Liam, thanks very much for your informative reply - greatly appreciated. I had a look through the course you sent there. It seems that course is more based on the theoretical aspects of mathematical finance and it seems to be somewhat lacking in computation? Whereas, the MSc I am interested in is more computational (of course I could be completely wrong about this). I probably should mention that the course was originally called 'Computational finance and trading' but was changed to quant finance to make it more marketable - apparently. I think the content between these two courses is roughly similar, maybe there is more probability and PDEs in the DCU one. However, the time series course doesn't look very 'advanced' at all.
Unfortunately this uni's course structure isn't quite as easy to get as DCU;
Module Information | Queen's University Belfast
here is a link to the module information of every course at the uni, if you pick finance from the list and scroll down to the bottom of the listed modules you will find the 8 modules I mentioned in my first post and the content of the module and learning outcomes etc.
You'll find out more information about the courses that way but I will copy and paste the course contents here to save trouble.
Asset pricing;
The aims of this module are to:
(i) provide students with the necessary theoretical and analytical tools which underpin the pricing of assets;
(ii) familiarize students with the environment of a trading room
Areas to be covered include:
Overview of main markets; how firms and governments raise finance; financial instruments; trading securities.
Valuation
Valuing stocks.
Asset returns and portfolio theory
Measuring asset returns; theory of choice under uncertainty; mean-variance portfolio theory.
Asset-pricing models
Assessing the theoretical and empirical validity of various asset pricing models.
Equity markets
EMH; anomalies; behavioural finance
Market Microstructure;
The aim of this module is to ensure that students understand the structure, dynamics and trading mechanisms of global financial markets, as well as appreciate the role of key institutions involved in these markets.
Areas to be covered:
1. Firstly, we analyse the role, structure and economic principles of the key players participating in financial markets.
2. Secondly, we examine the function and characteristics of two key markets: fixed income and foreign exchange.
3. Thirdly, we will analyse the trading mechanics of financial markets, and in doing so, we will examine the development and organisation of major exchanges.
Research methods in finance;
The purpose of this course is to provide a comprehensive introduction to econometric techniques used in finance. It contains a treatment of classical regression and an introduction to time series techniques. There will be an emphasis on applied work using econometric packages.
The course is designed to give students both theoretical and practical experience of statistical and econometric techniques. A wide range of topics is typically covered including the basic regression model, which includes a discussion of the classical violations of this model and methods for their correction. Students will learn a computer statistical software package, Stata (I'm going to use R since I already know it). Assessment: assignments and class based assessments.
Also, I already know most of the stuff that will be covered in this module so I am thinking I could use the time to go through Hastie - 'Introduction to Statistical learning' (And hopefully be able to use some techniques in my project) which I think will be valauble to have a working knowledge of for industry
Corporate finance;
Course Description:
The purpose of this course is to analyse how corporations make major financial decisions. The theory of corporate behaviour is discussed and the relevance of each theoretical model is examined by an empirical analysis of actual corporate decision making.
Course Aim:
The aims of this module are to:
(i) familiarize students with the issues confronting corporations when making investment and financing decisions;
(ii) develop the ability of students to obtain corporate information from the Bloomberg database.
Course Coverage:
. Corporate Governance
. Investment Appraisal
. Dividend Policy
. Capital Structure
. Initial Public Offerings
. Mergers and Acquisitions
Derivatives;
The aim of this course is to develop in students a theoretical and practical knowledge of derivative instruments.
This module provides participants with an exhaustive coverage of widely used derivative products stressing pricing and uses for financial engineering and risk management. The module provides an overview of derivative instruments, markets, participants and uses. It focuses on the pricing and uses of futures, forwards and options. The cost of carry relationship, the binomial approach, the Black-Scholes model and its variants are detailed to equip participants with the basic tools for pricing derivatives. The module examines practical uses of derivative securities as risk management tools for corporations and financial institutions.
Areas to be covered include:
THE MOVEMENT OF FUTURES PRICES: some basic facts. CTAs, managed futures, hedge funds. Financialization of Commodity Markets. Time series momentum.
MEAN VARIANCE APPROACHES TO HEDGE RATIO DETERMINATION, STOCK INDEX FUTURES AND HEDGING EFFECTIVENESS: The mean-variance approach to hedge ratio construction. Hedging with stock index futures. Hedging effectiveness and hedge ratio estimation - OLS, ECM and GARCH procedures. Duration and Expiration effects.
