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Long memory suggests that past returns can influence future returns over extended periods, indicating potential predictability. The aim of this study was to empirically examines the interplay between long memory effects, downside risk, and stock returns across five major international equity markets (S&P 500, FTSE 100, Nikkei 225, DAX, and ASX) from 2000 to 2022. An ARFIMA model which quantify fractional differencing was used in conjunction with a correlation matrix, regression and Granger causality test to investigate the interplay. The findings revealed a significant long memory across all markets, with Nikkei 225 exhibiting the strongest persistence, attributed to Japan’s prolonged economic stagnation. Counterintuitively, lagged downside risk positively predicts returns, aligning with behavioural theories of investor overreaction to tail events. Bidirectional causality between risk and returns underscores reflexive market dynamics. The study bridges a critical gap by integrating long memory into downside risk models, demonstrating that persistent volatility amplifies tail risk premiums. The findings of this study suggest that financial market regulators need to seriously considers adaptive regulatory tools due to the dual long memory in returns and downside risk necessitates. To the author´s knowledge, this study is the first to bridge a critical gap in the literature by systematically integrating long memory effects with downside risk metrics so as to provide a more robust framework for understanding risk-return dynamics, as it accounts for the persistent nature of volatility and tail risk, which traditional short-memory models often overlook.

The national development plan (NDP) clearly states that communities should take initiatives to solve their own problems. One of the socio-economic problems faced by communities in South Africa is the effects of inequality, which is largely the apartheid era is responsible for. In trying to deal with the socio-economic problem of inequality, many communities in South Africa have resorted to forming and running cooperatives in different sectors of the economy. However, these cooperatives are reported to be facing numerous challenges, and in some cases, many of them fail or close down. The challenges mentioned range from a lack of capacity to manage, poor economic performance in the country, heavy reliance on government grants and many others. Despite the challenges faced by cooperatives in South Africa, they are still believed to be a very important tool in dealing with the problem of inequality, which mostly affects rural areas where most of these cooperatives operate. To understand the potential impact of cooperatives in dealing with inequality, this paper uses a qualitative approach. Secondary sources of published information by government agencies and cooperatives themselves will be used, as well as literature published by experts in the area of study. Preliminary findings of the paper are that cooperatives in South Africa are very small in size, and the majority of them are in primary sectors of the economy. If cooperatives are to play a significant role in dealing with inequality, there is a need for them to be capacitated to grow and also participate in upper value chains of the economy.  

Financial markets are a system of complex price dynamics that are often influenced by various nonlinear factors. Traditional linear models such as Autoregressive Moving Average also known as ARMA and the GARCH model often fail to capture the inherent nonlinearities observed in financial market returns and trading volumes which poses significant problems in modelling financial time series. The aim of this study was to investigates the role of threshold nonlinearity in modelling financial market returns and trading volume. Daily share prices and trading volumes from the S&P 500 and the Nasdaq indices spanning from January 2000 to December 2020 were used as the sample financial markets. The findings revealed that the threshold model improves predictive performance over traditional linear models, highlighting the importance of incorporating nonlinearity for understanding the dynamics of financial markets. A threshold autoregressive model was proposed to account for sudden shifts in market conditions and nonlinear dependencies between returns and trading volumes.

Prior research on volatility spillover indicates that financial market spillovers are not uniform across all markets. Their intensity and direction depend on factors such as market size, economic linkages, and the nature of the shock. The aim of this study was to comprehensive investigation of volatility spillovers across five major international equity markets from January 2008 to January 2024, a period encompassing multiple financial crises. Utilizing a conditional volatility model, followed by the construction of a spillover index and Granger causality tests to investigate the direction and magnitude of volatility transmission, the findings of this study revealed that the S&P 500 is a net receiver of volatility, acting as a global shock absorber rather than a primary transmitter. In contrast, European markets and the Nikkei 225 emerged as net transmitters, with a tightly coupled, bidirectional relationship observed between the United Kingdom and European markets. The Shanghai Composite was also found to be nearly neutral, likely due to capital controls. These results reiterated the notion that volatility transmission networks are dynamic and not solely dictated by market size. The findings of this study suggest that asset managers and other investors can significantly increase the value of their portfolios if they adopt a multi-polar risk management framework, moving beyond a United States centric view for international diversification to include more European and Asian markets assets in their portfolios.

