Course Structure

The MSc Risk and Investment Management is a year-long full-time programme delivered in-person. Students start with an intensive induction week before undertaking eight modules across the first and second semesters. In the final semester, students either study an Applied Research Project or Dissertation.

Semester 1	Asset Pricing Corporate Finance Financial Regulation and Risk Management Financial Data Analytics
Semester 2	Credit Risk Management Derivatives Enterprise Risk Management and Risk Analytics Advanced Financial Data Analytics
Semester 3	Dissertation Or Applied Research Project

Career Prospects

Introduction
Risk management is currently, and will continue to be, a hot spot for graduate recruitment. As many financial firms are substantially expanding their risk management functions, this Masters degree is likely to open a wide range of new and exciting career opportunities. After graduation there is a wide variety of roles available for our students, some of which will suit those with mathematical skills who wish to utilise cutting-edge quantitative modelling techniques and work in collaboration with traders to develop bespoke financial products. Other roles involve portfolio and product risk management or monitoring firm-wide business risks either in-house or as part of an external regulatory team. Graduates from this degree have quickly secured high-quality posts, most of which are in risk management functions.

Graduates have gone on to work for companies/institutions such as: EY, Accenture, HSBC, KMC Wealth Management, University of Bath, Queen’s University Belfast, CCGroup Inc., CNO Financial Group, Clune Construction Company, Edelweiss Financial Services, Home Credit China.

Working in roles such as: Financial Services Assurance, Management Consultant (Finance and Risk), Global Graduate Analyst, Product Support Associate, Data Analyst, Assistant Project Manager, Associate — Structured Finance, Team Lead, PhD Candidate.

Located in: Frankfurt, Dublin, San Francisco Bay Area, Chicago, Belfast, Greater New York City Area, New Delhi, Shenzhen, Kuala Lumpur.

Queen's postgraduates reap exceptional benefits. Unique initiatives, such as Degree Plus and Researcher Plus bolster our commitment to employability, while innovative leadership and executive programmes alongside sterling integration with business experts helps our students gain key leadership positions both nationally and internationally.
http://www.qub.ac.uk/directorates/sgc/careers/

Learning and Teaching

Learning opportunities available with this course are outlined below:

Assessment

Assessments associated with the course are outlined below:

Modules

• Year 1

  Core Modules

  Asset Pricing (15 credits)

  ×

  Asset Pricing

  Overview

  Course Content
  
  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:
  
  Financial markets
  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

  Learning Outcomes

  Upon successful completion of this module, students will:
  1. Be familiar with the various theories on individuals’ investment decision making
  2. apply techniques for formally assessing risk.
  3. understand the methodologies employed in investigating asset pricing behaviour in the capital market
  4. be able to critically evaluate the various asset pricing models in terms of both theory and empirical evidence
  5. be able to critically appraise the EMH, anomalies and behavioural finance.
  6. be familiar with the trading-room environment and the Bloomberg database.

  Skills

  This module provides opportunities for the student to acquire or enhance the following skills:-
  • Subject-specific skills
  o Use of computer-based packages to analyse and evaluate relevant data
  o Ability to criticially read and evaluate finance and risk-related academic literature
  o Appreciation, construction and analysis of financial and economic models of practical risk situations
  
  • Cognitive Skills
  o Problem solving
  o Logical reasoning
  o Independent enquiry
  o Criticial evaluation and interpretation
  o Self-assessment and reflection
  
  • Transferable Skills
  o The ability to synthesis information/data from a variety of sources
  o Preparation and communication of ideas in both written and presentational forms
  o Ability to work both independently and in groups
  o Organisation and Time Management
  o Use of IT.

  Coursework

  40%

  Examination

  60%

  Practical

  0%

  Credits

  15

  Module Code

  FIN7026

  Teaching Period

  Autumn

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Times-Series Financial Econometrics (15 credits)

  ×

  Times-Series Financial Econometrics

  Overview

  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

  Learning Outcomes

  Upon successful completion of this module students will be able to:
  1. critically analyse, estimate and forecast using AR, MA, and ARMA models
  2. apply the Box-Jenkins approach to time series models
  3. model and forecast volatility using autoregressive conditional heteroscedastic (ARCH) models
  4. estimate, interpret, and forecast with generalised autoregressive conditional heteroscedastic (GARCH) models
  5. test for spill-over of volatility between assets
  6. use vector autoregressive (VAR) models to analyse and interpret interaction between financial variables
  7. examine critically evaluate the impact of shocks on financial variables using impulse response analysis

