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Russia and Ukraine are in a war, with the former invading the latter. This puts the latter under great stress, many have died in the process and many more have been displaced and many more have fled from Ukraine. This has resulted in intervention in many time series related to Ukraine. For example, the time series of the daily exchange rates of Ukrainian Hryvnia (UAH) and United States Dollars (USD) experienced an intervention on the first day of Russian incursion. By Box and Tiao (1975) approach, a realization of the time series from 1 January 2022 to 15 March 2022 is analyzed. The intervention model arrived at is found adequate. It can be the basis for management and planning., {"references":["Box, G. E. P. and Jenkins, G. M. (1976) Time Series Analysis, Forecasting and Control. Holden-Day, San Francisco.","Box, G. E. P. and Tiao, G. C. (1975). Intervention analysis with applications to economic and environmental problems. Journal of the American Statistical Association, Volume 70, No. 349, pp. 70-79.","Etuk, E. H. and Eleki, A. G. (2016). Intervention Analysis of Daily Yuan-Naira Exchange Rates. CARD International Journal of Science and Advanced Innovation Research, Volume 1, Number 1. http://www.casirmediapublishing.com","Giordano, G., Blanchini, F., Bruno, R., Colaneri, P., Fillipo, A. D., Matteo, A. D. and Colaneri, M. (2020). Modelling the covid-19 epidemic nd implementation in Italy. Nature Medicine 26, 855-860.","Helfenstein, U. (1991). The use of transfer function models, intervention analysis and related time series methods in epidemiology. Int J Epidemiology, 1991 Sep., 20(3): 808-815. Doi:10.1093/ije/20.3.808. PMID: 1955267.","Ma, Z., Kuller, L. H., Fisher, M. A. and Ostroff, S. M. (2013). Use of interrupted time series method to evaluate the impact of cigarette excise tax increases in Pennsylvania, 2000-2009. Preventing Chronic Disease, 2013;10:120268. DOI: http://dx.doi.org/10.5888/pcd10.120268","Mohammed, H., Abdul-Aziz, A. R. and Saeed, B. I. I. (2016). Modeling the Ghanaian inflation rates using interrupted Time Series Analysis Approach. Mathematical Theory and Modelling, Volume 6, No. 2. https://www.iiste.org","Oreko, B. U., Nwobi-Okoye, C. C., Okyl, S. and Igboanugo, A. C. (2017). Modeling the impact of intervention measures on total accident cases in Nigeria using Box-Jenkins methodology: A case study of federal road safety commission. Cogent Engineering, Volume 4, Issue 1. https://doi.org/10.1080/23311916.2017.1345043","Ray, M., Ramasubramanian, V., Kumar, A. and Rai, A. (2014). Application of Time Series Intervention Modelling and Forecasting Cotton Yield. Statistics and Applications, Volume 12, Nos. 1&2, pp. 61-70.","Yaacob, W.F.W., Husin, W.Z.W., Aziz, N. A., and Nordin, N. I. (2011). An Intervention Model of Road Accidents: The Case of OPS Sikap Intervention. Journal of Applied Sciences, 11: 1105-1112."]}

Background: In modeling heteroscedasticity of returns, it is often assumed that the series are uncorrelated. In practice, such series with small time periods between observations can be observed to contain significant serial correlations, hence the motivation for this research. Aim: The aim of this research is to assess the existence of serial correlations in the return series of Zenith Bank Plc, which is targeted at identifying their effects on the parameter estimates of heteroscedastic models. Materials and Methods: The data were obtained from the Nigerian Stock Exchange spanning from January 3, 2006, to November 24, 2016, having 2690 observations. The hybridized Autoregressive Integrated Moving Average-Generalized Autoregressive Conditional Heteroscedasticity (ARIMA-GARCH-type) models such as Autoregressive Integrated Moving Average-Generalized Autoregressive Conditional Heteroscedasticity (ARIMA-GARCH), Autoregressive Integrated Moving Average-Exponential Generalized Autoregressive Conditional Heteroscedasticity (ARIMA-EGARCH) and the Autoregressive Integrated Moving Average-Glosten, Jagannathan and Runkle Generalized Autoregressive Conditional Heteroscedastic (ARIMA-GJRGARCH) under normal and student-t distributions were employed to model the conditional variance while the GARCH-in-Mean-GARCH-type model corresponding to the selected ARIMA-GARCH-type model was applied to appraise the possible existence of serial correlations. Results: The findings of this study showed that heteroscedasticity exists and appeared to be adequately captured by ARIMA(2,1,1)-EGARCH(1,1) model under student-t distribution but failed to account for the presence of serial correlations in the series. Meanwhile, its counterpart, GARCH-in-Mean-EGARCH(1,1) model under student-t distribution sufficiently appraised the existence of serial correlations. Conclusion: One remarkable implication is that the estimates of the parameters of ARIMA-GARCH-type model are likely to be biased when the presence of serial correlations is ignored. Also, the application of GARCH-in-Mean-GARCH-type model possibly provides the feedback mechanism or interaction between the variance and mean equations.

