ACCOUNTING AND FINANCE
For undergraduates only. Examines the fundamental concepts of financial accounting and finance, from the perspective of both managers and investors. Key topics covered include principles of accrual accounting; recognizing and recording accounting transactions; preparation and analysis of financial statements; ratio analysis; pro-forma projections; time value of money (present values, future values and interest/discount rates); inflation; discounted-cash-flow (DCF) project evaluation methods; deterministic and probabilistic measures of risk; capital budgeting.
- F 10:10 a.m.-12:40 p.m.
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Instructor:
Nadejda Zaets
CULPA: 3
Info
- 3 points
FOUNDATIONS OF DATA SCIENCE
Designed to provide an introduction to data science for sophomore SEAS majors. Combines three perspectives: inferential thinking, computational thinking, and real-world applications. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? Teaches critical concepts and skills in computer programming, statistical inference, and machine learning, in conjunction with hands-on analysis of real-world datasets such as economic data, document collections, geographical data, and social networks. At least one project will address a problem relevant to New York City.
STOCHASTIC SYSTEMS AND APPLICATIONS
Some of the main stochastic models used in engineering and operations research applications: discrete-time Markov chains, Poisson processes, birth and death processes and other continuous Markov chains, renewal reward processes. Applications: queueing, reliability, inventory, and finance. IEOR E3106 must be completed by the fifth term. Only students with special academic circumstances may be allowed to take these courses in alternative semesters with the consultation of CSA and Departmental advisers.
- TR 4:10 p.m.-5:25 p.m.
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Instructor:
Kaizheng Wang
CULPA: 1 h-index: 8 Info - 3 points
FOUNDATIONS OF OPTIMIZATION
This first course in optimization focuses on theory and applications of linear optimization, network optimization, and dynamic programming.
- MW 11:40 a.m.-12:55 p.m.
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Instructor:
Eric Balkanski
h-index: 13 Info - 3 points
PROBABILITY FOR ENGINEERS
Introductory course to probability theory and does not assume any prior knowledge of subject. Teaches foundations required to use probability in applications, but course itself is theoretical in nature. Basic definitions and axioms of probability and notions of independence and conditional probability introduced. Focus on random variables, both continuous and discrete, and covers topics of expectation, variance, conditional distributions, conditional expectation and variance, and moment generating functions. Also Central Limit Theorem for sums of random variables. Consists of lectures, recitations, weekly homework, and in-class exams.
- TR 10:10 a.m.-11:25 a.m.
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Instructor:
Daniel Lacker
CULPA: 4
h-index: 13
Info
- 3 points
UNDERGRAD RESEARCH OR PROJECT
Independent work involving experiments, computer programming, analytical investigation, or engineering design.
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Instructor:
Shipra Agrawal
h-index: 24 Info -
Instructor:
Eric Balkanski
h-index: 13 Info -
Instructor:
Daniel Bienstock
h-index: 38 Info -
Instructor:
Agostino Capponi
h-index: 19 Info -
Instructor:
Rachel Cummings
h-index: 16 Info -
Instructor:
Emanuel Derman
CULPA: 1 Wikipedia h-index: 30 Info -
Instructor:
Antonius B Dieker
CULPA: 10 h-index: 19 Info -
Instructor:
Christopher J Dolan
CULPA: 4
Info
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Instructor:
Adam N Elmachtoub
CULPA: 1 h-index: 13 Info -
Instructor:
Yuri Faenza
h-index: 11 Info -
Instructor:
Donald Goldfarb
CULPA: 1 Wikipedia h-index: 55 Info -
Instructor:
Vineet Goyal
h-index: 21 Info -
Instructor:
Ali Hirsa
h-index: 8 Info -
Instructor:
Garud Iyengar
CULPA: 6
h-index: 30
Info
-
Instructor:
Hardeep Johar
Info -
Instructor:
Cedric Josz
CULPA: 2 h-index: 13 Info -
Instructor:
Soulaymane Kachani
CULPA: 12 Info -
Instructor:
Christian Kroer
h-index: 16 Info -
Instructor:
Daniel Lacker
CULPA: 4
h-index: 13
Info
-
Instructor:
Henry Lam
h-index: 17 Info -
Instructor:
Uday Menon
Info -
Instructor:
Jay Sethuraman
CULPA: 10 Info -
Instructor:
Karl Sigman
CULPA: 15
Info
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Instructor:
Clifford Stein
CULPA: 31 Wikipedia h-index: 53 Info -
Instructor:
Wenpin Tang
h-index: 6 Info -
Instructor:
Van Anh Truong
CULPA: 2 Info -
Instructor:
Kaizheng Wang
CULPA: 1 h-index: 8 Info -
Instructor:
David D Yao
CULPA: 5
Info
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Instructor:
Yi Zhang
CULPA: 2 Info -
Instructor:
Xunyu Zhou
h-index: 54 Info -
Instructor:
Jenny S Mak
Info - 1-3 points
FIELDWORK
Prerequisites: Obtained internship and approval from faculty advisor. Only for IEOR undergraduate students who need relevant work experience as part of their program of study. Final reports are required. This course may not be taken for pass/fail credit or audited.
