<p>copy and paste from another thread
School:Berkeley
Major:Math/Econ</p>
<p>Econometrics – Economics (ECON) 240A [5 units]
Description: Basic preparation for the Ph.D. program including probability and statistical theory and the classical linear regression model.</p>
<p>Financial Engineering Systems I – Industrial Engineering (IND ENG) 222 [3 units]
Description: Introductory graduate level course, focusing on applications of operations research techniques, e.g., probability, statistics, and optimization, to financial engineering. The course starts with a quick review of 221, including no-arbitrage theory, complete market, risk-neutral pricing, and hedging in discrete model, as well as basic probability and statistical tools. It then covers Brownian motion, martingales, and Ito’s calculus, and deals with risk-neutral pricing in continuous time models. Standard topics include Girsanov transformation, martingale representation theorem, Feyman-Kac formula, and American and exotic option pricings. Simulation techniques will be discussed at the end of the semester, and MATLAB (or C or S-Plus) will be used for computation.</p>
<p>Applied Stochastic Process I – Industrial Engineering (IND ENG) 263A [4 units]
Description: Conditional Expectation. Poisson and renewal processes. Renewal reward processes with application to inventory, congestion, and replacement models. Discrete and continuous time Markov chains; with applications to various stochastic systems–such as exponential queueing systems, inventory models and reliability systems.</p>
<p>Game Theory in the Social Sciences – Economics (ECON) C110 [4 units]
Description: A non-technical introduction to game theory. Basic principle, and models of interaction among players, with a strong emphasis on applications to political science, economics, and other social sciences. Also listed as Political Science C135.</p>
<p>will probably drop game theory to do the senior honors thesis.</p>