Courses

The listing of a course description here does not guarantee a course’s being offered in a particular semester. Please refer to the published schedule of classes on the Student Link for confirmation a class is actually being taught and for specific course meeting dates and times.

• QST MF 731: Corporate Risk Management
  This course is an introduction to modern methods of risk management. The first half of the course focuses on market risk. Here, lectures cover risk measures (such as Value at Risk and Expected Shortfall), with a focus on computation of such measures in a dynamic, multi-asset environment using real-world data. In particular, students will learn to compute, back-test, and account for risk measures when both monitoring and constructing portfolios. Additionally, lectures cover scenario analysis, stress testing, and the measurement of severe tail risk via extreme value theory. In the second half of the course, lectures cover alternate types of risk. These include operational, liquidity, model, and counter-party credit risk. In particular, students will derive formulas for the valuation adjustments due to counter-party default. The approach to the topic is quantitative. The course is ideal for students with strong quantitative backgrounds who are seeking to understand issues pertaining to risk management and to master modern methods and techniques of risk control. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)
• QST MF 740: Economics of FinTech
  The course covers the following topics: introduction to Blockchains and cryptocurrencies; contract theory for initial coin offerings; robo-advising; crowd wisdom; and privacy issues. Although the course introduces some Blockchain programming languages, e.g. Solidity, the emphasis of the course is on the economics of FinTech rather than on programming. Students are expected to be familiar with basic financial economics, econometrics, and stochastic processes.
• QST MF 770: Advanced Derivatives
  This course provides a comprehensive and in-depth treatment of valuation methods for derivative securities. Extensive use is made of continuous time stochastic processes, stochastic calculus and martingale methods. The main topics to be addressed include (i) European option valuation, (ii) Exotic options, (iii) Multiasset options, (iv) Stochastic interest rate, (v) Stochastic volatility, (vi) American options and (vii) Numerical methods. Additional topics may be covered depending on time constraints. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)
• QST MF 772: Credit Risk
  The derivatives market has experienced tremendous growth during the past decade as credit risk has become a major factor fostering rapid financial innovation. This course will provide an in-depth approach to credit risk modelling for the specific purpose of pricing fixed income securities and credit-risk derivatives. The course will explore the nature of factors underlying credit risk and develop models incorporating default risk. Types and structures of credit-derivatives will be presented and discussed. Valuation formulas for popular credit-derivatives will be derived. Numerical methods, for applications involving credit derivative structures and default risks, will be presented. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)
• QST MF 793: Statistics for Mathematical Finance
  This course covers the fundamental principles of statistics and econometrics. It is mandatory for all tracks of the MSc. program. The course first reviews the needed concepts in probabilities, properties of random variables, the classic distributions encountered in Finance. Then, we cover the principles of random sampling, properties of estimators, e.g., the standard moment estimators (sample mean, variance, etc..). The next major topic is the regression analysis. We study the OLS and GLS principles, review their properties, in the standard case and when ideal assumptions are not correct. The course ends with a study of time series ARMA models and volatility models such as GARCH and Risk-Metrics. The course makes intensive use of the R package. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)
• QST MF 794: Stochastic Methods in Asset Pricing II
  The course covers: the Feynman-Kac formula and the Fokker-Plank equation, stochastic calculus with jumps, Levy processes and jump diffusion models in finance, Bellman's principle of dynamic programming and the Hamilton-Jacobi- Bellman equation, classical problems for optimal control in finance (Merton's problem, etc.), investment-consumption decisions with transaction costs, the connection between asset pricing and free-boundary problems for PDEs, optimal stopping problems and the exercise of American-style derivatives, capital structure and valuation of real options and corporate debt, exchange options, stochastic volatility models, and Dupire's formula. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)
• QST MF 795: Stochastic Methods in Asset Pricing I
