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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.

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<p>Road accidents continuously become a major problem in Malaysia and consequently cause loss of life or property. Due to that, many road accident data have been collected by highway concessionaries or build–operate–transfer operating companies in the country meant for coming up with proper counter measures. Several analyses can be done on the accumulated data in order to improve road safety. In this study the reported road accidents cases in North South Expressway (NSE) from Sungai Petani to Bukit Lanjan during 2011 to 2014 period is analyzed. The aim is to determine whether the pattern is clustered at certain area and to identify spatial pattern of hot spots across this longest controlled-access expressway in Malaysia as hotspot represents the location of the road which is considered high risk and the probability of traffic accidents in relation to the level of risk in the surrounding areas. As no methodology for identifying hotspot has been agreed globally yet; hence this study helped determining the suitable principles and techniques for determination of the hotspot on Malaysian highways. Two spatial analysis techniques were applied, Nearest Neighborhood Hierarchical (NNH) Clustering and Spatial Temporal Clustering, using CrimeStat® and visualizing in ArcGIS™ software to calculate the concentration of the incidents and the results are compared based on their accuracies. Results identified several hotspots and showed that they varied in number and locations, depending on their parameter values. Further analysis on selected hot spot location showed that Spatial Temporal Clustering (STAC) has a higher accuracy index compared to Nearest Neighbor Hierarchical Clustering (NNH). Several recommendations on counter measures have also been proposed based on the details results.</p>