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IEF 217a: Computer Simulations and Risk Assessment

IEF 217a: Computer Simulations and Risk Assessment. Blake LeBaron blebaron@brandeis.edu www.brandeis.edu/~blebaron/classes/ief217a TA: Ritirupa Samanta. Introduction. Description Prerequisites Readings Computer issues Grading Outline. What is this course?. Computer

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IEF 217a: Computer Simulations and Risk Assessment

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  1. IEF 217a: Computer Simulations and Risk Assessment • Blake LeBaron • blebaron@brandeis.edu • www.brandeis.edu/~blebaron/classes/ief217a • TA: Ritirupa Samanta

  2. Introduction • Description • Prerequisites • Readings • Computer issues • Grading • Outline

  3. What is this course? • Computer • Probability/Statistics • Finance • Psychology/Philosophy

  4. Topics • Computational tools • Probability basics • Finance applications • Value-at-Risk • Stress testing • Multiperiod investments • Dynamic trading strategies • Liquidity risk

  5. Prerequisites • Required: • IEF 205 (basic finance knowledge) • Or Econ 171 for BA/MA students • Recommended: • Probability/Statistics • Computer skills (enthusiasm) • Who can take this course? • 2nd year MA, MBAi • MSF, BA/MA • 2nd year and beyond Ph.D

  6. Readings/Software • Books • Jorion, Value at Risk • Sigmon and Davis, Matlab Primer • Papers • Brandeis Electronic Reserves • Password “gambles” • Software • Matlab (personal version) • Internet (email/web)

  7. Computer Issues • Personal Computer (Windows) • Matlab student edition (cd rom: bookstore) • Can also use Sachar machines • Programs from course website

  8. Grading • Problem sets (25%) • Midterm exam (30%) • Group project (20%) • Take home final (25%)

  9. Course Outline • Introduction and philosophy • Tools • Risk measures • Financial meltdowns • Value-at-Risk • VaR methods • VaR extensions • Stress testing • Time, dynamics, and uncertainty • More finance examples • Advanced monte-carlo methods • Liquidity risk

  10. Introduction and philosophy • Basic ideas of probability • Quantifying risky situations • Expected values/St. Petersburg paradox • Variance • Histograms/distributions • Further questions about risk • Frank Knight: Risk versus uncertainty • Ellsberg paradox • Computing power and risk assessment

  11. Tools • The Matlab computer language • Probability basics • Sampling, monte-carlo, and bootstrapping

  12. Risk Measures • Histograms • Variance • Beta • Value-at-Risk (VaR) • Expected utility • Time and risk • Chaos and complexity • Types of risk

  13. Financial Meltdowns

  14. Value-at-Risk • Computing VaR • Interpreting VaR • Time scaling • Regulation and VaR • Estimation errors

  15. VaR Methods • Delta normal • Historical simulation • Monte-carlo • Bootstrap

  16. VaR Extensions • Testing VaR • VaR and portfolios • VaR and changing volatility

  17. Stress Testing

  18. Time, Dynamics, and Uncertainty • Multiperiod investments • Retirement problems • Dynamic trading strategies

  19. Further Financial Examples • Short positions and VaR • Exotic option pricing • Portfolio selection

  20. Final Topics • Advanced monte-carlo tools • Liquidity risk

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