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MS&E 245A Investment Science
This course covers basic concepts of modern quantitative finance and investments. The focus is on the financial theory and empirical evidence that are useful for investment decisions. The concepts are applied in a stock market simulation with real data. The topics include:

  1. Time is money: understand basic interest rates
  2. Evaluating investments: present value and internal rate of return
  3. Fixed-income markets: bonds, yield, duration, portfolio immunization
  4. Term structure of interest rates
  5. Measuring risk: volatility and expected utility
  6. Designing optimal security portfolios: Risk-return trade-off and mean-variance optimization
  7. The capital asset pricing model
  8. Arbitrage pricing theory and factor modeling

MS&E 349 Financial Statistics
This Ph.D. course covers topics in financial statistics with a focus on current research. Topics will include time-series modeling, volatility modeling, high-frequency statistics, large dimensional factor modeling, estimation of continuous time processes and machine-learning methods in finance. The goal of this course is to introduce students to the frontiers of financial statistics. The course will focus on the statistical theory of the estimation approaches, but we will also spent time covering a range of significant applications to the estimators. Here is a selection of topics covered in previous classes:

  • Linear-time series models: ARIMA Models
  • Volatility Modeling: GARCH
  • General theory of extremum estimators: Probabilistic models, consistency, asymptotic normality, test statistics, maximum likelihood and general method of moments
  • Linear factor modeling in low and high dimensions: Test statistics and estimation
  • High-dimensional latent factor modeling: Curse of dimensionality, principal component analysis, random matrix theory and spiked covariance models, number of factors
  • High-frequency statistics: Limit theorems, non-parametric volatility and jump estimation
  • Machine-learning in empirical asset pricing
  • Operator methods for continuous-time Markov processes: Transition dynamics of non- linear Markov processes
  • Nonstationary continuous-time processes: Nonparametric estimation of continuous-time Markov processes
  • Simulated score methods and indirect inference for continuous-time models: Efficient method of moment estimation
  • Markov Chain Monte Carlo methods for continuous-time financial econometrics: Bayesian inference for latent variables and high-dimensional distributions

MS&E 108 Senior Project Course
Students carry out a major project in groups of four, applying techniques and concepts learned in the major. Project work includes problem identification and definition, data collection and synthesis, modeling, development of feasible solutions, and presentation of results.

MS&E 145 Introductory Financial Analysis
Evaluation and management of money, complicated by temporary distributions and uncertainty. The "time-value of money" and its impact on economic decisions (both personal and corporate) with the introduction of interest rate (constant or varying over time); several approaches critically examined and made consistent as suitable metrics of comparison. The concept of investment diversification in the presence of uncertainty; portfolio selection and efficient frontier analysis leading to the formulation of the Capital Asset Pricing Model; practical implementation of the concepts, including comparison of loan terms, interest rate term structure and its relationship to rate-of-return analysis, and graphical presentation of uncertain investment alternatives; and current economic news of interest. Critical thinking, discussion, and interaction, using group and computer labs assignments.

Professional Education
Courses in Investment and Asset Management In the Age of Machine Learning and Big Data, Blockchain for Finance and Insurance, Financial Data Analytics, Financial Technology and Machine Learning in Finance, AI in Investment