NYU Stern School of Business

Undergraduate College


Fall 2011

Instructor Details

White, Norman



M-W 3:30-5pm and as available

KMEC 8-88

I am generally around from about 11 am until 6pm, M-Th. My assistant, Sirley Lau, 212-998-0810 has my schedule.


Course Meetings

MW, 2:00pm to 3:15pm

KMC 4-120

Final Exam:

Schedule exceptions
    Class will not meet on:
    Class will meet on:


Course Description and Learning Goals

"Computational finance (also known as financial engineering) is a cross-disciplinary field which relies
 on mathematical finance, numerical methods and computer simulations to make trading, hedging and investment decisions,
 as well as facilitating the risk management of those decisions. Utilizing various methods, practitioners of 
 computational finance aim to precisely determine the financial risk that certain financial instruments create." 


The field of Financial Engineering has grown rapidly, following a number of theoretical breakthroughs and aided by the dramatic drop in the price of computation.  Financial Engineering techniques have allowed the development of many new financial derivatives and “structured” products whose risks and expected returns demand substantial amounts of computation.  Trading systems are built which rely on the rapid pricing of derivatives from options to CMOs.  “Quant” hedge funds make extensive use of the enormous amount of data and computation ability to look for arbitrage opportunities. Corporate Finance departments use Value at Risk calculations to track their portfolios of both real and financial assets.   New security types such as Exchange Traded Funds allow new approaches to hedging risk and portfolio optimization.  In addition to market risk, financial institutions also need to be able to estimate credit risk. Credit risk is more complicated in the case of complex derivatives.


This course will introduce the student to a variety of financial engineering problems and solution approaches using software systems like Excel, Matlab and Sas. Emphasis will be on the underlying data and how to access it, as well as techniques for attacking common problems such as the pricing of derivatives, evaluating risk, approaches to building quantitative trading systems, and monte carlo simulation approaches in Value at Risk calculations.


While by its nature, the course requires some mathematical skills and an understanding of probability theory, there is no assumption that the student has a background in many of the advanced mathematical concepts used in the theory of financial engineering. Necessary mathematical and statistics concepts will be introduced and covered as necessary, but always in the context of a real problem. The emphasis will be on how to use computer based tools to solve the problems.  Real data will be used whenever possible to illustrate not only the financial computational problems, but also the inherent data processing problems which need to be solved in the real world before the financial calculations can be done.


Course Pre-Requisites

Core Finance Course, IS Core course, Core Statistics class,  some minor programming background.

Co-requisite Future and Options course or experience in Futures and Options


Course Outline

Lectures, Assignments and Readings:


Week 1 – Course overview and introduction to financial engineering concepts and problems.

The buying and selling of risk.

Basic concepts of arbitrage and replication.

Examples: Option pricing models, securities pricing models, Value at Risk calculations, hedging strategies, portfolio optimization.

Reading: Brandimarte Chapters 1, 2.1, 2.2,2.3, Appendix B


Week 2 – Technical tools - Monte Carlo Simulation, random number generation, generating data

from different probability distributions, Inverting the cumulative distribution function. Reducing simulation variance, antithetic variables, control variates …

Reading: Brandimarte, Chapter 4.1, 4.2, 4.3, 4.4


Week 3 – Introduction to Matlab

              matlab examples.

Reading: Brandimarte ,  Appendix A

http://www.math.mtu.edu/~msgocken/intro/intro.html (first 4 topics)

http://www.maths.uq.edu.au/~gac/mlb/mlb.html (Chapters 1-5)


Week 4 – Overview of the Matlab Financial toolkit.

 Portfolio optimization in Matlab

Reading: Brandimarte, Chapters 2.4 – 2.6 (skim 2.7 – 2.8)


Week 5 - Producing graphical output in Matlab. How does one display multidimensional data?

            Yield curve calculations in Matlab.

            Fixed income applications, pricing a callable bond.

                        Reading:  see Blackboard


Week 5 – Options Pricing in Matlab, continuous and discrete approaches.

            Homework 3 due – Bootstrapping the  yield curve

Reading: Brandimarte,  Chapter 7


Week 7 – Mid-Term review, mid-term



Week 8 – Data problems. Data sources, data consistency, accessing data remotely using Matlab,

             Grid computing and financial engineering.

Reading:  see Blackboard


Week 9 -  Optimization techniques in Matlab, Linear Programming, Dynamic Programming,

 non-convex optimization. Applications to finance.


Reading: Brandimarte, Chapter 6.1, 6.2, Chapter 10,12


Week 10 – Introduction to Sas, using Sas to analyze data from Matlab

Reading: http://www.psych.yorku.ca/lab/sas/introsas.htm



Week 11 – Building financial systems with Matlab. The Matlab compiler, matlab gui generator,

 data feeds, linking other code to Matlab.


Week 12 – Other topics, volatility smiles, trinomial trees, credit risk, variance reduction



Week 13 -  Course Review, Final Project Presentations (food)


Required Course Materials

Text: Numerical Methods in Finance and Economics, A MATLAB Based Introduction, 2nd Edition, Paolo Brandimarte, Wiley 2006


Optional Text: Options, Futures and Other Derivatives, 6th Edition, John Hull,  Pearson 2006



Assessment Components

Course requirements: biweekly homework assignments, a mid-term exam, final quiz  and a final project.  (Homework assignments will be posted in Blackboard, along with hints at solutions.  Look in the assignments folder)


Grading:  homeworks 20%, Midterm 30%,  Group Project 35%  (25% +10% peer assessment),  Presentation 5%,  Class participation 10%


At NYU Stern we seek to teach challenging courses that allow students to demonstrate differential mastery of the subject matter. Assigning grades that reward excellence and reflect differences in performance is important to ensuring the integrity of our curriculum.