  Skills

  This module provides opportunities for the student to acquire or enhance the following skills:-
  Subject-specific Skills
  • The ability to construct arguments and exercise problem solving
  skills in the context of theories of finance and risk management
  • The ability to use computer-based mathematical / statistical /
  econometric packages to analyse and evaluate relevant data
  • The ability to read and evaluate finance and risk-related
  academic literature
  
  Cognitive Skills
  • Problem solving
  • Logical reasoning
  • Independent enquiry
  • Critical evaluation and interpretation
  • Self assessment and reflection
  
  Transferable Skills
  • The ability to synthesise information/data from a variety of sources including from databases, books, journal articles and the internet
  • The preparation and communication of ideas in finance, information economics and risk management in both written and presentational forms
  • The ability to work both independently and in groups
  • Organisation and time management
  • Problem solving and critical analysis
  • Work-based skills; use of IT, including word-processing, email, internet and statistical/econometric/risk management packages
  • The ability to communicate quantitative and qualitative information together with analysis, argument and commentary

  Coursework

  100%

  Examination

  0%

  Practical

  0%

  Credits

  15

  Module Code

  FIN7028

  Teaching Period

  Spring

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Enterprise Risk Management & Risk Analytics (15 credits)

  ×

  Enterprise Risk Management & Risk Analytics

  Overview

  Course Description:
  
  Enterprise Risk Management & Risk Analytics examines two distinct and increasingly important areas of risk management. The first part of the course focuses on the management of risk at the enterprise level, examining how companies are addressing all their key risks on a consistent integrated basis. The second half of the course covers the most important principles, techniques and tools in Quantitative Risk Analysis with a focus on Monte Carlo simulation. Students will be introduced to risk analysis and modelling using ModelRisk from Vose Software.
  
  Course Aim:
  The aims of part one of this module are to:
  
  (i) Enable students to develop an understanding of how corporate risk management has progressed from a traditional approach to a more holistic integrated approach.
  
  (ii) Provide students with an appreciation of the benefits of an integrated approach to risk management and why it should create value.
  
  (iii) Properly define and categorise risks within an Enterprise Risk Management framework and contrast this with a small to medium enterprise risk framework.
  
  (iv) Provide an understanding of Economic Capital calculation and illustrate how quantitative risk management is utilised in corporate decision making.
  
  The aims of part two of this module are to:
  (i) Introduce the most important techniques and tools in Quantitative Risk Analysis.
  
  (ii) Familiarise students with risk analysis modelling environments in Microsoft Excel using ModelRisk software from Vose Software.

  Learning Outcomes

  Upon successful completion of this module, students will be able to:
  
  • Describe and synthesize the theory and rationale behind a corporation’s motivation to implement an Enterprise Risk Management programme.
  • Understand how risks may be categorised and thus develop an appropriate business risk framework.
  • Outline the main components of the Enterprise Risk Management framework
  • Understand how Economic Capital measurement and management are key components in shaping corporate strategy
  • Build a Monte Carlo risk model within Microsoft Excel using ModelRisk from Vose Software.
  • Present the results of a quantitative risk model in an appropriate format which can be effectively interpreted by the end-user.

  Skills

  This course provides opportunities for the student to acquire or enhance the following skills:-
  
  Subject-specific skills:
  
  • The ability to construct arguments and exercise problem solving skills in the context of theories of risk management.
  • The ability to read and evaluate finance and risk-related academic literature.
  • The ability to appreciate, construct and analyse mathematical, statistical, financial and economic models of practical risk situations
  • The ability to use Microsoft Excel based quantitative risk analysis software (ModelRisk) to analyse and evaluate uncertainty.
  
  Cognitive skills:
  
  • Problem solving
  • Logical reasoning
  • Independent enquiry
  • Critical evaluation and interpretation
  • Self assessment and reflection
  
  Transferrable skills:
  
  • The ability to synthesise information/data from a variety of sources including from databases, books, journal articles and the internet.
  • The preparation and communication of ideas in finance, information and risk management in both written and presentation forms.
  • The ability to work both independently and in groups.
  • Organisation and time management.
  • Problem solving and critical analysis.
  • Work-based skills; use of IT, including word-processing, email, internet and statistical risk management packages.
  • The ability to communicate quantitative and qualitative information together with analysis, argument and commentary in a form appropriate to different intended audiences.

