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 …

This paper presents a case study from the Center for International Research (CIBR) at HAN International School of Business, which has employed a systematic change management approach to transition toward sustainability. The objective of this paper is to clarify this process through the introduction of …

-

Striving for positive GDP growth often comes at the expense of sustainability indicators, highlighting a crucial dilemma in modern economic development. This empirical study delves into the intricate relationship between sovereign sustainability indicators and GDP growth by using panel data that spans …

This research aims to initiate academic discussion on the European Union (EU) social taxonomy for the purpose of promoting sustainable economic activities that ultimately contribute to achieving the United Nations’ sustainable development goals (SDGs) in the EU. The paper examines the historical evolution …

This study investigates the Topological Tail Dependence (TTD) theory’s applicability to individual stock volatility and high dimensions. Utilizing a comprehensive dataset from the S&P 100, the research employs various methodologies to test the predictions and implications of the TTD theory. The theory’s …

-

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 …

This paper presents a Python script that automates the estimation of Yang & Zhang’s stock realized volatility proxy for univariate and multivariate cases. Yang & Zhang’s realized volatility is a stock volatility proxy commonly used by financial researchers and practitioners due to its unbiasedness in …

This study proposes a novel loss function for neural network models that explores the topological structure of stock realized volatility (RV) data by adding Wasserstein Distance (WD). The study shows that the proposed loss statistically significantly improves the forecast accuracy of neural network models …