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Elective Courses
Each candidate must take at least six approved elective courses from the list below. Descriptions for the prerequisite and elective courses can be found in the Stanford Bulletin.
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At least two electives in Stochastic Processes, Statistics or Probability from: |
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Statistics:
202 (Data Analysis), 206 (Applied Multivariate Analysis), 207 (Introduction to Time Series Analysis), 219 (Stochastic Processes - same as Math 136), 220 (Continuous Time Stochastic Control), 237 (Time Series Modeling and Forecasting), 240 (Statistical
Methods in Finance), 252 (Data Mining and Electronic Business), 254 (Correspondence Analysis and Related Methods) one time offering Aut 08-09, 305 (Introduction to Statistical Modeling), 306A (Methods for Applied Statistics), 310A/B/C (Theory of Probability), 315A/B/C (Modern Applied Statistics), 317 (Stochastic Processes), 318 (Modern Markov Chains), 324 (Multivariate and Random Matrix Theory), 343 (Time Series Analysis), 376A (Information Theory)
Mathematics:
136 (Stochastic Processes - same as Stats 219), 205A/B (Real Analysis), 237 (Stochastic Equations and Random Media)
Economics:
275 (Time Series Econometrics)
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| 2. |
At least two electives in Differential Equations, Simulation, Computing or Optimization from: |
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Mathematics:
220 (PDE of Applied Mathematics), 222A (Computational Methods for Fronts, Interfaces, and Waves), 227 (Partial Differential Equations and Diffusion Processes), 256A/B (Partial Differential Equations), 261A/B (Functional Analysis), 266 (Time Frequency Analysis and Wavelets)
Statistics:
212 (Applied Statistics with SAS), 227 (Statistical Computing),
235 (Decision Making in Financial Services),
322 (Function Estimation in White Noise)
CS:
106B (Programming Abstractions), 106X [Programming Abstractions (Accelerated)], 193D (C++), 229 (Machine Learning), 249A (Object-Oriented Programming : A Modeling and Simulation Perspective), 261 (Optimization and Algorithmic Paradigms), 339 (Topics in Numerical Analysis), 365 (Randomized Algorithms)
MS&E:
310 (Linear Programming), 311 (Optimization), 312 (Advanced Methods in Numerical Optimization), 313 (Vector Space Optimization), 323 (Simulation Theory), 339 (Approximate Dynamic Programming), 348 (Optimization of Uncertainty and Applications in Finance), 351 (Dynamic Programming and Stochastic Control)
CME:
340 (Computational Methods in Data Mining)
GSB:
Operations, Information, and Technology:
OIT 667* (Revenue Management)
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| 3. |
At least two electives in Finance or Economics from: |
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Economics:
202N-203N (Core Economics: Modules 1 and 2, 5 and 6 - For Non-Economics Ph.D. Students), 210 (Core Economics: Modules 3 and 7), 211 (Core Economics: Modules 11 and 12), 269 (International Financial Markets and Monetary Institutions), 281 (Economics of Uncertainty), 284 (Topics in Dynamic Economics)
Mathematics:
180 (Introduction to Financial Mathematics)
Statistics:
243 (Introduction to Mathematical Finance) (summer version of Math 180)
MS&E:
242H (Investment Science Honors), 247G (same as GSB F323)* (International Financial Management), 247S (International Investments), 272 (Entrepreneurial Finance), 341 (Advanced Economic Analysis), 342 (Advanced Investment Science), 345 (Advanced Topics in Financial Engineering), 444* (Investment Practice),
445 (Projects in Wealth Management)
GSB:
Finance:
F320* (Debt Markets), F326* (Derivative Securities), F328* (Portfolio Management), F620* (Financial Markets I), F621* (Financial Markets II),
F622* (Dynamic Asset Pricing Theory), F629* (Tax and Finance seminar),
Economic
Analysis and Policy:
MGTECON600* (Microeconomic Analysis I), MGTECON604* (Econometric Methods II), MGTECON609* (Applied Econometric and Economics Research)
* - indicate courses of limited enrollment and/or the instructor's preapproval is needed for registration.
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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.
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