TIME SERIES FORECAST MODELS FOR FOREIGN EXCHANGE MARKET IN A DEVELOPING ECONOMY

Technicalities in foreign exchange forecasting have been of interest to investors and academia, particularly in a developing economy. Data of foreign exchange are time series in nature and several techniques have been developed to modeling and forecasting foreign exchange rates. In this study, Nigeria's foreign exchange rate against three world-leading currencies (US Dollars, Euro and Pounds Sterling) are modeled with ARIMA, Auto. arima, Box-Cox, random walk forecast, and Exponential Smoothing and subjected to comparative tests using  Diebold-Mariano criteria with a modern model time series model. The empirical analysis shows that that the modern model outperforms some of the other techniques in forecasting Nigeria exchange rates against world-leading currencies particularly when the forecast horizon is low

Forecasting Nigeria Foreign Exchange Risk with Extreme Value Theory

Foreign exchange forecasting is important in all forms of foreign investments and transactions, the skills which compliments the field of finance and related disciplines. This paper uses extreme value theory to estimate the Value-at-Risk (VaR) and Expected Shortfall (ES), by fitting negative log returns of Nigeria Naira (NGN) against nine other regional and world currencies into the Generalized Pareto Model. In this paper VaR is being used to determine daily foreign exchange risk on investments and the ES is used to determine the average risk over a period of time. Excess distribution and the tail of the underlying distribution were obtained over a required threshold. Empirical analysis shows that parameter estimate of the underlying distribution can be used to describe the performance of the VaR and ES. The findings contributes to the knowledge on foreign exchange forecasting and helps investors and policy makers in Nigeria to measure daily and possible risk over a period of time on certain investments

Using Extreme Value Theory to Model Insurance Risk of Nigeria's Motor Industrial Class of Business

Extreme losses have been recorded in Nigeria insurance companies due to motor insurance class claims; Nigeria Insurance market being a developing one requires building the confidence of the public to subscribe to their products. Nigeria’s motor industrial insurance claim data for five insurance companies in a two year period is modelled in this paper with extreme value theory (EVT) to estimate the Value-at-Risk (VaR), where VaR gives estimate of the minimum amount of claims an insurance company would pay in a given period of time. The time series plot was obtained which aimed at capturing the trend of the claims over the two-year period, the mean excess plot was obtained which helped to determine threshold and the shape of the distribution in the tail area. The returns were then fitted in a Generalized Pareto model (GPD), a similar model that would have been used is the Generalized Extreme Value model (GEV) but the GPD is used in this study because it describes what happens in the tail area of the distribution and not just the maximum tail. A linear Q-Q plot reveals that parametric model fits the data well. VaR estimate was finally obtained using the extreme value method and other two methods of Historical and Gaussian at 5% confidence interval. The three methods of estimating VaR were compared and the empirical result shows that extreme VaR is most suitable to calculate VaR as compared to the Historical and Gaussian method

EXPLORING THE METHODS OF COINTEGRATION PROCEDURES USING STOCK PRICES

Stationary models are an essential class of stochastic models for describing time series data which have received a great attention. In reality, however, business and economic data are non-stationary multivariate time series that are often better understood by cointegration analysis. This study investigates the cointegration testing methods of Engle-Granger two-step estimation technique, Phillip-Ouliaris test, and Johansen's multivariate test. The stock prices of selected companies in Nigeria from 2008-2014 are used in the study. Findings revealed that the three techniques produced different results and that the Johansen's method and Engle-Granger two steps procedure exhibits higher efficiencies than Phillips-Ouliaris methods but their efficiency is dependent on the number of variables and correct selection

Knowledge, Attitude, and Perception of Health and Non-Healthcare Workers Towards COVID-19 Vaccination: Machine Learning Approach

There have been concerns globally as to whether taking COVID-19 vaccination is harmful or not. In this study, we conducted an online survey to measure the knowledge and attitude of people, first about COVID-19, and second about COVID-19 vaccination—various analyses such as descriptive statistics, logistic regression, and support vector regression with k-fold cross-validation. The support vector machine and tuned support vector machine suggest a better fit based on cross-validation error. The results show that immigration requirements significantly explain why an individual would accept the COVID-19 vaccine. This study suggests that people in authority should look into people's concerns regarding taking the COVID-19 vaccine and address them accordingly. The study aims to draw the attention of the people to the concern that surrounds taking COVID-19 vaccination and explored various statistical techniques to draw inference

Bayesian Models for Zero Truncated Count Data. Asian Journal of Probability and Statistics.

