An Analysis of Option Pricing Models Charles Vu 2002-2003 Topic Analysis
Some university courses that have relevance to my project:

• GMU: CS 310 (Computer Science III):Tools and techniques required to develop moderate to large programs. Topics include continued study of object-oriented techniques, data structures, recursion, and problem-solving skills. Students complete several moderate-size programs.
• GMU: SWE 432 (Design and Implementation of Software for the Web): This course teaches students how to develop software for web applications. The concepts of client-server computing, theories of usable graphical user interfaces, and models for web-based information retrieval and processing are covered. Goals are to understand how to design usable software interfaces and implement them on the web, learn how to build software that accepts information from users across the web and returns data to the user, and understand how to interact with database engines to store and retrieve information. Specific topics that are included are HTML, CGI programming, Java, Java applets, Javascripts, and Java servlets.
• Princeton: ORF 335 (Introduction to Financial Engineering) (also ECO 335): Financial engineers design and analyze products that improve the efficiency of markets and create mechanisms for reducing risk. This course introduces the basics of financial engineering: the notions of arbitrage and risk-neutral probability measure are developed in the case of discrete models; Black-Scholes theory is introduced in continuous-time models, and interest rate derivatives and the term structure of interest rates are discussed.
• Princeton: ORF 435 (Financial Risk Management): This course is about measuring, modeling and managing financial risks. It introduces the variety of instruments that are used to this effect and the methods of designing and evaluating such instruments. Topics covered include risk diversification, planning models, market and nonmarket risks, and portfolio effects.
• Princeton: ORF 417 (Dynamic Programming): An introduction to stochastic dynamic programming and stochastic control. The course deals with discrete and continuous-state dynamic programs, finite and infinite horizons, stationary and nonstationary data. Applications drawn from inventory management, sequential games, stochastic shortest path, dynamic resource allocation problems. Solution algorithms include classical policy and value iteration for smaller problems and stochastic approximation methods for large-scale applications.

Assignments First Quarter
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