Bakri Abdul Karim

Does trade matter for stock market integration

Purpose - The purpose of this paper is to re-examine the stock market integration and short-run dynamic interactions between the Malaysian stock market and the stock markets of its major trading partners (the USA, Japan, Singapore, China and Thailand). Design/methodology/approach - Weekly stock indices spanning from January 1992 to May 2008 is analysed using autoregressive distributed lag (ARDL) bound testing approach and vector autoregression (VAR) framework. Findings - Stock markets of Malaysia and its major trading partners are found to be integrated. To some extent, it is found that trade does matter for stock market integration. Additional, geographical proximity and close relationship between the countries further contributes towards a greater integration between them. To move forward to a greater financial integration among these countries, trade liberalisation, including reduction or removal of trade and investment barriers would be necessary. Originality/value - This paper is among the first attempts to use ARDL and VAR frameworks to examine integration among the stock markets of Malaysia and its major trading partners. The findings of the study would shed some empirical lights for the purpose of policy making.

International Linkages among Stock Markets of Malaysia and Its Major Trading Partners

This study empirically examines the short- and long-run dynamic causal linkages between Malaysia and its major trading partners (the United States, Japan, Singapore, China, and Thailand) based on a two-step estimation, Autoregressive distributed lag (ARDL) and Generalized Method of Moments (GMM) during the period 1992–2008. The study documents that the stronger the trade ties among the countries, the higher the degree of comovements among their stock markets. The Japanese stock market, to some extent, is found to be more important than the United States over these markets. In designing stock market policies, each country should take into consideration of any shocks in its major trading partners.

Wavelet Transform and Fast Fourier Transform for signal compression: A comparative study

Wavelet and Fourier transform are the common methods used in signal and image compression. Wavelet transform (WT) are very powerful compared to Fourier transform (FT) because its ability to describe any type of signals both in time and frequency domain simultaneously while for FT, it describes a signal from time domain to frequency domain. Because of that, the performance of FT is outperformed by the impressive ability of WT for most type of signals (stationary or non-stationary). In this paper, we will discuss the use of Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) for signal compression. We do the numerical experiment by considering three types of signals and by applying FFT and DWT to decompose those signals. For DWT, various wavelet filters such as Haar (2 filters) and Daubechies (up to 10 filters) are used. All the numerical results were done by using Matlab programming.

Driving forces of the ASEAN-5 stock markets integration

Purpose – The purpose of this study is to examine the determinants of stock market integration among five selected Association of Southeast Asian Nations (ASEAN) emerging stock markets (Malaysia, Thailand, Indonesia, the Philippines and Singapore). Design/methodology/approach – Both pooled OLS and panel data regressions were used over the period spanning from 2001 to 2010. Findings – This study finds that trade and stock markets volatility significantly influence stock market integration in this region. This finding is consistent with the view that the stronger the bilateral trade ties among the countries, the higher the degree of co-movements. In addition, if one markets volatility increases relative to another markets volatility, the first markets returns should also increase relative to the second markets return. Practical implications – As the ASEAN-5 stock markets are integrated, there is limited room to gain benefits from international investment diversification in the region. There is a need fo...

Solar radiation data analysis by using Daubechies wavelets

The aim of this paper is to study the possibility of using wavelet transform method for solar radiation data compression. The Daubechies wavelets were utilized for analyzing the transient nature of solar radiation. The measured solar radiation was subjected to the wavelets techniques for smoothing and filtering of data that was decomposed up to level 5. The findings from this study show that the Daubechies wavelets method can be used for filtering and smoothing of solar radiation. The results presented indicate that Daubechies 10 (D10) wavelet approach is suitable for data filtering and smoothing of the solar radiation data.

Denoising solar radiation data using Meyer wavelets

Signal processing is important in solar energy data analysis since the received solar radiation data fluctuates continuously. Some of the fluctuations can be considered as noise, and need to be filtered out before the signal will be used for other analysis. There exist various methods in order to filter the noise and one of the promising methods is wavelets transform. This paper utilized the use of wavelet transform method for solar radiation denoising. The Meyer wavelets have been utilized, instead of the usual sinusoidal or Gaussian type functions. Since Meyer wavelets are obtained directly from its Fourier transform which is in terms of sinusoidal functions, optimized Meyer wavelets may give a good indication of the solar radiation data. Results showed Heuristic Stein Unbiased Estimate of Risk (SURE) and SURE gave better denoised results as compared to Minimax and Fixed Form methods.

Image reconstruction using singular value decomposition

The singular value decomposition (SVD) is an effective tool to reconstruct the image approximately towards the original image. This paper will introduce and explores image reconstruction by applying the SVD on gray-scale image. As quality measurements, we used Compression Ratio (CR) and Root-Mean Squared Error (RMSE). The results indicated that for certain images the value of k is smaller than for other images. The value of k is defined as the rank for the closet matrix and the constant integer k can be chosen expectantly less than diagonal matrix n, and the digital image corresponding to outer product expansion, Qk still have very close to the original image.

Predicting Malaysia business cycle using wavelet analysis

Wavelet transforms are capable to decompose time series at various level which corresponds to the resolution of the decomposition. We can find the trend, cycle, noise, structural break etc. This is where wavelets are so efficient in studying characteristics of the any time series. In this present article, we study the use of wavelet (symlet 16) to detect the business cycle in Malaysia. Firstly we decompose the time series then we study the long-run trend and we filtered the high frequency components and finally we find the business cycle in Malaysia. The results indicated the existence of business cycles for GDP data in Malaysia which is strongly counter-cyclical.