This study investigates the volatility in daily stock returns for Total Nigeria Plc using nine variants of GARCH models: sGARCH, girGARCH, eGARCH, iGARCH, aGARCH, TGARCH, NGARCH, NAGARCH, and AVGARCH along with value at risk estimation and backtesting. We use daily data for Total Nigeria Plc returns for the period January 2, 2001 to May 8, 2017, and conclude that eGARCH and sGARCH perform better for normal innovations while NGARCH performs better for student t innovations. This investigation of the volatility, VaR, and backtesting of the daily stock price of Total Nigeria Plc is important as most previous studies covering the Nigerian stock market have not paid much attention to the application of backtesting as a primary approach. We found from the results of the estimations that the persistence of the GARCH models are stable except for few cases for which iGARCH and eGARCH were unstable. Additionally, for student t innovation, the sGARCH and girGARCH models failed to converge; the mean reverting number of days for returns differed from model to model. From the analysis of VaR and its backtesting, this study recommends shareholders and investors continue their business with Total Nigeria Plc because possible losses may be overcome in the future by improvements in stock prices. Furthermore, risk was reflected by significant up and down movement in the stock price at a 99% confidence level, suggesting that high risk brings a high return.

COVID-19 (Coronavirus Disease-2019) is regarded as a public health emergency of international concern. Patients contracting the severe form of the disease constitute approximately 15% of the cases [WHO). The covid-19 is affecting 203 countries and territories around the world. An epidemiological threat such as COVID-19 can have destructive effect on the economy.it is of great importance not to focus only on the epidemiological profile of the virus but also its impact on the economy. As much as economists think about risk-taking as a key driver of the economy, an economy only works if risks are largely known. With the impact of the covid-19 on travel services, durable expenditure, on supply chain and on social isolation (high skilled working from home, home schooling) and impact on demand and supply. On the bases of the listed impact on the economy global recession seems inevitable, there is also possibility of emerging markets. The overall demand effect is probably higher than the initial supply shock. There will be uncertainties, panic, a lot of panic buying and lock-down policies is a key to drive large drop in demand. The investment in a lot of firms especially the small and young firms, spending for households such as rent and mortgagor’s depend largely on cash flow. Large drop in demand will lead to force closure in a lot of firms and this will lead to an increase in lay-offs and hence further drop in consumption, and sadly the economy leads to depressing loop.

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In this article, we studied the comparative model profile of COVID-19 occurrence in Nigeria. The model was analyzed using Simple Regression Model in GRETL. The data adopted a non-stationary time series forecasting approach. We used the Mean Error (ME), Mean Square Error (MSE) and Root Mean Square Error (RMSE) to determine the performance measures that fit the COVID-19 confirmed cases Nigeria. The model was used Linear, Quadratic and Exponential Model to determine the best performing model for predicting COVID-19 cases. The findings showed that the RMSE value (84.60) for the linear model is the smallest compared to the RMSE of quadratic and exponential models with values 32492.29 and 136.32 respectively. This showed that the linear model was the best model that fitted COVID-19 cases in Nigeria both in terms of data fit and for prediction purposes. Moreover, the results also showed that the figure of the actual cases were above the predicted cases in all the three models, which could be as a result of some parameters like infectious contact rate or inaccurate COVID-19 cases data.

Time series of count with over-dispersion is the reality often encountered in many biomedical and public health applications.  Statistical modelling of this type of series has been a great challenge. Rottenly, the Poisson and negative binomial distributions have been widely used in practice for discrete count time series data, their forms are too simplistic to accommodate features such as over-dispersion. Unable to account for these associated features while analyzing such data may result in incorrect and sometimes misleading inferences as well as detection of spurious associations. Therefore, the need for further investigation of count time series models suitable to fit count time series with over-dispersion of different level. The study therefore proposed a best model that can fit and forecast time series count data with different levels of over-dispersion and sample sizes Simulation studies were conducted using R statistical package, to investigate the performances of Autoregressiove Conditional Poisson (ACP) and Poisson Autoregressive (PAR) models. The predictive ability of the models were observed at different steps ahead. The relative performance of the models were examined using Akaike Information criteria (AIC) and Hannan-Quinn Information Criteria (HQIC). Conclusively, the best model to fit was ACP at different sample sizes. The predictive abilities of the four fitted models increased as sample size and number of steps ahead were increased