Browsing by Author "Nweze, N.O."

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    Analysis of the Effects of Climate Change on Jamila Rice Output in Kaduna-Nigeria
    (Department of Statistics, Nasarawa Sate University Keffi., 2018-01-01) Adehi, Mary Unekwu; Nweze, N.O.; Chaku, Shammah Emmanuel
    The effect of climate change factors and non- climate change factors on Jamila Rice output have been investigated, using secondary sources of time series annual data obtained from Zaria and Rumi area of Kaduna State for the period of 1980-2013. The error correction mechanism was analyzed, and it was shown that in the short run, only rainfall tested significantly positive to rice output among the climate change factors. In the long run, temperature, carbon dioxide emission, carbon emission and rainfall tested significantly to rice output. There is a need to formulate policies that will aid farmers towards adaptation practices to mitigate the effects of climate change and motivate them to increase their involvement in rice production.
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    A Class of Block Multi-Step Methods for the Solutions of Ordinary Differential Equation (Ode)
    (Department of Mathematical Science, Nasarawa State University Keffi., 2016-04-13) Nweze, N.O.; Chaku, Shammah Emmanuel; Offiong, M.N.; Maijamaa, Bilkisu
    In this research, an attempt is made to derive a self starting block procedure for some K-step linear multi-step methods (for K=1, 2 and 3), using Chebyshev polynomial as the basis function. The continuous interpolant were derived and collocated at grid and off-grid points to give the discrete methods used in block and applied simultaneously for the solution of non stiff initial value problem.The regions of absolute stability of the methods are plotted and are shown to be A (α) stable. The methods for K=2 and K=3 were experimented on initial value problems and the results reveal that the newly constructed block methods have good error stability and are efficient.
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    Coronavirus Disease (Covid-19), is Global Recession Evitable?
    (Department of Statistics, Nasarawa State University Keffi., 2020-04-21) Maijamaa, Bilkisu; Nweze, N.O.; Bagudu, Hauwa Daniyan
    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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    MEAN PARAMETER MODELING FOR AN AUTOMOBILE INSURANCE PORTFOLIO USING GENERALIZED ADDITIVE MODELS FOR LOCATION, SCALE AND SHAPE (GAMLSS)
    (Department of Statistics, Nasarawa Sate University Keffi., 2020-01-08) Chaku, Shammah Emmanuel; Adenomon, Monday Osagie; Nweze, N.O.
    In this study, Generalized Additive Models for location, scale and shape was deployed to model a typical automobile insurance portfolio. The data set used for this study comprises of seven variables, which are: Kilometres, Zone, Bonus, Make, Insured, Claims and Payments, it was compiled by a Swedish Committee on the Analysis of Risk Premium in Motor Insurance. The mean was modeled in terms of the explanatory variables although the GAMLSS has the capacity to model up to four parameters unlike the Generalized Lineal Models (GLMs) and Generalized Additive Models (GAMs). This allows for greater flexibility in modeling. In checking for over-dispersion, the negative binomial was used such that terms were dropped or added. Analysis revealed that all term were important and as such no terms could be dropped. When terms were added, analysis further showed that all the two way interaction terms are needed in the model except for the interaction between Kilometers and Zone. Results from the optimal model check gives the best model as those with separate smoothing terms for both Bonus and Kilometers.
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    MODELING AND FORECASTING DAILY STOCK RETURNS OF GUARANTY TRUST BANK NIGERIA PLC USING ARMA-GARCH MODELS, PERSISTENCE, HALF-LIFE VOLATILITY AND BACKTESTING
    (Department of Statistics, Nasarawa Sate University Keffi., 2019-06-05) Emenogu, N.G.; Adenomon, Monday Osagie; Nweze, N.O.
    This study investigated the forecasting ability of GARCH family models, and to achieve superior and more reliable models for volatility persistence, half-life volatility and backtesting, the study combined the ARMA and GARCH models. The study modeled and forecasted the Guaranty Trust Bank (GTB) daily stock returns using data from January 2, 2001 to May 8, 2017 obtained from a secondary source. The ARMA-GARCH models, persistence, half- life and backtesting were used to analyse the data using student t and skewed student t distributions, and the analyses were carried out in R environment using rugarch and performanceAnaytics Packages. The study revealed that using the lowest information criteria values alone could be misleading so backtesing was also carried out. The ARMA(1,1)-GARCH(1,1) models fitted exhibited high persistency in the daily stock returns while it took about 6 days for mean-reverting of the models, but failed backtesting. However, backtesting showed that ARMA(1,1)-eGARCH(2,2) model with student t distribution passed the test and was suitable for evaluating the GTB stock returns, and required about 16 days for the persistence volatility to return to its average value of the stock returns. The study recommended addition of backtesting approach in evaluating the performance of GARCH model in order to avoid misleading results. Also, the GTB stocks can be predicted since most of the estimated models were stable.
