At the end of the module, the learner should be able to explain confidently basic concepts used in financial mathematics, to simulate the prices of financial products (fixed income products and derivatives) using available IT software (R, Python). The contents of this module to include arbitrage theory, pricing derivatives, martingales and martingale representations, differentiation in stochastic environments, the Wiener process, Levy processes and rare events in financial markets, Integration in stochastic environments and Ito's formula and its usage in financial mathematics.
The module general objective is to introduce the leaners to survival and clinical data analysis. The contents for the module shall include survival, hazard and cumulative hazard functions, censoring, Kaplan Meier survival curve, parametric models and estimation of parameters in these models, nonparametric models, comparison of two groups including log-rank test, Inclusion of the covariates. Proportional hazard model including application of model checking, computation of risks and extensions, clinical trials, uncontrolled and blind trials, forms of data and data management. Some computer labs will also be organized where R or Stata can be used.
Actuarial mathematics is a field of financial mathematics which focus on risks measurements particularly insurance industry. Students undertaking this module are thus trained to apply financial mathematics in insurance industry. It is thus reasonable in the contents of the course to have some reviews of financial mathematics, mortality concepts and mortality tables, some annuities computation, life insurance, premium computations and reinsurance.
The aims of this course is to enable students to acquire active knowledge and understanding of some basic concepts in Decision analysis, It gives a mathematical description of the decision analysis under certain circumstances. Decision under Certainty, uncertainty and under risk. Decision tree and ends with the decision making in light of competitive actions (game theory).
During the teaching sessions, the following will be covered: Introduction to Decision Analysis, Decision making under Certainty, Decision making under uncertainty, Decision making under risk, Decision making with perfect information, Decision making with imperfect information, Decision tree, Decision making and utility, Decision making criteria. Decision making in light of competitive actions, Network analysis and Game theory.
Learning Outcomes
- Should have a reasonable understanding of the definitions and terms related to the Module aims at as well as the Course Contents.
- Should have a reasonable understanding of the statements, proofs and implications of the basic results.
- should be able to practice the application of theoretical results using SAS
- should be able to present simple arguments and conclusions using Decision analysis in making decisions
This module focuses on analysis of problems in biology by applying the techniques of mathematical modelling mainly using differential equations along with numerical solution techniques. The module presents models of population dynamics, epidemics, biochemical reaction networks and molecular networks (metabolic reactions and gene regulation).
This module is intended to provide students with the theoretical understanding needed to underpin econometric work in the business area. It will create an improved awareness, through the analysis of economic data, of the econometric techniques available to decision makers, and use practical applications to explore the value of econometric methods in decision support and evaluate their limitations.