Exposures to environmental pollutants have been associated with respiratory diseases in humans and Continuous exposure to non-combusted liquefied petroleum gas (LPG) is suspected as a leading hazardous factor that might result in the development of impaired pulmonary functions. The study is aimed at assessing the effects of chronic exposure to non-combusted LPG on the prevalence of respiratory symptoms and appraising the potential pulmonary impairments among LPG vendors. Seventy five (75) apparently healthy LPG vendors and Seventy five (75) apparently healthy non LPG vendors, aged 18 to 50 years were recruited into this study. The Forced expiratory volume in 1second (FEV1), forced vital capacity (FVC) and peak expiratory flow (PEF) were obtained using a Spirometer while FEV1/FVC was calculated. Independent t-test was applied to determine the mean difference between the exposed and control groups at 5% level of significance. Chi-square test/Fisher’s exact test was used to investigate all forms of associations in the study. The prevalence of respiratory symptoms in LPG vendors was highest in nasal irritation/sneezing (56%), followed by cough (53.3%), wheeze (40%) and chest tightness (26.7%), respectively. Only the symptoms of nasal irritation/sneezing and cough showed significant association with the LPG vendors (P). Association between respiratory symptoms and age, association between respiratory symptoms and duration of exposure were not significant (P >0.05). There was a recorded significant decrease in FEV1, FVC, PEF except FEV1/FVC for the LPG vendors (P ) compared to the non LPG vendors. The health implications of exposure to LPG are high prevalence rate of respiratory symptoms (nasal irritation/sneezing and cough) and impaired pulmonary functions. Journal Name: Journal of Biodiversity and Environmental Sciences | JBES, {"references":["Ajah J. 2013. Households' access and preference to cooking fuels in Abuja, Nigeria. Journal of Environmental Science and Technology 6, 91-98. https://doi.org/10.3923/jest.2013.91.98","Alphonsus RI, Adesuwa QA. 2014. Household Cooking Fuel Use among Residents of a Sub-Urban Community in Nigeria: Implications for Indoor Air Pollution. Eurasian Journal of Medicine 46, 203-208. https://doi.org/10.5152/eajm.2014.0051","Cecelski E, Matingamm. 2014. Cooking with Gas: Why women in developing countries want LPG and how they can get it. WLPGA 17-20.","Corbo GM, Forastiere F, Agabiti N, Dell'Orco V, Pistelli R, AebischermL, Valente S, Perucci C. 2001. Effect of Gas Cooking on Lung Function in Adolescents: Modifying Roe of Sex and Immunoglobulin E. Thorax 56, 536-540.","Fedak KM, Good N, Walker ES, Balmes J, Brook RD, ClarkmL, Cole-Hunter T, Devlin R, L'Orange C, Luckasen G, Mehaffy J, Shelton R, Wilson A, Volckens J, peel JL. 2019. Acute Effects on Blood Pressure Following Controlled Exposure to Cookstove Air Pollution in the STOVES Study. Journal of American Heart Association 8, 1-10. https://www. ahajournals.org/doi/suppl/10.1161/JAHA.119.012246","Ferkol T, Schraufnagel D. 2014. The Global Burden of Respiratory Disease. Annals of American Thoraxic Society 11, 404-406.","Ghulam S, Muhamed Y, Shafi M, Tanzeel-Ahmed R, Muhammad S, Yahauzib M, Basher A, Munir A. 2017. Spirometric evaluation of lung function of coal workers, working at Mach (Bolam District). Indo American Journal of Pharmaceutical Research 7, 866-5667.","Kaur-Sidhu M, Ravindra K, Mor S, John Siby, Aggarwal AN. 2019. Respiratory Health States of Rural Women Exposed to Liquefied Petroleum Gas and Solid Biomass Fuel Emissions. Air, Soil and Water Research 12, 