Honours Moudles

 

Lecturer: Dr J Hugo

 

This module aims to integrate theoretical and practical components of multivariate statistical analysis. 

 

Core content: Matrix theory, partition matrices and Kronecker products, solutions of linear equations, latent roots and latent vectors of matrices, multivariate normal distribution, matrix normal distribution, moments, quadratic forms, estimation of parameters, the Wishart distribution, assessing multivariate normality, outliers in multivariate samples, tests on one or two mean vectors, profile analysis, tests on covariance matrices, discriminant analysis (description of group separation), classification analysis (allocation of observations to groups), principal component analysis, multivariate multiple regression. 

 

 

 

Lecturer: Dr WJ Brettenny

 

This module covers the theory and application of quantitative statistical method used for data analytics. R is used as the software of tuition. 

 

Core content: Cross validation methods, bias-variance trade-off, linear regression methods, knn regression, nonparametric regression, local regression, knn classification, naïve Bayes, discriminant analysis, logistic regression, support vector machines, classification trees, random forests, model accuracy assessments, ROC curves, precision-recall curves, k-means clustering, hierarchical clustering. 

 

 

 

Lecturer: Mr Friskin - friskin@gmail.com

 

This module covers the theory and application of quantitative statistical method used for data analytics. R is used as the software of tuition. 

 

Core content: Cross validation methods, bias-variance trade-off, linear regression methods, knn regression, nonparametric regression, local regression, knn classification, naïve Bayes, discriminant analysis, logistic regression, support vector machines, classification trees, random forests, model accuracy assessments, ROC curves, precision-recall curves, k-means clustering, hierarchical clustering. 

 

 

 

Lecturer: Prof I Litvine

 

This module will prepare students to use various mathematical and computational financial models for statistical analysis of financial data. 

 

Core Content: Critical and coherent understanding and application of the underlying principles of: risk-return relationships, concepts of risk free investment, adjusted performance; shortfall risk, and safety rule, Sharpe ratio, security market line, Models for risk diversification, systematic and non-systematic risks. Markovitz efficient frontier, Models for asset price fluctuations, volatility, Introduction to efficient market theory, Introduction to asset valuations: arbitrage-free valuation principles, Models for arbitrage-free valuation of bonds and shares. 

 

Note: The honours students will be required to complete a research project in addition to the aforementioned modules. The credit and NQF levels are provided below.