- 1-1.5 points
CORPORATE FINANCE FOR ENGINEERS
Introduction to the economic evaluation of industrial projects. Economic equivalence and criteria. Deterministic approaches to economic analysis. Multiple projects and constraints. Analysis and choice under risk and uncertainty.
- R 7:10 p.m.-9:40 p.m.
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Instructor:
David Derosa
CULPA: 4 h-index: 9 Info - 3 points
OPTIMIZATION MODELS AND METHODS
A graduate course only for MS&E, IE, and OR students. This is also required for students in the Undergraduate Advanced Track. For students who have not studied linear programming. Some of the main methods used in IEOR applications involving deterministic models: linear programming, the simplex method, nonlinear, integer and dynamic programming.
- TR 10:10 a.m.-11:25 a.m.
- U 4:10 p.m.-6:40 p.m.
- F 1:10 p.m.-3:40 p.m.
- TR 10:10 a.m.-11:25 a.m.
- MW 5:40 p.m.-6:55 p.m.
- TR 2:40 p.m.-3:55 p.m.
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Instructor:
Vineet Goyal
h-index: 21 Info -
Instructor:
Cedric Josz
CULPA: 2 h-index: 13 Info -
Instructor:
Shipra Agrawal
h-index: 24 Info -
Instructor:
Yuri Faenza
h-index: 11 Info -
Instructor:
Daniel Bienstock
h-index: 38 Info -
Instructor:
Vineet Goyal
h-index: 21 Info - 3 points
OPT MODELS & METHODS FOR FE
Linear, quadratic, nonlinear, dynamic, and stochastic programming. Some discrete optimization techniques will also be introduced. The theory underlying the various optimization methods is covered. The emphasis is on modeling and the choice of appropriate optimization methods. Applications from financial engineering are discussed.
- MW 8:40 a.m.-9:55 a.m.
- MW 2:40 p.m.-3:55 p.m.
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Instructor:
Garud Iyengar
CULPA: 6
h-index: 30
Info
- 3 points
STATISTICS & SIMULATION
Probability and simulation. Statistics building on knowledge in probability and simulation. Point and interval estimation, hypothesis testing, and regression. A specialized version of IEOR E4150 for MSE and MSBA students who are exempt from the first half of IEOR E4101. Must obtain waiver for E4101.
- MW 10:10 a.m.-11:25 a.m.
- MW 11:40 a.m.-12:55 p.m.
- MW 8:40 a.m.-9:55 a.m.
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Instructor:
Henry Lam
h-index: 17 Info - 1.5 points
PROBABILITY STAT & SIMULATION
Basic probability theory, including independence and conditioning, discrete and continuous random variable, law of large numbers, central limit theorem, and stochastic simulation, basic statistics, including point and interval estimation, hypothesis testing, and regression; examples from business applications such as inventory management, medical treatments, and finance. A specialized version of IEOR E4150 for MSE and MSBA students.
- MW 10:10 a.m.-11:25 a.m.
- MW 11:40 a.m.-12:55 p.m.
- MW 8:40 a.m.-9:55 a.m.
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Instructor:
Henry Lam
h-index: 17 Info - 3 points
STOCHASTIC MODELING FOR MSE
Introduction to stochastic processes and models, with emphasis on applications to engineering and management; random walks, gambler’s ruin problem, Markov chains in both discrete and continuous time, Poisson processes, renewal processes, stopping times, Wald’s equation, binomial lattice model for pricing risky assets, simple option pricing; simulation of simple stochastic processes, Brownian motion, and geometric Brownian motion. A specialized version of IEOR E4106 for MSE students.
- MW 4:10 p.m.-5:25 p.m.
