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The aim of this paper is to assess whether the availability of high-frequency data enhances the accuracy of extreme market risk estimation in comparison to low-frequency data by using Value-at-risk (VaR) and Expected shortfall (ES). The sample data used for analysis comprised the daily closing stock prices and 5-minute intraday stock prices of DJIA, FTSE100, BOVESPA, and MERVAL Index from 2014 to 2022. The data analysis was done to compare the performance of two-stages hybrid methods called conditional EVT that combined the GARCH, RV and HAR specification models with the EVT approach. To assess the accuracy of the VaR forecasts, out-of-sample VaR forecast was backtested by using unconditional coverage (UC) and conditional coverage (CC) tests. The VaR backtesting procedure also incorporated the utilization loss function which are the regulatory loss function (RLF) and the firm’s loss function (FLF). The accuracy of the forecasted ES was backtested by using the generalized breach indicator (GBI) method. The findings of this research emphasized that high-frequency conditional EVT, incorporating the HAR specification outperformed the low-frequency conditional EVT in predicting market risk during periods characterized by extreme returns. Based on the VaR and ES measure, the HAR-EVT typed models are the best performance model compared to the GARCH-EVT and RV-EVT typed models during both crisis and non-crisis periods. This research study contributes to the current literature on the forecasting ability of risk models by concentrating on the hybrid model of long-memory models (FIEGARCH, RV-FIEGARCH and HAR-FIEGARCH) for with the EVT approach.

Knowledge sharing can enhance learning and help to build the knowledge workforce. This paper reports on a study of knowledge sharing behaviour among undergraduate students in Malaysia. Knowledge sharing was found to be influenced by the mechanisms used, various barriers to communication and the motivations behind knowledge sharing. The mechanisms include online communication, social interaction, off-campus activities and learning activities. It was found that problems with information and communications technology (ICT), lack of self confidence, external constraints, self-centredness and social attributes can create barriers to knowledge exchange. It is concluded that the motivation to share knowledge appears to be affected by relationships, rewards and the level of satisfaction with knowledge sharing activities. (Contains 1 note and 4 tables.)