Courses

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

• 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 790: STOCH CALCULUS
  STOCH CALCULUS
• 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 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: FinTech Programming
  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: Portfolio Construction
  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
  Graduate Prerequisites: (QSTMF793) - 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: Deep Learning, Statistical Learning
  Graduate Prerequisites: (QSTMF796) - 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 998: Directed Study: Mathematical Finance
  Graduate Prerequisites: Consent of instructor and the program director - 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
  Graduate Prerequisites: Consent of instructor and the program director - PhD-level directed study in Mathematical Finance. 1, 2, or 3 cr. Application available on the Graduate Center website.
• QST MG 730: Ethical Leadership in the Global Economy
  The purpose of this course is to explore ethical issues throughout our global economy in a pragmatic, responsible, and decisive manner in order to prepare you to resolve these issues when faced with them in your personal and professional lives. This course will bridge the gap between an individual's personal moral values and the challenges presented by corporate activity in a marketplace -- be it local or global. Our work in this course will raise your awareness of the interrelated legal, moral, and ethical challenges inherent in business. We will critically examine the ethical implications of business decisions and equip you with frameworks and strategies for managing your own and others' behavior. We will formulate a process to evaluate complex leadership decisions and enhance your own ability to effectively navigate multi-faceted decision-making scenarios.
• QST MG 735: Crisis Management
  Dealing with a crisis is not just one particular event, it is about positioning your business to minimize the effect if a crisis occurs. In fact, it is an issue that needs constant attention. Failure to do so can destroy value and even threaten the very survival of your company. Risks can arise from various sources. Some risks are external to the company, such as interest rates or demand fluctuations or systemic forces like climate change. Others are internal, like technology innovations, model error, brand crisis, or cyberattacks. Managers from different departments may perceive or prioritize risks differently or deal with different risks, but they all must manage risk while balancing resource constraints. In this one-day class, students will learn how to effectively identify, assess, and manage crisis situations, from natural disasters to organizational failures. You will develop your decision-making skills and learn strategies to minimize the impact of crises on your organization. A team of instructors will guide you through real-world case studies and interactive exercises, providing you with the tools to handle crisis situations with confidence and proficiency. By the end of this workshop, you will have a deep understanding of crisis management principles and be able to apply them.
• QST MK 200: Principles of Marketing
  Open only to non-Questrom students. Marketing elective for Business minors. How is it that some products succeed and some fail? In many instances, the difference is in their marketing. The course examines key areas of marketing including product development, advertising, promotions, pricing, and channels. It uses a combination of in-class exercises, real world examples, cases, lecture, and discussion
• QST MK 323: Marketing Management
  Undergraduate Prerequisites, Questrom students only: QST AC221; MO221; QM221; QM222 or BA222; SM131; SM132; SM275 - Component of QST SM323, The Cross Functional Core. Introduces students to the field of marketing management: analysis, planning and implementation of marketing strategies as the means for achieving an organization's objectives. Students analyze cases and participate in workshops that focus on key marketing management tasks: marketing research, consumer behavior, segmentation and targeting, sales forecasting, brand management, distribution channels, pricing, promotion and advertising strategies, and marketing ethics. A semester-long business plan project where students collect primary and secondary research explores the interactions and the cross functional integrations between marketing, operations, and finance, while leveraging business analytics. cr. 4
• QST MK 345: Consumer Insights
  Undergraduate Prerequisites: QST SM131 and sophomore standing - Formerly MK445. Provides insight into the motivations, influences, and processes underlying consumption behavior. Considers relevant behavioral science theories/frameworks and their usefulness in formulating and evaluating marketing strategies (i.e., segmentation, positioning, product development, pricing, communications).
• QST MK 435: Introduction to the Music Business and Music Marketing
  Undergraduate Prerequisites: (QSTMK323) - Survey of the music industry with a focus on understanding of its structure and the intersection of business and music. Discusses key areas of music marketing, including opportunities for musicians, including publicity, advertising, promotion (online and traditional), digital distribution, touring, licensing/synch, and radio.
• QST MK 442: Digital Marketing Analytics
  Undergraduate Prerequisites: QST MK323. Pre-req for SHA students: SHA HF260; CAS MA115 (or MA113); CAS MA116 - This is an introductory course on Digital Marketing emphasizing analytics that seeks to familiarize students with digital marketing tactics. At the heart of marketing lies consumers and their marketing journey through the stages of awareness, intent, conversion and finally retention. In this course, we will learn how digital has revolutionized the interactions between firms and consumers along this journey. Digital offers powerful tactics to reach consumers along the funnel: online display ads raise awareness, search listings reach consumers with intent, on-site e-commerce marketing facilitate conversion, and social medial both energizes and retains customers. The course develops essential data analytics skills--critical thinking, data mining, experimental analysis and design--applied to ad campaign, ad attribution, and social media data.