  This course develops the basic tools from measure-theoretic probability theory and stochastic calculus that are needed for an in-depth study of continuous time finance. Some related tools from asset pricing (e.g., risk-preferences and state-price densities) are introduced as well, and the basic ingredients of continuous time financial modeling are developed. The following topics are covered: probability and measure, the coin-toss space and the random walk, random variables and convergence, Gaussian distribution, martingales, Brownian motion, stochastic integration for semi-martingales and Ito formula, Girsanov's theorem, stochastic differential equations, continuous time market models and pricing by arbitrage, resume of Malliavin calculus, replication and pricing of contingent claims, market completeness and the fundamental theorems of asset pricing. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)
• QST MF 796: Computational Methods of Mathematical Finance
  This course introduces common algorithmic and numerical schemes that are used in practice for pricing and hedging financial derivative products. Among others, the course covers Monte-Carlo simulation methods (generation of random variables, exact simulation, discretization schemes), finite difference schemes to solve partial differential equations, numerical integration, and Fourier transforms. Special attention is given to the computational requirements of these different methods, and the trade-off between computational effort and accuracy. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)
• QST MF 810: Advanced Programming: Data Structures and Algorithms
  The course introduces students to a number of efficient algorithms and data structures for computational problems across a variety of areas within FinTech. In the first half of the course, a special programming language for blockchains, such as Solidity, is taught, and TensorFlow, a special Python library for deep learning models, is used to solve stochastic control problems in finance. In the second half of the course, advanced techniques for improving computational performance, including the use of parallel computation and GPU acceleration are surveyed; frameworks for big data analysis such as Apache Hadoop and Apache Spark are studied. Students will have the opportunity to employ these techniques and gain hands-on experience developing advanced applications. (This course is reserved for students enrolled in the Graduate Certificate in Financial Technology.)
• QST MF 815: Advanced Machine Learning Applications for Finance
  This course surveys applications of machine learning techniques to various types of financial datasets. This course starts with financial data structure and features, then introduces deep learning and advanced supervised learning techniques. We will examine several machine learning applications in pricing, hedging, and portfolio management. Advanced methods for clustering and classification such as support vector machine and unsupervised learning will be introduced. Reinforcement learning and its connection with optimal control will be discussed. Text data will be introduced and analyzed using text mining techniques. Machine learning techniques will be applied to asset allocation. Strategy back-testing and strategy risk will also be discussed. (This course is reserved for students enrolled in the Graduate Certificate in Financial Technology.)
• QST MF 821: Algorithmic and High-Frequency Trading
  This course will introduce concepts of electronic markets, and statistical and optimal control techniques to model and trade in these markets. We will begin with a description of the basic elements of electronic markets, some of the features of the data, its empirical implications and simple microeconomic models. Next, we will study statistical tools to estimate and predict price and volatility of the high-frequency price. Then we will investigate algorithmic trading problems from the stochastic optimal control perspective, including the optimal execution problem and show how to modify the classical approaches to include order-flow information and the effect that dark pools have on trading. Trading pairs of assets that mean-revert is another important algorithmic strategy, and we will see how stochastic control methods can be utilized to inform agents how to optimally trade. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)
• QST MF 825: Advanced Topics in Investments
  This course is designed for students seeking to work as quants in a quantitative finance investments group. It covers utility theory, portfolio optimization, asset pricing, and some aspects of factor models, incorporating the impact of parameter uncertainty. The course does not cover risk management or fixed income instruments, nor does it describe how the financial services industry works. Rather, it teaches how a quant should optimize a portfolio. The course makes extensive use of R (Excel or VBA are not substitutes), optimization theory, statistics, regression theory (OLS, GLS, testing theory), and matrix algebra. Students should be very comfortable with these concepts before taking the course; further, students should already have taken a finance course covering expected returns models (CAPM), options and futures. The course emphasizes the ability to prove theoretical results and their validity, an essential trait for investments quants. Students who completed QST FE825 may not take this course for credit. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)
