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Required Courses
  Course Descriptions
Elective Courses
Financial Industry Related Courses
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Elective Courses

Each candidate must take at least six approved elective courses from the list below, with two from each of the three core areas:

  1. Stochastic Processes and Statistics
  2. Differential Equations, Modeling, Simulation and Computing
  3. Finance

Other elective courses may be authorized by the Program Director if they provide skills relevant to financial mathematics and do not overlap with courses in the candidate's program.

Descriptions for the prerequisite and elective courses can be found in the Stanford Bulletin.

Mathematics (MATH):
136 Stochastic Processes - same as STATS 219 (A)

180 Introduction to Financial Mathematics

205A/B Real Analysis (A/W)

220 PDE of Applied Mathematics (A)

222A Computational Methods for Fronts, Interfaces, and Waves

227 Partial Differential Equations and Diffusion Processes

236 Introduction to Stochastic Differential Equations (W)

237 Default and Systemic Risk

238 (same as STATS 250) Mathematical Finance (W)

239 Computation and Simulation in Finance

240 Topics in Financial Mathematics: Fixed Income Models

256A/B Partial Differential Equations (Spr/W)

261A/B Functional Analysis

266 Time Frequency Analysis and Wavelets

Statistics (STATS):

202 Data Mining and Analysis (A/Smr)

206 Applied Multivariate Analysis (A)

207 Introduction to Time Series Analysis (Spr)

212 Applied Statistics with SAS

219 Stochastic Processes - same as MATH 136 (A)

220 Continuous Time Stochastic Control

227 Statistical Computing

235 Decision Making in Financial Services

237 Theory of Investment Portfolios and Derivative Securities

238 Policy & Strategy Issues in Financial Engineering

240 Statistical Methods in Finance (A)

241 Financial Modeling Methodology and Applications

242 Algorithmic Trading and Quantitative Strategies (Smr)

243 Statistical Methods and Models for Risk Management and Surveillance (W)

252 Data Mining and Electronic Business

254 Correspondence Analysis and Related Methods one time offering Aut 08-09

305 Introduction to Statistical Modeling (A)

306A Methods for Applied Statistics (W)

310A/B/C Theory of Probability (A/W/Spr)

315A/B Modern Applied Statistics (W/Spr)

317 Stochastic Processes

318 Modern Markov Chains

322 Function Estimation in White Noise

324 Multivariate and Random Matrix Theory

343 Time Series Analysis

362 Monte Carlo (Spr)

376A Information Theory

Computer Science (CS):
106A Introduction to Computers (A/W/Spr/Smr)
106B
Programming Abstractions (A/W/Spr/Smr)

106XProgramming Abstractions (Accelerated) (A/W)

193D C++

224M Multi-Agent Systems (Spr)

229 Machine Learning (A)

249A Object-Oriented Programming : A Modeling and Simulation Perspective (A)

261 Optimization and Algorithmic Paradigms (W)

295 Software Engineering

339 Topics in Numerical Analysis

365 Randomized Algorithms

Economics (ECON):
190 Introduction to Financial Accounting (A/W)
202N-203N Core Economics: Modules 1 and 2, 5 and 6 - For Non-Economics Ph.D. Students (A/W)

210 Core Economics: Modules 3 and 7 (A)

211 Core Economics: Modules 11 and 12 (W)

269 International Financial Markets and Monetary Institutions

275 Time Series Econometrics

281 Economics of Uncertainty

284 Topics in Dynamic Economics

Management Science & Engineering (MS&E):
242H Investment Science Honors

247G (same as GSB F323)* International Financial Management

247S International Investments

272 Entrepreneurial Finance (Spr)

310 Linear Programming (A)
311 Optimization

312 Advanced Methods in Numerical Optimization (A)

313 Vector Space Optimization

322 Stochastic Calculus and Control

323 Stochastic Simulation

339 Approximate Dynamic Programming

341 Advanced Economic Analysis (W)

342 Advanced Investment Science (W)

345 Advanced Topics in Financial Engineering

347 Credit Risk: Modeling and Management (W)

348 Optimization of Uncertainty and Applications in Finance

349 Capital Deployment

351 Dynamic Programming and Stochastic Control (A)
444* Investment Practice (Spr)

445 Projects in Wealth Management (Spr)


Computational & Mathematical Engineering (CME):

340 Computational Methods in Data Mining

Graduate School of Business (GSB):
FINANCE:

221* Finance for Non-MBAs (A)

320* Debt Markets (W)

326* Derivative Securities

328* Portfolio Management

620* Financial Markets I (A)

621* Financial Markets II (W)

622* Dynamic Asset Pricing Theory (A)

629* Tax and Finance seminar

Economic Analysis and Policy:
MGTECON600*
Microeconomic Analysis I

MGTECON604* Econometric Methods II

MGTECON609* Applied Econometric and Economics Research

MGTECON640* Quantitative Methods for Empirical Research

Operations, Information, and Technology:
OIT 667*
Revenue Management

* - indicate courses of limited enrollment and/or the instructor's preapproval is needed for registration