54690 – 879 (180) MCom (Financial Risk Management) – thesis option
54690 – 889 (180) MCom (Financial Risk Management) – assignment option
See the Faculty Calendar here for a detail description of this program. Modules presented in these
programmes:
NA - This module is not presented in 2023.
* Statistical Learning Theory (Honours module) - see content at Honours Mathematical Statistics
** Alternative Investments (new content)
Module Content - Masters
Extreme Value Theory A & B (10441-813 & 10442-843)
Objectives and
content: Extreme value Theory (EVT) entails the study of extreme events, i.e. unusual events rather than usual events as in more traditional statistics. In order to do
this, theory has been developed that describes behavior in the tails of distributions. These results are analogous to the results of central limit theory and in a similar way
transforms problems of unknown underlying distributions to parametric problems where only parameters are unknown. Techniques have been developed to carry out inference on these
parameters and to apply them to data sets where understanding behavior in the tails of distributions, is important. In these modules the mathematical and practical aspects of the
theory and inference techniques will be studied.
Advance Financial Risk Management A & B(Matlab) (10501-831 & 10503-861)
Objectives and content (Adv FRM-A): The aim of this model is to give the Financial Risk Management Masters students some
introductory background to Statistical Learning theory. Statistical learning is a relatively new area in statistics. It is concerned with modeling and understanding patterns in
complex datasets. With to the explosion of "Big Data", there is currently a high demand for individuals with expertise in statistical learning. The methods studied in
this module include regularised regression by means of ridge regression and the lasso; classification using linear discriminant analysis, logistic regression, quadratic
discriminant analysis and k-nearest neighbors; resampling methods such as k-fold cross-validation, leave-one-out cross-validation and the bootstrap; linear model selection and
dimension reduction methods; handling non-linearity via regression splines, smoothing splines, local regression, generalised additive models, bagging, random forests and boosting;
and non-linear classification and regression by means of support vector machines. The objectives of the module are to equip students with the following knowledge and skills:
- the theory underlying the above statistical learning techniques;
- application of statistical learning methods in a programming environment;
- assessment and comparison of various models;
- interpretation and effective (written and verbal) communication of results.
We extensively make use of the R programming language, therefore note that the R course is a prerequisite.
Objectives and content (Adv
FRM-B{Matlab}): The aim of the model is to teach students how to apply MATLAB in advance
Financial Risk modelling. The module consists of a series of lectures, demonstrations, and assignments covering the key ideas and applications in finance and risk management of
quantitative modelling. It covers a variety of practical quantitative models and building blocks that will allow you to create your own models using MATLAB. The topics covered
include the fundamentals of Monte Carlo and Quasi Monte Carlo simulation techniques, Financial Instrument Pricing models, Interest Rate models, Value at Risk and Principal
Components Analysis.
Advance Financial Risk Management Programming
(10504-835)
Objectives and content: This module has been compiled in such a manner that it provides to the student an overview of credit risk from a scoring, accounting impairments and regulatory
impairments perspective and the using of SAS in this respect. The major topics that will be covered in this module are as follows: Introduction to Credit Risk Analytics,
Introduction to SAS Software, Exploratory Data Analysis, Data Preprocessing for Credit Risk Modelling, Credit Scoring, IFRS 9 in a nutshell, Probability of Default, Loss Given
Default, Basel in a nutshel.
Advance Portfolio Management Theory A & B(VaR) (10517-833 &
10518-863)
Objectives and content (Adv PMT-A): The
overriding aim of this module is to provide students with a background to the risks in the asset management. Students will be encouraged to evaluate the relevance of information
that is controversial, ambiguous and requires (ethical) discretion in their decision making. The intention is to keep the course substantially less quantitative in its content
than the other course offered in the Department. The following topics are covered: Fiduciary preferences/utility function, Habits of Prudence, Benchmarks (Arnott),
Generalised Law of Active Management, Investment philosophy (Minahan), Holdings data analysis, Manager’s incentives, Liquidity risk: Forecasting crisis & Risk Management
lessons, Strategy for gated assets in Hedge Funds, Liquidity risk & horizon uncertainty, Volatility/Risk management, Commodities and portfolio construction, Currency market
operations, Transition management, Portfolio optimization with Black-Litterman.
Objectives and content (Adv PMT-B {VaR}): In this
module the underlying theory regarding Value-at-Risk (VaR) is studied and practically applied. The following topics are covered: Value at Risk (VaR) and Other Risk Metrics,
Parametric Linear VaR Models, Historical simulation, Monte Carlo VaR, VaR for Option Portfolios, Risk model risk, Scenario analysis and stress testing, Capital
allocation.
Credit Derivative Instruments A
(10575-834)
Objectives and content:
This module has been compiled in
such a manner that it provides to the student an overview of the nature and scope of credit related fair valuation adjustments, or in general, xVA. The major topics that will be
covered in this module are as follows: Introduction, Global financial crisis, and the general OTC derivatives market, counterparty risk, netting, close-out and related aspects,
collateral, credit exposure and funding, capital requirements and regulation, counterparty risk intermediation, quantifying credit exposure, exposure and the impact of collateral,
default probabilities, credit spreads and funding costs, discounting and collateral, credit and debit value adjustments, and funding value adjustments.