Modelling The Impact Of Us Stock Market On Asean Countries Stock Markets.

The ASEAN countries which located on the Southeast Asian consist of 10 countries. Most of these countries depend on US as their main trading partner

Detecting The Regime Shift Via Wavelet Transform.

Recently, regime shifts or structure breaks had acquired very high attention in analyzing financial time series data

Cross-cultural challenges and adjustments of expatriates: a case study in Malaysia

Due to globalization and vision to be an industrialized nation, Malaysia acknowledges the inflow of expatriates into the country to meet the demands for skilled and professional manpower. This paper reports on a study conducted among a group of expatriates in Malaysia. The objectives of the study are to examine challenges faced by the expatriates and adjustments made to the challenges. Cultural clashes between foreign and local values are inevitable in which expatriates experience challenges. Indepth interviews were conducted with 20 male and female expatriates working in various firms and institutions in Malaysia. The study highlighted the psychological, socio-cultural and work challenges. Adjustments were based on individual initiatives based on the psychological and mental strengths of the expatriates, combined with efforts of peer expatriates, parent firms and host organizations

Volatility forecasting with the wavelet transformation algorithm GARCH model: Evidence from African stock markets

The daily returns of four African countries' stock market indices for the period January 2, 2000, to December 31, 2014, were employed to compare the GARCH(1,1) model and a newly proposed Maximal Overlap Discreet Wavelet Transform (MODWT)- GARCH(1,1) model. The results showed that although both models fit the returns data well, the forecast produced by the GARCH(1,1) model underestimates the observed returns whereas the newly proposed MODWT-GARCH(1,1) model generates an accurate forecast value of the observed returns. The results generally showed that the newly proposed MODWT-GARCH(1,1) model best fits returns series for these African countries. Hence the proposed MODWT-GARCH should be applied on other context to further verify its validity

Comparison of forecasting performance between MODWT-GARCH(1,1) and MODWT-EGARCH(1,1) models: Evidence from African stock markets

Many researchers documented that if stock markets' returns series are significantly skewed, linear-GARCH(1,1) grossly underestimates the forecast values of the returns. However, this study showed that the linear Maximal Overlap Discreet Wavelet Transform MODWT-GARCH(1,1) actually gives an accurate forecast value of the returns. The study used the daily returns of four African countries' stock market indices for the period January 2, 2000, to December 31, 2014. The Maximal Overlap Discreet Wavelet Transform-GARCH(1,1) model and the Maximal Overlap Discreet Wavelet Transform-EGARCH(1,1) model are exhaustively compared. The results show that although both models fit the returns data well, the forecast produced by the Maximal Overlap Discreet Wavelet Transform-EGARCH(1,1) model actually underestimates the observed returns whereas the Maximal Overlap Discreet Wavelet Transform-GARCH(1,1) model generates an accurate forecast value of the observed returns

A Study of Intercept Adjusted Markov Switching Vector Autoregressive Model in Economic Time Series Data

Commodity price always related to the movement of stock market index. However real economic time series data always exhibit nonlinear properties such as structural change, jumps or break in the series through time. Therefore, linear time series models are no longer suitable and Markov Switching Vector Autoregressive models which able to study the asymmetry and regime switching behavior of the data are used in the study. Intercept adjusted Markov Switching Vector Autoregressive (MSI-VAR) model is discuss and applied in the study to capture the smooth transition of the stock index changes from recession state to growth state. Results found that the dramatically changes from one state to another state are continuous smooth transition in both regimes. In addition, the 1-step prediction probability for the two regime Markov Switching model which act as the filtered probability to the actual probability of the variables is converged to the actual probability when undergo an intercept adjusted after a shift. This prove that MSI-VAR model is suitable to use in examine the changes of the economic model and able to provide significance, valid and reliable results. While oil price and gold price also proved that as a factor in affecting the stock exchange

Empirical Mode Decomposition Combined with Local Linear Quantile Regression for Automatic Boundary Correction

Empirical mode decomposition (EMD) is particularly useful in analyzing nonstationary and nonlinear time series. However, only partial data within boundaries are available because of the bounded support of the underlying time series. Consequently, the application of EMD to finite time series data results in large biases at the edges by increasing the bias and creating artificial wiggles. This study introduces a newtwo-stagemethod to automatically decrease the boundary effects present inEMD.At the first stage, local polynomial quantile regression (LLQ) is applied to provide an efficient description of the corrupted and noisy data.The remaining series is assumed to be hidden in the residuals. Hence, EMD is applied to the residuals at the second stage. The final estimate is the summation of the fitting estimates from LLQ and EMD. Simulation was conducted to assess the practical performance of the proposed method. Results show that the proposed method is superior to classical EMD

Linear Vector Error Correction Model Versus Markov Switching Vector Error Correction Model To Investigate Stock Market Behaviour

The stock market can reflect the economy of a country. The movement of the stock market index may imply the economic condition in general. The 1997 Asian Financial Crisis and the 2008 Global Economic Crisis are examples of share depressions that impacted countries’ inflation, unemployment rates and gross national product (GNP). This study investigates how oil and gold prices impact the stock exchange using a linear vector error correction model (VECM) and a Markov switching vector error correction model (MS-VECM). The results show that oil and gold prices affect the stock market returns for the four selected countries, namely Malaysia, Singapore, Thailand and Indonesia. The MS-VECM is able to capture every change in the transition probabilities of the financial time series data and is more reliable than the linear VECM for examining the effect of oil and gold prices on the stock market

A Study Of Structure Breaks In Amman Stocks Market By Using Wavelet Transform.

Regime shifts or structure breaks acquire very high attention in analyzing financial time series data