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 delivery dispatching problem with time windows for urban consolidation centers

This paper addresses the dispatching problem faced by an urban consolidation center. The center receives orders according to a stochastic arrival process and dispatches them in batches for the last-mile distribution. The operator of the center aims to find the cost-minimizing consolidation policy, depending on the orders at hand, preannounced orders, and stochastic arrivals. We present this problem as a variant of the delivery dispatching problem that includes dispatch windows and define a corresponding Markov decision model. Larger instances of the problem suffer from intractably large state-, outcome-, and action spaces. We propose an approximate dynamic programming (ADP) algorithm that can handle such instances, using a linear value function approximation to estimate the downstream costs. To design the value function approximation, we construct various sets of basis functions, numerically evaluate their suitability, and discuss the properties of good basis functions for the dispatching problem. Numerical experiments on toy-sized instances show that the best set of basis functions approximates the optimal values with an error of less than 3%. To cope with large action spaces, we formulate an integer linear program to be used within our ADP algorithm. We evaluate the performance of ADP policies against four benchmark policies: two heuristic policies, a direct cost minimization policy, and a post-decision rollout policy. We test the performance of ADP on a variety of networks. ADP consistently outperforms the benchmark policies, performing particularly well when there is sufficient flexibility in dispatch times