1-8. https://doi.org/10.1177%2F1178622119874314","Kim KY, Lee E, Kim Y. 2019. The Association between Bisphenol A Exposure and Obsity in Children-A Systematic Review with Meta-Analysis. International Journal of Environmental Research and Public Health 16, 2521.","Miller MR, Hankinson J, Brusasco V, Burgos F, Casaburi R, Coates A, Crapo R, Enright P, van der Grinten CPM, Gustafsson P, Jensen R, Johnson DC, Maclntyre N,mcKay R, Navajas D, Pedersen OF, Pellegrino R, Viegi G, Wanger J. 2005. Standardization of Spirometry. European Respiratory Journal 26, 319-338.","Moitra S, Blanc PD, Brashier BB. 2014. Airflow Obstruction among Street Vendors who refill Cigarette Lighters with Liquefied Petroleum Gas. International Journal of Tuberculosis and Lung Disease 18, 1126-1131. https://doi.org/10.5588/ijtld.14.0016","Moore VC. 2012. Spirometry: Step by step. Breathe 8, 233-240. https://doi.org/10.1183/20734735.0021","Moran SE, Strachan DP, Johnston ID, Anderson HR. 1999. Effects of Exposure to Gas Cooking in Childhood and Adulthood on Respiratory Symptoms, Allergic and Sensitization and Lung Function in Young British Adults. Chemical Experimental Allergy 29, 1033-1041","Nazurah bt Abdul Wahid NN, Balalla NBP, Koh D. 2014. Respiratory symptoms of vendors in an open-air hawker center in Brunei Darussalam. Frontier in Public Health 2, 167.","Pena MB, Romero KM, Velazquez EJ, Davila-Roman VG, Gilman RH, Wise RA, Miranda JJ, Checkley W. 2015. Relationship between Daily Exposure to Biomass Fuel Smoke and Blood Pressure in High-Altitude Peru. Hypertension 65,1134-1140. https://doi.org/10.1161/HYPERTENSIONAHA.114.","Petty TL, Enright PI. 2003. Simple Office Spirometry for Primary Care Practitioners. National Lung Health Education Program (NLHEP). Alphamedica, Inc. USA p. 9-29.","Quinn AK, Ae-Ngibise KA, Jack DW, Boamah EA, Enuameh Y, Mujtaba MN, Chillrud SN, Wylie BJ, Owusu-Agyei S, Kinney PL, Asante KP. 2016. Association of Carbon Monoxide Exposure With Blood Pressure Among Pregnant Women in Rural Ghana: Evidence From GRAPHS. International Journal of Hygiene and Environmental Health 219, 176-183. https://doi.org/10.1016/j.ijheh.2015.10.004","Rehfuess, Eva & World Health Organization. 2006. Fuel for Life: Household Energy and Health. World Health Organization. Available from https://apps.who.int/handle/10665/43421 (Accessed April 20, 2020).","Sirdahmm, Al Laham NA, El Madhoun RA. 2013. Possible Health Effects of Liquefied Petroleum Gas on Workers at Filling and Distribution Stations of Gaza Governorates. Eastern Mediterranean Health Journal 17, 289-294.","Svedahl S, Svendsen K, Qvenild T, Sjaastad AK, Hilt B. 2009. Short Term Exposure to Cooking Fumes and Pulmonary Function. Journal of Occupational Medicine and Toxicology 4, 9. https://doi.org/10.1186/1745-6673-4-9","Thompson LM. 2018. Cooking with GAS: How children in the developing world benefit from switching to LPG. Prepared for the World LPG Association. WLPGA 8-34.","Vainiotaloa S, Matveinena K. 1993. Cooking Fumes as a Hygienic Problem in the Food and Catering Industries. American Industrial Hygene Association Journal 54, 376-82.","Willers SM, Brunekreef B, Oldenwening M, Smit HA, Kerkhof M, Gerritsen J, De Jongste JC, De Vries H. 2006. Gas Cooking, Kitchen Ventilation, and Asthma, Allergic Symptoms and Sensitizationin Young Children- the PIAMA Study. Allergy 61, 563-568. https://doi.org/10.1111/j.1398-9995.2006.01037.x","World Health Organization. 2014. WHO Guidelines for Indoor Air Quality: Household Fuel Combustion. Available from www.who.int/airpollution/guidelines /household-fuel-combustion/IAQHHFC_guidelines,pdf (Accessed April 25, 2020)."]}

