FE622 Simulation Methods in Computational Finance and Economics

Course Catalog Description


Simulation methods present a new approach to economic and financial modeling. This course introduces the fundamentals of computer-based simulation techniques used in computational finance and economics. The major categories of simulation methods include Discrete-event simulation, Monte Carlo simulation, and Agent-based simulation. With the introduction of basic philosophy and methodology behind simulation in computational finance and economics, students will learn advanced topics such as Markov Chain Monte Carlo, Bayesian Monte Carlo, Zero Intelligent Agent Model, and Utility Optimization Agent Model. On the subject of the agent-based approach, this course will introduce a widely used agent-based modeling framework where financial market prices and information are modeled from the bottom up with large numbers of interacting agents. Students will learn simulation packages in R and RNetlogo. At the end, students will be able to formulate simulation models and perform statistical analysis, evaluation and sensitivity analysis using these tools.

Campus Fall Spring Summer
On Campus X
Web Campus X


Professor Email Office
Steve Yang
steve.yang@stevens.edu Babbio 536

Course Resources


  • Simulation and the Monte Carlo Method, by Reuven Y. Rubinstein, Dirk P. Kroese (Required)

Additional References

  • Modern Portfolio Management: from Markowitz to Probabilistic Scenario Optimization by Paolo Sironi, 2015 [OPTIONAL]
  • Agent-based Computational Finance: Handbook of Computational Economics, Second Edition, by Blake LeBaron, Leigh Tesfatsion and Kenneth L. Judd – Wiley 2006. [OPTIONAL]
  • Monte Carlo Methods in Financial Engineering, by Paul Glassermen, - Springer Springer; 2003 edition (OPTIONAL)


Grading Policies

Generally the grade distribution follows the following percentages:

  • HomeWork Assignments 30%
  • Midterm 30%
  • Final 40%

Lecture Outline

Topic Reading
Week 1 Introduction – Foundation of Simulation Rubinstein Chapter 1
Week 2 Random Numbers and Random Variables, and Stochastic Process Generation Rubinstein Chapter 2
Week 3 Simulation and Analysis of Discrete-Event Systems Rubinstein Chapter 3
Week 4 Monte Carlo Methods and Finance Applications Rubinstein Chapter 4/Classermen 3
Week 5 Controlling Variance Techniques Rubinstein Chapter 5/Glassermen 4
Week 6 Markov Chain Monte Carlo & Pricing American Options Rubinstein Chapter 6/Glassermen 6, 7
Week 7 Quasi-Monte Carlo and Applications Glassermen 5
Week 8 Midterm Exam
Week 9 Beyond Modern Portfolio Theory for Long-term and Goal-based Investing Paolo Chapter 1 and 2
Week 10 Real Securities and Reinvestment Strategies with Risk-Return Time Profile Paolo Chapter 3, 4 and 5
Week 11 Probabilistic Scenario Optimization Paolo Chapter 5 and 8
Week 12 Agent-Based Modeling Framework, ABM Noise Traders and Behavioral Finance Handbook 16, 18, 19
Week 13 Heterogeneous Agent Models in Economics and Finance Handbook 23, 24
Week 14 Final Project