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.)
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• 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.
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• 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.
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