• QST MF 840: Data Analysis and Financial Econometrics
  This is the second course of the econometrics sequence in the Mathematical Finance program. The course quickly reviews OLS, GLS, the Maximum Likelihood principle (MLE). Then, the core of the course concentrates on Bayesian Inference, now an unavoidable mainstay of Financial Econometrics. After learning the principles of Bayesian Inference, we study their implementation for key models in finance, especially related to portfolio design and volatility forecasting. We also briefly discuss the Lasso and Ridge methods, and contrast them with the Bayesian approach Over the last twenty years, radical developments in simulation methods, such as Markov Chain Monte Carlo (MCMC) have extended the capabilities of Bayesian methods. Therefore, after studying direct Monte Carlo simulation methods, the course covers non-trivial methods of simulation such as Markov Chain Monte Carlo (MCMC), applying them to implement models such as stochastic volatility. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)
• QST MF 850: Advanced Computational Methods
  This course explores algorithmic and numerical schemes used in practice for the pricing and hedging of financial derivative products. The focus of this course lies on data analysis. It covers such topics as: stochastic models with jumps, advanced simulation methods, optimization routines, and tree-based approaches. It also introduces machine learning concepts and methodologies, including cross validation, dimensionality reduction, random forests, neural networks, clustering, and support vector machines. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)
• QST MF 921: Topics in Dynamic Asset Pricing
  This course provides a comprehensive and in-depth treatment of modern asset pricing theories. Extensive use is made of continuous time stochastic processes, stochastic calculus and optimal control. Particular emphasis will be placed on (i) stochastic calculus with jumps; (ii) asset pricing models with jumps; (iii) the Hamilton-Jacobi-Bellman equation and stochastic control; (iv) numerical methods for stochastic control problems in finance. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)
• QST MF 922: Advanced Mathematical Finance
  This course provides a rigorous introduction to the modern theory of mathematical finance in incomplete markets. The first part of the course covers the fundamental theorem of asset pricing, as well as super-hedging of contingent claims, in full generality for discrete time models. The second half of the class focuses on optimal investment, pricing and hedging in continuous time incomplete markets. Here, lectures will cover optimal investment and duality for general semi-martingale models; and portfolio optimization and pricing in Markovian factor models, non-Markovian Brownian models, models with trading constraints, and models with transactions costs. Throughout this half, lectures will highlight connections to the dynamic programming principle, the Martingale optimality principle, semi-linear Cauchy partial differential equations, backward stochastic differential equations, and the theory of viscosity solutions.
• QST MF 930: Advanced Corporate Finance
  This doctoral level class on corporate finance covers both theoretical and empirical work. Rather than explaining the underpinnings of basic corporate research (e.g., model/applications dealing with asymmetric information, agency problems, and capital market frictions), we go deeper in understanding "how to operationalize" research on concrete topics that are central to contemporary corporate finance, such as bankruptcy, capital structure, mergers and acquisitions, the firm boundaries, investment, and much more. The class also looks at the interface between corporate finance and other research areas, such as asset pricing and banking. The course is a blend of new approaches to modeling in corporate research (e.g., dynamic, structural models of financial policy that generate typically quantitative predictions) and new approaches to testing design (e.g., regression discontinuities and natural experiments). The goal is to expose the students to the "state-of-the-art" of research in corporate finance and prepare them to do research in corporate finance using new methods and tools. (Mathematical Finance courses are reserved for students enrolled in the Mathematical Finance program.)
• QST MF 990: Current Topics Seminar
  For PhD students in the Mathematical Finance program. Registered by permission only.
• QST MF 998: Directed Study: Mathematical Finance
  PhD-level directed study in Mathematical Finance. 1, 2, or 3 cr. Application available on the Graduate Center website.
• QST MF 999: Directed Study: Mathematical Finance
  PhD-level directed study in Mathematical Finance. 1, 2, or 3 cr. Application available on the Graduate Center website.

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