THE STOCHASTIC PROCESS OF ASSET PRICES AND THE DERIVATION OF THE BLACK-SCHOLES MODEL:The Wiener process and rare events in financial markets; Ito processes; Ito's lemma; generalised Ito's lemma; Black-Scholes differential equation; Black-Scholes pricing formula; options on stocks paying known dividends; pseudo-American model; option on stock indices, currency options and options on futures;
VOLATILITY: Estimating volatility: historical; implied - application of Newton-Raphson. Empirical characteristics of volatility: smiles; term structure skew; mean reversion; Forecasting volatility: application of GARCH; empirical evidence of volatility forecasts - implied versus historical; Bisection.
EXOTIC OPTIONS: Types of exotic options - barrier options; lookback options; strike options; binary or digital options; compound options; and chooser options.
INTEREST RATE DERIVATIVES: The standard market models; models of short rate; HJM and LMM models.
RISK AND REGULATION WITH EMPHASIS ON VALUE AT RISK: Regulation of Financial Institutions; value at risk and forecast accuracy; capital adequacy and value at risk; value at risk and the variance covariance approach; value at risk and non-parametric methods such as historical simulation and bootstrapping; value at risk and linear and non-linear positions.
CREDIT RISK AND CREDIT DERIVATIVES: Default probabilities; Recovery rates; Default correlation; Credit default swaps; Asset-backed securities.
REAL OPTIONS: The option to expand, contract, default, abandon and switch. The valuation of real options in the face of compoundness, interaction between options and ownership. Real options and the valuation of internet companies.
Computational methods in finance;
The aims of this module are to:
i. develop the students' computational skills
ii. introduce a range of numerical techniques of importance to financial engineering
iii. understand how to develop models using MATLAB
Areas to be covered include:
A primer on derivatives pricing
o Bonds, forwards, options
o Yield curves
o Probability distributions
o Expectation theory
MATLAB
o Arrays and matrices
o Scripts, functions and classes
o Programming constructs
Numerical Methods
o Root finding
o Interpolation
o Linear Algebra
Lattice based models
o Binomial trees
Numerical solutions to stochastic differential equations
o Finite difference methods
The fundamentals of Monte Carlo simulation
o Random number generation
o Monte Carlo integration
o Monte Carlo simulation
o Variance Reduction
Principal Component Analysis
o Dimension reduction
Numerical optimisation
o Model calibration
Time series econometrics;
The aims of this module are to:
(i) provide students with knowledge of the econometric methods and techniques used in the analysis of time series finance information.
(ii) apply the empirical techniques using economic and financial data.
Statistical Properties of Financial Returns
Stylised Facts about Financial Returns; Distribution of Asset Returns; Time Dependency; Linear Dependency across Asset Returns
Univariate Time Series and Applications to Finance
Wold's Decomposition Theory; Properties of AR Processes; Properties of Moving Average Processes; Autoregressive Moving Average (ARMA) Processes; The Box-Jenkins Approach; Example: A Model of Stock Returns
Modelling Volatility - Conditional Heteroscedastic Models
ARCH Models; GARCH Models; Estimation of GARCH Models; Forecasting with GARCH Model; Asymmetric GARCH Models; The GARCH-in-Mean Model
Modelling Volatility and Correlations - Multivariate GARCH Models
Multivariate GARCH Models; The VECH Model; The Diagonal VECH Model; The BEKK Model; The Constant Correlation Model; The Dynamic Correlation Model; Estimation of a Multivariate Model
Vector Autoregressive Models
Vector Autoregressive Models; Issues in VAR; Hypothesis Testing in VAR; Example: Money Supply, Inflation and Interest Rate
Trading principles;
Part 1
These three simulation topics will focus on three distinct issues that form the foundation of modern finance:
Law of one price
Market efficiency
Price formation
Part 2
Automation in trading
Aims and objectives
Part 1
Students will derive core finance concepts by making their own financial decisions under real world conditions, and study several strategies that form the basis of many investment management practices. Students experience a range of environments that require actual valuation, investment, and risk management decisions, that will promote a deep understanding of the functioning of capital markets. The goal is to teach students how to learn finance through a unified framework for understanding relative valuation. This will help them stay current throughout their career.
Part 2
Students will understand the automating of trading and have a broad understanding of the prevalence of algorithms in the financial markets. Students will have hands on experience of algorithmic trading using a python based platform (iPython).
Also, I have uploaded a screenshot from the courses webinar giving details of past student placement. What (I think) is apparent is that there are not really any quant positions listed. Only the data analyst (for local machine learning fintech firm which is pretty cool), trade analyst and trader jobs look decent.
Hope this is helpful/good amount of information about the course.
Thanks very much!