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Instructor:
Karl Sigman
CULPA: 15
Info
- 3 points
STOCHASTIC MODELS
Some of the main stochastic models used in engineering and operations research applications: discrete-time Markov chains, Poisson processes, birth and death processes and other continuous Markov chains, renewal reward processes. Applications: queueing, reliability, inventory, and finance.
- MW 2:40 p.m.-3:55 p.m.
- MW 10:10 a.m.-11:25 a.m.
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Instructor:
Karl Sigman
CULPA: 15
Info
- 3 points
OPERATIONS CONSULTING
Aims to develop and harness the modeling, analytical, and managerial skills of engineering students and apply them to improve the operations of both service and manufacturing firms. Structured as a hands-on laboratory in which students "learn by doing" on real-world consulting projects (October to May). The student teams focus on identifying, modeling, and testing (and sometimes implementing) operational improvements and innovations with high potential to enhance the profitability and/or achieve sustainable competitive advantage for their sponsor companies. The course is targeted toward students planning careers in technical consulting (including operations consulting) and management consulting, or pursuing positions as business analysts in operations, logistics, supply chain and revenue management functions, positions in general management, and future entrepreneurs.
- R 7:10 p.m.-9:40 p.m.
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Instructor:
Soulaymane Kachani
CULPA: 12 Info - 3 points
INTRO-PROBABILITY & STATISTICS
Covers the following topics: fundamentals of probability theory and statistical inference used in engineering and applied science; Probabilistic models, random variables, useful distributions, expectations, law of large numbers, central limit theorem; Statistical inference: pint and confidence interval estimation, hypothesis tests, linear regression. For IEOR graduate students.
- MW 11:40 a.m.-12:55 p.m.
- R 7:10 p.m.-9:40 p.m.
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Instructor:
Christopher J Dolan
CULPA: 4
Info
- 3 points
INTRO-PROBABILITY & STATISTICS
EXEMPTION:INTRO PROB/STAT
Covers the following topics: fundamentals of probability theory and statistical inference used in engineering and applied science; Probabilistic models, random variables, useful distributions, expectations, law of large numbers, central limit theorem; Statistical inference: pint and confidence interval estimation, hypothesis tests, linear regression. For IEOR graduate students.
- 0 points
MSIEOR Quantitative Bootcamp
Zero-credit course. Primer on quantitative and mathematical concepts. Required for all incoming MSOR and MSIE students.
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Instructor:
Michael Miller
CULPA: 18 h-index: 14 Info - 0 points
HUMAN-CENTERED DESIGN AND INNOVATION
HUMAN-CENTERED DESIGN & I
Open to SEAS graduate and advanced undergraduate students, Business School, and GSAPP. Students from other schools may apply. Fast-paced introduction to human-centered design. Students learn the vocabulary of design methods, understanding of design process. Small group projects to create prototypes. Design of simple product, more complex systems of products and services, and design of business.
- T 1:10 p.m.-3:40 p.m.
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Instructor:
Harry West
CULPA: 1 h-index: 1 Info - 3 points
HUMAN FACTORS: PERFORMANCE
Required for undergraduate students majoring in IE. Sensory and cognitive (brain) processing considerations in the design, development, and operations of systems, products, and tools. User or operator limits and potential in sensing, perceiving decision making, movement coordination, memory, and motivation.
- M 4:10 p.m.-6:40 p.m.
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Instructor:
Leon S Gold
CULPA: 6
Info
- 3 points
Data Analytics & Machine Learning for OR
Data Analytics for OR
Surveys tools available in Python for getting (web scraping and APIs) and visualizing data (charts and maps). Introduction to analytics through machine learning (ML algorithms, model evaluation, text analytics, network algorithms, deep learning).
- MW 2:40 p.m.-3:55 p.m.
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Instructor:
Hardeep Johar
Info - 3 points
STATISTICS AND DATA ANALYSIS
Descriptive statistics, central limit theorem, parameter estimation, sufficient statistics, hypothesis testing, regression, logistic regression, goodness-of-fit tests, applications to operations research models.
- TR 11:40 a.m.-12:55 p.m.
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Instructor:
Kaizheng Wang
CULPA: 1 h-index: 8 Info - 3 points
MSE Quantitative Bootcamp
Zero-credit course. Primer on quantitative and mathematical concepts. Required for all incoming MSOR and MSIE students.
