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The study analyzes and assess the traditional generalized autoregressive conditional heteroscedasticity (GARCH) model with the Markov regime-switching GARCH model (MRS GARCH). Evaluation based on the accuracy of forecasting volatility and risk of Malaysia natural rubber grade Standard Malaysia Rubber 20 (SMR20). We fitted these models under six distributions which are normal, Student-t, generalized error distribution (GED), and their skewed version. Based on log-likelihood (LL), Akaike information criterion (AIC), and Bayesian information criterion (BIC), we found that the MRS GARCH models outperform the traditional GARCH models. We also found that the SMR20 returns are fat tails and skewedly distributed. Further, we forecasted one-day Value-at-Risk (VaR) for each model and compared their adequacy using the conditional coverage (CC) test and Dynamic Quantile (DQ) test. We discovered that the best accurate VaR prediction for SMR20 risk management comes from the MRS GARCH model of skewed Student-t distribution. [ABSTRACT FROM AUTHOR]

Measuring company performance-based financial ratios is common among decision makers such as business analysts, shareholders, creditors, prospective investors and financial managers. Ratio analysis is a powerful tool to enable stakeholders to analyze the health of companies' financial status. Financial ratios provide comparative financial status among companies within an industry or within a company itself. The decision maker can use ratios to identify internal strengths and weaknesses, and estimate future financial performance to compare companies in order to make a decision. Using econometric technique, this study employed the production function of the frontier to determine the performance and to analyse the productivity of companies. The purpose of this paper is to evaluate and measure the performance of 115 samples oflisted companies in Bursa Malaysia which will be discussed in terms of mathematical analysis. The Cobb-Douglas function was estimated using Maximum Likelihood to determine the values of parameters in the model. This study will utilize input of debt-equity ratio and asset turnover to maximize the output, return on equity ratio. This paper will mainly contribute in showing how the selection output based on DuPont theory was selected where the highest return on stockholders' investment is set as main objective and estimating the technical efficiency of companies using financial ratio. [ABSTRACT FROM AUTHOR]

This paper examines the existence of long memory in the daily stock market EGX 30 index. The long memory of the series was investigated and a fractionally integrated autoregressive moving-average (ARFIMA) model was fitted using 3152 daily data (from June 20, 2004 to July 18, 2017). This paper shows the efficiency of several methods used to estimate fractional parameter in the ARFIMA model, long memory was investigated by Rescaled Range statistic (R/S), Aggregated variance, Higuchi and absolute moment and estimates has obtained by semi parametric methods such as GPH and modified GPH. The results indicate that the modified GPH outperformed the GPH. [ABSTRACT FROM AUTHOR]

Many scholars have shown their interest into the relationship between energy consumption (EC), gross domestic product (GDP) and emissions. The main objective of this study is to investigate the relationship between GDP, EC and CO2 within multivariate model by using panel data method in Asian countries; Korea, Malaysia, Japan and China for annually data during the period 1960 to 2010. The main finding shows that CO2 can be explained more than 86% & 78% by EC and GDP in each of cross section model and period model respectively. As a result of that, CO2 emissions should be considered as an important factor in energy consumption and gross domestic product by policy maker. [ABSTRACT FROM AUTHOR]

Normal mixture (NM) GARCH model can capture time variation in both conditional skewness and kurtosis. In this paper, we present the general framework of Normal mixture GARCH (1,1). An empirical application is presented using Malaysia weekly stock market returns. This paper provides evidence that, for modeling stock market returns, twocomponent Normal mixture GARCH (1,1) model perform better than Normal, symmetric and skewed Student's t-GARCH models. This model can quantify the volatility corresponding to stable and crash market circumstances. We also consider Value-at-Risk (VaR) estimation for Normal mixture GARCH model. [ABSTRACT FROM AUTHOR]

This study investigates the heterogeneous market hypothesis using high frequency data. The cascaded heterogeneous trading activities with different time durations are modelled by the heterogeneous autoregressive framework. The empirical study indicated the presence of long memory behaviour and predictability elements in the financial time series which supported heterogeneous market hypothesis. Besides the common sum-of-square intraday realized volatility, we also advocated two power variation realized volatilities in forecast evaluation and risk measurement in order to overcome the possible abrupt jumps during the credit crisis. Finally, the empirical results are used in determining the market risk using the value-at-risk approach. The findings of this study have implications for informationally market efficiency analysis, portfolio strategies and risk managements. [ABSTRACT FROM AUTHOR]

This paper focuses on the Stochastic cost Frontier Analysis (SFA) approach, in an attempt to measure the relationship between efficiency and organizational structure for Takaful and insurance operators in Malaysia's dual financial system. This study applied a flexible cost functional form i.e., Fourier Flexible Functional Form, for a sample consisting of 19 firms, chosen between 2002 and 2010, by employing the Battese and Coelli invariant efficiency model. The findings show that on average, there is a significant difference in cost efficiency between the Takaful industry and the insurance industry. It was found that Takaful has lower cost efficiency than conventional insurance, which shows that the organization form has an influence on efficiency. Overall, it was observed that the level of efficiency scores for both life insurance and family Takaful do not vary across time. [ABSTRACT FROM AUTHOR]

Normal mixture distributions model has been successfully applied in financial time series analysis. In this paper, we estimate the return distribution, value at risk (VaR) and conditional value at risk (CVaR) for monthly and weekly rates of returns for FTSE Bursa Malaysia Kuala Lumpur Composite Index (FBMKLCI) from July 1990 until July 2010 using the two component univariate normal mixture distributions model. First, we present the application of normal mixture distributions model in empirical finance where we fit our real data. Second, we present the application of normal mixture distributions model in risk analysis where we apply the normal mixture distributions model to evaluate the value at risk (VaR) and conditional value at risk (CVaR) with model validation for both risk measures. The empirical results provide evidence that using the two components normal mixture distributions model can fit the data well and can perform better in estimating value at risk (VaR) and conditional value at risk (CVaR) where it can capture the stylized facts of non-normality and leptokurtosis in returns distribution. [ABSTRACT FROM AUTHOR]

