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The impact of COVID-19 on the educational systems across the globe was characterized by the shutdown of university education systems. In the post COVID-19 era, examining the prospect of on-line education becomes demanding with emphasis on promoting effective and uninterrupted university education sector in Nigeria.

This study employed phenomenological and descriptive research design. The sample size for the study was 15, comprising university lecturers, students and school social workers. In-depth Interview (IDI) interview was the instrument for data collection.

Findings revealed that university education in Nigeria was adversely disrupted by COVID-19 lockdown during the pandemic. Similarly, the study revealed that online education will facilitate effective and uninterrupted university education in post-COVID-19 era. Challenges such as lack of access to laptops, smartphones, reliable internet services, electricity etc. are major obstacles to an effective transition to online education in the post-COVID-19 University education era. The students lamented that poor knowledge of ICT by their lecturers and inclusivity of indigent/disabled students were major concerns.

Revitalizing the education sector is a core concern for low-income countries. Online education presents the lasting solution to the constant interruption of university education in Africa. Promoting effective and uninterrupted university education is pegged on an adequate government education budgetary allocation targeted towards providing an enabling environment for online education to thrive. The study recommends that school social workers' engagement in the educational sector would help advocate for the availability of technological infrastructures and mainstreaming of the poor and disabled in the online education dispensation.

This paper aims to introduce a heteroskedastic hidden truncation normal (HTN) model that allows for conditional volatilities, skewness and kurtosis, which evolve over time and are linked to economic dynamics and have economic interpretations.

The model consists of the HTN distribution introduced by Arnold et al. (1993) coupled with the NGARCH type (Engle and Ng, 1993). The HTN distribution nests two well-known distributions: the skew-normal family (Azzalini, 1985) and the normal distributions. The HTN family of distributions depends on a hidden truncation and has four parameters having economic interpretations in terms of conditional volatilities, kurtosis and correlations between the observed variable and the hidden truncated variable.

The model parameters are estimated using the maximum likelihood estimator. An empirical application to market data indicates the HTN-NGARCH model captures stylized facts manifested in financial market data, specifically volatility clustering, leverage effect, conditional skewness and kurtosis. The authors also compare the performance of the HTN-NGARCH model to the mixed normal (MN) heteroskedastic MN-NGARCH model.

The paper presents a structure dynamic, allowing us to explore the volatility spillover between the observed and the hidden truncated variable. The conditional volatilities and skewness have the ability at modeling persistence in volatilities and the leverage effects as well as conditional kurtosis of the S&P 500 index.