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LSA Course Guide Search Results: UG, GR, Winter 2007, Dept = IOE
 
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Title
Section
Instructor
Term
Credits
Requirements
IOE 265 — Probability and Statistics for Engineers
Section 100, LEC

Instructor: Jin,Jionghua

WN 2007
Credits: 4

Credit Exclusions: No credit granted to those who have completed or are enrolled in STATS 311, 400, 405, or 412, or ECON 405.

Graphical representation of data; axioms of probability; conditioning, Bayes Theorem; discrete distributions (geometric, binomial, Poisson); continuous distributions (Normal Exponential, Weibull), point and interval estimation, likelihood functions, test of hypotheses for means, variances, and proportions for one and two populations.

Enforced Prerequisites: MATH 116 and ENGR 101; (C->)

IOE 466 — Statistical Quality Control
Section 001, LEC

Instructor: Garcia-Guzman,Luis Manuel
Instructor: Suriano,Saumuy

WN 2007
Credits: 3

Quality improvement philosophies; Modeling process quality, statistical process control, control charts for variables and attributes, CUSUM and EWMA, short production runs, multivariate quality control, auto correlation, engineering process control economic design of charts, fill control, precontrol, adaptive schemes, process capability, specifications and tolerances, gage capability studies, acceptance sampling by attributes and variables, international quality standards.

IOE 510 — Linear Programming I
Section 001, LEC

Instructor: Cohn,Amy Ellen Mainville

WN 2007
Credits: 3

Background and Goals: A fundamental problem is the allocation of constrained resources such as funds among investment possibilities or personnel among production facilities. Each such problem has as it's goal the maximization of some positive objective such as investment return or the minimization of some negative objective such as cost or risk. Such problems are called Optimization Problems. Linear Programming deals with optimization problems in which both the objective and constraint functions are linear (the word "programming" is historical and means "planning" rather that necessarily computer programming). In practice, such problems involve thousands of decision variables and constraints, so a primary focus is the development and implementation of efficient algorithms. However, the subject also has deep connections with higher-dimensional convex geometry. A recent survey showed that most Fortune 500 companies regularly use linear programming in their decision making. This course will present both the classical and modern approaches to the subject and discuss numerous applications of current interest.

Content: Formulation of problems from the private and public sectors using the mathematical model of linear programming. Development of the simplex algorithm; duality theory and economic interpretations. Postoptimality (sensitivity) analysis; algorithmic complexity; the ellipsoid method; scaling algorithms; applications and interpretations. Introduction to transportation and assignment problems; special purpose algorithms and advanced computational techniques. Students have opportunities to formulate and solve models developed from more complex case studies and use various computer programs.

Alternatives: Cross-listed as IOE 510.

Subsequent Courses: IOE 610 (Linear Programming II) and IOE 611 (Nonlinear Programming)

Advisory Prerequisite: MATH 217, 417, or 419

IOE 552 — Financial Engineering Seminar I
Section 001, LEC

Instructor: Keppo,Jussi Samuli; homepage

WN 2007
Credits: 3

Outline: The objective of the course is to present arbitrage theory and its applications to pricing for financial derivatives. The main mathematical tool used in the course is the theory of stochastic differential equations (SDEs). We treat basic SDE techniques, including martingales, Feynman-Kac representation, and the Kolmogorov equations. We also consider, in some detail, stochastic optimal control.

The mathematical models are applied to the arbitrage pricing of financial instruments. We consider Black-Scholes theory and its extensions, as well as incomplete markets. We cover several interest rate theories.

Course Material:

  • Lecture notes
  • Björk, T.: Arbitrage Theory in Continuous Time, 2nd edition, Oxford University Press.

Homework Problems: Students are expected to solve the selected homework problems. Homework must be submitted individually.

Grading: Exam 60% (closed book; midterm 20% and final 40%), homework 40%. If you believe an exam question was graded in error and wish to have the exam regraded, you must submit the exam to the GSI together with a written explanation for requesting the regrade. This must be done within one week from the date the exam was returned.

Schedule:

 
Date Topic 					Chapters 
1/10 Introduction and binomial trees 		1, 2, 3 
1/17 Stochastic calculus 			4, 5 
1/24 Arbitrage pricing 				6, 7 
1/31 Hedging 					8, 9 
2/7  Several underlying assets 			13 
2/14 Incomplete markets 			15 
2/21 Dividends and foreign exchange rates 	16, 17 
2/28 Spring break 
3/7  Midterm 
3/14 Exotic options 				18 
3/21 Optimal portfolio selection 		19 
3/28 Bonds and interest rates	 		20 
4/4  Short rate models 				21, 22 
4/11 Short rate models and review 		21, 22 
4/18 Final   

Enforced Prerequisites: IOE 552 or MATH 552

Advisory Prerequisite: MATH,IOE 453 or MATH 423. Business School students: FIN 580 or 618 or BA 855

IOE 623 — Computational Finance
Section 001, LEC

Instructor: Conlon,Joseph G; homepage

WN 2007
Credits: 3

This course is a course on mathematical finance with an emphasis on numerical and statistical methods. It is assumed that the student is familiar with basic theory of arbitrage pricing, equity and fixed income (interest rate) derivatives in discrete and continuous time. The course will focus on numerical implementations of these models as well as statistical methods for calibration, i.e., obtaining the parameters of the models. Specific topics include finite-difference methods, trees and lattices and Monte Carlo simulations with extensions.

Texts:

  1. James and Webber: Interest Rate Modelling, Wiley, 2000.
  2. Wilmott, Howison, Dewynne: The Mathematics of Financial Derivatives, Cambridge, 1995.
  3. Jaeckel: Monte Carlo Methods in Finance, Wiley, 2002.

Advisory Prerequisite: MATH,MATH 316 and MATH 425 or 525.

 
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