Registry

Module Specifications

Current Academic Year 2012 - 2013
Please note that this information is subject to change.

Module Title	Quantitative Methods for Finance
Module Code	EF5104
School	DCUBS
Online Module Resources
Module Co-ordinator	Semester 1: Julie Byrne Semester 2: Julie Byrne Autumn: Julie Byrne
Module Teacher	Julie Byrne
NFQ level	8	Credit Rating	5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

Description

The course aims to introduce the essential mathematical and statistical concepts and methods required by the quantitative finance and financial economics courses offered on the program throughout the academic year. To this end, it will include an intensive week-long ‘block-release’ module that will be delivered at the beginning of the academic year (typically during so called “orientation week”) complemented by intense tutorials and continuous assessment. This will be followed by shorter sessions and continuous assessment of progress throughout the academic year, to help students acquire familiarity with mathematical and statistical concepts and techniques as they become relevant. This year-long module will be delivered in one intensive week during orientation week and in additional shorter units (seminars) throughout the academic year.

Learning Outcomes

1. Use matrix algebra to solve systems of linear equations akin to those that describe basic problems encountered in financial decision making, e.g. linear pricing;
2. Use differential and integral calculus to describe and analyze the dynamics of quantities of interest in economic and financial studies;
3. Apply basic notions of uni-variate and multi-variate calculus to solve computational problems commonly encountered in financial economics, e.g. optimization;
4. Use descriptive statistics to characterize the moments of asset returns and apply the central limit theorem to make elementary inferences about their mean;
5. Use spreadsheets and write simple programs to solve basic computing problems.

Workload	Full-time hours per semester
*Type*	*Hours*	*Description*
Lecture	24	No Description
Assignment	50	No Description
Examination	10	No Description
Independent learning time	41	No Description
Total Workload: 125

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Introduction.
Sets, relations and functions.

Matrix Algebra.
Determinants and matrix inversion, matrix rank, eigenvalues; quadratic forms; static partial equilibrium analysis..

Differential and integral calculus.
Sequences and continuity of functions; limits and derivatives, rules of differentiation; integrals, exponential and logarithmic functions; comparative static and implicit functions..

Optimization.
Convexity and concavity; necessary and sufficient conditions for finding stationary points; unconstrained and constrained optimization..

Elements of descriptive statistics.
Discrete and continuous random variables; frequencies vs. probabilities; histograms; distributions and cumulative distributions of discrete random variables; density and cumulative density functions of continuous random variables; moments of random variables and their estimators; central limit theorem (law of large numbers) and sampling; introduction to the use of common statistical software (e.g., statistical functions in MS Excel, Stata)..

Elements of programming (year-long).
Reading data into a program (STATA, RATS, Visual Basic per Excel, R, Matlab or similar), writing simple routines to perform repetitive calculations, using matrix algebra to simplify coding tasks..

Assessment Breakdown
Continuous Assessment	0%	Examination Weight	0%

Course Work Breakdown
Type	Description	% of total	Assessment Date

Reassessment Requirement
Resit arrangements are explained by the following categories; 1 = A resit is available for all components of the module 2 = No resit is available for 100% continuous assessment module 3 = No resit is available for the continuous assessment component
This module is category 1

Indicative Reading List