Bootstrapping the OIS curve in a South African bank

The financial crisis in 2007 highlighted the credit and liquidity risk present in interbank (LIBOR) rates, and resulted in changes to the pricing and valuation of financial instruments. The shift to Overnight Indexed Swap (OIS) discounting and multi-curve framework led to changes in the construction of interest rate zero curves, with the OIS curve being central to this methodology. Developed markets, such as the European (EUR), were able to adopt this framework due to the existence of a liquid OIS market. In the case of the South African (ZAR) market, the lack of such tradeable instruments poses the issue of how to construct or infer the OIS curve. Jakarasi et al. (2015) proposed a method to infer the OIS curve through the statistical relationship between SAFEX ROD and 3M JIBAR. The extension of the statistical relationship used by Jakarasi et al. (2015) to more statistically rigorous models, capable of capturing more information relating to the relationship between the rates, arises from the expected cointegrating relationship exhibited between rates. This dissertation investigates the implementation of such statistical models to infer the OIS curve in the ZAR market

Confirmation: A crucial step in copy number variation analysis after exome sequencing in intellectual disabilities

Intellectual disability (ID) comprises a group of mental disorders which have underlying genetic causes, among which the monogenic causes are one of the causes for ID. One kind of a monogenic cause is the copy number variations (CNVs). These CNVs can be indicated using exome sequencing (ES) and the CoNVex and CoNIFER algorithms. To confirm the possible causative CNVs quantitative PCR (QPCR) was used. In a Pakistani ID patient a homozygous deletion of ENTPD3 was indicated and in an Estonian ID patient CPVL-CHN2 a homozygous duplication was indicated. However the QPCR showed that ENTPD3 did not segregate and CPVL-CHN2 was only duplicated heterozygous. Confirmation, like QPCR, is therefore a crucial step in confirming CNVs analysis of ES in ID patients

Donald Duck Holiday Game: A numerical analysis of a Game of the Goose role-playing variant

The 1996 Donald Duck Holiday Game is a role-playing variant of the historical Game of the Goose, involving characters with unique attributes, event squares, and random event cards. The objective of the game is to reach the camping before any other player does. We develop a Monte Carlo simulation model that automatically plays the game and enables analyzing its key characteristics. We assess the game on various metrics relevant to each playability. Numerical analysis shows that, on average, the game takes between 69 and 123 rounds to complete, depending on the number of players. However, durations over one hour (translated to human play time) occur over 25% of the games, which might reduce the quality of the gaming experience. Furthermore, we show that two characters are about 30% likely to win than the other three, primarily due to being exposed to fewer random events. We argue that the richer narrative of role-playing games may extend the duration for which the game remains enjoyable, such that the metrics cannot directly be compared to those of the traditional Game-of-the-Goose. Based on our analysis, we provide several suggestions to improve the game balance with only slight modifications. In a broader sense, we demonstrate that a basic Monte Carlo simulation suffices to analyze Game-of-the-Goose role-playing variants, verify how they score on criteria that contribute to an enjoyable game, and detect possible anomalies

The Stochastic Dynamic Post-Disaster Inventory Allocation Problem with Trucks and UAVs

Humanitarian logistics operations face increasing difficulties due to rising demands for aid in disaster areas. This paper investigates the dynamic allocation of scarce relief supplies across multiple affected districts over time. It introduces a novel stochastic dynamic post-disaster inventory allocation problem with trucks and unmanned aerial vehicles delivering relief goods under uncertain supply and demand. The relevance of this humanitarian logistics problem lies in the importance of considering the inter-temporal social impact of deliveries. We achieve this by incorporating deprivation costs when allocating scarce supplies. Furthermore, we consider the inherent uncertainties of disaster areas and the potential use of cargo UAVs to enhance operational efficiency. This study proposes two anticipatory solution methods based on approximate dynamic programming, specifically decomposed linear value function approximation and neural network value function approximation to effectively manage uncertainties in the dynamic allocation process. We compare DL-VFA and NN-VFA with various state-of-the-art methods (exact re-optimization, PPO) and results show a 6-8% improvement compared to the best benchmarks. NN-VFA provides the best performance and captures nonlinearities in the problem, whereas DL-VFA shows excellent scalability against a minor performance loss. The experiments reveal that consideration of deprivation costs results in improved allocation of scarce supplies both across affected districts and over time. Finally, results show that deploying UAVs can play a crucial role in the allocation of relief goods, especially in the first stages after a disaster. The use of UAVs reduces transportation- and deprivation costs together by 16-20% and reduces maximum deprivation times by 19-40%, while maintaining similar levels of demand coverage, showcasing efficient and effective operations