As investment portfolios become increasingly diversified and financial asset risks grow more complex, accurately forecasting the risk of multiple asset classes through mathematical modeling and identifying their heterogeneity has emerged as a critical topic in financial research. This study examines the volatility and tail risk of gold, crude oil, Bitcoin, and selected stock markets. Methodologically, we propose two improved Value at Risk (VaR) forecasting models that combine the autoregressive (AR) model, Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model, Extreme Value Theory (EVT), skewed heavy-tailed distributions, and a rolling window estimation approach. The model’s performance is evaluated using the Kupiec test and the Christoffersen test, both of which indicate that traditional VaR models have become inadequate under current complex risk conditions. The proposed models demonstrate superior accuracy in predicting VaR and are applicable to a wide range of financial assets. Empirical results reveal that Bitcoin and the Chinese stock market exhibit no leverage effect, indicating distinct risk profiles. Among the assets analyzed, Bitcoin and crude oil are associated with the highest levels of risk, gold with the lowest, and stock markets occupy an intermediate position. The findings offer practical implications for asset allocation and policy design.

This main purpose of this article investigated the impact of ageing population on economic growth in Malaysia. Annual time series data for 27-year duration (1990-2017) was used and the autoregressive distributed lag (ARDL) was applied. This study will focuses on addressing role of ageing population in Malaysia by context that failed to receive much attention especially in employment sector. By using Romer [1] endogenous theory, the cointegration result revealed that exists a long run relationship exists between ageing population in Malaysia government development expenditure in education and economic growth. Our analysis recommends further investment in government expenditure in education sector to achieving higher human capital capability as a towards high income country and ageing phenomena.

This study aims to apply the technology assessment (TA) model on e-banking perceptions in the context of Malaysia, which involves aspects of supply and demand, and increasing the cashless concept in the country. A sample of 470 respondents were randomly selected from high density state capitals and major cities, through the convenience sampling method. Respondents were requested to complete a questionnaire developed from the basic model by forming UTAUT constructs, including quality, skills, transaction costs, user satisfaction, role of service providers (banks), and the influence of environment. Based on the results obtained, the model suggests that transaction costs, as direct costs by service providers, have a significant impact on the overall assessment of the performance of retail e-banking. Data analysis was performed using structured equation modeling (SEM), with the use of AMOS V22 as a method of trajectory analysis. Keywords: technology, SEM, performance, e-banking

HRMARS is well established journal publisher based in Pakistan. It is publishing prominent journals in social sciences since 2011.

With the digital transformation of the global economy and the deepening of the concept of sustainable development, China's construction machinery industry faces major opportunities and challenges in the reconstruction and upgrading of the global value chain. Based on the dynamic capability theory and the collaborative innovation theory, this study constructs a theoretical framework of "digital transformation-green innovation-global value chain upgrading" to explore the impact of the synergy between digital transformation and green innovation on improving the position of Chinese construction machinery companies in the global value chain. This study uses a multi-source data fusion method, combined with patent data, financial statements and questionnaires, to construct an unbalanced panel data set of 189 listed construction machinery companies in China from 2010 to 2022, and uses econometric methods such as dynamic panel modeling, threshold regression and machine learning to analyze. The study finds that: (1) The impact of digital transformation on the global value chain upgrading of construction machinery companies presents a nonlinear "U-shaped" curve relationship; (2) Green innovation plays a positive regulatory role in the relationship between digital transformation and value chain upgrading; (3) There is a significant synergy between digital transformation and green innovation ; (4) The quality of the institutional environment has an important impact on the synergy, and a high-quality institutional environment enhances the synergy; (5) Enterprise heterogeneity significantly moderates the impact of synergy on value chain upgrading. The study expands the theory of global value chains, enriches the theory of collaborative innovation, provides a new theoretical perspective for the integration of digital economy and green development, and provides practical guidance for enterprises and governments to formulate relevant strategies and policies. Future research can further explore the cross-national comparison and long-term dynamic evolution of synergy effects.Digital transformation