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Instructor:
Michael Miller
CULPA: 18 h-index: 14 Info - 0 points
Corporate Finance, Accounting & Investme
CorpFin, Account & Invest
Interpret financial statements, build cash flow models, value projects, value companies, and make Corporate Finance decisions. Additional topics include: cost of capital, dividend policy, debt policy, impact of taxes, Shareholder/Debtholder agency costs, dual-class shares, using option pricing theory to analyze management behavior, investment banking activities, including equity underwriting, syndicated lending, venture capital, private equity investing and private equity secondaries. Application of theory in real-world situations: analyzing financial activities of companies such as General Electric, Google, Snapchat, Spotify, and Tesla.
- M 7:10 p.m.-9:40 p.m.
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Instructor:
Rodney Sunada-Wong
Info - 3 points
QUANTITATIVE CORPORATE FINANCE
Required for students in the Undergraduate Advanced Track. Key measures and analytical tools to assess the financial performance of a firm and perform the economic evaluation of industrial projects. Deterministic mathematical programming models for capital budgeting. Concepts in utility theory, game theory and real options analysis.
- F 10:10 a.m.-12:40 p.m.
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Instructor:
David Derosa
CULPA: 4 h-index: 9 Info - 3 points
SIMULATION
Generation of random numbers from given distributions; variance reduction; statistical output analysis; introduction to simulation languages; application to financial, telecommunications, computer, and production systems. Graduate students must register for 3 points. Undergraduate students must register for 4 points. Note: Students who have taken IEOR E4703 Monte Carlo simulation may not register for this course for credit. Recitation section required.
- TR 5:40 p.m.-6:55 p.m.
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Instructor:
Jay Sethuraman
CULPA: 10 Info - 3 points
GAME THEOR MODELS OF OPERATION
Required for undergraduate students majoring in OR:FE and OR. A mathematically rigorous study of game theory and auctions, and their application to operations management. Topics include introductory game theory, private value auction, revenue equivalence, mechanism design, optimal auction, multiple-unit auctions, combinatorial auctions, incentives, and supply chain coordination with contracts. No previous knowledge of game theory is required.
- TR 11:40 a.m.-12:55 p.m.
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Instructor:
Jay Sethuraman
CULPA: 10 Info - 3 points
APPLICATIONS PROGRAMMNG FOR FE
We will take a hands-on approach to developing computer applications for Financial Engineering. Special focus will be placed on high-performance numerical applications that interact with a graphical interface. In the course of developing such applications we will learn how to create DLLs, how to integrate VBA with C/C++ programs, and how to write multithreaded programs. Examples of problems settings that we consider include simulation of stock price evolution, tracking, evaluation and optimization of a stock portfolio; optimal trade execution. In the course of developing these applications, we review topics of interest to OR:FE in a holistic fashion.
- MW 4:10 p.m.-5:25 p.m.
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Instructor:
Daniel Bienstock
h-index: 38 Info - 3 points
FOUNDATIONS OF DATA SCIENCE
Designed to provide an introduction to data science for sophomore SEAS majors. Combines three perspectives: inferential thinking, computational thinking, and real-world applications. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? Teaches critical concepts and skills in computer programming, statistical inference, and machine learning, in conjunction with hands-on analysis of real-world datasets such as economic data, document collections, geographical data, and social networks.
- F 10:10 a.m.-12:40 p.m.
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Instructor:
Manuel Balsera
Info - 3 points
TOOLS FOR ANALYTICS
MS IEOR students only. Introduction programming in Python, tools with the programmer's ecosystem. Python, Data Analysis tools in Python (NumPy, pandas, bokeh), GIT, Bash, SQL, VIM, Linux/Debia, SSH.
- M 7:10 p.m.-9:40 p.m.
- T 7:10 p.m.-9:40 p.m.
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Instructor:
Julian H Berman
Info - 3 points
TOOLS FOR ANALYTICS
EXEMPTION:TOOLS ANALYTICS
MS IEOR students only. Introduction programming in Python, tools with the programmer's ecosystem. Python, Data Analysis tools in Python (NumPy, pandas, bokeh), GIT, Bash, SQL, VIM, Linux/Debia, SSH.
- 0 points
Python for Analytics
Zero-credit course. Primer on Python for analytics concepts. Required for MSBA students.
- 0 points
DESIGN DIGITAL OPERATING MODELS
IEOR students only. Understand digital businesses, apply scientific, engineering thinking to digital economy. Data-driven digital strategies and operating models. Sectors: ecommerce, advertising technology, and marketing technology. Automation of the marketing, sales, and advertising functions. Algorithms, patents, and business models. Business side of the digital ecosytem and the digital economy.
- M 7:10 p.m.-9:40 p.m.