  Coursework

  40%

  Examination

  60%

  Practical

  0%

  Credits

  15

  Module Code

  FIN7031

  Teaching Period

  Spring

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Applied Research Project (60 credits)

  ×

  Applied Research Project

  Overview

  The applied research project provides students with the opportunity to utilise the knowledge and skills acquired over the previous two semesters to plan, develop and produce a substantial piece of original, independent applied research.
  Lectures and computer-based workshops will cover the following areas:
  1. Research Methodology
  2. Fundamental analysis and strategy analysis
  3. Data Management, Analysis, Visualisation and Inference
  4. Financial analysis [ratios/cash flows], forecasting profit & EPS.
  5. Valuation 1: DDM and DCF approach
  6. Valuation 2: EVA and Price- multiples
  7. Critical assessment of model adequacy
  8. Presenting Information and Data

  Learning Outcomes

  Upon successful completion of this project, students will:
  1. Demonstrate an ability to design and manage a piece of individual research.
  2. Apply knowledge and skills developed in previous modules to contemporary issues in financial markets.
  3. Establish links between financial theory and financial practice.
  4. Exhibit intellectual discipline in identifying and critique the appropriate information.
  5. Identify appropriate econometric methods for critically analysing a contemporary issue in finance.
  6. Critically evaluate the appropriateness of modelling assumptions.
  7. Present their thinking in a professional industry-style research paper.

  Skills

  This applied research project provides opportunities for the student to acquire or enhance the following skills:-
  · Subject-specific skills
  -Use of computer-based packages to analyse and evaluate relevant data
  -Ability to critically read and evaluate finance and risk-related academic literature
  -Appreciation, construction and analysis of financial and economic models of practical risk situations
  · Cognitive Skills
  -Problem solving
  -Logical reasoning
  -Independent enquiry
  -Critical evaluation and interpretation
  -Self-assessment and reflection
  -Intellectual humility
  -Intellectual discipline
  · Transferable Skills
  -The ability to synthesis information/data from a variety of sources
  -Preparation and communication of ideas in both written and presentational forms
  -Ability to work both independently
  -Organisation and Time Management
  -Use of IT

  Coursework

  70%

  Examination

  0%

  Practical

  30%

  Credits

  60

  Module Code

  FIN9100

  Teaching Period

  Summer

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  MSc Risk Management and Financial Valuation Dissertation (60 credits)

  ×

  MSc Risk Management and Financial Valuation Dissertation

  Overview

  The aim of the dissertation is to provide students with the skills needed for the advanced analysis of relevant datasets, to allow them to demonstrate an understanding of the relevant literature and to derive and test hypotheses and to draw appropriate conclusions.

  Learning Outcomes

  On completion of the dissertation students will have an understanding of:-
  
  • how to conduct a review of the current and relevant literature of the subject area chosen for the research study;
  • how to derive hypotheses or formulate research questions;
  • how to use data extracted from datasets or interviews to test hypotheses or answer research questions;
  • how to draw conclusions and identify the limitations of the study and scope for further research.

  Skills

  This course provides opportunities for the student to acquire or enhance the following skills:-
  
  • Communication
  • Effective and independent learning
  • Specific research skills relevant to the chosen research topic
  • Data analysis skills relevant to the chosen research topic
  • Econometric skills

  Coursework

  100%

  Examination

  0%

  Practical

  0%

  Credits

  60

  Module Code

  FIN7025

  Teaching Period

  Summer

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Credit Risk Management (15 credits)

  ×

  Credit Risk Management

  Overview

  The aims of this course are to:
  
  i. Profile the development of credit culture and its key role in the global economy. Identify the key credit players, their roles and the interaction between key players.
  
  ii. Introduce students to the techniques used to quantify credit risk. The incremental nature of model development is highlighted and the strengths and weaknesses of various techniques are discussed.
  
  iii. Provide students with an understanding of basic credit derivatives and securitised credit products set against a backdrop of how the complexity of these instruments contributed to the recent financial crisis.
  
