Why attending this Program
Sharpening the quantitative skills in
 Pricing, hedging and risk measurement of
derivative securities
 Implementing risk measurement and
valuation models in software
Developing the abilities in
 Identifying and monitoring risk in valuation
models
 Assessment of the appropriateness of
quantitative models and their limitations
Roles and responsibilities
as a quantitative analyst

Develop mathematical models for pricing,
hedging and risk management of derivative
securities.

Support of trading activities by explaining
model behavior, identifying risk sources in
portfolios, carrying out scenario analysis.

Design efficient numerical algorithms and
implement high performance computing
solutions – delivery to systems and
applications.
How to prepare yourself for a
Quants job in the financial
market?



Strong knowledge of option pricing theory
(quantitative models for pricing and
hedging)
Strong software design and development
skills using C++
Mastery of advanced mathematics and
numerical analysis arising in financial
modeling (probability theory, stochastic
processes, numerical analysis)
General skills:
Analytic, quantitative and problem solving
skills; strong communication skills
Courses in MSc Program
Financial Mathematics
MATH571 Mathematical Models of Financial Derivatives
[Fall, 08]
MAFS526 Fixed Income Derivatives
[Fall, 08]
MAFS513 Mathematical Models of Investment [Summer, 08]
[Summer, 09]
MATH572 Interest Rate Models
[Spring, 09]
MAFS523 Advanced Credit Risk Models
[Summer, 09]
MAFS524 Software Development with C++ for Quantitative
Finance
[Spring, 09]
MAFS525 Computational Methods for Pricing Structured
Financial Products
[Spring, 08]
MAFS527 Computational Tools and Technologies
for Building Financial Applications
[Fall, 08]
Statistics courses
MAFS513 Quantitative Analysis of Financial Time Series
[Spring, 09]
MAFS511 Advanced Data Analysis with Statistical
Programming
[Fall, 08]
MAFS512 Applied Multivariate Analysis
[Spring, 09]
MAFS522 Quantitative and Statistical Risk Analysis
[Summer, 09]
Foundation courses
MAFS501 Stochastic Calculus
[Fall, 08]
MAFS502 Advanced Probability and Statistics
[Fall, 08]
MATH 571
Mathematical Models of
Financial Derivatives [3-0-0:3]
Fundamental Theorem of Asset
Pricing. Risk neutral valuation
approach. Black-Scholes-Merton
framework, dynamic hedging,
replicating portfolio. Martingale
theory of option pricing, risk
neutral measure. Stochastic
volatility models.
MATH 572
Interest Rate Models
[3-0-0:3]
Yield curves. Sort rates and
forward rates. Short rate models:
Vasicek and CIR models. Term
structure models: Hull-white fitting
procedure. Heath-Jarrow-Morton
pricing framework. LIBOR and
swap market models. Affine
models.
MAFS 521
Mathematical Models of
Investment
[3-0-0:3]
Utility theory, stochastic
dominance. Portfolio analysis:
mean-variance approach, TwoFund Theorem. Capital asset
pricing models. Arbitrage pricing
theory. Consumption-investment
models.
MAFS 523
Advanced Credit Risk Models
[3-0-0:3]
Credit spreads and bond pricebased models. Credit spread
models. Intensity based models.
Credit rating models. Firm value
models. Industrial codes: KMV,
CreditMetrics and CreditRisk+.
Default correlation. Pricing of
correlation products.
MAFS 525
Computational
Methods for Pricing Structured
Products
[3-0-0:3]
Lattice tree methods, finite
difference methods, Monte Carlo
simulation. Structured products
analyzed include: Convertible
bonds, equity-linked notes, quanto
currency swaps, collateralized
debt obligations, mortgage backed
securities, volatility swaps.
MAFS 501
Stochastic Calculus
[3-0-0:3]
Random walk models. Filtration.
Martingales. Brownian motions.
Diffusion processes. Forward and
backward Kolmogorov equations.
Ito’s calculus. Stochastic
differential equations. Stochastic
optimal control problems in
finance.
MAFS 502
Advanced Probability and
Statistics
[3-0-0:3]
Probability spaces, measurable
functions and distributions, conditional
probability, conditional expectations,
asymptotic theorems, stopping times,
martingales, Markov chains, Brownian
motion, sampling distributions,
sufficiency, statistical decision theory,
statistical inference, unbiased
estimation, method of maximum
likelihood.
Upon completion of the program,
students are expected to achieve
the following intellectual abilities:

A broad knowledge and understanding of
the financial products commonly traded in
the markets and various practical aspects
of risk management.

Use of mathematical and statistical tools to
construct quantitative models in derivative
pricing, quantitative trading strategies, risk
management, and scenario simulation,
including appropriate solution methods and
interpretation of results.
To graduate from the MSc
program, each student is required
to complete 30 credits of which
 6
credits from the list of
foundation courses
 9 credits from the list of courses
in statistics
 9 credits from the list of courses
in financial mathematics
 6 credits as free electives*
Needs to maintain a graduation grade
point average of B grade or above.