This paper is an humble attempt to add value to the existing literature by empirically testing the "time-varying" and "scale dependent" volatilities of and correlations of the sample commodities. Particularly, by incorporating scale dependence, it is able to identify unique portfolio diversification opportunities for different set of investors bearing different investment horizons or holding periods. In order to address the research objectives, we have applied the vector error-correction test and several recently introduced econometric techniques such as the Maximum Overlap Discrete Wavelet Transform (MODWT), Continuous Wavelet Transform (CWT) and Multivariate GARCH - Dynamic Conditional Correlation. The data used in this paper is the daily data of seven commodities (crude oil, gas, gold, silver, copper, soybean and corn) prices from 1 January 2007 until 31 December 2013. Our findings tend to suggest that there is a theoretical relationship between the sample commodities (as evidenced in the cointegration tests) and that the crude oil, gas, gold and copper variables are leading the other commodities (as evidenced in the Vector Error-Correction models).

This paper is the first attempt at testing the "time-varying" and "time-scale dependent" volatilities of and correlations between the selected Islamic stock indices of South East Asian countries and selected commodities for enhancing portfolio diversification benefits. Consistent with the results of our VECM, our analysis based on the application of the recent wavelet technique MODWT, indicates that the Singapore Islamic index is leading the other Islamic indices and the commodities. From the point of view of portfolio diversification benefits, based on the extent of dynamic correlations between variables, our results suggest that an investor should be aware that the Philippine Islamic stock index is less correlated with the crude oil in the short run (as evidenced in the continuous wavelet transform analysis) and that an investor holding the crude oil can gain by including the Malaysian Islamic stock index in the portfolio (as evidenced in the Dynamic conditional correlation analysis).

Financially distressed companies always bring about enormous financial and economic losses to many stakeholders, such as management, stockholders, employees and customers together with a substantial social and economic cost to the country. Therefore, a distress prediction model that can predict the event and identify the determinants in advance would serve to reduce such losses by providing a pre-warning signal to stakeholders. Preventive actions can then be taken by stakeholders if they can obtain early warning signal of probable failure that will lead to the efficient allocation of available resources to reduce losses. For these reasons, identifying the financial distress determinants that can give accurate prediction has become an essential aspect of finance. Our study will try to identify the financial distress determinants in Malaysia by applying logistic regression analysis (LRA) method together with multivariate discriminant analysis (MDA) and artificial intelligence algorithm (ANN) for robustness. Twenty-one independent variables were selected in our study that can be categorised as financial ratio variables, market variables and non-financial data variables. Data samples were derived from 158 Malaysian public listed companies and the assessment period was from the year 2001 until 2012 ...