  Areas to be covered include:
  
  Background:
  History & Evolution of Credit Culture
  
  The Key Players:
  Banks, Savings Institutions, Insurance Companies, Finance Companies, Special Purpose Entities, Rating Agencies
  
  Classic Credit Risk Modelling
  Financial Ratio Analysis, Strategic Industry Analysis
  
  Accounting Based Credit Risk Models
  The Altman Z Score, The Zeta Score, RiskCalc by Moody’s, S&P Credit Model, Neural Networks, Mortality Models
  
  Market Based Credit Risk Models
  Options theory, The Merton Model, The KMV Model, The ‘Distance to Default’ Derivation, Reduced Form Models
  
  Analysis of Changing Default Rates
  Default Recovery Rates, Loss Given Default, Credit Risk Migration Matrices
  
  Portfolio Approaches to Credit Risk
  Portfolio Theory, Correlated Defaults, Credit VaR, Copula Modelling
  
  Credit Derivatives & Structured Credit Products
  The Credit Default Swap Market, Credit Linked Notes, Asset Backed Securitisation, Credit Derivatives and the Financial Crisis

  Learning Outcomes

  Upon successful completion of this module, students will:
  
  1. understand how corporate credit culture and the lending decisions have evolved over the past 50 years
  2. be able to identify and critically access the roles played by key institutional and market players in credit markets
  3. be able to demonstrate knowledge of the structure and application of accounting and market based credit risk models
  4. be familiar with portfolio based techniques for credit risk modeling
  5. understand the features of credit derivatives and securitized credit products with an appreciation of how these instruments contributed to the recent financial crisis

  Skills

  This course provides opportunities for the student to acquire or enhance the following skills:
  
  Subject-specific skills:
  
  o The ability to critically read and evaluate finance and risk specific academic literature
  o The ability to apply financial data to a series of models in order to make a appropriate estimate of credit risk exposure
  o The understanding of the relative strengths and weaknesses of credit risk models
  o The ability to analyse various credit derivatives from a risk mitigation perspective
  o The ability to identify and debate issues pertaining to credit risk set within a broader context of the current financial climate
  
  Cognitive skills:
  
  o Problem solving
  o Logical reasoning
  o Independent enquiry
  o Critical evaluation and interpretation
  o Self assessment and reflection
  
  
  Transferable Skills:
  
  o The ability to synthesise financial information/data from a variety of sources
  o Preparation and communication of ideas in both written and presentational forms
  o Ability to work both independently and in groups
  o Organisation and Time Management
  o Use of IT

  Coursework

  40%

  Examination

  60%

  Practical

  0%

  Credits

  15

  Module Code

  FIN7022

  Teaching Period

  Spring

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Corporate Finance (15 credits)

  ×

  Corporate Finance

  Overview

  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

  Learning Outcomes

  Upon successful completion of this module, students will be able to:
  • describe and synthesize academic theories which explain the approaches of corporations to investment and financing decisions;
  • analyse how corporations can increase shareholder value;
  • evaluate empirical evidence regarding whether corporate decision making is consistent with academic theories;
  • apply theoretical principles to hypothetical situations;
  • use the Bloomberg database in a trading-room environment.

  Skills

  This course provides opportunities for the student to acquire or enhance the following skills:
  
  Subject-specific Skills
  • The ability to construct arguments and exercise problem solving skills in the context of theories of finance and risk management
  • The ability to use computer-based mathematical / statistical / econometric packages to analyse and evaluate relevant data
  • The ability to read and evaluate finance and risk-related academic literature
  • The ability to appreciate, construct and analyse mathematical, statistical, financial and economic models of practical risk situations
  
  Cognitive Skills
  • Problem solving
  • Logical reasoning
  • Independent enquiry
  • Critical evaluation and interpretation
  • Self assessment and reflection
  
  Transferable Skills
  • The ability to synthesise information/data from a variety of sources including from databases, books, journal articles and the internet
  • The preparation and communication of ideas in finance, information economics and risk management in both written and presentational forms
  • The ability to work both independently and in groups
  • Organisation and time management
  • Problem solving and critical analysis
  • Work-based skills; use of IT, including word-processing, email, internet and statistical/econometric/risk management packages
  • The ability to communicate quantitative and qualitative information together with analysis, argument and commentary in a form appropriate to different intended audiences.

  Coursework

  40%

  Examination

  60%

  Practical

  0%

  Credits

  15

  Module Code

  FIN9005

  Teaching Period

  Autumn

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Research Methods in Finance (15 credits)

  ×

  Research Methods in Finance

  Overview

  The purpose of this course is to provide an 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 (R).

  Learning Outcomes

  Upon successful completion of this course students will have an understanding of:-
  • the main issues relating to the appropriate econometric modelling of financial and economic time series;
  • and have gained experience in the use of econometric software and be able to demonstrate their software skills in completing assignments;
  • and be able to discuss, applied econometric research topics in finance;
  • and have improved their data management, programming and research skills.

