during uncertain times (e.g., COVID-19 and the Russia–Ukraine conflict). Our portfolio analysis shows that NFTs provide diversification and hedging in all market conditions. However, the period of turmoil dampened the diversification potential, and hedging became expensive. Our study offers detailed and insightful information about the transmission mechanism and enables the participants of financial markets to diversify and hedge their portfolio.

Purpose This study aims to investigate factors that influence the attitudes and intentions of investors towards environmental, social and governance (ESG) stocks in the presence of perceived risk as a moderator. Design/methodology/approach Data was collected through an online survey method from 341 investors with more than three years of investing experience. Smart PLS was used to analyse the data using two-stage structural equation modelling. First, a measurement model was performed for construct reliability and validity, followed by path analysis (structural model) for hypothesis testing and overall model predictability. Findings The findings show that both environmental concern (altruistic value) and economic concern (egoistic value) are crucial for the attitude and intention of investors to invest in ESG-backed stocks; however, environmental concern was found to be a more significant predictor of their behaviour, showing evidence of pro-environmental values in the decision-making of utility-seeking individuals. No significant impact of perceived risk was evident as a moderator of the relationship between attitude and intention towards ESG stocks. Practical implications The study's findings have implications for fund managers, policymakers, and the government. Values as antecedents were found to be influential in shaping investors’ attitudes and intentions towards the environmental cause. Fund managers could include more ESG-compliant companies in their portfolios, and the government can play an important role in encouraging investors by providing financial incentives. Corporates should also take strategic steps to adopt green production processes to secure long-term, sustainable capital funding. Originality/value To the best of the authors’ knowledge, there has been no research done in the field of ESG investing that takes into account the values (both altruistic and egoistic) of investors as potential antecedents of their attitudes and intentions.

In this study, we attempted to analyze the determinants of capital structure for Indian firms using a panel framework and to investigate whether the capital structure models derived from Western settings provide convincing explanations for capital structure decisions of the Indian firms. The investigation is performed using balanced panel data procedures for a sample 298 firms (from the BSE 500 firms based on the availability of data) during 2001-2010. We found that for lowest quantile LnSales and TANGIT are significant with positive sign and NDTS and PROFIT are significant with negative sign. However, in case of 0.25th quantile LnSales and LnTA are significant with positive sign and PROFIT is significant with negative sign. For median quantile PROFIT is found to be significant with negative sign and TANGIT is significant with positive sign. For 0.75th quantile, in model one, LnSales and PROFIT are significant with negative sign and TANGIT and GROWTHTA are significant with positive sign whereas, in model two, results of 0.75th quantile are similar to the median quantile of model two. For the highest quantile, in case of model one, results are similar to the case of 0.75th quantile with exception that now GROWTHTA in model one (and GROWTHSA in model two).

This study utilizes two complementary models, the Time-Varying Parameter Vector Autoregressive Diebold–Yilmaz (TVP-VAR-DY) and the Time-Varying Parameter Vector Autoregressive Baruník–Křehlík (TVP-VAR-BK), to investigate the dynamic volatility transmission between exchange rates and stock returns in major commodity-exporting and -importing countries. The analysis focuses on periods of quantitative easing (QE) and quantitative tightening (QT) from March 15, 2020 to December 30, 2022. The countries examined are Canada and Australia (major commodity exporters) and the UK and Germany (major commodity importers). An essential contribution of this paper is new empirical insights into the dynamics of stock market returns and the transmission of volatility between these markets and exchange rates during the QE and QT periods. The results reveal that causality primarily flows from stock markets to exchange rates, especially during the QT period across all investment horizons. The Toronto Stock Exchange (TSX) emerges as the principal net driver among the markets under study. Furthermore, the Canadian exchange rate (USDCAD) and the Australian Stock Exchange (ASX) are the most significantly affected indices within the network across various investment horizons (excluding the long-term). These findings underscore the importance for investors and policymakers to consider the interplay between exchange rates and stock market returns, particularly in the context of the QE and QT periods, as well as other economic, political, and health-related events. Our findings are relevant to various stakeholders, including governments, traders, portfolio managers, and multinationals.

This study explores and evaluates cash holdings patterns, including cash-driven resilience capabilities for the manufacturing and service industries, and distinguishes between business group firms and stand-alone firms. Specifically, this study uses the ANOVA Kruskal-Wallis test to examine various cash-driven resilience capabilities and the weighted least-square (WLS) to test the stated research questions. The empirical outcomes uncover that non-resilient organizations predominate over resilient ones. Moreover, the study finds that various cash-driven resilience capabilities differ significantly from a statistical viewpoint. In the process, it contributes to the literature on the impacts of COVID-19 on both manufacturing and services industries. It also uses different empirical methodologies, including Driscoll-Kraay, pooled ordinary least squares, Rogers, White, and Newey-West Fixed effects between the group estimations and the generalised method of moments (GMM) estimator to check the robustness of the findings. Based on the findings, this study recommends that the management of manufacturing and service organizations focus on increasing organizational resilience potential. This study provides a platform for managers of the business group and the standalone firms to manage the liquidity so the companies should not face any liquidity crunch during adverse economic or epidemic conditions.

The current study intends to empirically test a relationship between long-memory features in returns and volatility of Dhaka Stock Exchange market. As such, the study uses the ARFIMA-FIGARCH and FIPARCH structure for the daily data ranging from 15 December 2003 to July 31, 2013 of Dhaka Stock Exchange market index, i.e., DSE General Index (DGEN). The observed indication assembled from long-memory tests supports the occurrence of long memory in Bangladesh stock returns. The study aims at doing research work with long-memory data set, as it provides a superior strategy, as well as gives real picture with short-memory data set. Moreover, the backup indication for existence of long memory in both return and volatility denies the efficient market hypothesis of Fama (1970) that the future return and volatility values are unpredictable. Extra measures ought to be given for the smooth functioning of the Dhaka Stock Exchange market so that both individual and institutional investors can get congenial atmosphere to invest. Authors’ suggested that Bangladesh Bank must play vital role as share market of Bangladesh is dominated by banking shares and in case of other listed shares of the Dhaka Stock Exchange, market authority should deal with transparently and fairly so that the market can be transformed into strong efficient market. This requires suitable directives, groundwork, removing malpractices and also implementation of investors’ friendly decisions. Further, fiscal policy of the country should be pro investor friendly, as well as monetary policy should work as complementary towards investment at stock exchange market as suggested by the authors.

After a long transition period, the Central and Eastern European (CEE) capital markets have consolidated their place in the financial systems. However, little is known about the price behavior and efficiency of these markets. In this context, using a battery of tests for nonlinear and chaotic behavior, we look for the presence of nonlinearities and chaos in five CEE stock markets. We document, in general, the presence of nonlinearities and chaos which questions the efficient market hypothesis. However, if all tests highlight a chaotic behavior for the analyzed index returns, there are noteworthy differences between the analyzed stock markets underlined by nonlinearity tests, which question, thus, their level of significance. Moreover, the results of nonlinearity tests partially contrast the previous findings reported in the literature on the same group of stock markets, showing, thus, a change in their recent behavior, compared with the 1990s.