The aim of this paper is to appraise if there is any improvement subtracting the effects of outliers from existing heteroscedastic models and whether this improvement makes difference with the existing models in achieving efficiency in capturing excess kurtosis in the returns series. The study employed both existing and outlier modified autoregressive conditional heteroscedastic (ARCH), generalized autoregressive conditional heteroscedastic (GARCH), exponential GARCH (EGARCH), Glosten, Jagnnathan and Runkle GARCH (GJR-GARCH) models with respect to normal and student-t distributions to assess the portion of excess kurtosis of the returns series expressed compare to the theoretical value of kurtosis. The data applied were the share prices of Union bank of Nigeria and Unity bank from January 3, 2006 to November 24, 2016, comprising 2690 observations and were obtained from Nigerian Stock Exchange. The results obtained revealed that the Outlier Modified GARCH-type models chosen were adequate and sufficiently reducing the value of excess kurtosis in close proximity to the theoretical value. Therefore, the modification of existing GARCH-type models by subtracting the effects of outliers seems to show a substantive improvement in the portion of excess kurtosis captured and thus proves that the Outlier Modified GARCH-type models make difference with the existing ones.

The purpose of this study is to apply white noise process in measuring model adequacy targeted at confirming the assumption of independence. This ensures that no autocorrelation exists in any time series under consideration, and that the autoregressive integrated moving average (ARIMA) model entertained is able to capture the linear structure in such series. The study explored the share price series of Union bank of Nigeria, Unity bank, and Wema bank obtained from Nigerian Stock Exchange from January 3, 2006 to November 24, 2016 comprising 2690 observations. ARIMA models were used to model the linear dependence in the data while autocorrelation function (ACF), partial autocorrelation function (PACF), and Ljung-Box test were applied in checking the adequacy of the selected models. The findings revealed that ARIMA(1,1,0) model adequately captured the linear dependence in the return series of both Union and Unity banks while ARIMA(2,1,0) model was sufficient for that of Wema bank. Also, evidence from ACF, PACF and Ljung-Box test revealed that the residual series of the fitted models were white noise, thus satisfying the conditions for stationarity.

To overcome the weaknesses of in-sample model selection, this study adopted out-of-sample model selection approach for selecting models with improved forecasting accuracies and performances. Daily closing share prices were obtained from Diamond Bank and Fidelity Bank as listed in the Nigerian Stock Exchange spanning from January 3, 2006 to December 30, 2016. Thus, a total of 2713 observations were explored and were divided into two portions. The first which ranged from January 3, 2006 to November 24, 2016, comprising 2690 observations, was used for model formulation. The second portion which ranged from November 25, 2016 to December 30, 2016, consisting of 23 observations, was used for out-of-sample forecasting performance evaluation. Combined linear (ARIMA) and Nonlinear (GARCH-type) models were applied on the returns series with respect to normal and student-t distributions. The findings revealed that ARIMA (2,1,1)-EGARCH (1,1)-norm and ARIMA (1,1,0)-EGARCH (1,1)-norm models selected based on minimum predictive errors throughout-of-sample approach outperformed ARIMA (2,1,1)-GARCH (2,0)-std and ARIMA (1,1,0)-EGARCH (1,1)-std model chosen through in-sample approach. Therefore, it could be deduced that out-of-sample model selection approach was suitable for selecting models with improved forecasting accuracies and performances.

