Estimating the Parameters of GARCH Models and Its Extension: Comparison between Gaussian and non- Gaussian Innovation Distributions

Innovation distributions play significant role in determining the fitness as well as forecasting performance of volatility models. Several studies aimed at comparing the performance of volatility have been carried out but most of the studies focused on the use of Gaussian innovation distribution. Hence, this study compares the performance of GARCH models and its extensions using five innovation distributions, one Gaussian distribution (normal distribution) and four non- Gaussian innovation distributions(Student –t distribution, generalized error distribution, skewed Student- t and skewed generalized error distribution). Data on the daily closing prices of Zenith bank (04/01/2007 to 31/12/2019) and ETI (04/01/2007 to 31/12/2019) were obtained from cashcraft website and then converted to daily returns. Hence, using these five innovation distributions, the parameters of GARCH(1,1), TGARCH(1,1), EGARCH(1,1), IGARCH(1,1) and GJR- GARCH(1,1) were estimated. The performances of these models were compared in terms of fitness using AIC and forecasting performance based on Root Mean Square Error. Result of analysis revealed that GARCH models and its extensions estimated using non- Gaussian innovation distributions outperformed other innovation distributions both in terms of fitness and forecasting accuracy. Result also shows that among the non-Gaussian innovation distributions considered, the skewed generalized error distribution performed better than other non-Gaussian innovation distributions. The TGARCH (1,1)-sged and E-GARCH (1,1)-sged were recommended as the best model for predicting the volatility in ETI and Zenith bank stocks respectively

Childbirth Practices in the Akpabuyo Rural Health and Demographic Surveillance System

Maternal and neonatal mortality remain high in Nigeria. The State and Federal governments have adopted several strategies to prevent maternal and infant deaths such as the Cross River State Free Health Services to pregnant women and infants, and the National Midwives’ Service Scheme. This study assessed pregnancy and childbirth practices of Nigerian women in rural communities located in Akpabuyo in the Niger Delta region of Nigeria. Women who were pregnant or had recently given birth in a population of 5,668 people under surveillance in some rural communities of Akpabuyo were interviewed to obtain information on pregnancy and childbirth practices. Validated semi-structured questionnaires were administered by well-trained field workers. Completed questionnaires were entered into electronic data forms in OpenHDS software and exported to STATA for analysis. Results showed that, 39.5% of women reported that they had sought prenatal care from a traditional birth attendant (TBA). 84.6% of all births occurred outside the formal health system with the majority attended by TBAs. Only 15.4% of births occurred in hospitals or health centres. The implements used to cut the umbilical cord were knives (46.2%), new razor blades, old razor blades, sharp stone and scissors. The materials used for treating the umbilical cord were mostly methylated spirit (63.1%); other treatment materials were“western medicine”, “black powder” and others including herbs and earth. The study concluded that, childbirth practices that pose significant risk to maternal and newborn health remain common in these rural communities. Majority of births were attended by TBAs despite free delivery services available at the formal health facilities. TBAs should be assisted to enhance their role in health care delivery. Effort should be made to increase public awareness and interest in facility-based services. Keywords: Maternal health, neonatal infection, longitudinal data, pregnancy

Optimum hardware, software and personnel requirements for a paperless health and demographic surveillance system: a case study of Cross River HDSS, Nigeria

Health and Demographic Surveillance Systems (HDSS) are a robust and rigorous data collection, validation, storage, analysis and reporting platforms for community-based data on vital events. These processes make high demands on paper and man-hours with attendant implications on running costs and environmental impact. However, with the rapid development of ICT and increasing affordability of computing devices, some of the manual processes can be replaced with ICT tools. This paper presents a case study of the Cross River HDSS in Akpabuyo Southern Nigeria with a view to highlighting the essential personnel, hardware and software requirements for running an IT-based paperless HDSS in low income settings. The DSA comprised of 22 contiguous EAs of 1370 households. The case study entailed four update rounds, each of which involved field workers visiting households and obtaining information on vital events. The first update round was purely paper-based involving the use of large collections of paper forms for interviews. The last three rounds were IT-based, devoid of paper questionnaires and ran on web-based open source software. Hardware was a set of high-end servers, desktops, tablet PCs and android phones for data collection.   The case study demonstrated the feasibility of running a paperless IT-based HDSS in a resource-poor setting using free and open source software, such as the web-based OpenHDS, MySQL, ODK, MirthConnect, etc. This overcomes the limitations of the popular HRS2 in terms of costs, complexities, and lack of compatibility with changing hardware and system software configurations. However, running IT-based paperless HDSS threw up some challenges, such as cases of poor internet connectivity, absence of GSM network connectivity using mobile devices, and having the right mix of staff with sufficient IT skills. This paper recommended solution strategies for overcoming these challenges. The need for the development of new set of protocols for data quality in a paperless HDSS is also discussed.   Keywords: Health, demographic surveillance system, information technology, paper, environment, enumeration area

A calibrated synthetic estimator for small area estimation

Synthetic estimators are known to produce estimates of population mean in areas where no sampled data are available, but such estimates are usually highly biased with invalid confidence statements. This paper presents a calibrated synthetic estimator of the population mean which addresses these problematic issues. Two known special cases of this estimator were obtained in the form of combined ratio and combined regression synthetic estimators, using selected tuning parameters under stratified sampling. In result, their biases and variance estimators were derived. The empirical demonstration of the usage involving the proposed calibrated estimators shows that they provide better estimates of the population mean than the existing estimators discussed in this study. In particular, the estimators were examined through simulation under three distributional assumptions, namely the normal, gamma and exponential distributions. The results show that they provide estimates of the mean displaying less relative bias and greater efficiency. Moreover, they prove more consistent than the existing classical synthetic estimator. The further evaluation carried out using the coefficient of variation provides additional confirmation of the calibrated estimator's advantage over the existing ones in relation to small area estimation

Estimating the Parameters of GARCH Models and Its Extension: Comparison between Gaussian and non- Gaussian Innovation Distributions

Innovation distributions play significant role in determining the fitness as well as forecasting performance of volatility models. Several studies aimed at comparing the performance of volatility have been carried out but most of the studies focused on the use of Gaussian innovation distribution. Hence, this study compares the performance of GARCH models and its extensions using five innovation distributions, one Gaussian distribution (normal distribution) and four non- Gaussian innovation distributions(Student –t distribution, generalized error distribution, skewed Student- t and skewed generalized error distribution). Data on the daily closing prices of Zenith bank (04/01/2007 to 31/12/2019) and ETI (04/01/2007 to 31/12/2019) were obtained from cashcraft website and then converted to daily returns. Hence, using these five innovation distributions, the parameters of GARCH(1,1), TGARCH(1,1), EGARCH(1,1), IGARCH(1,1) and GJR- GARCH(1,1) were estimated. The performances of these models were compared in terms of fitness using AIC and forecasting performance based on Root Mean Square Error. Result of analysis revealed that GARCH models and its extensions estimated using non- Gaussian innovation distributions outperformed other innovation distributions both in terms of fitness and forecasting accuracy. Result also shows that among the non-Gaussian innovation distributions considered, the skewed generalized error distribution performed better than other non-Gaussian innovation distributions. The TGARCH (1,1)-sged and E-GARCH (1,1)-sged were recommended as the best model for predicting the volatility in ETI and Zenith bank stocks respectively