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Sense of place is important in sustaining the socio-cultural heritage of a place. It can be nurtured through a systematic place-based approach. However, the lack of a critical perspective and often under-theorized place-based education has been criticized as this results in limitations to achieve its original intention. This research aims to comprehend the concepts of place-based learning by identifying the socio-cultural elements, particularly from a clan/geographical heritage perspective, and how it might cultivate a sense of place for locals. Interviews and observations were conducted in Melaka and George Town, the World Heritage Sites of Malaysia. A total of twenty documents, including primary and secondary data, were analysed. The findings reveal five themes to be developed as a place-based approach for nurturing the sense of place of locals. Theoretically, it extends the literature of place-based learning, and the ontological appreciations of place. Practically, related stakeholders might use this as a reference to preserve their clan/geographical association heritage.

This study estimates if Malaysian finance professionals' investment characteristics and stock characteristics' preferences affect their portfolio diversification, and whether the effects of these predictors vary across professionals' gender, income and experience. Employing a survey and ordinal regression models, the findings demonstrate that investment characteristics such as active trading, usage of internet and telephone, and saving for retirement objective are likely to improve diversification, whereas diversification worsens with the spending less time on researching investment. With respect to stock's characteristics, trading volume and return on asset are related to better diversification, whereas profit margin and return on equity are related to least diversification. The predictors are found to vary across professionals' gender, income and experience. The results suggest that finance professionals need to consider spending more time on researching investment and stock's characteristics in portfolio composition.

One of the main applications of conditional volatility modeling and forecasting of financial assets is the value-at-risk (VaR) estimation that is used by financial institutions for reporting the daily capital in risk. It remains a question on whether realized volatility (RV) models that incorporate the use of intraday data produce better VaR forecasts compared to methodologies that are based solely on daily returns. This study provides extensive comparison of out-of-sample volatility and VaR forecast performance on three equity market indices: S&P500, FTSE100, and DAX30 using 13 risk models that consist of 5 GARCH specifications, 4 ARFIMAX specifications and 4 HARX specifications. The out-of-sample volatility forecasts are evaluated by various loss functions and simple scoring procedures in order to identity the model that produces the overall best volatility forecasts. For VaR forecasts, the models are evaluated using a two-stage backtesting procedure where the models undergo unconditional and conditional coverage tests to eliminate underperforming models and the qualified models are then evaluated using the quadratic probability score (QPS) function that is computed based on various VaR loss functions. The results showed that RV models outperform GARCH models for volatility forecasts, but a simple EGARCH model outperforms the rest models for most of the VaR forecasts. The results also indicated that capturing the asymmetric behavior of volatility dynamics is essential for accurate volatility and VaR forecasts. The findings of this study provide useful information for market risk regulation, financial risk management and further investigations such as extension to derivative markets and options pricing.

AbstractThis paper applies autoregressive heteroscedasticity (ARCH) family models for the purpose of comparing stylized facts such as volatility clustering, leverage effect, long memory volatility and risk-return tradeoff for energy and stock markets. Empirical results have found that the presences of volatility clustering in both markets and the impact of volatility shocks to the conditional volatility display hyperbolic rather than exponential rate of decay. Meanwhile, only stock markets denote the leverage effect, which implies that ‘bad’ news has a greater impact on volatility than ‘good’ news at the same magnitude. Additionally, empirical results also highlighted that Kerosene and Brent crude oil are the only energy commodities exhibit risk-return tradeoff. For forecast evaluations, the FIAPARCH model indicates superior out of sample forecasts over short and long time horizon for stock markets. Nevertheless, FIAPARCH model suits better over long term as compared to short term for energy markets. Finally, the Superior Predictive Ability (SPA) tests suggested that overall asymmetric long memory GARCH models display higher forecasting accuracy than the standard GARCH models.

Abstract:This study employs a cross-sectional design with stratified random sampling method to examine if the microcredit programme of the Amanah Ikhtiar Malaysia (AIM) improves the income of hardcore poor households. This study uses a quasi-experimental approach and selected control and treatment group members from AIM’s client’s base — new and old clients respectively. It is evident that the AIM’s microcredit programme increased the household’s income and reduced the poverty rate among their hardcore poor clients in Peninsular Malaysia. Findings of the study indicate the effectiveness of microcredit in eradicating poverty. This programme should, therefore, focus on diversifying their products and services in order to increase outreach and decrease inactive borrowers and dropouts.

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.

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, offcampus 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.