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Instructor:
Anthony Effik
Info - 3 points
Industry Projects in Analytics & Operati
Industry Projs in Analyti
Teams of students work on real-world projects in analytics. Focus on three aspects of analytics: identifying client analytical requirements; assembling, cleaning and organizing data; identifying and implementing analytical techniques (e.g., statistics and/or machine learning); and delivering results in a client-friendly format. Each project has a defined goal and pre-identified data to analyze in one semester. Client facing class. Class requires 10 hours of time per week and possible client visits on Fridays.
- M 9 a.m.-11:30 a.m.
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Instructor:
Michael Robbins
Wikipedia Info - 3 points
SYSTEM ENGI TOOLS/METHODS
Applications of SE tools and methods in various settings. Encompasses modern complex system development environments, including aerospace and defense, transportation, energy, communications, and modern software-intensive systems.
- W 4:10 p.m.-6:40 p.m.
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Instructor:
Ebad Jahangir
Info - 3 points
DATA ANALYTICS
IEOR students only; priority to MSBA students. Survey tools available in Python for getting, cleaning, and analyzing data. Obtain data from files (csv, html, json, xml) and databases (Mysql, PostgreSQL, NoSQL), cover the rudiments of data cleaning, and examine data analysis, machine learning, and data visualization packages (NumPy, pandas, Scikit-lern, bokeh) available in Python. Brief overview of natural language processing, network analysis, and big data tools available in Python. Contains a group project component that will require students to gather, store, and analyze a data set of their choosing.
- TR 4:10 p.m.-5:25 p.m.
- TR 2:40 p.m.-3:55 p.m.
- R 7:10 p.m.-9:40 p.m.
- None None-None
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Instructor:
Uday Menon
Info - 3 points
ANALYTICS IN PRACTICE
MSOR students only. Groups of students will work on real world projects in analytics, focusing on three aspects: identifying client analytical requirements; assembling, cleaning, and organizing data; identifying and implementing analytical techniques (statistics, OR, machine learning); and delivering results in a client-friendly format. Each project has a well-defined goal, comes with sources of data preidentified, and has been structured so that it can be completed in one semester. Client-facing class with numerous on-site client visits; students should keep Fridays clear for this purpose.
- W 7:10 p.m.-9:40 p.m.
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Instructor:
Uday Menon
Info - 3 points
MACHINE LEARNING FE & OPR
MS IEOR students only. Introduction to machine learning, practical use of ML algorithms and applications to financial engineering and operations. Supervised learning: regression, classification, resampling methods, regularization, support vector machines (SVMs), and deep learning. Unsupervised learning: dimensionality reduction, matrix decomposition, and clustering algorithms.
- F 10:10 a.m.-12:40 p.m.
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Instructor:
Christian Kroer
h-index: 16 Info - 3 points
DATA MINING
Course covers major statistical learning methods for data mining under both supervised and unsupervised settings. Topics covered include linear regression and classification, model selection and regularization, tree-based methods, support vector machines, and unsupervised learning. Students learn about principles underlying each method, how to determine which methods are most suited to applied settings, concepts behind model fitting and parameter tuning, and how to apply methods in practice and assess their performance. Emphasizes roles of statistical modeling and optimization in data mining.
- W 7:10 p.m.-9:40 p.m.
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Instructor:
Krzysztof M Choromanski
Info - 3 points
TOPICS IN OPERATIONS RESEARCH
ENTREPRENEURSHIP BOOTCAMP
Each offering of this course is devoted to a particular sector of Operations Research and its contemporary research, practice, and approaches. If topics are different, then course can be taken more than once for credit.
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Instructor:
David B Lerner
Info - 1.5 points
TOPICS IN OPERATIONS RESEARCH
FINANCE & STRUCTURING FOR DS
Each offering of this course is devoted to a particular sector of Operations Research and its contemporary research, practice, and approaches. If topics are different, then course can be taken more than once for credit.
- F 1:30 p.m.-3:40 p.m.
- 3 points
TOPICS IN OPERATIONS RESEARCH
VISUALIZATION & STORYTELLING W
Each offering of this course is devoted to a particular sector of Operations Research and its contemporary research, practice, and approaches. If topics are different, then course can be taken more than once for credit.
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Instructor:
Michelle Glaser
Info - 1.5 points
TOPICS IN OPERATIONS RESEARCH
POLICY FOR PRIVACY TECHNOLOGY
Each offering of this course is devoted to a particular sector of Operations Research and its contemporary research, practice, and approaches. If topics are different, then course can be taken more than once for credit.