Traditional financial models assume constant variance in asset returns however, empirical evidence highlights volatility persistence, which are effectively captured by ARCH and GARCH models. The aim of this study was to examine the heteroskedastic behaviour of stock prices across international financial markets. Using a GARCH (1,1) model with a student’s t-distribution, this study analyses daily stock returns from the NYSE, FTSE 100, Nikkei 225, and Shanghai Composite (SSE) between 2018 and 2023. The findings confirm significant autoregressive conditional heteroskedasticity in all markets, supporting the notion that financial volatility is dynamic and clustered. Developed markets, such as the NYSE and FTSE 100, exhibit lower baseline volatility but more persistent shocks, indicative of market efficiency and stability. In contrast, emerging markets, particularly SSE, demonstrate higher volatility and greater sensitivity to past shocks, reflecting heightened uncertainty. The results re-iterate the importance of incorporating dynamic volatility models in risk assessments. Given the increasing complexity of global financial systems, this study advocates for enhanced regulatory frameworks and adaptive econometric methodologies to account for evolving market structures and external macroeconomic shocks.

High-frequency data offers unparalleled insights into market dynamics, facilitating the analysis of statistical properties, dynamic correlations, and scaling behaviours with a precision previously unattainable. These Statistical Properties are essential framework for understanding and modelling the interactions between global financial markets over time. The aim of this study was to investigate the dynamic conditional correlations and scaling behaviour of high-frequency data from January,1 2010 to December 31, 2020. Using the S&P 500, FTSE 100, Nikkei 225, HKEX and DAX as sampled financial markets, the results revealed significant differences in correlation patterns across the markets, as well as fractal-like scaling behaviour. The relationship between the S&P 500-FTSE 100 and S&P 500-DAX supports trend-following strategies, while the Nikkei 225-HKEX and Nikkei 225-FTSE 100 may be closer to random-walk behaviour. This study advances the frontier of knowledge on high-frequency data and offers insights into the temporal relationships and scaling properties between financial markets.

Capital structure defined by the proportion of debt and equity financing has always been an important component in managerial finance due to its relative importance in measuring a company's risk. Arguably, it is among the most controversial topics in corporate finance and has gain recent recognition of its importance when considering the emergence of different sources of financing across industry. The purpose of this study was to explore the relationship between capital structure and a firm's profitability in other to determine whether a significant connection exists between how a firm finances its operations and its financial performance. A comprehensive Lilliefors’s, Kolmogorov Smirnov and Chi square tests analysis were utilised for a sample of 23 peer reviewed journal articles across various industries. The findings revealed that capital structure cannot be used to explain variations in profitability and profitability is independent of capital structure. This paper contributes to the ongoing debate on optimal capital structure by strengthening methodological rigor and some of the existing theories on how capital structure can be used to maximise profitability. It may be very helpful for further studies to first establish the direction of impact and the size effect of the independent variables.

Conventional intrusion detection systems encounter difficulties in addressing advanced cyber threats and handling the increasing volume of network data.  This research presents a modernisation strategy by integrating swarm intelligence algorithms to enhance the efficiency and efficacy of intrusion detection.  This research employs qualitative observational and content analysis methodologies to investigate the utilisation of swarm intelligence in improving intrusion detection systems.  Findings demonstrate substantial enhancements in detection rates and system efficacy, with swarm intelligence algorithms attaining a true positive detection rate of over 99% and minimising false positives to as low as 2%.  These findings highlight the impending substitution of conventional intrusion detection systems with swarm intelligence-based alternatives, offering significant enhancement in cybersecurity capabilities.

Financial markets areconstantly fluctuating in response to a range of factors. These fluctuations connote volatility which are unpredictable behaviour that deviates significantly from the patterns observed in stable conditions. It is therefore important to use standard stochastic volatility models to capture the time-varying nature of volatility during periods of financial distress in other to make informed decisions. The aim of this study was to explore stochastic volatility in financial markets during periods of financial distress using a Heston model. The sample financial markets were, the CAC 40, the DAX, the JSE, the Nasdaq Index and the Nikkei-225. The 2007/2008 financial crisis and the Covid-19 pandemic were used as sample time frames. The findings of this study revealed extortionate stochastic volatility during the Covid-19 pandemic compared to the 2007/2008 financial crisis. Since the 2007/ 2008 crisis stemmed from a collapse in the financial system, the nature of the event was more localized within the financial sector and had a somewhat more predictable unfolding than the Covid-19 pandemic. By Implication, it may be suggested that it was more difficult to price assets during the Covid-19 pandemic due to extreme uncertainty which resulted in inflated option premiums as investors sought hedging strategies.