It is important to fit count data with suitable model(s), models such as Poisson Regression, Quassi Poisson, Negative Binomial, to mention but a few have been adopted by researchers to fit zero truncated count data in the past. In recent times, dedicated models for fitting zero truncated count data have been developed, and they are considered sufficient. This study proposed Bayesian multi-level Poisson and Bayesian multi-level Geometric model, Bayesian Monte Carlo Markov Chain Generalized linear Mixed Models (MCMCglmms) of zero truncated Poisson and MCMCglmms Poisson regression model to fit health count data that is truncated at zero. Suitable model selection criteria were used to determine preferred models for fitting zero truncated data. Results obtained showed that Bayesian multi-level Poisson outperformed Bayesian multi-level Poisson Geometric model; also MCMCglmms of zero truncated Poisson outperformed MCMCglmms Poisson

Exploring Robust Methods for Testing Equality of Stock Price Variances

Assessment and test of equality of volatilities of stocks with similar returns is important for decision makers in their portfolio selection. Volatility estimation is equally important for pricing stocks and derivative securities. In this study, three robust tests for homogeneity of variance are considered in the selection of stocks under the conditions of high kurtosis and large skewness. Investigations involved the effectiveness of the Ballett’s test, robust methods of Levene's Test and Fligner-Killeen test in the test of homogeneity of variance of stocks. Simulated and insurance stock data were employed and empirical results findings show that the three test statistics are equally efficient in testing homogeneity of variance even as the datasets deviates from normality

Radiation Effects: Recommendations for Safe Plasma/Flame Cutting Operation

Plasma cutting has been a revolutionary method of processing metals as it provides precision cutting with a smooth finish. The Plasma Arc Cutting machine is an important machine used for producing fine cuts and creating shapes in materials. In addition to high energy radiation (ultraviolet and visible) which plasma arc cutting generates, the intense heat of the arc also generates substantial quantities of fumes and smoke from vaporizing metal in the kerf. With a reflection on a case study, this paper examines the working processes of the machine and the effect on the health of operators or any unsuspecting member of the public. With the safety regulations and recommendations from certified bodies, recommendations are made to ensure safety and limitations of health hazards during the use of the machine

On modelling tail risk of electrical energy production level

Fitting the correct type of model to a particular set of data is key to proffering solution to bugging issues. Electricity production in Nigeria has been faced with various challenges for over ten decades since the first production and supply. Consequently, there is a need to measure electricity production risk. Extreme Value Theory (EVT) is considered sufficient in measuring such risk by modelling tails of the distribution. Adopting EVT, there is a need to measure Value-at-risk and Expected Shortfall which can be adequately done with Generalized Pareto Distribution (GPD); one of the models for extreme events. The preference for GPD is because it models the distribution of exceedances over a high threshold rather than the individual observations. In this study, diagnostics tests were carried out in order to determine the suitability of GPD for fitting the data, and GPD was found adequate modelling future risk of electricity production for the given data. The GPD was then used to fit the electricity production data in Nigeria at 1%, 0.5%, and 0.1% probability. Following the result, measures to avoid electricity production risks were recommended

A Lotka-Volterra Non-linear Differential Equation Model for Evaluating Tick Parasitism in Canine Population

This research employs a modified version of the Lotka-Volterra non-linear first-order ordinary differential equations to model and analyze the parasitic impact of ticks on dogs. The analysis reveals that fluctuations in pesticide effects significantly influence tick populations and the size of the canine host. The study also uncovers that alterations in the size of the interacting species can lead to both stable and unstable states. Interestingly, in a pesticide-free environment, a decline in the inter-competition coefficient catalyzes an increase in the sizes of both interacting species. This increase, although marginal for the tick population, contributes to overall system stability. The findings underscore the utility of the Lotka-Volterra non-linear first-order ordinary differential equations in modeling the parasitic effect of ticks on dogs. To protect pets, particularly dogs, from the harmful effects of tick infestation, this study recommends the appropriate and regular application of disinfectants