The article is aimed at assessing the effects of chronic exposure to non-combusted LPG on the prevalence of respiratory symptoms and appraising the potential pulmonary impairments among LPG vendors. A case control design in which vendors and non-vendors of LPG from Calabar, Nigeria were used for the data collection. Seventy five (75) apparently healthy LPG vendors and Seventy five (75) apparently healthy non LPG vendors, aged 18 to 50 years were considered. A structured questionnaire was randomly administered to the participants to obtain information on age, family history, medical history, physical lifestyle, drug usage, occupation and duration on the job. The Forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC) and peak expiratory flow (PEF) were obtained using a Spirometer while FEV1/FVC was calculated. Independent t-test was applied to determine the mean difference between the exposed and control groups at 5% level of significance. Chi-square test/Fisher's exact test was used to investigate all forms of associations in the study. It is evident in the data that nasal irritation/sneezing and cough were significantly associated with the LPG vendors. The pulmonary function parameters except FEV1/FVC indicated significant reduction among LPG vendors. The data can further be reused by applying regression analysis, correlation analysis to determine the relationship between pulmonary function indices and duration of exposure. Also, analysis of variance (ANOVA) can be used for determining the effect of interaction between age of exposed group and duration of exposure on pulmonary function parameters.

This study traced the patterns of discrete time series over time with respect to GARCH effect and asymmetric GARCH effect. Particularly, we paid attention to the weakness of the GARCH model in modeling the asymmetry of GARCH effect. In order to handle this weakness, we applied the sign and size bias test which comprises sign bias test, negative size bias test, positive size bias test, and Lagrange Multiplier test in order to identify the asymmetric effect in the residual series of the GARCH model. Where the asymmetric effect is present and significant, we fit the asymmetric GARCH models. Exploring the share price returns of Zenith bank plc obtained from the Nigerian Stock Exchange from January 4, 2006 to May 26, 2015, our findings indicated the presence of GARCH effect and was adequately captured by GARCH(0,1) model. Also, the sign and size bias test for asymmetric GARCH effect on the residual series of GARCH(0,1) model showed a joint significance as indicated by the Lagrange Multiplier test. Moreover, the asymmetric GARCH effect was adequately captured by EGARCH(0,1) and TGARCH(0,1) models. In addition, the significance of the size bias test indicated that the size of negative and positive returns has an impact on the predicted heteroscedasticity. Hence, we concluded that GARCH(0,1) model adequately predicted the GARCH effect but failed to capture the asymmetric effect in the share price returns of the discrete series. However, this was complemented by both EGARCH(0,1) and TGARCH(0,1) models with the size of both the negative and positive effects taken into consideration.

This study was prompted by the fact that the presence of outliers in discrete-time stochastic series may result in model misspecification, biases in parameter estimation and in addition, it is difficult to identify some outliers due to masking effects. However, the iterative approach which involves joint estimation of outliers effects and model parameters appears to be a panacea for masking effects. Considering the dataset on credit to private sector in Nigeria from 1981 to 2014, we found that ARIMA (1, 1, 1) model fitted well to the series without considering the presence of outliers. Using the iterative procedure method to reduce masking effects, the following outliers, IO (t = 24), AO (t = 33) and TC (t = 22) were identified. Adjusting the series for outliers and iterating further, ARIMA (2, 0, 1) model alongside AO (t = 33) and TC (t = 22) outliers was found to fit the series better than ARIMA (1, 1, 1) model. The implication is that in the presence of outliers, ARIMA (1, 1, 1) model was misspecified, the order of integration was wrong and by extension, autocorrelation and partial autocorrelation functions were misleading, and the estimated parameters were biased.

In this paper, we study a certain type of second order linear neutral differential equation with constant impulsive jumps. This type of equation is known always to possess an unbounded non-oscillatory solution. The method and technique of impulse imposition used here is due to studies by Bainov and Simeonov [1]. By assuming, amongst other conditions, that the constant coefficient of the equation in question lies between zero and one and the delay function is non-decreasing, it is shown that all bounded solutions of the said neutral impulsive equation are oscillatory.

This study looks at a possible combination of both the ARMA and ARCH-types models to form a single model such as ARMA-ARCH that will completely model the linear and non-linear features of financial data. The data used for this study are daily closing share prices of First Bank of Nigeria plc from January 4, 2000 to December 31, 2013 and were obtained from the Nigerian Stock Exchange. The share price series was found to be nonstationary while the returns series which is the first difference of log of the share price series was found to be stationary. This study provides evidence to show that ARMA(2,2) model is found to be adequate in the modeling the linear dependence in the returns of First Bank of Nigeria while the ARCH(1) model is adequate in modeling the changing conditional variance in the returns of First Bank of Nigeria. Therefore, combining the two models results in a single ARMA(2,2)-ARCH(1) model that completely models the returns series of First Bank of Nigeria.