What is the Financial Risk Analysis (FRA) Track offered under Finance?
Financial Risk Analysis Track (FRA) Track was launched in 2009 under the Bachelor of Business Management Programme. The creation of the FRA track is in line with the industry-wide recognition of the prominence of the role of risk management professionals in the finance industry and beyond. The recent global financial crisis has highlighted the importance to understand, monitor and manage risks that are pertinent in transactions of financial nature and, by extension, in activities that can ultimately affect the smooth functioning of enterprises. Increasingly, professionals who are familiar with the business environment and who are well-grounded in quantitative and statistical skills are sought for to analyze numerical data for hints of risks and to quantify risk for proper monitoring.
Students interested in the FRA Track are required to pursue Finance as a major.
Students will need to declare FNCE with Track in FRA as their major and take the following courses :
1. Corporate Reporting & Analysis of Financial Statements
2. Financial Instruments, Institutions & Markets
3. Linear Algebra
4. Complete one of the following courses:
• Quantitative Risk Analysis
• Enterprise Risk Management
5. Any 4 Financial Risk Analysis Track electives
SMU students should not offer Quantitative Finance and Finance (Financial Risk Analysis) as a double major.
How is Quantitative Finance different from Finance?
Course offerings under this Major include traditional science-based mathematics topics such as Linear Algebra, Real Analysis, and Differential Equations, and will be taught at the same type and level of rigor as would be expected in a science school.
The course will carry a strong entrepreneurial and innovative flavor characteristic of the strengths of the dynamic Business School . Cross-disciplinary courses from the Economics School , the Information Systems School and the Accounting School all add full color to the rich spectrum and thorough requirements in this demanding major meant to prepare students to meet the stringent expectations of the competitive investment banking, financial risk, and funds management industries.
A significant feature of the curriculum will be an intensive computing laboratory environment at the QF Computing Lab (previously named The Simulated Trading Lab) where students will learn the skills of risk analyses, structuring, pricing, and coding numerical methods and solutions. In many of the required courses or modules, lectures will be accompanied by computing laboratory sessions where students compute and solve applied problems in derivatives and portfolio risk management.
Students from all other Schools as well as Students from the Business School who have a different first major, are able to take Quantitative Finance as a second major.
How many more courses do I need to take?
Quantitative Finance Compulsory Courses
1. QF201 Linear Algebra and Regression
2. QF202 Differential Equations
3. QF203 Real Analysis
4. QF301 Structured Finance
5. QF302 Investment & Financial Data Analysis
6. QF303 Stochastic Calculus & Finance Theory
7. QF304 Numerical Methods
8. QF.305 Global Financial Risk Management
9. Either one of the following
• QF204 Probability & Finance Theory or
• Stochastic Processes & Modelling or
• Risk Theory & Loss Models
10. Either one of the following
• Computing Technology for Finance or
• IS Software Foundations or
• Data Management or
• Object Oriented Application Development or
• Software Engineering
Why should I take Quantitative Finance?
(extracted from http://www.quanthome.com/)
Quantitative Finance integrates Mathematics, Statistics, and Computer Science with the objective of practical application towards financial markets. One of the primary concerns of quantitative finance is risk. Some of the primary concerns of
Quantitative Finance include:
• The pricing of securities, especially derivative securities
• Risk
A large amount of work in quantitative finance relies on the following two assumptions:
• Markets are efficient
• There is no free lunch (no arbitrage assumption)
Two common approaches to the pricing of derivatives are the risk-neutral pricing framework and the differential equations approach. Binomial Trees are an example of risk-neutral pricing, and the Black-Scholes equation is an application of partial differential equations.
The differential equations approach often leads to closed-form solutions, leading to straight-forward pricing formulas such as the Black-Scholes formula. These are referred to as analytical solutions. In other cases, differential equations have no known analytical solution, and thus must be solved numerically. For instance, there is no closed-form solution for the price of an American Put option; though solving for the price of an American Put numerically is not computationally intense.
The risk-neutral approach can also entails use of stochastic calculus. While analytical solutions are possible, numerical solutions are more common in the risk-neutral approach. The risk-neutral approach is more flexible than the differential equations approach and can be used to price derivitives that cannot be valued using differential equations or Black-Scholes.
Stochastic calculus often requires a “change of measure” between the “real world” of probabilities and the “risk-neutral world” of probabilities.