Module Code: |
H9FQM |
Long Title
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Financial and Quantitative Modelling
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Title
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Financial and Quantitative Modelling
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Module Level: |
LEVEL 9 |
EQF Level: |
7 |
EHEA Level: |
Second Cycle |
Module Coordinator: |
COLETTE DARCY |
Module Author: |
CORINA SHEERIN |
Departments: |
School of Business
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Specifications of the qualifications and experience required of staff |
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Learning Outcomes |
On successful completion of this module the learner will be able to: |
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Learning Outcome Description |
LO1 |
Identify and analyse spreadsheet modelling techniques applied to a financial modelling environment. |
LO2 |
Implement elementary control structures (conditional statements and loops) in a high-level programming language such as VBA, MATLAB and R will be covered. |
LO3 |
Apply these programming techniques to solve a variety of financial modelling problems. |
LO4 |
Apply matrix algebra techniques to solve financial problems. |
LO5 |
Use Monte Carlo simulation in financial modelling, with an emphasis on derivative valuation and risk measurement. |
Dependencies |
Module Recommendations
This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
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No recommendations listed |
Co-requisite Modules
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No Co-requisite modules listed |
Entry requirements |
There are no additional entry requirements for this module. The programme entry requirements apply.
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Module Content & Assessment
Indicative Content |
Spreadsheet Modelling
History and use of spreadsheets.
Principles of spreadsheet model design.
MS Excel Functionality
Financial Functions
Statistical Functions
Lookup Functions
Array Function
Identifying and managing errors in MS Excel
Use of the Solver add-in.
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Visual Basic for Applications (VBA)
Automation of tasks using macros; recording macros.
Modifying recorded macro code.
Simple VBA programs; functions and subroutines
Control structures:
If then else
Select case
For Next Loops
While Loops
Data structures in VBA:
Data types
Arrays
Application of control / data structures to solve financial problems:
Implementing VBA code for the Black Scholes Option Pricing Model
Forward Price / Value Function
Determining the Internal Rate of Return using a While loop with interval bisection.
Implementing a Cox Rubenstein Tree to value American Option
Error handling and debugging
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Matrices Applied to Finance
Recap on matrices; matrix structure, addition, subtraction, and multiplication.
Vectors, matrices as operators on vectors.
Representation of data in matrix form, computation of covariance matrices.
Application of matrices and vectors to portfolio management; determination of a portfolio standard deviation.
Positive definite matrices; prevalence in finance.
Cholesky decomposition; application to Monte Carlo Simulation.
Identity Matrices and Inverse Matrices
Solving simultaneous equations with matrices
Eigenvectors and Eigen Values
Orthogonal Matrices
Singular Value Decomposition
Principal Component Analysis
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Monte Carlo Simulation
Generating uniformly distributed random numbers.
Transforming uniformly distributed random numbers to normally distributed random numbers with arbitrary mean and standard deviation.
Modelling independent and correlated random variables.
Use of the Central Limit Theorem to estimate the accuracy of a Monte Carlo simulation model.
Modelling discrete processes such as default events.
Application to modelling structured credit products such as Credit Default Swaps and Collateralised Debt Obligations.
Application to modelling equity derivatives through Monte Carlo Simulation of Geometric Brownian Motion.
Application to Corporate Finance; modelling Real Options and solving NPV / IRR problems with uncertain inputs.
Use of Cholesky Decomposition to construct a Monte Carlo VaR Model.
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Modelling with MATLAB
Introduction to MATLAB Integrated Development Environment; Command Window, Workspace and Script Editor.
Use of MATLAB as a financial calculator.
Exploration of a selection of financial functions in MATLAB; bond pricing and Black Scholes option model.
Use of MATLAB for time series analysis, Econometric Toolbox and Distribution Fitter Application.
Creating scripts in MATLAB.
Use of graphics in MATLAB.
Application, the use of MATLAB for Monte Carlo Simulation, speed comparison versus Excel and VBA.
Application of MATLAB to investigate the Weak form of the Efficient Market Hypothesis using the Econometrics Toolbox.
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Modelling with R-Studio
Introduction to R and R Studio, overview of the R-Studio Integrated Development Environment.
Data Structures in R
Vectors
Matrices
Arrays
Data Frames
Lists
Time Series
Importing Data
Writing Functions and Scripts
Plotting Data
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Assessment Breakdown | % |
Coursework | 50.00% |
End of Module Assessment | 50.00% |
AssessmentsFull Time
Coursework |
Assessment Type: |
Continuous Assessment |
% of total: |
50 |
Assessment Date: |
n/a |
Outcome addressed: |
1,2 |
Non-Marked: |
No |
Assessment Description: The minor modelling assessment will be designed to test certain principles of modelling, spreadsheet design or coding. Examples of these assignment include 1) modelling a random walk in either Excel, VBA, MATLAB or Python; 2) solving a Real Option problem with Monte Carlo Simulation; 3) building and implementing a function to compute implied volatility given an option price and the remaining pricing inputs. |
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End of Module Assessment |
Assessment Type: |
Terminal Exam |
% of total: |
50 |
Assessment Date: |
End-of-Semester |
Outcome addressed: |
1,2,3,4,5 |
Non-Marked: |
No |
Assessment Description: The major assignment will be a more involved assignment designed to test the ability of the student to apply a range of modelling principles to a financial problem. Examples include building a spreadsheet model to value a company, construction and back-testing of VaR models, valuation of options with non-standard payoffs.
The students will be required to prepare and deliver a presentation of their work. Marks will be awarded for the quality of this presentation. |
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Reassessment Requirement |
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.
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Reassessment Description Repeat assessment of this module will consist of a repeat examination which will test all the learning outcomes.
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NCIRL reserves the right to alter the nature and timings of assessment
Module Workload
Module Target Workload Hours 0 Hours |
Workload: Full Time |
Workload Type |
Workload Description |
Hours |
Frequency |
Average Weekly Learner Workload |
Lecture |
Classroom and demonstrations |
36 |
Per Semester |
3.00 |
Directed Learning |
Directed e-learning |
36 |
Per Semester |
3.00 |
Independent Learning |
Independent learning |
178 |
Per Semester |
14.83 |
Total Weekly Contact Hours |
6.00 |
Module Resources
Recommended Book Resources |
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Van Niekerk, M. (2020), VBA Automation for Excel 2019 Cookbook, Packt.
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Alexander, C. (2008), Market Risk Analysis Volume I: Quantitative Methods in Finance, Wiley.
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Gilat, A. (2014), MATLAB: An Introduction with Applications, 5th Edition. FT Prentice Hall.
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Medeiros, K. (2018), R Programming Fundamentals, Packt.
| This module does not have any article/paper resources |
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Other Resources |
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[Journal], Journal of Finance.
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[Journal], Journal of Quantitative Finance.
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[Journal], Quarterly Journal of Finance.
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[Journal], Journal of Economics and Finance.
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[Journal], Journal of Financial and Quantitative
Analysis.
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[Journal], Journal of Mathematical Finance.
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[Journal], Journal of Computational Finance.
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[Journal], Journal of Current Issues in Finance,
Business and Economics.
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[Website], http://www.economist.com.
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[Website], http://www.ft.com.
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[Website], http://www.wsj.com.
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[Website], http://www.bloomberg.com.
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[Website], http://www.reuters.com.
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[Website], http://www.centralbank.ie.
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[Website], www.imf.org.
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[Website], http://epp.eurostat.ec.europa.eu/.
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