Details about the program

The Financial Mathematics Program provides technically trained professionals with an understanding of how to value financial derivatives and complex investments, and assess the associated risks. Graduates must have a rigorous training in mathematics, especially in the area of stochastic processes and probability, in statistics, and in computation, together with a foundation in the institutional operation of financial markets. The Program also provides a focal point for Financial Mathematics activities such as research seminars and workshops.

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The 18-month curriculum requires 7 core courses, 3 electives, and a private-industry internship (or project).

Core Classes

  • Capital Investment Economic Analysis (ISE 711) Financial Mathematics (MA 547)
  • Statistical Theory I (ST 521) Statistical Theory II (ST 522)
  • Asset Pricing (ECG 528) Computational Methods in Econ. and Finance (ECG 766)
  • Monte Carlo Methods in Finance (MA 591)

Sample Electives

  • BUS 522 Portfolio and Capital Market Theory
  • ECG/ST 751 Econometrics
  • ISE 712 Bayesian Decision Analysis for Engineers and Managers
  • MA/ST 747 Probability and Stochastic Processes II
  • ST 810 Credit Risk in Financial Derivatives (currently special topics)

Students have held internships and jobs at Duke Energy, Bank of America, Wells Fargo, BB&T, SAS, Credit Suisse, Genworth, Goldman Sachs and companies all over the world. 


Current students description of the program: 

NC State's Masters in Financial Mathematics Program can be categorized into four tracks:

(1) Practical Financial Applications

This track is mainly focus on professional practice in the finance field. Courses in this track can help students without solid finance background to improve knowledge of finance.

Courses selected:

  • ECG528 Asset Pricing: Introduction of financial derivatives and basic methods of pricing models. Introduction to major fundamental assets (stocks and bonds), interest rates, and derivative assets, such as put and call options. Arbitrage theorem, present value, risk aversion, hedging, duration, properties of derivative assets, binomial trees, elementary stochastic calculus, Black-Scholes option pricing formula, implied volatility, capital asset pricing model. Emphasis on mathematical methods used to price derivative assets.

  • MBA524 Equity Valuation: Students will develop will learn SAS programming and statistical analysis, quantitative stock selection methods used in industry, the trading mechanism in US equity markets, and basics of creating diversified portfolio for a mutual fund

  • MBA526 International Finance: Theory and practice of financial management in the international arena, including spot and forward markets for foreign exchange, currency futures and options contracts, international arbitrage conditions, foreign exchange exposure, foreign trade financing instruments, direct and portfolio investment abroad, and the role of country risk in determining investments

(2) Financial theory and econometrics

This track is mainly focus on quantitative methods in finance. The topic covers from option pricing models, interest rate model to Monte Carlo and computational methods in finance. Courses in this track are dedicated to develop students’ quantitative ability.

Courses selected:

  • MA547 Financial Mathematics: Stochastic models of financial markets, no-arbitrage derivative pricing, from discrete to continuous time models. Brownian motion, stochastic calculus, Feynman-Kac formula and tools for European options and equivalent martingale measures. Black-Scholes formulaOptimal stopping and American options

  • MA590 Monte Carlo methods in Financial Engineering: Principles of Monte Carlo, random variables generating methods, variance reduction techniques, discretization methods and quasi-Monte Carlo.

  • ST730 Applied Time Series Analysis: An introduction to use of statistical methods for analyzing and forecasting data observed over time. Trigonometric regression, periodogram/spectral analysis. Smoothing. Autoregressive moving average models. Regression with autocorrelated errors. Linear filters and bivariate spectral analysis. Stress on methods and applications; software implementations described and used in assignments.

(3) Mathematical Tools:

This track is mainly focused on advanced mathematical skills that could be applied in quantitative finance. Courses in this track will help students to develop high-level mathematical skills combined with solid theoretical knowledge.

Courses selected:

  • MA(ST)546 Probability and Stochastic Process: Modern introduction to Probability Theory and Stochastic Processes. The choice of material is motivated by applications to problems such as queueing networks, filtering and financial mathematics. Topics include: review of discrete probability and continuous random variables, random walks, markov chains, martingales, stopping times, erodicity, conditional expectations, continuous-time Markov chains, laws of large numbers, central limit theorem and large deviations.

  • MA584 Numerical Solution of Partial Differential Equations--Finite Difference Methods: Survey of finite difference methods for partial differential equations including elliptic, parabolic and hyperbolic PDE's. Consideration of both linear and nonlinear problems. Theoretical foundations described; however, emphasis on algorithm design and implementation

  • MA(ST)748 Stochastic Differential Equations: Theory of stochastic differential equations driven by Brownian motions. Current techniques in filtering and financial mathematics. Construction and properties of Brownian motion, wiener measure, Ito's integrals, martingale representation theorem, stochastic differential equations and diffusion processes, Girsanov's theorem, relation to partial differential equations, the Feynman-Kac formula.

(4) Computational Skills:

Courses in this track are mainly designed to provide students resources to learn programming skills that can be applied in quantitative finance. The programming skills cover R, SAS, MATLAB and C++.

Courses selected:

  • ST590G Computation for Data Analysis:  Create data mining diagrams, discriminant analysis, ordinary and logistic regression and association analysis coded with SAS.
  • CSC114 Introduction to Computing with C++:  An introductory course in computing in C++. Emphasis on algorithm development and problem solving. Particular elements include: careful and methodical development of C++ programs from specifications; documentation and style; appropriate use of control structures, data types and subprograms; data abstraction and verification; numeric and nonnumeric applications; introduction to object-oriented programming and design
  • CSC780 Numerical Analysis:  Approximation and interpolation, Fast Fourier Transform, numerical differentiation and integration, numerical solution of initial value problems for ordinary differential equations, coded with C++ language.


Director-  Dr. Jeff Scroggs 
Director of Career Services-  Leslie Bowman, M.A. 
Graduate Student Services Coordinator-  Di Bucklad

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