Instructions to Authors for SA Actuarial Journals
Individuals or teams planning to submit papers to the SAAJ for publication are advised to read through the following documents to ensure that their drafts are consistent with SAAJ standards. For those papers accepted for publication, this would help to ensure a smoother transition to completion of the process.
South African Actuarial Journals
For assistance or additional information regarding the SA Actuarial Journals you can contact us on: email@example.com
- SA Actuarial Journal 2017
- SA Actuarial Journal 2016
- SA Actuarial Journal 2015
- SA Actuarial Journal 2014
- SA Actuarial Journal 2013
- SA Actuarial Journal 2012
- SA Actuarial Journal 2011
- SA Actuarial Journal 2010
- SA Actuarial Journal 2009
- SA Actuarial Journal 2008
- SA Actuarial Journal 2007
- SA Actuarial Journal 2006
- SA Actuarial Journal 2005
- SA Actuarial Journal 2004
- SA Actuarial Journal 2003
- SA Actuarial Journal 2002
- SA Actuarial Journal 2001
Authors: Dave Strugnell and Shivani Ranchod
ABSTRACT: We employ survival analysis to investigate throughput rates, and certain demographic and educational factors that exert a significant influence on them, in the Actuarial Science programme at the University of Cape Town. The results contextualise the huge transformation challenge facing the profession, and also point to some of the features of the educational landscape which have the power to overcome them.
Authors: Nick (Nikolaos) Georgiopoulos
ABSTRACT: Primary life insurers need to calculate life reinsurance recoverables for excess-of-loss life reinsurance treaties for solvency purposes as in Solvency II. However, assuming deterministic mortality, the recoverables of excess-of-loss treaties could be zero because the surviving lives are too few to trigger the excess-of-loss barrier. Resorting to simulation may be cumbersome as it may call for blending into a deterministic mortality model such as those of commercial vendors. In this paper we describe an alternative method to avoid simulation that is fast and accurate and can easily be blended into existing commercial software. The results can be used in many instances such as supervisory reporting, reinsurance pricing and risk management.
Authors: Emlyn Flint and Eben Maré
ABSTRACT: In this research we describe how forward-looking information on the statistical properties of an asset can be extracted directly from options market data and demonstrate how this can be practically applied to portfolio management. Although the extraction of a forward-looking risk-neutral distribution is well-established in the literature, the issue of estimating distributions in an illiquid market is not. We use the deterministic SVI volatility model to estimate weekly risk-neutral distribution surfaces. The issue of calibration with sparse and noisy data is considered at length and a simple but robust fitting algorithm is proposed. We further attempt to extract real-world implied information by implementing the recovery theorem introduced by Ross (2015). Recovery is an ill-posed problem that requires careful consideration. We describe a regularisation methodology for extracting real-world implied distributions and implement this method on a history of SVI volatility surfaces. We analyse the first four moments
from the implied risk-neutral and real-world implied distributions and use them as signals within a simple tactical asset allocation framework, finding promising results.
Editorial: Research is a sound investment
Abstracts of recent postgraduate theses and dissertations at South African universities
Abstracts of articles in other South African journals
Author: N van Zyl and DJJ van Zyl
ABSTRACT: The significant shift from defined benefit to defined contribution retirement funds in South Africa has led to many fund members bearing responsibility for a range of risks. Many of these risks, such as those related to investment, longevity and cognitive deterioration are unavoidable. Another category of risk is that related to the choices made by government, employers, trustees, advisors and/or individuals at either national, scheme or individual level. These choices may also pose a threat to a member’s financial wellbeing in retirement. Behavioural economics and finance helps to explain the choices made by these stakeholders in the retirement industry. The authors explain this concept in the context of industry stakeholders and the unique South African economic and demographic landscape, focusing on defined contribution retirement funds. Key behavioural insights applicable to the retirement industry are explored and, where practical, illustrated by stakeholder behaviour. Possible ways to harness these insights in order to improve retirement wellbeing are then discussed.
Authors: ML Strydom, D Corubolo and C Nel
ABSTRACT: This research investigates the impact of improved (and improving) mortality experience in South Africa as a result of the increased (and increasing) access to antiretroviral treatment on South African life assurers, the entry-level insurance market and the wider South African economy. The research focuses on various potential impacts on the entry-level insurance market, including new business profitability, product development and pricing, market penetration and the potential for increased savings. This research has been done with the assistance of four of the main South African life offices and also draws on the new THEMBISA AIDS model on which a working paper has been produced. The research is based on the THEMBISA model in order to investigate the potential impact of alternative mortality scenarios on typical entry-level products within the industry where the scenarios have been based on actual current and proposed antiretroviral roll-out strategies by the Department of Health. Potential improvements to profitability, premium reductions, benefit enhancements and cashback benefits are quantified using a profit test model for entry-level market products.
Authors: ML Smith, FJC Beyers and JP de Villiers
ABSTRACT: No analytic procedures currently exist for determining optimal artificial neural network structures and parameters for any given application. Traditionally, when artificial neural networks have been applied to financial modelling problems, structure and parameter choices are often made a priori without sufficient consideration of the effect of such choices. A key aim of this study is to develop a general method that could be used to construct artificial neural networks by exploring the model structure and parameter space so that informed decisions could be made relating to the model design. In this study, a formal approach is followed to determine suitable structures and parameters for a Feed Forward
Multi-layered Perceptron artificial neural network with a Resilient Propagation learning algorithm with a single hidden layer. This approach is demonstrated through the modelling of four South African economic variables, namely the average monthly returns on the money, bond and equity markets as well as monthly inflation. Artificial neural networks can be constructed on the aforementioned variables in isolation or, jointly, in an integrated model. The performance of a range of more traditional time series models is compared with that of the artificial neural network models. The results suggest that, on a statistical level, artificial neural networks perform as well as time series models at forecasting the returns for financial markets. Hybrid models, combining artificial neural networks with the time series models, are constructed, trained and tested for the money market and for the rate of inflation. They appear to add value to the time series models when forecasting inflation, but not for the money market.