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Siti Meriam Zahari
Universiti Teknologi MARA Shah Alam

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Modelling volatility of Kuala Lumpur composite index (KLCI) using SV and garch models Ezatul Akma Abdullah; Siti Meriam Zahari; S.Sarifah Radiah Shariff; Muhammad Asmu’i Abdul Rahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 3: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i3.pp1087-1094

Abstract

It is well-known that financial time series exhibits changing variance and this can have important consequences in formulating economic or financial decisions. In much recent evidence shows that volatility of financial assets is not constant, but rather that relatively volatile periods alternate with more tranquil ones. Thus, there are many opportunities to obtain forecasts of this time-varying risk. The paper presents the modelling volatility of the Kuala Lumpur Composite Index (KLCI) using SV and GARCH models.  Thus, the aim of this study is to model the KLCI stock market using two models; Stochastic Volatility (SV) and Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH). This study employs an SV model with Bayesian approach and Markov Chain Monte Carlo (MCMC) sampler; and GARCH model with MLE estimator. The best model will be used to forecast the future volatility of stock returns. The study involves 971 daily observations of KLCI Closing price index, from 2 January 2008 to 10 November 2016, excluding public holidays. SV model is found to be the best based on the lowest RMSE and MAE values.
Fuzzy time series forecasting in determining inventory policy for a small medium enterprise (SME) company S.Sarifah Radiah Shariff; Nurul Nadiah Abdul Halim; Siti Meriam Zahari; Zuraidah Derasit
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i3.pp1654-1660

Abstract

Fuzzy time series models have been widely used to handle forecasting problems, such as forecasting consumer demand and production volume. It is of greater benefits if we have good forecasting accuracy rates especially in managing inventory in a Small and Medium Enterprise (SME) company.  This study focuses on multiple products with single production line.  The aims of this study are to propose the appropriate the forecasting method for the products, to develop new inventory policy that minimizes the total inventory cost for the company.  Simple forecasting methods like trend line, three month moving average (MA (3) and fuzzy time series forecasting are used in this study.  The result shows that fuzzy time series forecasting model is suitable to be used in forecasting future demand for all products.  The proposed inventory policy is based on the number of cycle per year and the number of production for each product has helped the company to minimize total inventory cost and schedule the production process accordingly.  The proposed inventory policy resulted in lower total inventory cost when compared to current practice. 
Co-Authors Ezatul Akma Abdullah Muhammad Asmu’i Abdul Rahim Nurul Nadiah Abdul Halim S.Sarifah Radiah Shariff S.Sarifah Radiah Shariff Zuraidah Derasit