Determining stock performance using financial ratio is challenging for many investors and researchers. Financial ratio can indicate the strengths and weaknesses of a company's stock performance. There are five categories of financial ratios namely liquidity, efficiency, leverage, profitability and market ratios. It is important to interpret the ratio correctly for proper financial decision making. The purpose of this study is to compare the performance of listed companies in Bursa Malaysia using Data Envelopment Analysis (DEA) and DuPont analysis Models. The study is conducted in 2015 involving 116 consumer products companies listed in Bursa Malaysia. The estimation method of Data Envelopment Analysis computes the efficiency scores and ranks the companies accordingly. The Alirezaee and Afsharian's method of analysis based Charnes, Cooper and Rhodes (CCR) where Constant Return to Scale (CRS) is employed. The DuPont analysis is a traditional tool for measuring the operating performance of companies. In this study, DuPont analysis is used to evaluate three different aspects such as profitability, efficiency of assets utilization and financial leverage. Return on Equity (ROE) is also calculated in DuPont analysis. This study finds that both analysis models provide different rankings of the selected samples. Hypothesis testing based on Pearson's correlation, indicates that there is no correlation between rankings produced by DEA and DuPont analysis. The DEA ranking model proposed by Alirezaee and Asharian is unstable. The method cannot provide complete ranking because the values of Balance Index is equal and zero. [ABSTRACT FROM AUTHOR]

The objective of this study is to estimate the retirement wealth adequacy of future retirees under defined contribution (DC) plan in Malaysia using wealth-need model and simulation approach. Several feasible scenarios are projected, including the best case (or optimistic) and worse case (or pessimistic) scenarios, based on several related assumptions. The results show that 36% of households are projected to have adequate retirement benefits under the pessimistic scenario, while 84% of households are projected to have adequate retirement benefits under the optimistic scenario. [ABSTRACT FROM AUTHOR]

Quantile regression is a statistical analysis that does not restrict attention to the conditional mean and therefore, permitting the approximation of the whole conditional distribution of a response variable. Quantile regression is a robust regression to outliers compared to mean regression models. In this paper, we demonstrate how quantile regression approach can be used to analyze the ratio of projected wealth to needs (wealth-needs ratio) during retirement. [ABSTRACT FROM AUTHOR]

Adequacy of retirement income is very important to maintain a comfortable living standard during retirement. Under a life cycle model, assets are mainly accumulated during an individual's work life to finance consumption after retirement. A generally accepted goal of retirement planning is to provide enough income during retirement to prevent the level of living from dropping much below the pre-retirement level. Retirement wealth can be defined as adequate if the total retirement income is equal or greater than the desired total retirement consumption (or needs). In this study, retirement adequacy is projected using the Malaysian Household Income Survey (HIS) 2009 data which is based on 5881 sample of households and contains information on income, demographic and socioeconomic status of each household. Besides the projection of retirement adequacy, a regression of the ratio of projected wealth to needs (or wealth-needs ratio) is performed to investigate the demographic and socioeconomic determinants of retirement adequacy in Malaysia. The results show that 69% of households in Malaysia are adequately prepared for retirement. [ABSTRACT FROM AUTHOR]

Mean and variance of return distributions are two important parameters of the mean-variance model in portfolio optimization. However, the mean-variance model will become inadequate if the returns of assets are not normally distributed. Therefore, higher moments such as skewness and kurtosis cannot be ignored. Risk averse investors prefer portfolios with high skewness and low kurtosis so that the probability of getting negative rates of return will be reduced. The objective of this study is to compare the portfolio compositions as well as performances between the mean-variance model and mean-variance-skewness-kurtosis model by using the polynomial goal programming approach. The results show that the incorporation of skewness and kurtosis will change the optimal portfolio compositions. The mean-variance-skewness-kurtosis model outperforms the mean-variance model because the mean-variance-skewness-kurtosis model takes skewness and kurtosis into consideration. Therefore, the mean-variance-skewness-kurtosis model is more appropriate for the investors of Malaysia in portfolio optimization. [ABSTRACT FROM AUTHOR]

Risk management is very important in portfolio optimization. The mean-variance model has been used in portfolio optimization to minimize the investment risk. The objective of the mean-variance model is to minimize the portfolio risk and achieve the target rate of return. Variance is used as risk measure in the mean-variance model. The purpose of this study is to compare the portfolio composition as well as performance between the optimal portfolio of mean-variance model and equally weighted portfolio. Equally weighted portfolio means the proportions that are invested in each asset are equal. The results show that the portfolio composition of the mean-variance optimal portfolio and equally weighted portfolio are different. Besides that, the mean-variance optimal portfolio gives better performance because it gives higher performance ratio than the equally weighted portfolio. [ABSTRACT FROM AUTHOR]

The forecasting of weekly return is one of the most challenging tasks in investment since the time series are volatile and non-stationary. In this study, an integrated model of wavelet transform and artificial neural network, WT-ANN is studied for modeling and forecasting of KLCI weekly return. First, the WT is applied to decompose the weekly return time series in order to eliminate noise. Then, a mathematical model of the time series is constructed using the ANN. The performance of the suggested model will be evaluated by root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE). The result shows that the WT-ANN model can be considered as a feasible and powerful model for time series modeling and prediction. [ABSTRACT FROM AUTHOR]