Professional Master in Quantitative Finance

The importance of the financial markets and their impact on the global economy has grown steadily for more than forty years. This phenomenon is closely linked to the deregulation that began in the early 1970s, notably through the emergence of floating exchange rates. This expansion could not have taken place without the parallel development of a financial risk industry; many insurance contracts come to the aid of industrialists, states and investors to protect their activity or their investments against contrary market movements. The most classic is the buy option which allows one to buy or sell on a given date at a guaranteed price. The objective of this professional master is to meet the demand for highly qualified executives in mathematical finance in the domains of Investment and insurance, financial consulting, financial engineering, risk Management, fund managing. It covers the main mathematical tools to study financial products. These products include European and American options, interest rates, energy prices, weather derivatives, etc. Financial models are based on Probability theory and Ito's stochastic calculus, more precisely on stochastic differential equations, optimization methods, stochastic optimal stopping, stochastic optimal control, time series and various computational methods used in financial applications.

Admission requirements

Admission to the professional master's program in Quantitative Finance  (MxQF) is a competitive process. The application must give evidence that the candidate possesses a potential for strong academic performance. We select the best applicants based on the overall undergraduate GPA and transcript, general GRE (quantitative) score, and the recommendation or reference letters.

The minimum requirements for applicants to MxQF are: 

1. A four-year bachelor's or masters' degree (or equivalent) in Mathematics, Statistics, Computer Science or any related area  in Science and Engineering 
   

2. Minimum Grade-Point Average (GPA): 2.5 on a scale of 4.00 (or equivalent)

3. Grades of at least B (or equivalent) in most Mathematics and Statistics courses

4. Minimum GRE score in quantitative section: 156

5. IELTS score of 6+ or TOEFL of 70+ (waived for KFUPM graduates)

6. Two recommendation or reference letters

7. Required preparatory courses include undergraduate courses in calculus, linear algebra, probability and statistics, differential equations, numerical methods, and programming. (See details below for each program) 

The admission process goes beyond meeting the minimum requirements.

The list of courses, offered at KFUPM, which are equivalent to the required preparatory undergraduate instruction in calculus, linear algebra, probability and statistics, differential equations, numerical methods, and programming are given below:

• Math  101, Math 102, Stat 301, Fin 250

Degree Plan

Two Years Degree Plan of MX-Quantitative Finance

Course Descriptions:

FIN 524 Financial Derivatives and Risk Management (3-0-3)

Covers the trading and pricing of a wide array of derivative securities including financial options, futures, forwards, and swaps. The topics covered include that operation of derivatives markets, trading strategies of derivatives, the payoff and valuation of divertive securities, the theory and approaches of derivative pricing. The course will also cover the economic role of derivatives and their use in managing risks such as currency risk, interest rate risk, market risk, commodity risk, and general business risks.

Prerequisite: FIN 510 or FIN 540

FIN 540 Financial Markets and investment Theory (3-0-3)

Financial markets and their role in economic activity. Types of financial markets, market indexes, financial securities and crypto assets, and types of orders. Risk and expected return, modern portfolio theory, and asset pricing theories and their implications. Efficient market hypothesis and an introduction to behavioral finance.  Macroeconomic environment and industry analysis. Basic valuation of equity securities. Bonds yields, prices, and risk measures. Basics of options strategies and valuation.

Note: Not to be taken for credit with FIN 523

Prerequisite: Graduate Standing

FIN 545 Financial Econometrics and Inference (3-0-3)

Applications of statistical methods and econometric techniques in finance problems using statistical packages. Introduction to classical linear regression model and OLS estimates, assumptions, and diagnostic tests. Univariate and multivariate time series models, modelling long-run relationships, volatility and correlations models, switching models, panel data, limited dependent variable models, and various simulation methods.

Prerequisite: Graduate Standing

ISE 514 Stochastic Systems Simulation (3-0-3)

Basic discrete-event simulation modeling, queuing models, simulation languages, review of basic probability and statistics, random-number generators, generating random variables, output data analysis, validation of simulation models. Simulation language, simulation models, real case studies.

Note: Not to be taken for credit with ISE 405

Prerequisite: Graduate Standing

MATH 561 Time Series (3-0-3)

Description: : Examples of simple time series. Stationary time series and autocorrelation. Autoregressive moving average processes. Modeling and forecasting with ARMA processes. Maximum likelihood and least squares estimator. Nonstationary time series.

Note: Not to be taken for credit with STAT 460

Prerequisite: Graduate standing

MATH 562 Fundamentals of Option Pricing (3-0-3)

Basic principles of option pricing, binomial model, the Black-Scholes model, arbitrage, complete and incomplete markets, trading strategies, European options, American options. Topics include Risk-neutral Valuation, options on stock Indices, currencies, futures, the Greek letters, Interest Rate Derivatives,  Black-Scholes PDE and formula.

Prerequisite: MATH 564

MATH 564 Stochastic Analysis in Finance (3-0-3)

differential equations, Geometric Brownian motion,  financial examples., Feynman-Kac formula, Description: Stochastic processes, Gaussian processes, Brownian motion, Ito stochastic integral, the Ito lemma, the geometric Brownian motion and its application to finance. Introduction to stochastic Girsanov Formula and  application to Black Scholes PDE and formula.

Prerequisite:  Graduate Standing

MATH 619 Project (0-0-6)

A graduate student will arrange with a faculty member to conduct an industrial research project related to the computational finance field. Subsequently the students shall acquire skills and gain experiences in developing and running actual industry-based project. This project culminates in the writing of a technical report, and an oral technical presentation in front of a board of professors and industry experts.

Prerequisite: Graduate Standing

STAT 501 Probability and Mathematical Statistics (3-0-3)

Axioms and foundations of probability. Conditional probability and Bayes' theorem, independence, random variables and distribution functions and moments. Topics include random vectors and their distributions, convergence of sequences of random variables, laws of large numbers, the central limit theorem and sample moments and their distributions.  Order statistics and their distributions

Prerequisite: Graduate Standing