This study investigates the time series beaviour of daily stock returns of four firms listed in the Nigerian Stock Market from 2nd January, 2002 to 31st December, 2006, using three different models of heteroscedastic processes, namely: GARCH (1,1), EGARCH (1,1) and GJR-GARCH models respectively. The four firms whose share prices were used in this analysis are UBA, Unilever, Guiness and Mobil. All the return series exhibit leverage effect, leptokurtosis, volatility clustering and negative skewness, which are common to most economic financial time series. Except for Guiness, other series display significant level of second-order autocorrelation, satisfying covariance-stationary condition. These models were estimated assuming a Gaussian distribution using Brendt-Hall-Hall-Hausman (BHHH) algorithm's program in Eview software platform. The estimation results reveal that the GJR-GARCH (1, 1) gives better fit to the data and are found to be superior both in-sample and out-sample forecasts evaluation.

One of the key objectives of every good economy, whether or not developing or developed is to achieve a high and sustainable economic growth rate coupled with the economic indicators. The research on the Multivariate Time Series Modeling of Major Economic Indicators in Nigeria, aims at providing quantitative analysis of the dynamics on currency in circulation, exchange rate, external reserve, gross domestic product, money supply and price deflator. This study utilizes secondary data obtained from the Central Bank of Nigeria, Statistical Bulletin (vol. 21: 2010), of all variables investigated in the model. The sample covers quarterly data from 1981 to 2010. The study employed the newly developed multivariate time series estimation technique via Vector Autoregressive modeling to model the economic indicators in Nigeria. The empirical result yields a stable and sustainable economic model for the six economic variables in the study. The Granger causality analysis indicates that there exists unidirectional and bidirectional causality between the economic variables. Gross domestic product and external reserve was seen as a good predictor to other economic indicators. The relationship between these economic indicators is however significantly determined which is positive in either direction. The empirical model provides forecast value for the next two years.

This study examines the long run behaviour of the closing prices of the Nigerian bank stocks using Markov Chain. A total of eight (8) Nigerian bank stocks were randomly selected and data on their daily closing prices between 1st January, 2004 and 29th May, 2013 were collected secondarily through cashcrasft website. Then, variations in daily closing prices were classified into three states Markov Chain of drop, rise and stable. If the closing at day t+1 is greater than that of the day t, it was classified as rise, if that of the day t+1 is less than that of the day t, it was classified as drop but when the closing price for day t is same as that of the day t+1, it was classified as stable. Based on these classifications, transition probabilities, the limiting distribution of the transition matrix as well as the steady state transition probabilities were computed for each stock. Finding suggests that despite the current situation in the market, there is still hope for Nigerian bank stocks as some of these bank stocks tend to experience an increase in price in the long run as shown by the results of the steady state probability. Although, this finding is very informative and crucial to investors, stock brokers and other regulator in this sector, this finding is subject to unforeseen circumstances such as change in government policy, among many other factors. Despite these limitations, it is hope that the results of this study will very useful to investors, intending investors and other relevant stakeholders who are involve in stock trading.

Increasing the parameter of a distribution helps to capture the skewness and peakedness characteristic in the data sets. This allows a more realistic modeling of data arising from different real life situations. In this paper, we modified Laplace distribution using the exponentiation method. The study proved that the modified Laplace distribution (MLD) is a probability density function. Some of the basic statistical properties of the modified Laplace distribution are obtained. We applied the proposed modified Laplace distribution on two life datasets and simulated data. Parameters of the distributions were estimated using method of maximum likelihood estimation. The study compared the modified Laplace distribution with Laplace distribution and Generalized error distribution using Schwartz Criteria (SC) measure of fitness. The results obtained revealed that the modified Laplace distribution has a better fit than the Laplace and Generalized error distributions and can be used for more realistic modeling of data arising from different real life situations. The simulation results obtained shows that as the sample size increases, the Biasedness and Root Mean Square Error (RMSE) of the proposed modified Laplace distribution reduces.

Single-linkage is one of the methods in cluster analysis, which is used, for determining natural groupings in multi-variate data. Given a data set with one or more characteristics, singlelinkage system classifies the data into clusters so that they are as similar as possible within each cluster and as different as possible between clusters. The objective is to show the closeness or similarity in the growth rate of GDP. Using the MINITAB software the similarity of the growth rate of GDP and the similarity in the years of production were shown.