Mathematics 16:642:621 Mathematics Finance I

Schedule

The course is offered during the Fall semester.

• Class meeting dates: Please visit the University's academic calendar.
• Schedule and Instructor: Please visit the University's schedule of classes for the instructor, time, and room.
• Instructor and Teaching Assistant Office Hours: Please visit the Mathematical Finance program's office hour schedule.

Note: Please avoid stopping by the course assistant or instructor offices outside of their posted hours without an appointment. You are welcome to alert the instructor if you believe an assignment contains a typographical error, but the course assistants and instructor will rarely answer homework questions by email, except for students who are working full-time off campus and are unable to attend any scheduled office hour. We recommend emailing the instructor or teaching assistant in advance to alert them to your visit, as schedules can sometimes change unexpectedly.

Course Abstract

This course is an introduction to the mathematical theory of derivative security (or option) pricing. Fundamental concepts are briefly introduced first using the discrete-time binomial model: financial markets, derivative securities, arbitrage, hedging and replicating portfolios, risk-neutral probabilities, risk-neutral pricing formula, and market completeness. Basic ideas of probability and stochastic processes are reviewed for finite probability spaces and discrete-time processes: conditional expectation, martingales, and Markov processes. After this introduction to finance using discrete-time models, the emphasis shifts to continuous-time models and the main part of the course. Topics covered include a summary of probability measure theory and conditional expectation, Brownian motion and quadratic variation, martingales, Ito integral, stochastic calculus, replicating portfolios and hedging, Black-Scholes-Merton formulae for a European-style call option price, change of measure and Girsanov's Theorem, risk-neutral pricing pricing theory, no-arbitrage and existence of risk-neutral measure, market completeness and uniqueness of risk-neutral measure, Markov property, Feyman-Kac theorem and the connection between stochastic calculus and partial differential equations, and local volatility and stochastic volatility models.

Pre-requisites and Co-requisites

Ordinary differential equations (01:640:244 or 01:640:252), multivariable calculus (01:640:251), linear algebra (01:640:250), and undegraduate probability theory with calculus (01:640:477 or 01:960:381). An undergraduate course on analysis (01:640:311-312 or 01:640:411-412) or engineering mathematics (01:640:421) or partial differential equations (01:640:423) is recommended but not required.

Please visit the prerequisites page for descriptions of Rutgers undergraduate course prerequisites. A solid understanding of undergraduate probability at the level of the textbook by Sheldon Ross, A First Course in Probability, is especially important. Given this background, the course should be accessible to Mathematical Finance master's degree students and graduate students in Computer Science, Economics, Finance, Engineering, Mathematics, Physics, Operations Research, and Statistics.

Required Textbooks

Stephen E. Shreve, Stochastic Calculus for Finance II: Continuous-Time Models, Springer Verlag, 2004, ISBN 0-387-40101-8. (Text errata available from author's web site.)

Supplementary Textbooks: Stephen E. Shreve, Stochastic Calculus for Finance I: The Binomial Asset Pricing Model, Springer Verlag, 2004; John C. Hull, Options, Futures, and other Derivatives, 7th Edition, Prentice Hall, 2008.

Note: The textbook may be purchased either new or used at significant discounts to the list price from online sellers such as Amazon.com, Buy.com, Half.com, and others.

Sakai

All course content – lecture notes, homework assignments and solutions, exam solutions, supplementary articles, and computer programs – are posted on Sakai and available to registered students.

Grading

Class attendance 5%, homework 25%, midterm exam 30%, and final exam 40%. Exams are in-class.

Class Policies

• Class attendance: Attendance is taken every week and students will be dropped from the course for missing an excessive number of class periods. Students can be absent for one week (two periods) without an excuse. Additional absences must be excused in writing. Unexcused absences will negatively impact course grade.
• Homework: Assignments may be submitted at the beginning of class each week only – assignments left in mailboxes, under office doors, given to departmental staff, emailed, faxed, or mailed are not accepted. No late homework is accepted, for any reason – instead, the two (2) lowest scores are dropped. Students may work together on assignments provided their submissions represent fair individual efforts. Assignments must be legible, stapled (no paper clips or loose sheets), and use US letter-size paper.
• Exam attendance: Make-up exams are not permitted. In a genuine emergency, such as a documented medical condition, students are expected to contact the instructor in advance or as soon as possible after the event.
• Incomplete grades: Incomplete grades are not given. Students who do not have adequate preparation or time to complete assignments or study for exams during the semester should not take the course.
• Academic integrity: Students are expected to adhere to the academic integrity code.
• Work-study balance: If you work part or full time, please read our guidelines for balancing work and study.
• Withdrawal dates: You are responsible for being aware of all deadlines, including those for course refunds or withdrawals. Please contact the Registrar if you are in any doubt regarding drop or refund deadlines.

Weekly Lecturing Agenda and Readings

This page lists the topics we shall cover in each week, with links and information on the related readings. Reading assignments should be completed prior to each class. The schedule will be updated regularly.

