New Jersey Institute of Technology
Department of Mathematical Sciences
Math 666, Simulation for Finance
Instructor: David J. Horntrop
Contact Information: 520 Cullimore Hall,
4:00pm - 5:30pm Mon.
4:00pm - 5:30pm Tues.
or by appointment
Paul Glasserman, Monte Carlo Methods in Financial
Engineering, Springer, 2004.
- P. Jackel, Monte Carlo Methods in Finance, Wiley, 2002.
- D. McLeish, Monte Carlo Simulation and Finance
, Wiley, 2005.
Prerequisites: Prior coursework in probability/stochastic
processes, linear algebra,
and differential equations; ability to write computer programs.
Examinations: There will be a midterm examination
and a final examination.
The midterm examination will occur before the "drop'' deadline. The final
examination date, time, and location will be determined by the university.
Homework: Homework assignments/projects will be given frequently.
Each assignment must be turned in at the beginning of class. Late
assignments are NOT accepted. Early assignments are always welcomed
and are appropriate for preplanned absences from class.
As a standing assignment,
you should read the relevant sections of the textbook prior to lecture.
Quizzes: From time to time, quizzes may be given. Make up
quizzes are NOT given.
Grading: The midterm examination will represent 35% of your grade.
The final examination will be worth 35% of your grade. The remaining
30% of your grade will be determined by your homework/quizzes/projects.
Attendance: Attendance at and participation in all lectures
is expected. If you know in advance that you will be absent from
class for a legitimate reason, please tell me prior to your absence
so that appropriate arrangements (if any) can be made.
Tardiness to class is very disruptive of the classroom environment
and should be avoided.
Academic Integrity Code: The
NJIT Academic Integrity Code applies
to all activities associated with the course, including but not limited to
homework, quizzes, examinations, and projects. As an example, when you submit
a homework assignment, you are certifying that your paper contains only
your work and is not copied from other people or sources.
Major topics for this course include:
Simulation of random variables
Simulation of stochastic processes
Discretization of stochastic differential equations
Variance reduction techniques
Introduction to Quasi Monte Carlo