Mohd Tahir Ismail

Modeling The Interactions Of Stock Price And Exchange Rate In Malaysia

After the East Asian crisis in 1997, the issue of whether stock prices and exchange rates are related or not have received much attention. This is due to realization that during the crisis the countries affected saw turmoil in both their currencies and stock markets. This paper studies the non-linear interactions between stock price and exchange rate in Malaysia using a two regimes multivariate Markov switching vector autoregression (MS-VAR) model with regime shifts in both the mean and the variance. In the study, the Kuala Lumpur Composite Index (KLCI) and the exchange rates of Malaysia ringgit against four other countries namely the Singapore dollar, the Japanese yen, the British pound sterling and the Australian dollar between 1990 and 2005 are used. The empirical results show that all the series are not cointegrated but the MS-VAR model with two regimes manage to detect common regime shifts behavior in all the series. The estimated MS-VAR model reveals that as the stock price index falls the exchange rates depreciate and when the stock price index gains the exchange rates appreciate. In addition, the MS-VAR model fitted the data better than the linear vector autoregressive model (VAR).

Robust Wavelet Estimation to Eliminate Simultaneously the Effects of Boundary Problems, Outliers, and Correlated Noise

Classical wavelet thresholding methods suffer from boundary problems caused by the application of the wavelet transformations to a finite signal. As a result, large bias at the edges and artificial wiggles occur when the classical boundary assumptions are not satisfied. Although polynomial wavelet regression and local polynomial wavelet regression effectively reduce the risk of this problem, the estimates from these two methods can be easily affected by the presence of correlated noise and outliers, giving inaccurate estimates. This paper introduces two robust methods in which the effects of boundary problems, outliers, and correlated noise are simultaneously taken into account. The proposed methods combine thresholding estimator with either a local polynomial model or a polynomial model using the generalized least squares method instead of the ordinary one. A primary step that involves removing the outlying observations through a statistical function is considered as well. The practical performance of the proposed methods has been evaluated through simulation experiments and real data examples. The results are strong evidence that the proposed method is extremely effective in terms of correcting the boundary bias and eliminating the effects of outliers and correlated noise.

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.

Assessing the responses of physical parameters in ocean via statistical approach

It is essential to assess a physical parameter in response to the changes in other physical parameter. Exploration on the type of relationship between two physical parameters depends on models and expert view due to the complexity of the ecosystems. These need validation with actual data over a certain periods. Innovative statistical approaches, particularly the nonparametric regression is presented to investigate the ecological relationships. These are achieved by demonstrating the features of salinity, conductivity and temperature at a sampling point in Selat Tebrau. Observed data monitored for 10 years from 2004–2013 are examined. Testing for no-effect and linearity for salinity and temperature; log conductivity and temperature, and log conductivity and salinity, with the ecological objectives of investigating the evidence of changes in each of the above physical parameter. The results show the appropriateness of smooth function to explain the variation of salinity in response to the changes in tempera...

Modelling nonlinear relationship among vegetable oil price time series

The study of commodity price behaviour has attracts the attention of many economists and finance specialists. This is due to the fact that many less developed countries rely on the revenues generated by the commodity exports. In this paper, the nonlinear relationship because of regime shifts in four vegetable oil price series was investigated. The multivariate Markov switching vector autoregressive (MS-VAR) model with regime shifts in both the mean and the variance was employed to capture common regime shifts behaviour among the four price series. Results revealed that all the series demonstrate common regime shifts trend of declining and increasing. In addition, the MS-VAR model fitted the data better than the linear vector autoregressive model (VAR).

A hybrid approach EMD-HW for short-term forecasting of daily stock market time series data

Recently, forecasting time series has attracted considerable attention in the field of analyzing financial time series data, specifically within the stock market index. Moreover, stock market forecasting is a challenging area of financial time-series forecasting. In this study, a hybrid methodology between Empirical Mode Decomposition with the Moving Average Model (EMD-MA) is used to improve forecasting performances in financial time series. The strength of this EMD-MA lies in its ability to forecast non-stationary and non-linear time series without a need to use any transformation method. Moreover, EMD-MA has a relatively high accuracy and offers a new forecasting method in time series. The daily stock market time series data of 10 countries is applied to show the forecasting performance of the proposed EMD-MA. Based on the five forecast accuracy measures, the results indicate that EMD-MA forecasting performance is superior to traditional Moving Average forecasting model.

Application of Empirical Mode Decomposition with Local Linear Quantile Regression in Financial Time Series Forecasting

This paper mainly forecasts the daily closing price of stock markets. We propose a two-stage technique that combines the empirical mode decomposition (EMD) with nonparametric methods of local linear quantile (LLQ). We use the proposed technique, EMD-LLQ, to forecast two stock index time series. Detailed experiments are implemented for the proposed method, in which EMD-LPQ, EMD, and Holt-Winter methods are compared. The proposed EMD-LPQ model is determined to be superior to the EMD and Holt-Winter methods in predicting the stock closing prices.

A study of stationarity in time series by using wavelet transform

In this work the core objective is to apply discrete wavelet transform (DWT) functions namely Haar, Daubechies, Symmlet, Coiflet and discrete approximation of the meyer wavelets in non-stationary financial time series data from US stock market (DJIA30). The data consists of 2048 daily data of closing index starting from December 17, 2004 until October 23, 2012. From the unit root test the results show that the data is non stationary in the level. In order to study the stationarity of a time series, the autocorrelation function (ACF) is used. Results indicate that, Haar function is the lowest function to obtain noisy series as compared to Daubechies, Symmlet, Coiflet and discrete approximation of the meyer wavelets. In addition, the original data after decomposition by DWT is less noisy series than decomposition by DWT for return time series.

Denoising solar radiation data using coiflet wavelets

Signal denoising and smoothing plays an important role in processing the given signal either from experiment or data collection through observations. Data collection usually was mixed between true data and some error or noise. This noise might be coming from the apparatus to measure or collect the data or human error in handling the data. Normally before the data is use for further processing purposes, the unwanted noise need to be filtered out. One of the efficient methods that can be used to filter the data is wavelet transform. Due to the fact that the received solar radiation data fluctuates according to time, there exist few unwanted oscillation namely noise and it must be filtered out before the data is used for developing mathematical model. In order to apply denoising using wavelet transform (WT), the thresholding values need to be calculated. In this paper the new thresholding approach is proposed. The coiflet2 wavelet with variation diminishing 4 is utilized for our purpose. From numerical results it can be seen clearly that, the new thresholding approach give better results as compare with existing approach namely global thresholding value.

Impact of Global Financial Crisis on Precious Metals Returns: An Application of ARCH and GARCH Methods

This paper is focusing on seeing the resilient of precious metals returns in facing the global financial crisis and provides a new guide for the investors before making investment decisions on precious metals. Four types of precious metals returns which are the variables selected in this study. The precious metals are gold, silver, bronze and platinum. All the variables are transferred to natural logarithm (ln). Daily data over the period 2 January 1995 to 30 December 2011 is used. Unit root tests that involve Augmented Dickey- Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests have been employed in determining the stationarity of the variables. Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) methods have been applied in measuring the impact of global financial crisis on precious metals returns. The result shows that investing in platinum is less risky compared to the other precious metals because it is not influence by the crisis period.