Week	Topics	Reading
1	Financial markets and derivative securities; No-arbitrage condition; One bond, one-stock model; Forward contracts No arbitrage pricing; No arbitrage price of an option for the binomial model	Hull, § 1, 2, & 5 Hull, § 11; Shreve-I, § 1; Pliska, § 1.1, 1.2
2	First Fundamental Theorem of Asset Pricing for a one period, finite state model; State-price vector State-price vectors and risk-neutral measure; Risk neutral pricing formula. Examples.	Shreve-I, § 1; Hull, § 11; Pliska, § 1.3, 1.4, 1.5 Optional: Duffie, § 1;
3	Binomial trees (continued); Probability theory and discrete-time stochastic processes Binomial trees (continued); Risk-neutral measure and option pricing	Shreve-I, § 2 & 3;
4	Binomial trees (continued) Probability spaces	Shreve-I, § 2 & 3 Shreve-I, § 2; Shreve-II, § 1.1, 1.2, 1.3
5	Expectation, information, and σ-algebras Conditional expectation	Shreve-I, § 2.2; Shreve-II, § 1.3, 1.5 Shreve-I, § 2.3, 2.4, 2.5; Shreve-II, § 2.1, 2.2, 2.3 Rutgers Math 591 Notes, Chicago Stat 313 Notes, Lyons Notes, Harvey Mudd Math 157 Notes (pdf)
6	Brownian motion: Random walks and the central limit theorem Brownian motion: Definition, martingale property, quadratic variation	Shreve-II, § 3.2 Shreve-II, § 3.3
7	Brownian motion: Markov property The Itô integral: Introduction	Shreve-II, § 3.3 Shreve-II, § 4.2, 4.3, & 4.4
8	The Itô formula The Black-Scholes-Merton PDE and its solution for European-style call and put option prices.	Shreve-II, § 4.4 Shreve-II, § 4.5
9	The Black-Scholes-Merton formula, geometry of hedging, put-call parity Multivariable stochastic calculus, Lévy's characterization of Brownian motion, Gaussian processes, Brownian bridge.	Shreve-II, § 4.5 Shreve-II, § 4.6 & 4.7
10	Change of measure, Radon-Nikodym derivative, Girsanov's theorem for single Brownian motion Discounted stock and portfolio processes as martingales	Shreve-II, § 1.6, 5.1, & 5.2.1 Shreve-II, § 5.2.2, 5.2.3, & 5.2.4
11	Pricing under risk-neutral measure, derivation of Black-Scholes-Merton formula Martingale representation theorem, Multi-dimensional market model	Shreve-II, § 5.2.4, & 5.2.5 Shreve-II, § 5.3, 5.4.1 & 5.4.2
12	Existence of risk-neutral measure, no arbitrage, and First fundamental theorem of asset pricing Uniqueness of risk-neutral measure, completeness, and Second fundamental theorem of asset pricing	Shreve-II, § 5.4.3 Shreve-II, § 5.4.4
13	Option pricing and PDEs	Shreve II, § 6.1, 6.2, 6.3, 6.4, & 6.6
14	Risk-neutral, martingale measure pricing theory and explicit portfolio hedge ratios Overview of Dupire local volatility, Heston stochastic volatility, and jump models Course sequels Introduction	Steele § 14.3, Shreve II chapters 5 & 6 Shreve II, chapters 6 and 11 Mathematical Finance II, Computational Finance

Library Reserves

All textbooks referenced on this page should be on reserve in the Hill Center Mathematical Sciences Library (1st floor). Please contact the instructor if reserve copies are insufficient or unavailable. Please visit the Mathematical Finance Reference Text List blog for additional textbook suggestions.

Additional Textbooks

Class lectures will draw on material from the following texts and current research articles. Please see the Rutgers Mathematical Finance Reference Texts blog for additional textbooks. K. Back, A Course in Derivative Securities: Introduction to Theory and Computation, Springer, 2005
M. Baxter and A. Rennie, Financial Calculus: An Introduction to Option Pricing, Cambridge, 1996
T. Björk, Arbitrage Theory in Continuous Time, Oxford, 2004
J. C. Hull, Options, Futures, and other Derivatives, 6th Edition, Prentice Hall, 2006
P. Hunt and J. Kennedy, Financial Derivatives in Theory and Practice, Wiley, 2004
M. Jackson and M. Staunton, Advanced Modelling in Finance using Excel and VBA, Wiley, 2001
R. Jarrow and S. Turnbull, Derivative Securities, 2nd edition, South-Western College,1999
I. Karatzas and S. E. Shreve, Brownian Motion and Stochastic Calculus, Springer, 1997
D. G. Luenberger, Investment science, Oxford, 1997
S. R. Pliska, Introduction to Mathematical Finance: Discrete Time Models, Blackwell, 1997
S. E. Shreve, Stochastic calculus and Finance I: Binomial Model, Springer, 2004
J. M. Steele, Stochastic calculus and financial applications, Springer, 2000
P. Wilmott, Paul Wilmott on Quantitative Finance, 2nd edition, 3 volume set, Wiley, 2006

Software

Depending on the application, Excel/VBA or MATLAB may be used in the course. Please visit the Quantitative Finance Software blog for a guide to platforms, installation guides, and sample code.

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