The goal of a {\em foreign exchange} (Forex) trader is to trade currency pairs for one another, based on observed market behaviour, with the goal of finding a suitable trade-off between maximising returns and minimising risk. Human traders are, however, both ineffective and inefficient decision makers. This prevents them from making consistent and unbiased trading decisions. A logical solution to this problem is the adoption of a trading algorithm capable of making trading decisions without human intervention. Frameworks in the literature that guide human traders during the algorithm development process, however, fail to exploit the potential benefits of incorporating ensembling principles and are often not applicable to the Forex market. A framework for the design of an intraday ensemble-based trading algorithm is therefore proposed in this paper, tailored for the Forex market. Ensemble principles are incorporated into this framework which may be thought of as a roadmap for designing a trading procedure capable of trading signal generation based on market forecasts. The practicality of the framework is illustrated in the form of a validation case study during which a proof-of-concept trading algorithm is designed according to the framework and tested in respect of a portfolio of four Forex currency pairs. The trading results returned by the algorithm demonstrate that it benefits from an ensemble process in terms of both forecasting accuracy achieved and profits generated.
This paper applies the symmetric and asymmetric Heston-Nandi models to the South African equity options market. The overall objective is the pricing of South African Volatility Index (SAVI) futures, which can be used as volatility hedge instruments. The pricing performance of the symmetric and asymmetric Heston-Nandi models is tested. In addition, the accuracy of the implied SAVI is also illustrated. The asymmetric model is more reliable when the pricing performance and implied SAVI are considered, consistent with previous findings in the literature.
Following the discussions documented by [3], guidelines were devised to support active earlycareer,emerging doctoral supervisors in Statistics in South Africa. These guidelines developedare incorporated into a guiding rubric and this associated usage manual. The rubricis not presented as a formal set of rules, but rather a dynamic document encouraging thegrowth of both the novice supervisor and the doctoral student. The rubric need not be usedin its entirety either, since it is only intended to aid in the supervision process within thediscipline of Statistics; it is not meant to be overwhelming or overbearing in the supervisionprocess.We present the current evolution of this guiding rubric, after discussions with, and feedbackfrom, both novice and senior supervisors within South Africa over the period from 2022-2024.If the rubric is used, the current paper and the conference paper [14] should be cited.This document is compiled with the intention of improving the potential for emerging supervisorsin Statistics to be appointed as primary supervisor, while allowing for the inclusion ofsenior academics as expert co-supervisors or mentors in the team. While the developmentof the doctoral student is the primary goal of this guiding rubric, the development of thenovice supervisor is also important as a secondary goal, contributing to the sustainability ofacademia in Statistics.
The study aims to improve average emergency vehicle (EV) response times by proactivelyavoiding pre-emption conflicts by integrating emergency vehicle signal pre-emption (EVSP)and route selection. Pre-emption in this work means to anticipate an emergency vehicleapproaching a signalled intersection, and attempting to ensure it is provided with right ofway. While literature highlights the success of EVSP strategies, particularly when integratedwith route selection, few strategies consider pre-emption conflicts that can cause delays toEVs. An EVSP and route selection strategy capable of proactively avoiding pre-emptionconflicts was subsequently proposed in this study. Using simulation modelling, the twostrategies were compared in a series of carefully constructed experimental runs. To testthe robustness of the proposed strategy, each experimental run was configured accordingto a unique combination of variables. It was empirically observed that, while not robustagainst these variables, the proposed strategy could perform equally well or better than thereference strategy (when considering average EV response times) in most instances. Thisindicates that an EVSP and route selection strategy capable of proactively anticipating andattempting to avoid pre-emption conflicts is a promising approach to reducing the responsetimes of EVs.
Portfolio decisions involve selecting a subset of alternatives that together maximize some measure of value, subject to resource constraints. Exact methods are available to solve portfolio decision problems, but these require time, expertise and effort that may not always be available. In response, recent research has proposed a number of computationally simple, psychologically plausible rule-based heuristics for portfolio decision making. Simulation studies have shown that these portfolio heuristics perform well relative to exact approaches, but portfolio selection heuristics have yet to be applied in a real-world setting. Our study addresses this gap by using portfolio heuristics to support the selection of research grant proposals at a research institute in South Africa. We compare results obtained with portfolio heuristics to those obtained using two more traditional form of decision support, the standard linear-additive portfolio model, and robust portfolio modelling. We found that portfolios constructed using portfolio heuristics yielded over 90% of the value of optimal portfolios, selected slightly different portfolios potentially useful in a sensitivity analysis role, and were experienced as providing transparent and easy to understand decision support. Heuristic portfolios were slighly but consistently outperformed by those generated by robust portfolio modelling. Collectively, our study contributes to the growing body of evidence supporting the use of psychological heuristics in the realm of portfolio decision-making.