- TR 2:40 p.m.-3:55 p.m.
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Instructor:
Rachel Cummings
h-index: 16 Info - 3 points
TOPICS IN OPERATION RESEARCH
STARTUP FUNDAMENTALS
Each offering of this course is devoted to a particular sector of Operations Research and its contemporary research, practice, and approaches. If topics are different, then course can be taken more than once for credit.
- R 4:10 p.m.-6:40 p.m.
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Instructor:
Owen E Davis
Info - 3 points
MSBA Quantitative Bootcamp
Primer on quantitative and mathematical concepts. Required for all incoming MSBA students.
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Instructor:
Michael Miller
CULPA: 18 h-index: 14 Info - 0 points
QUANTITATIVE RISK MANAGEMENT
Risk management models and tools; measure risk using statistical and stochastic methods, hedging and diversification. Examples include insurance risk, financial risk, and operational risk. Topics covered include VaR, estimating rare events, extreme value analysis, time series estimation of extremal events; axioms of risk measures, hedging using financial options, credit risk modeling, and various insurance risk models.
- TR 11:40 a.m.-12:55 p.m.
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Instructor:
Agostino Capponi
h-index: 19 Info - 3 points
Pricing Models for Financial Engineering
PRICING MODELS FOR FIN EN
In this course, you will be introduced to pricing models for some of the most widely traded securities in today�s financial markets. These securities include futures, forwards, bonds, options, credit default swaps, and mortgage-backed securities. We will examine not only how pricing models work under normal market conditions, but also in extreme market environments. In other words, we examine not only how these financial instruments are supposed to work in theory, but how they actually work in the real world. Along the way, we will discuss the history of various financial products, and how financial markets are likely to evolve in the future.
- T 7:10 p.m.-9:40 p.m.
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Instructor:
Michael Miller
CULPA: 18 h-index: 14 Info - 3 points
BUSINESS ANALYTICS
Prepares students to gather, describe, and analyze data, using advanced statistical tools to support operations, risk management, and response to disruptions. Analysis is done by targeting economic and financial decisions in complex systems that involve multiple partners. Topics include probability, statistics, hypothesis testing, experimentation, and forecasting.
INTRO TO FINANCIAL ENGINEERING
Prerequisite(s): IEOR E4106 or E3106. Required for undergraduate students majoring in OR:FE. Introduction to investment and financial instruments via portfolio theory and derivative securities, using basic operations research/engineering methodology. Portfolio theory, arbitrage; Markowitz model, market equilibrium, and the capital asset pricing model. General models for asset price fluctuations in discrete and continuous time. Elementary introduction to Brownian motion and geometric Brownian motion. Option theory; Black-Scholes equation and call option formula. Computational methods such as Monte Carlo simulation.
- MW 1:10 p.m.-2:25 p.m.
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Instructor:
Emanuel Derman
CULPA: 1 Wikipedia h-index: 30 Info - 3 points
STOCHASTIC MODELS FOR FIN ENG
This graduate course is only for M.S. Program in Financial Engineering students, offered during the summer session. Review of elements of probability theory, Poisson processes, exponential distribution, renewal theory, Wald’s equation. Introduction to discrete-time Markov chains and applications to queueing theory, inventory models, branching processes.
- TR 10:10 a.m.-11:25 a.m.
- TR 11:40 a.m.-12:55 p.m.
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Instructor:
David D Yao
CULPA: 5
Info
- 3 points
FOUNDATIONS FR FINANCIAL ENGIN
This graduate course is only for M.S. Program in Financial Engineering students, offered during the summer session. Discrete-time models of equity, bond, credit, and foreign-exchange markets. Introduction to derivative markets. Pricing and hedging of derivative securities. Complete and incomplete markets. Introduction to portfolio optimization and the capital asset pricing model.
- MW 10:10 a.m.-11:25 a.m.
- MW 5:40 p.m.-6:55 p.m.
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Instructor:
Wenpin Tang
h-index: 6 Info - 3 points
FE CONTINUOUS TIME MODELS
This graduate course is only for MS program in FE students. Modeling, analysis, and computation of derivative securities. Applications of stochastic calculus and stochastic differential equations. Numerical techniques: finite-difference, binomial method, and Monte Carlo.
- TR 5:40 p.m.-6:55 p.m.
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Instructor:
Xunyu Zhou
h-index: 54 Info - 3 points