STATISTICAL ANALYSIS AND TIME SERIES

STAT ANALYSIS & TIME SERI

This graduate course is only for M.S. Program in Financial Engineering students. Empirical analysis of asset prices: heavy tails, test of the predictability of stock returns. Financial time series: ARMA, stochastic volatility, and GARCH models. Regression models: linear regression and test of CAPM, non-linear regression and fitting of term structures.

• TR 10:10 a.m.-11:25 a.m.
• Instructor: Agostino Capponi
  h-index: 19 Info
• 3 points

GLOBAL CAPITAL MARKETS

An introduction to capital markets and investments providing an overview of financial markets and tools for asset valuation. Topics covered include the pricing of fixed-income securities (treasury markets, interest rate swaps futures, etc.), discussions on topics in credit, foreign exchange, sovereign ad securitized markets—private equity and hedge funds, etc.

• M 7:10 p.m.-9:40 p.m.
• Instructor: Siddhartha Ghosh Dastidar
  CULPA: 2 Info
• 3 points

TOPICS IN QUANTATIVE FINANCE

FINANCIAL ENG. FOR ESG FINANCE

Selected topics of interest in the area of quantitative finance. Offerings vary each year; some topics include energy derivatives, experimental finance, foreign exchange and related derivative instruments, inflation derivatives, hedge fund management, modeling equity derivatives in Java, mortgage-backed securities, numerical solutions of partial differential equations, quantitative portfolio management, risk management, trade and technology in financial markets.

• T 7:10 p.m.-9:40 p.m.
• Instructor: Cyril Shmatov
  Info
• 1.5 points

TOPICS IN QUANTATIVE FINANCE

TERM STRUCTURES& CREDIT MODELS

Selected topics of interest in the area of quantitative finance. Offerings vary each year; some topics include energy derivatives, experimental finance, foreign exchange and related derivative instruments, inflation derivatives, hedge fund management, modeling equity derivatives in Java, mortgage-backed securities, numerical solutions of partial differential equations, quantitative portfolio management, risk management, trade
and technology in financial markets.

• R 7:10 p.m.-9:40 p.m.
• Instructor: Luca Capriotti
  Info
• 3 points

TOPICS IN QUANTITATIVE FINANCE

PREDICTION MARKETS IN FE

Selected topics of interest in the area of quantitative finance. Offerings vary each year; some topics include energy derivatives, experimental finance, foreign exchange and related derivative instruments, inflation derivatives, hedge fund management, modeling equity derivatives in Java, mortgage-backed securities, numerical solutions of partial differential equations, quantitative portfolio management, risk management, trade and technology in financial markets.

• TR 1:10 p.m.-2:25 p.m.
• Instructor: Yanwei Jia
  Info
• 3 points

TOPICS IN QUANTITATIVE FINANCE

BIG DATA - INVESTMENT RESEARCH

Selected topics of interest in the area of quantitative finance. Offerings vary each year; some topics include energy derivatives, experimental finance, foreign exchange and related derivative instruments, inflation derivatives, hedge fund management, modeling equity derivatives in Java, mortgage-backed securities, numerical solutions of partial differential equations, quantitative portfolio management, risk management, trade and technology in financial markets. Java, mortgage-backed securities, numerical solutions of partial differential equations, quantitative portfolio management, risk management, trade and technology in financial markets.

• T 4:10 p.m.-6:40 p.m.
• Instructor: Ajit Agrawal
  Info
• 1.5 points

TOPICS IN QUANTATIVE FINANCE

ALT CRYPTO PORTFOLIO STRATEGY

Selected topics of interest in the area of quantitative finance. Offerings vary each year; some topics include energy derivatives, experimental finance, foreign exchange and related derivative instruments, inflation derivatives, hedge fund management, modeling equity derivatives in Java, mortgage-backed securities, numerical solutions of partial differential equations, quantitative portfolio management, risk management, trade and technology in financial markets.

• Instructor: Kenneth Goodman
  Info
• 1.5 points

STRUCTURED&HYBRID PRODUCTS

Prerequisite(s): IEOR E4700. Conceptual and practical understanding of structured and hybrid products from the standpoint of relevant risk factors, design goals and characteristics, pricing, hedging, and risk management. Detailed analysis of the underlying cash-flows, embedded derivative instruments, and various structural features of these transactions, both from the investor and issuer perspectives, and analysis of the impact of the prevailing market conditions and parameters on their pricing and risk characteristics. Numerical methods for valuing and managing risk of structured/hybrid products and their embedded derivatives and their application to equity, interest rates, commodities and currencies, inflation, and credit-related products. Conceptual and mathematical principles underlying these techniques, and practical issues that arise in their implementations in the Microsoft Excel/VBA and other programming environments. Special contractual provisions encountered in structured and hybrid transactions, and incorporation of yield curves, volatility smile, and other features of the underlying processes into pricing and implementation framework for these products.

• R 7:10 p.m.-9:40 p.m.
• Instructor: Alireza Javaheri
  Info
• 3 points

Programming for Financial Engineering

Covers C++ programming language, applications, and features for financial engineering, and quantitative finance applications. Note: restricted to IEOR MS FE students only.

• W 7:10 p.m.-9:40 p.m.
• Instructor: Sebastien Donadio
  Info
• 3 points

Deep Learning for OR and FE

Selected topics of interest in area of quantitative finance. Some topics include energy derivatives, experimental finance, foreign exchange and related derivative instruments, inflation derivatives, hedge fund management, modeling equity derivatives in Java, mortgage-backed securities, numerical solutions of partial differential equations, quantitative portfolio management, risk management, trade and technology in financial markets. Note: open to IEOR students only.

