Download computational finance programme

Opportunities in
Quantitative
Finance
in the Department of Mathematics
Introduction and Background
•
In 1973 Black and Scholes developed the option pricing
models based on advanced mathematics.
•
Today the financial practice has become very quantitative.
•
Sophisticated mathematical models are used to support
investment decisions, to develop and price new financial
products or to manage risk.
What is Quantitative Finance?
•
Multidisciplinary programme that combines Mathematics,
Finance and Computing with a practical orientation that is
designed for high-caliber students who wish to become
professionals in the finance industry.
•
Covers the following areas
– Mathematical Theory and Tools
– Statistical Methods
– Computing Theory and Techniques
– Financial Theory and Principles
– Core Financial Product Knowledge
•
Plays an increasingly important role in the financial services
industry and the economy.
Examples
Risk Management
• Banks in the course of their business take on risk?
• How do we measure the risk that the bank is exposed to?
• How do we hedge and manage the risk?
• Tools
– Linear Algebra and Calculus
– Advanced probability and statistics
– Time Series Analysis
– Simulation Methodologies
Derivatives Trading
• Pricing and Hedging of Complex Derivatives
• Tools
– Advanced Stochastic Processes
– Numerical solutions to partial differential equations
Career Opportunities
•
Potential Employers
– Banks
– Investment Companies
– Securities Firms
– Insurance Companies
– Multinationals
•
Increase in demand for graduates with high level of
quantitative and analytical skills
•
Jobs
– Financial Product Development and pricing (Structured
Deposits, Derivatives etc.)
– Risk Management
– Investment decision making and fund management
– Wealth Management
Skills Required
of Quantitative Analysts/Risk Managers
•
Basic Quantitative Skills
– Mathematics (Linear Algebra, Calculus)
– Probability
– Statistics
•
Computer programming
– Excel, VBA, Matlab, SAS
•
Knowledge of Derivatives and Fixed Income
Objective of Programme
•
To equip graduates for the finance industry with
– Technical knowledge and skills in quantitative finance
and risk management
– Strong quantitative modeling skills
– Analytical mind
•
This programme is uniquely positioned to meet the increasing
demand for graduates with quantitative modeling and risk
management skills.
Key Features
•
Multi-disciplinary curriculum integrating mathematical
methods and statistical tools, computing techniques with
applications to finance.
•
Use of quantitative tools with state-of-the-art financial
systems in the computing laboratory.
•
Projects with financial engineering applications.
•
Honours track programme with option to exit earlier and
graduate with B.Sc.
•
Typically, it takes 3 and 4 years to complete the requirements
for B.Sc. and B.Sc.(Hons), respectively. A shorter timeframe
is possible for some.
Curriculum Structure
•
Satisfy University requirements for B.Sc./B.Sc.(Hons)
•
Satisfy Faculty requirements for B.Sc./B.Sc.(Hons)
•
Pass at least 70 MCs/102 MCs to satisfy the major
requirements for B.Sc./B.Sc.(Hons)
•
Obtain at least 120 MCs/160 MCs to graduate with
B.Sc./B.Sc.(Hons) in Quantitative Finance
•
Maximum candidature for students reading B.Sc.(Hons) is 4
years
Curriculum Structure
•
Core modules cover topics in
– Calculus
– Data structures and algorithms
– Finance
– Financial accounting
– Financial time series
– Financial trading and modeling
– Financial mathematics
– Investment instruments
– Linear algebra
– Linear programming
– Mathematical statistics
– Numerical analysis
– Probability
– Programming methodology
Curriculum Structure
•
Elective modules cover topics in
– Basic derivatives and bonds
– Computer intensive statistical methods
– Corporate finance
– Design & analysis of algorithm
– Discrete time finance
– Equity products and exotics
– Financial markets
– Financial risk management
– Game theory
– Linear models
– Mathematical programming
– Nonlinear programming
– Numerical partial differential equations
– Ordinary different equations
– Regression analysis
– Stochastic analysis
Admission Requirements
•
Enrolled in Faculty of Science
•
Studied for two semesters in NUS
•
Obtained an overall CAP of 3.50 or higher at the end of the
two semesters
•
Passed the following four qualifying modules in the two
semesters
– CS1010/CS1010E (Programming Methodology)
– MA1102R (Calculus)
– MA1101R (Linear Algebra)
– ST2131/MA2216 (Probability)
•
The group average point (GAP) for the qualifying modules
must be at least 3.50
Application
•
Application deadline and form will be posted on the
department’s web in due course.
•
Submit a hard copy of the application form with a copy of the
NUS academic results to the general office by the application
deadline.
•
For more information, visit http://www.math.nus.edu.sg