  Skills

  Subject-specific Skills
  • The ability to construct arguments and exercise problem solving skills in finance
  • The ability to use computer-based mathematical/statistical/econometric packages to analyse and evaluate relevant data
  • The ability to read and evaluate finance and risk-related academic literature
  Cognitive Skills
  • Problem solving
  • Logical reasoning
  • Independent enquiry
  • Critical evaluation and interpretation
  • Self-assessment and reflection
  Transferable Skills
  • The ability to synthesise information/data from a variety of sources
  • The preparation and communication of ideas in finance, information economics and risk management
  • Organisation and time management
  • Problem solving and critical analysis
  • Work-based skills; use of IT, including word-processing, email, internet and statistical/econometric/risk management packages
  • The ability to communicate quantitative and qualitative information together with analysis, argument and commentary

  Coursework

  30%

  Examination

  70%

  Practical

  0%

  Credits

  15

  Module Code

  FIN9008

  Teaching Period

  Autumn

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Financial Regulation and Risk Management (15 credits)

  ×

  Financial Regulation and Risk Management

  Overview

  COURSE DESCRIPTION
  
  This module considers both risk and regulation in financial services. With regard to risk the module introduces students to the risks that institutions must take if they are to survive and prosper including market risk, credit risk, liquidity risk and operational risk. Emphasis is on the quantification of these risks, decisions about what level of such risks are acceptable and the action required to mitigate unacceptable levels of risk.
  
  MODULE AIMS
  
  (i) to offer a rigorous and intellectually demanding course of study of the techniques, principles and underpinning theories behind effective financial regulation
  
  (ii) to offer a rigorous and intellectually demanding course of study of risk assessment and risk mitigation techniques applicable to financial institutions
  
  (iii) to provide students with an understanding of current thoughts on financial market reform.
  
  
  With regard to regulation the emphasis is on identifying the objectives of the regulatory system. The course stresses that being clear about the objectives of regulation is essential from the point of view of ensuring that the system is operated efficiently, that priorities are correctly assigned and weighted, and that the spirit as well as the letter of regulatory requirements is observed.

  Learning Outcomes

  LEARNING OUTCOMES
  
  Upon successful completion of this module students will have an understanding of:
  
  1. the economics of information and its importance in financial regulation
  2. the calculation of market risk, credit risk, liquidity risk and operational risk for financial institutions
  3. the relationship between the capital requirements faced by financial institutions and their levels of credit risk, market risk and operational risk.
  4. obstacles to efficient supervision and resolution
  5. working in small groups and making tutorial presentations
  6. how to use journal articles to build knowledge of the subject material

  Skills

  Skills
  
  This module provides opportunities for the student to acquire or enhance the following skills:-
  
  • Subject-specific skills
  o Ability to critically read and evaluate finance and risk-related academic literature;
  o Appreciation, construction and analysis of maths/statistical, financial and economic models of practical risk situations;
  o Ability to connect business problems with risk management;
  o Ability to marry regulatory structure with the principles of risk sharing and risk mitigation
  
  • Cognitive Skills
  o Problem solving
  o Logical reasoning
  o Independent enquiry
  o Criticial evaluation and interpretation
  o Self assessment and reflection
  
  • Transferable Skills
  o The ability to synthesis information/data from a variety of sources
  o Preparation and communication of ideas in both written and presentational forms
  o Ability to work both independently and in groups
  o Organisation and Time Management
  o Use of IT.

  Coursework

  40%

  Examination

  60%

  Practical

  0%

  Credits

  15

  Module Code

  FIN7021

  Teaching Period

  Autumn

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Derivatives (15 credits)

  ×

  Derivatives

  Overview

  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.

