Stochastic mean absolute deviation model with random transaction costs: securities from the Johannesburg stock market

Published ArticleWe propose a multi-stage stochastic mean absolute deviation model with random transaction costs in optimal portfolio selection. We take implicit costs incurred in trading as our transaction costs. The multi-stage stochastic model captures risk due to uncertainty, as well as implicit transaction costs incurred by an investor during initial trading and subsequent rebalancing of the portfolio. We apply the model to securities on the Johannesburg stock market and find out that implicit transaction costs are at least 14.3% of returns on investment

Fitting Statistical Parent Distributions to Quantify Financial Risk in the South African Financial Index (J580)

The purpose of this study is to investigate and describe the riskiness of an investment in the South African Financial Index (J580) using four relatively heavy tailed statistical parent distributions, viz: the Exponential, Weibull, Gamma and Burr distributions. The statistical distributions describe the Index returns, and quantify the riskiness of the monthly South African Financial Index (J580) for the period 1995-2018. The Maximum Likelihood Estimation (MLE) method is used to estimate the distribution parameters. The heavier-tailed Burr distribution in the heavy tailed Frétchet domain distribution is the best fitting statistical parent distribution for losses as evidenced by the AIC, BIC and other graphical measures of goodness of fit. The lighter tailed Exponential distribution is the best fitting statistical parent distribution for the positive returns (gains). The Exponential distribution is in the light tailed Gumbel domain distribution. Summary measures of financial risk, such as the Value at Risk (VaR) and Expected Shortfall (ES) are calculated using the two best fitting distributions. Financial risk (VaR and ES) quantification and risk mitigation is topical in light of the failure of the Normal distribution-based risk models, which under estimated risk in leading up to the Global Financial Crisis (GFC) of 2008-2009. The practical implications are that the Normal distribution-based risk measures ought to be replaced with other statistical parent distributions and even extreme value distributions (EVD) in order to accurately estimate financial risk. Given the limited empirical investigations on the South Africa Financial Index (J580), the results from this research provide additional and valuable information for both investors and practitioners on how to accurately estimate and assess financial risk. The study extends the empirical literature on more accurate financial risk assessment, more specifically in the context of the Financial Index in South Africa

Estimation of Diversification Effects/Benefits Using the Generalised Pareto Distribution - Extreme Value Gumbel Copula Model

The objective of this paper is to estimate the diversification effects/benefits of an investment in a portfolio consisting of the South African Industrial (J520) and the Financial (J580) Indices using the Generalised Pareto Distributions (GPDs) with an extreme value Gumbel copula. The GPD is used as the marginal distribution to both assets to better characterise the extreme risk of returns in both Indices tails. The extreme value Gumbel copula captures the dependence structure (co-movement) of the financial assets in the portfolio. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) goodness of fit tests and the scatterplots indicate that the upper tail of the gains (the larger gains) risk and the losses tail (the larger losses) are best captured using the extreme value Gumbel copula. Monte Carlo simulation of an equally weighted portfolio of the two Indices is used to estimate the portfolio risk. The univariate marginal risks and the portfolio risks are used to calculate the diversification effects/benefits. The results show that there are benefits in diversification since the riskiness of the portfolio is less than the sum of the risk of the two financial assets. This implies that VaR, although not additive theoretically, is sub-additive in this practical situation. This property of sub-additivity represents the benefits of diversification for a portfolio. The implication is that investors investing in individual risky assets can benefit from constructing such a portfolio to reduce extreme risk. Due to high dependence and contagion between developed markets/Global markets, this is useful information for local and international investors seeking a portfolio which includes developing countries market Indices, such as South African assets, which are less correlated with other Global markets, thereby reducing the risk of contagion

Analysis of the same day of the week increases in peak electricity demand in South Africa

Modelling of the same day of the week increases in peak electricity demand using the Generalized Pareto-type (GP-type) distribution is discussed. The GP-type distribution discussed in this paper has one parameter to estimate and as such, it is referred to as the Generalized Single Pareto (GSP). The data is from Eskom, South Africa's power utility company and is for the years 2000 to 2011. A comparative analysis is done with a Generalized Pareto Distribution (GPD). Although both the GSP and the GPD fit the data, the use of the GSP is easier since it has only one parameter to estimate instead of two as is the case with the GPD. Modelling of the same day of the week increases in peak electricity demand improves the reliability of a power network if an accurate assessment of the level and frequency of future extreme load forecasts is carried out

Global Research Priorities to Better Understand the Burden of Iatrogenic Harm in Primary Care: An International Delphi Exercise

There is a need to identify and reach agreement on key foci for patient safety research in primary care contexts and understand how these priorities differ between low-, middle-, and high-income settings. We conducted a modified Delphi exercise, which was distributed to an international panel of experts in patient safety and primary care. Family practice and pharmacy were considered the main contexts on which to focus attention in order to advance patient safety in primary care across all income categories. Other clinical contexts prioritised included community midwifery and nursing in low-income countries and care homes in high-income countries. The sources of patient safety incidents requiring further study across all economic settings that were identified were communication between health care professionals and with patients, teamwork within the health care team, laboratory and diagnostic imaging investigations, issues relating to data management, transitions between different care settings, and chart/patient record com- pleteness. This work lays the foundation for a range of research initiatives that aim to promote a more comprehensive appreciation of the burden of unsafe primary care, develop understanding of the main areas of risk, and identify interventions that can enhance the safety of primary care provision internationall

Daily peak electricity load forecasting in South Africa using a multivariate non-parametric regression approach

Accurate prediction of daily peak load demand is very important for decision makers in the energy sector. This helps in the determination of consistent and reliable supply schedules during peak periods. Accurate short term load forecasts enable effective load shifting between transmission substations, scheduling of startup times of peak stations, load flow analysis and power system security studies. A multivariate adaptive regression splines (MARS) modelling approach towards daily peak electricity load forecasting in South Africa is presented in this paper for the period 2000 to 2009. MARS is a non-parametric multivariate regression method which is used in high-dimensional problems with complex model structures, such as nonlinearities, interactions and missing data, in a straight forward manner and produces results which may easily be explained to management. The models developed in this paper consist of components that represent calendar and meteorological data. The performances of the models are evaluated by comparing them to a piecewise linear regression model. The results from the study show that the MARS models achieve better forecast accuracy