Why attending this Program

1
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

2
Roles and responsibilitiesas 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.

3
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
4
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 Computa
tional Methods for Pricing Structured Financial
Products Spring, 08 MAFS527 Computational
Tools and Technologies for Building Financial
Applications Fall, 08
5
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
6
Foundation courses
MAFS501 Stochastic Calculus Fall,
08 MAFS502 Advanced Probability and Statistics
Fall, 08
7
MATH 571 Mathematical Models of Financial
Derivatives 3-0-03

• 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.

8
MATH 572 Interest Rate Models 3-0-03

• 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.

9
MAFS 521 Mathematical Models of
Investment 3-0-03

• Utility theory, stochastic dominance. Portfolio
  analysis mean-variance approach, Two-Fund
  Theorem. Capital asset pricing models. Arbitrage
  pricing theory. Consumption-investment models.

10
MAFS 523 Advanced Credit Risk Models 3-0-03

• Credit spreads and bond price- based 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.

11
MAFS 525 Computational Methods for Pricing
Structured Products 3-0-03

• 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.

12
MAFS 501Stochastic Calculus 3-0-03

• Random walk models. Filtration. Martingales.
  Brownian motions. Diffusion processes. Forward
  and backward Kolmogorov equations. Itos
  calculus. Stochastic differential equations.
  Stochastic optimal control problems in finance.

13
MAFS 502 Advanced Probability and Statistics
3-0-03

• 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.

14
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.

15
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.