  Learning Outcomes

  Upon successful completion of this module, students will have an understanding of:-
  1. understand the mechanisms of futures and forward market
  2. price futures and forward instruments
  3. understand the mechanisms of options markets
  4. understand concepts of stochastic processes and its application in financial modelling
  5. understand and derive binomial tree model
  6. understand and derive Black-Scholes-Merton model
  7. estimate historical and implied volatility
  8. construct hedges using futures and options

  Skills

  This module provides opportunities for the student to acquire or enhance the following skills:
  Subject-specific Skills
  • The ability to construct arguments and exercise problem solving skills in the context of theories of finance and risk management
  • The ability to use computer-based mathematical / statistical / econometric packages to analyse and evaluate relevant data
  • The ability to read and evaluate finance and risk-related academic literature
  • The ability to appreciate, construct and analyse mathematical, statistical, financial and economic models of practical risk situations
  • The ability to connect business problems with risk management
  • The ability to marry regulatory structure with the principles of risk sharing and risk mitigation
  
  Cognitive Skills
  • Problem solving
  • Logical reasoning
  • Independent enquiry
  • Critical evaluation and interpretation
  • Self assessment and reflection
  
  Transferable Skills
  • The ability to synthesise information/data from a variety of sources including from databases, books, journal articles and the internet
  • The preparation and communication of ideas in finance, information economics and risk management in both written and presentational forms
  • The ability to work both independently and in groups
  • Organisation and time management
  • Problem solving and critical analysis
  • Work-based skills; use of IT, including word-processing, email, internet and statistical/econometric/risk management packages
  • The ability to communicate quantitative and qualitative information together with analysis, argument and commentary in a form appropriate to different intended audiences

  Coursework

  40%

  Examination

  60%

  Practical

  0%

  Credits

  15

  Module Code

  FIN9007

  Teaching Period

  Spring

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

Entrance requirements

Graduate
Normally a 2.1 Honours degree or equivalent qualification acceptable to the University in Mathematics, Accountancy, Finance, Economics or other relevant quantitative subject. Science and Engineering disciplines will be considered where there is a significant mathematical component. Performance in relevant modules must be of the required standard.

We welcome applications from a diverse range of applicants so will also consider previous work experience alongside academic qualifications. Prior experiential learning in quantitative analysis, Mathematics or Finance based subjects in lieu of academic qualifications will be considered on an individual basis. The University's Recognition of Prior Learning Policy provides guidance on the assessment of experiential learning (RPEL). Please visit http://go.qub.ac.uk/RPLpolicy for more information.

Applicants are advised to apply as early as possible and ideally no later than 11th August 2023 for courses which commence in late September. In the event that any programme receives a high number of applications, the University reserves the right to close the application portal. Notifications to this effect will appear on the Direct Application Portal against the programme application page.

Please note:  international applicants will be required to pay a deposit to secure a place on this course.

International Students

Our country/region pages include information on entry requirements, tuition fees, scholarships, student profiles, upcoming events and contacts for your country/region. Use the dropdown list below for specific information for your country/region.

Please Select Your Country/Region Please Select Your Country/Region Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bolivia Bosnia And Herzegovina Botswana Brazil Brunei Bulgaria Cambodia Cameroon Canada Chile China Mainland Colombia Croatia Cuba Cyprus Czech Republic Denmark Djibouti Ecuador Egypt Eritrea Estonia Ethiopia Finland France Georgia Germany Ghana Greece Guyana Haiti Honduras Hong Kong (SAR) Hungary India Indonesia Iran Iraq Ireland Israel Italy Ivory Coast Jamaica Japan Jordan Kazakhstan Kenya Kosovo Kuwait Kyrgyzstan Lativa Lebanon Liberia Libya Lithuania Luxembourg Macau (SAR) Madagascar Malawi Malaysia Malta Mauritius Mexico Mongolia Morocco Mozambique Myanmar Namibia Nepal Netherlands New Zealand Nigeria Norway Oman Pakistan Palestine Panama Paraguay Peru Philippines Poland Portugal Qatar Romania Russia Rwanda Saudi Arabia Sierra Leone Singapore Slovakia Slovenia South Africa South Korea Spain Sri Lanka St Vincent And The Grenadines Sudan Sweden Switzerland Syria Taiwan Tajikistan Tanzania Thailand Trinidad and Tobago Tunisia Turkey Uganda Ukraine United Arab Emirates Uruguay USA Uzbekistan Venezuela Vietnam Yemen Zambia Zimbabwe

English Language Requirements

Evidence of an IELTS* score of 6.5, with not less than 5.5 in any component, or an equivalent qualification acceptable to the University is required. *Taken within the last 2 years.

International students wishing to apply to Queen's University Belfast (and for whom English is not their first language), must be able to demonstrate their proficiency in English in order to benefit fully from their course of study or research. Non-EEA nationals must also satisfy UK Visas and Immigration (UKVI) immigration requirements for English language for visa purposes.

For more information on English Language requirements for EEA and non-EEA nationals see: www.qub.ac.uk/EnglishLanguageReqs.