In general, students in this course can expect a grading distribution where the median grade is a B+. i.e. 50% of the students will have a B+ or better. However, since the class is small the actual median grade could be above or below a B+.  In the event that a student performs only adequately or below, he or she can expect to receive a C or lower.


Note that the actual distribution for this course and your won grade will depend upon how well each of you actually performs in this course.



Homework grading: I typically grade homeworks on a 0 -10 point scale. 0 is if you don’t hand it in. 10 is for a homework that is exceptional and goes beyond the requirements of the assignment. Typically a very good homework will receive a 9.5.  Usually homeworks that satisfy the minimal requirements will receive an 8. Homeworks that are clearly incorrect will receive grades in the 5 – 7.5 range. Late homeworks are penalized. Homeworks that have to be handed in on paper, should be handed in at the beginning of class. Home works that are due electronically are due at midnight of the due date. They should be posted to the dropbox on Blackboard. If you post electronically, MAKE sure you actually SUBMIT the homework.


Regrading: If you feel that a grading error has been made, please submit a request in writing to me within 7 days of receiving the grade, including why you believe a grading error has been made.



Group Projects

Guidelines for Group Projects

Business activities involve group effort. Consequently, learning how to work effectively in a group is a critical part of your business education.

Every member is expected to carry an equal share of the group’s workload. As such, it is in your interest to be involved in all aspects of the project. Even if you divide the work rather than work on each piece together, you are still responsible for each part. The group project will be graded as a whole:   its different components will not be graded separately. Your exams may contain questions that are based on aspects of your group projects.

It is recommended that each group establish ground rules early in the process to facilitate your joint work including a problem-solving process for handling conflicts. In the infrequent case where you believe that a group member is not carrying out his or her fair share of work, you are urged not to permit problems to develop to a point where they become serious. If you cannot resolve conflicts internally after your best efforts, they should be brought to my attention and I will work with you to find a resolution.

You will be asked to complete a peer evaluation form to evaluate the contribution of each of your group members (including your own contribution) at the conclusion of each project. If there is consensus that a group member did not contribute a fair share of work to the project, I will consider this feedback during grading.


Peer assessment: Your grade on a group  project will be weighted by how well your peers (and I) believe you have performed on a project.  I am to be treated as a member of each project team, and can help you on your projects. 



At NYU Stern we seek to teach challenging courses that allow students to demonstrate their mastery of the subject matter.  In general, students in undergraduate core courses can expect a grading distribution where: 

Note that while the School uses these ranges  as a guide, the actual distribution for this course and your own grade will depend upon how well  you actually perform in this course.



The process of assigning grades is intended to be one of unbiased evaluation. Students are encouraged to respect the integrity and authority of the professor’s grading system and are discouraged from pursuing arbitrary challenges to it.

If you believe an inadvertent error has been made in the grading of an individual assignment or in assessing an overall course grade, a request to have the grade re-evaluated may be submitted. You must submit such requests in writing to me within 7 days of receiving the grade, including a brief written statement of why you believe that an error in grading has been made.


Professional Responsibilities For This Course




In-class contribution is a significant part of your grade and an important part of our shared learning experience. Your active participation helps me to evaluate your overall performance.
You can excel in this area if you come to class on time and contribute to the course by:




Classroom Norms


Stern Policies

General Behavior
The School expects that students will conduct themselves with respect and professionalism toward faculty, students, and others present in class and will follow the rules laid down by the instructor for classroom behavior.  Students who fail to do so may be asked to leave the classroom. 


Collaboration on Graded Assignments
Students may not work together on graded assignment unless the instructor gives express permission. 


Course Evaluations
Course evaluations are important to us and to students who come after you.  Please complete them thoughtfully.


Academic Integrity

Integrity is critical to the learning process and to all that we do here at NYU Stern. As members of our community, all students agree to abide by the NYU Stern Student Code of Conduct, which includes a commitment to:

The entire Stern Student Code of Conduct applies to all students enrolled in Stern courses and can be found here:

Undergraduate College: http://www.stern.nyu.edu/uc/codeofconduct
Graduate Programs: http://w4.stern.nyu.edu/studentactivities/involved.cfm?doc_id=102505

To help ensure the integrity of our learning community, prose assignments you submit to Blackboard will be submitted to Turnitin.  Turnitin will compare your submission to a database of prior submissions to Turnitin, current and archived Web pages, periodicals, journals, and publications.  Additionally, your document will become part of the Turnitin database.


Recording of Classes

Your class may be recorded for educational purposes


Students with Disabilities

If you have a qualified disability and will require academic accommodation of any kind during this course, you must notify me at the beginning of the course and provide a letter from the Moses Center for Students with Disabilities (CSD, 998-4980, www.nyu.edu/csd) verifying your registration and outlining the accommodations they recommend.  If you will need to take an exam at the CSD, you must submit a completed Exam Accommodations Form to them at least one week prior to the scheduled exam time to be guaranteed accommodation.


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