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    Modeling Of Species Interaction in a Habitat Using Lotka- Volterra Type Systems
    (Department of Statistics, Nasarawa Sate University Keffi., 2018-01-01) Nweze, N.O.; Offiong, N.M.; Adehi, Mary Unekwu; Chaku, Shammah Emmanuel; Abdullahi, A.S.; Muhammad, Mahammad N.
    Mathematical models have been useful in the area of modeling of real life situations; its application can be found in virtually all spheres of scientific researches. As such, we adopt its use in the field of ecology where preys have to compete with other prey for survival. In this paper, we considered Lotka-Volterra type systems, consisting of two first order differential equations which were used to model the population size of prey–predator interaction. We also proposed a system of first order differential equations to model the population sizes of a prey and two predators. Under these conditions one of the predators dies out while the remaining predator and prey approach periodic behavior as time increases. Also we model the population size of two preys and one predator where there may be interaction between the preys. Under these conditions we found that one of the preys died out while the remaining preys and predators approached periodic behavior as time increased. For critical cases, each positive solution of the system was seen to be periodic in nature. Various examples and results were presented and further study was proposed.
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    On the performance of GARCH family models using the root mean square error and the mean absolute error
    (Department of Statistics, Nasarawa Sate University Keffi., 2018-02-02) Emenogu, N.G.; Adenomon, Monday Osagie; Nweze, N.O.
    It is a common practice to detect outliers in a financial time series in order to avoid the adverse effect of additive outliers. This paper investigated the performance of GARCH family models (sGARCH; gjrGARCH; iGARCH; TGARCH and NGARCH) in the presence of outliers (small, medium and large) for different time series lengths (250, 500, 750, 1000, 1250 and 1500) using the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). In a simulation iteration of 1000 times in R environment using rugarch, results revealed that for small size of outliers, irrespective of the length of time series, iGARCH was superior, for medium size of outliers, it was sGARCH and gjrGARCH that were superior irrespective of the time series length, and for a large size of outliers, irrespective of the time series length, gjrGARCH was superior. The study leveled that in the presence of additive outliers, both RMSE and MAE values would increase as the time series length is increased.
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    On the volatility of daily stock returns of Total Nigeria Plc: evidence from GARCH models, value-at-risk and backtesting
    (Department of Statistics, Nasarawa Sate University Keffi., 2020-08-06) Emenogu, N.G.; Adenomon, Monday Osagie; Nweze, N.O.
    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.
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    Portfolio Management: Allowing Short Selling Using the Lagrange Multiplier Method in R
    (Department of Statistics, Nasarawa Sate University Keffi., 2019-03-29) Nweze, N.O.; Chaku, Shammah Emmanuel; Adehi, Mary Unekwu; Sulaiman, Alhaji Ismaila
    In this study, an inquest was carried out into the portfolio theory using historical prices of three stocks Astrazeneca Limited (AZN.L), British Telecommunications (BT-A.L) and United Utilities (UU.L) from the dates 2nd of January, 2018 to 31st of December, 2018. The data set was combined and edited leaving only the trading dates (Volume>0) in chronological order. The price of each stock was plotted against the trading days, the daily net returns for each stock and estimates of the mean, standard deviation and correlation between stocks were obtained. A graph of the efficient frontier and minimum variance portfolio was plotted after which the lagrange multiplier method was employed allowing short selling to decide how to invest in these assets to achieve a reasonable target return. Correlations between stocks were obtained and it was observed that stock prices of the Telecommunications and Water companies are more correlated, probably due to the fact that their products are consumed more regularly. The bold part of the efficient frontier plot indicates the efficient frontier, while the leftmost part of the curve is the Minimum Variance Portfolio (MVP) obtained with the help of the Lagrange multiplier method. Finally, Astra stocks was short and monies re- invested in BT and United Utilities.