• TR 8:40 a.m.-9:55 a.m.
• Instructor: Ali Hirsa
  h-index: 8 Info
• 3 points

Applied Financial Risk Management

Applied Financial Risk Mg

Introduces risk management principles, practical implementation and applications, standard market, liquidity, and credit risk measurement techniques, and their drawbacks and limitations. Note: restricted to IEOR students only.

• TR 2:40 p.m.-3:55 p.m.
• Instructor: Allan M Malz
  CULPA: 1 Info
• 3 points

Financial Engineering Practitioners Semi

FE Seminar Series

Degree requirement for all MSFE first-year students. Topics in Financial Engineering. Past seminar topics include Evolving Financial Intermediation, Measuring and Using Trading Algorithms Effectively, Path-Dependent Volatility, Artificial Intelligence and Data Science in modern financial decision making, Risk-Based Performance Attribution, and Financial Machine Learning. Meets select Monday evenings.

• M 7 p.m.-8:30 p.m.
• 0 points

MSFE Quantitative and Computational Boot

MSFE Quant/Comp Bootcamp

Primer on quantitative and mathematical concepts. Required of all incoming MSFE students.

• 0 points

MASTERS RESEARCH OR PROJECT

Prerequisite(s): Approval by a faculty member who agrees to supervise the work. Independent work involving experiments, computer programming, analytical investigation, or engineering design.

• 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
• 1-3 points

MASTERS RESEARCH OR PROJECT

Prerequisite(s): Approval by a faculty member who agrees to supervise the work. Independent work involving experiments, computer programming, analytical investigation, or engineering design.

• 1-3 points

MASTERS RESEARCH OR PROJECT

Prerequisite(s): Approval by a faculty member who agrees to supervise the work. Independent work involving experiments, computer programming, analytical investigation, or engineering design.

• 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
• 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
• 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
• Instructor: Yi Zhang
  CULPA: 2 Info
• Instructor: Xunyu Zhou
  h-index: 54 Info
• Instructor: Jenny S Mak
  Info
• 1-3 points

FIELDWORK

Only for IEOR graduate students who need relevant work experience as part of their program of study. Final reports required. May not be taken for pass/fail credit or audited.

• 1-1.5 points

OPTIMIZATION I

Theory and geometry of linear programming. The simplex method. Duality theory, sensitivity analysis, column generation and decomposition. Interior point methods. Introduction to nonlinear optimization: convexity, optimality conditions, steepest descent, and Newton’s method, active set, and barrier methods.

• TR 11:40 a.m.-12:55 p.m.
• Instructor: Vineet Goyal
  h-index: 21 Info
• 4.5 points

Machine Learning and High-Dimensional Da

Mchn lrning&Hgh-dmntnl da

Discusses recent advances in fields of machine learning: kernel methods, neural networks (various generative adversarial net architectures), and reinforcement learning (with applications in robotics). Quasi Monte Carlo methods in the context of approximating RBF kernels via orthogonal transforms (instances of the structured technique). Will discuss techniques such as TD(0), TD(λ), LSTDQ, LSPI, DQN.

• M 7:10 p.m.-9:40 p.m.
• Instructor: Krzysztof M Choromanski
  Info
• 3 points

STOCHASTIC MODELING I

Advanced treatment of stochastic modeling in the context of queueing, reliability, manufacturing, insurance risk, financial engineering and other engineering applications. Review of elements of probability theory; exponential distribution; renewal theory; Wald’s equation; Poisson processes. Introduction to both discrete and continuous-time Markov chains; introduction to Brownian motion.

• MW 4:10 p.m.-5:25 p.m.
• Instructor: Henry Lam
  h-index: 17 Info
• 4.5 points

OPTIMIZATION & SIMULATION BOOTCAMP

• Instructor: Michael Miller
  CULPA: 18 h-index: 14 Info
• 0 points

ADVANCED TOPICS IN IEOR

MATCHING MARKETS & ALGORITHMS

Selected topics of current research interest. May be taken more than once for credit.

• TR 2:40 p.m.-3:55 p.m.
• Instructor: Yuri Faenza
  h-index: 11 Info
• 3 points

Service Operations Management

• M 1 p.m.-4:15 p.m.
• W 8:30 a.m.-11:45 a.m.
• Instructor: Medini Singh
  Info
• 3 points

Game-Theoretic Business Strategy

BUSINESS ANALYTICS STRATE

• M 1:10 p.m.-3:40 p.m.
• Instructor: Harborne Stuart
  h-index: 10 Info
• 3 points

Applied Regression Analysis

• M 6 p.m.-9:15 p.m.
• Instructor: David Juran
  Info
• 1.5 points

Demand Analytics

• M 8:30 a.m.-11:45 a.m.
• Instructor: Awi Federgruen
  Wikipedia Info
• 3 points

Sports Analytics

• T 2 p.m.-5:15 p.m.
• Instructor: Mark N Broadie
  Info
• 3 points

MANAGERIAL NEGOTIATIONS

• M 7 p.m.-9:30 p.m.
• Instructor: Harborne Stuart
  h-index: 10 Info
• 3 points

Quantitative Pricing & Revenue

• F 8:30 a.m.-11:45 a.m.
• Instructor: Wei Ke
  Info
• 1.5 points

RESEARCH

Before registering, the student must submit an outline of the proposed work for approval by the supervisor and the chair of the Department. Advanced study in a specialized field under the supervision of a member of the department staff. May be repeated for credit.

• 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
• 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
• 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
• Instructor: Yi Zhang
  CULPA: 2 Info
• Instructor: Xunyu Zhou
  h-index: 54 Info
• Instructor: Jenny S Mak
  Info
• 1-6 points