Search results

Sustainable development hinges on a crucial shift to renewable energy, which is essential in the fight against global warming and climate change. This study explores the relationships between artificial intelligence (AI), fuel, green stocks, geopolitical risk, and Ethereum energy consumption (ETH) in an era of rapid technological advancement and growing environmental concerns.

This research stands at the forefront of interdisciplinary research and forges a path toward a comprehensive understanding of the intricate dynamics governing green sustainability investments. These objectives have been fulfilled by implementing the innovative quantile time-frequency connectedness approach in conjunction with geopolitical and climate considerations.

Our findings highlight coal market dominance and Ethereum energy consumption as critical short- and long-term market volatility sources. Additionally, geopolitical risks and Ethereum energy consumption significantly contribute to volatility. Long-term factors are the primary drivers of directional volatility spillover, impacting green stocks and energy assets over extended periods. Additionally, SHapley Additive exPlanations (SHAP) findings corroborate the quantile time-frequency connectedness outcomes.

This study highlights the critical importance of transitioning to sustainable energy sources and embracing digital finance in fostering green sustainability investments, illuminating their roles in shaping market dynamics, influencing geopolitics and ensuring the long-term sustainability required to combat climate change effectively.

The study offers practical sustainability implications by informing green investment choices, strengthening risk management strategies, encouraging interdisciplinary cooperation and fostering digital finance innovations to promote sustainable practices.

The implementation of the quantile time-frequency connectedness approach, in line with considering geopolitical and climate factors, marks the originality of this paper. This approach allows for a dynamic analysis of connectedness across different distribution quantiles, providing a deeper understanding of variable interactions under varying market conditions.

This study aims to investigate the dynamics of return connectedness of the Standard & Poor’s (S&P) Gulf Cooperation Council (GCC) composite index with five regional equity indices, three global equity indices and other different asset classes during the COVID-19 pandemic period.

This study uses daily data spanning from January 2, 2018, to December 23, 2021. A subsample analysis is conducted to determine the role of uncertainty in modifying the connectedness structure during the ongoing pandemic period.

The results of this study show that the nature of connectedness is time-frequent, with clear evidence for a higher level of connectedness during stress periods, especially after the onset of the pandemic. The GCC index is found to be a net receiver of shocks to other assets, with an increase in magnitude during the COVID period.

This study is limited by the use of only daily data, and future research could consider using higher frequency data.

The results of this study confirm the disturbing effects of the pandemic on the GCC index and its connectedness with other assets, which matters for policymakers and investors.

This study provides new insights into the dynamics of return connectedness of the GCC index with other assets during the COVID-19 pandemic period, which has not been previously explored.

In developing nations, the utility and intention to use algorithmic trading (AT) platforms and financial services are predominantly reliant on investors’ technological knowledge. This study aims to investigate the effect of investor awareness of AT (AAT), trust in AT (TAT) and acceptance of innovativeness (AOI) on intention to use the AT (IUAT) platforms among Indian investors.

The authors used a structured questionnaire with a five-point Likert scale to collect the data from 392 Indian retail investors through a purposeful sampling approach. And, the authors carried out structural equation modelling to analyse the serial mediation among the latent (independent) and observed (dependent) variables.

The findings suggest that investor awareness exerts a statistically significant and positive effect on the IUAT platforms. Additionally, TAT platforms and innovation acceptance, independently as well as mediator, significantly influences the usage decision of AT platforms among Indian investors.

The findings on determinants of AT platform usage can guide investment regulators to promote technological awareness, build trust and provide a safe algorithmic trading environment for retail investors in India. The suggestions may take the edge off a few behavioural impediments among the investors w.r.t. AT platform usage.

Off the back of extensive literary exploration our field research is among the first that probes an intellectual discourse and documents the empirical evidence on linkages between investor AAT, TAT, AOI and the IUAT platforms in the Indian stock market.

This paper introduces a novel method, Variance Rule-based Window Size Tracking (VR-WT), for deriving a sequence of estimation window sizes. This approach not only identifies structural change points but also ascertains the optimal size of the estimation window. VR-WT is designed to achieve accurate model estimation and is versatile enough to be applied across a range of models in various disciplines.

This paper proposes a new method named Variance Rule-based Window size Tracking (VR-WT), which derives a sequence of estimation window sizes. The concept of VR-WT is inspired by the Potential Scale Reduction Factor (PSRF), a tool used to evaluate the convergence and stationarity of MCMC.

Monte Carlo simulation study demonstrates that VR-WT accurately detects structural change points and select appropriate window sizes. The VR-WT is essential in applications where accurate estimation of model parameters and inference about their value, sign, and significance are critical. The VR-WT has also helped us understand shifts in parameter-based inference, ensuring stability across periods and highlighting how the timing and impact of market shocks vary across fields and datasets.

The first distinction of the VR-WT lies in its purpose and methodological differences. The VR-WT focuses on precise parameter estimation. By dynamically tracking window sizes, VR-WT selects flexible window sizes and enables the visualization of structural changes. The second distinction of VR-WT lies in its broad applicability and versatility. We conducted empirical applications across three fields of study: CAPM; interdependence analysis between global stock markets; and the study of time-dependent energy prices.

The study seeks evidence on the asymmetric effects of broad money growth on inflation in the short run and long run, in the context of emerging markets and developing economies (EMDEs).

Using a panel dataset of 122 EMDEs (by distinguishing between inflation-targeting and non-inflation-targeting EMDEs), we employ the nonlinear counterpart of the autoregressive distributed lag framework, which provides evidence of asymmetric dynamics between money growth and inflation in EMDEs.

In consonance with the quantity theory of money, we find a long-run relationship between money growth and inflationary outcomes. We also find that the response of inflation is higher to a tightening episode in the monetary policy stance than to a loosening episode. The study also provides evidence that adopting the inflation targeting framework in EMDEs has led to a significant reduction in the inflation rates along with ensuring a higher magnitude of transmission from money supply growth to inflationary outcomes.

To the best of our knowledge, the present study is one of the first attempts to evaluate the differential impact of broad money growth on inflationary outcomes, using a panel dataset of EMDEs. As a result of inherent differences in the financial structures of EMDEs vis-à-vis advanced nations, there is an imperative need to assess the dynamics of pass-through from money supply to inflation to gain an understanding of the mechanism of monetary transmission in these economies.