Purpose: This study aims to critically evaluate the competitiveness of Transformer-based models in financial forecasting, specifically in the context of stock realized volatility forecasting. It seeks to challenge and extend upon the assertions of Zeng et al. (2023) regarding the purported limitations of these models in handling temporal information in financial time series.Design/methodology/approach: Employing a robust methodological framework, the study systematically compares a range of Transformer models, including first-generation and advanced iterations like Informer, Autoformer, and PatchTST, against benchmark models (HAR, NBEATSx, NHITS, and TimesNet). The evaluation encompasses 80 different stocks, four error metrics, four statistical tests, and three robustness tests designed to reflect diverse market conditions and data availability scenarios.Findings: The research uncovers that while first-generation Transformer models, like TFT, underperform in financial forecasting, second-generation models like Informer, Autoformer, and PatchTST demonstrate remarkable efficacy, especially in scenarios characterized by limited historical data and market volatility. The study also highlights the nuanced performance of these models across different forecasting horizons and error metrics, showcasing their potential as robust tools in financial forecasting, which contradicts the findings of Zeng et al. (2023)Originality/value: This paper contributes to the financial forecasting literature by providing a comprehensive analysis of the applicability of Transformer-based models in this domain. It offers new insights into the capabilities of these models, especially their adaptability to different market conditions and forecasting requirements, challenging the existing skepticism created by Zeng et al. (2023) about their utility in financial forecasting.

This paper introduces and examines a novel realized volatility forecasting model that makes use of Long Short-Term Memory (LSTM) neural networks and the risk metric Financial Turbulence (FT). The proposed model is compared to five alternative models, of which two incorporate LSTM neural networks and the remaining three include GARCH(1,1), EGARCH(1,1), and HAR models. The results of this paper demonstrate that the proposed model yields statistically significantly more accurate and robust forecasts than all other studied models when applied to stocks with middle-to-high volatility. Yet, considering low-volatility stocks, it can only be confidently affirmed that the proposed model yields statistically significantly more robust forecasts relative to all other models considered.

The concept of climate justice extends traditional environmentalism by emphasizing the social dimensions of environmental issues. Recently, it has gained much attention due to the growing impacts of climate change, especially on vulnerable communities that contribute minimally to global emissions. Based on the need for research in this field, this paper reports on a study that examined how higher education institutions (HEIs) are integrating climate justice into education, based on a literature review and a set of case studies. These cases highlight actions undertaken by various HEIs to incorporate climate justice into education across the globe. Based on the increasing number of publications, the findings show a growing research interest in the topic and reveal that some countries such as the US, the UK, China, Australia, and Germany have a visible presence in the literature. It also provides a wide range of perspectives on educational practices in climate justice, including real-world examples of courses implemented in HEIs. The findings reveal that HEIs have significant potential to address climate justice in their educational programs, which may help to transform students’ values and actions despite challenges.