Career Prospects

Introduction
Risk management is currently, and will continue to be, a hot spot for graduate recruitment. As many financial firms are substantially expanding their risk management functions, this Masters degree is likely to open a wide range of new and exciting career opportunities. After graduation there is a wide variety of roles available for our students, some of which will suit those with mathematical skills who wish to utilise cutting-edge quantitative modelling techniques and work in collaboration with traders to develop bespoke financial products. Other roles involve portfolio and product risk management or monitoring firm-wide business risks either in-house or as part of an external regulatory team. Graduates from this degree have quickly secured high-quality posts, most of which are in risk management functions.

Graduates have gone on to work for companies/institutions such as: EY, Accenture, HSBC, KMC Wealth Management, University of Bath, Queen’s University Belfast, CCGroup Inc., CNO Financial Group, Clune Construction Company, Edelweiss Financial Services, Home Credit China.

Working in roles such as: Financial Services Assurance, Management Consultant (Finance and Risk), Global Graduate Analyst, Product Support Associate, Data Analyst, Assistant Project Manager, Associate — Structured Finance, Team Lead, PhD Candidate.

Located in: Frankfurt, Dublin, San Francisco Bay Area, Chicago, Belfast, Greater New York City Area, New Delhi, Shenzhen, Kuala Lumpur.

Queen's postgraduates reap exceptional benefits. Unique initiatives, such as Degree Plus and Researcher Plus bolster our commitment to employability, while innovative leadership and executive programmes alongside sterling integration with business experts helps our students gain key leadership positions both nationally and internationally.
http://www.qub.ac.uk/directorates/sgc/careers/

Additional Awards Gained(QSIS ELEMENT IS EMPTY)

Prizes and Awards(QSIS ELEMENT IS EMPTY)

Graduate Plus/Future Ready Award for extra-curricular skills

In addition to your degree programme, at Queen's you can have the opportunity to gain wider life, academic and employability skills. For example, placements, voluntary work, clubs, societies, sports and lots more. So not only do you graduate with a degree recognised from a world leading university, you'll have practical national and international experience plus a wider exposure to life overall. We call this Graduate Plus/Future Ready Award. It's what makes studying at Queen's University Belfast special.

PREV
Entry requirements

NEXT
Fees and Funding

Tuition Fees

Northern Ireland (NI) 1	£8,360
Republic of Ireland (ROI) 2	£8,360
England, Scotland or Wales (GB) 1	£8,360
EU Other 3	£23,100
International	£23,100

MSc (T) Finance 

¹ EU citizens in the EU Settlement Scheme, with settled status, will be charged the NI or GB tuition fee based on where they are ordinarily resident. Students who are ROI nationals resident in GB will be charged the GB fee.

² EU students who are ROI nationals resident in ROI are eligible for NI tuition fees.

³ EU Other students (excludes Republic of Ireland nationals living in GB, NI or ROI) are charged tuition fees in line with international fees.

All tuition fees quoted are for the academic year 2023-24, and relate to a single year of study unless stated otherwise. Tuition fees will be subject to an annual inflationary increase, unless explicitly stated otherwise.

More information on postgraduate tuition fees.

Additional course costs

All Students

Depending on the programme of study, there may be extra costs which are not covered by tuition fees, which students will need to consider when planning their studies.

Students can borrow books and access online learning resources from any Queen's library. If students wish to purchase recommended texts, rather than borrow them from the University Library, prices per text can range from £30 to £100. Students should also budget between £30 to £75 per year for photocopying, memory sticks and printing charges.

Students undertaking a period of work placement or study abroad, as either a compulsory or optional part of their programme, should be aware that they will have to fund additional travel and living costs.

If a programme includes a major project or dissertation, there may be costs associated with transport, accommodation and/or materials. The amount will depend on the project chosen. There may also be additional costs for printing and binding.

Students may wish to consider purchasing an electronic device; costs will vary depending on the specification of the model chosen.

There are also additional charges for graduation ceremonies, examination resits and library fines.

Risk and Investment Management costs

There are no specific additional course costs associated with this programme.

How to Apply

Apply using our online Postgraduate Applications Portal and follow the step-by-step instructions on how to apply.

Apply now

Terms and Conditions

The terms and conditions that apply when you accept an offer of a place at the University on a taught programme of study.
Queen's University Belfast Terms and Conditions.

---

Download Postgraduate Prospectus