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The paper’s objective was to investigate the relationship between COVID-19 and South Africa financial stock markets. The pandemic worsened South Africa's unstable financial condition and societal problems: business was severely disrupted, as were the travel industry, hospitality, food security, small businesses, and many other sectors. The results show that the COVID-19 pandemic triggered high and longer-lasting financial volatility in the markets. The higher level of volatility persistence suggests a prolonged period of increased uncertainty. Overall, the uncertainty about the vaccine's effectiveness towards the pandemic provides low expectations about the future of the stock market, since there are variants that are still being discovered.

In the preceding decade, the South African economy has experienced challenges due to global disruptive events, hence, the implementation of risk mitigation strategies becomes a priority in volatile markets. Stable distributions account for skewness and heavy-tailed behaviour which are frequently observed in financial data. This study aims to investigate the fit of stable distributions for three FTSE/JSE indices and the USD/ZAR currency exchange rate. The maximum likelihood method was applied to fit Nolan’s -parameterization stable distribution. Value at Risk (VaR) is measure assessing market risk, therefore, VaR estimates and Kupiec likelihood test are applied to evaluate the extreme tail behaviour of the fitted stable model. Results show the robustness of stable distributions in the long and short position for each daily returns. This research validates the use of stable distributions aimed at capturing the characteristics financial data. Those concerned with curtailing losses and investigating alternatives for financial modeling in the South African financial industry may benefit the most by using stable distributions.

This study aimed to investigate economic growth (RGDP) and its determinants such as internal debt (INDT), external debt (EXDT), interest rate (RINR), exchange rate (REXR) and trade openness (OPEN). Quarterly data from 1986-2021 used for this investigation were extracted from Central Bank of Nigeria.  Exploratory data analysis (EDA) revealed linear dependence of RGDP and the aforementioned determinants. The EDA and the variance inflation factor revealed the presence of multicollinearity caused by the INDT. As a result of this, the ridge regression method was used and it was found that the multicollinearity problem was addressed with appropriate ridge constant K= 0.29. Hence, ridge regression technique with constant K= 0.29 was a robust method to predict economic growth in Nigeria using the explanatory variables under consideration. Thus, served as a great benefit to the policy makers as the study provided a better understanding of the economic growth and the aforementioned determinants in terms of relationship and in particular the set back caused by EXDT on the economy of Nigeria. Also, the researchers benefited from this study as it engendered the understanding of the appropriate estimation technique to be used when the presence of multicollinearity was established in fitting a linear model. 

  Risk management and prediction of market losses of cryptocurrencies are of notable value to risk managers, portfolio managers, financial market researchers and academics. One of the most common measures of an asset’s risk is Value-at-Risk (VaR). This paper evaluates and compares the performance of generalized autoregressive score (GAS) combined with heavy-tailed distributions, in estimating the VaR of two well-known cryptocurrencies’ returns, namely Bitcoin returns and Ethereum returns. In this paper, we proposed a VaR model for Bitcoin and Ethereum returns, namely the GAS model combined with the generalized lambda distribution (GLD), referred to as the GAS-GLD model. The relative performance of the GAS-GLD models was compared to the models proposed by Troster et al. (2018), in other words, GAS models combined with asymmetric Laplace distribution (ALD), the asymmetric Student’s t-distribution (AST) and the skew Student’s t-distribution (SSTD). The Kupiec likelihood ratio test was used to assess the adequacy of the proposed models. The principal findings suggest that the GAS models with heavy-tailed innovation distributions are, in fact, appropriate for modelling cryptocurrency returns, with the GAS-GLD being the most adequate for the Bitcoin returns at various VaR levels, and both GAS-SSTD, GAS-ALD and GAS-GLD models being the most appropriate for the Ethereum returns at the VaR levels used in this study.    

The purpose of this study is to investigate the suitable arbitrage-free term-structure model that might be able to fit the South African inflation-indexed spot-rate curve. The instrument has relatively less tradability in the market, which then translates into a lack of adequate data for bond valuation/pricing. Pricing deviations might give inflated/deflated projections on the value of government debt; consequently, higher estimated interest cost to be paid. A proper valuation of these instruments is mandatory as they form part of government funding/borrowing and the country’s budgeting processes in the medium term. The performance of newly developed non-linear multifactor models that follows the Nelson-Siegel (1987) framework was compared to the arbitrage-free Vasicek (1977) model and linear parametric models to assess any significant deviations in forecasting the real spot-rate curve over a short period. Models with constant parameters (i.e. linear parametric, cubic splines, Nelson-Siegel (1987) and Svensson (1994)) gave a perfect fit, they proved to marginally lose fitting capabilities during periods of higher volatility. Therefore, it could be concluded that the application of either Nelson-Siegel (1987) model or Svensson (1994) model on forecasting South African real spot-rate curve gave a perfect fit. However, for a solid conclusion to be derived, it is imperative to explore the performance of these models over a period of stressed market and economic conditions.

The purpose of the study is aimed at determining the financial socialisation of accounting students at South African universities and the factors that influence financial socialisation. The research used statistical techniques such as the structural equation modelling methodology to identify financial socialisation influencers and regression analysis to analyse associations between financial socialisation and socio-demographic variables among 1582 students. The study's results indicate that financial socialisation agents such as relatives, friends, and social media have little impact on most students' financial decisions. The majority of accounting students are often found to be financially socialised by their immediate family members rather than peers or social media factors. The regression analysis results confirmed this observation, revealing a statistically significant association between the parents' level of education and the students' financial socialisation. This study recommends that financial socialisation can be greatly improved by raising parents' educational levels and instilling financial education in university students' curricula, especially in South Africa.