1.5 Hours; 1.5 Credits This course will cover linear and quadratic optimization as well as other nonlinear techniques. Applications from finance will include problems in game theory and portfolio optimization. Prerequisite: MTH 9814 and MTH 9821
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3.0 Hours, 3.0 Credits This course will cover probability and statistics from a Bayesian perspective, with applications to finance. Topics will include joint marginal and conditional probability; discrete and continuous random variables; Bayesian inferences for means and proportions compared with the corresponding frequentist ones; simple linear regression model analyzed in a Bayesian manner; and a [...]
3.0 Hours; 3.0 Credits Each student will be required to prepare a case study motivated by a real-world problem in finance whose solution requires the application of mathematical techniques presented in this program. The student's analysis and conclusions will be presented to faculty and students. Prerequisite: MTH 9852, MTH 9862. Corequisite: MTH 9871.
3.0 Hours; 3.0 Credits Finite difference methods are discussed and implemented for valuating derivative securities such as plain vanilla European and American options, Bermudan options and barrier options. Numerical linear algebra methods used for finite difference solvers, including LU and Cholesky decompositions and iterative (Jacobi, Gauss-Siedel, SOR, and PSOR) methods are also implemented. Prerequisite: None
3.0 Hours; 3.0 Credits This course involves the careful examination of software development techniques for solving problems in finance. Emphasis is placed on productivity and the development of software engineering skills including automation, source control, and API design. The course is aimed at students who have a basic understanding of C++ and quantitative finance. The [...]
The Baruch MFE team won the Fourth Annual International Association of Financial Engineers (IAFE) Academic Student Competition. In a competition of 25 teams from 17 programs, Baruch MFE students researched and wrote a paper on developing a product which can be used to reduce the risk in defined benefit pension plans. The Baruch MFE team [...]
Professor Andrew Lesniewski, a renowned expert on interest rate modeling and celebrated in particular as one of the originators of the SABR model, who joined the Baruch MFE Program and the mathematics department at Baruch College in August 2013 as tenured full professor, has assumed the role of Curriculum Director of the Baruch MFE [...]
1.5 (7 Weeks; 3.0 Hours per week) Hours; 1.5 Credits This course covers machine learning prediction techniques in the context of efficient markets. Students will test these techniques in the context of various trading strategies and investigate correlations with maker directional movements. (This course is not open to students who completed MTH 9884.) Prerequisites: MTH [...]
1.5 Hours; 1.5 Credits This course focuses on developing tools in C++ to work with financial big data. Topics include efficient file formats for big data, parallel data structure and algorithm, and data cleaning, pattern recognition using C++/GPU. The objective of the course is to have each student build a Big Data toolbox in C++. [...]
1.5 Hours; 1.5 Credits This course covers different areas of behavioral finance such as emotional finance, experimental finance, as well as psychological concepts and behavioral biases. Students will use heuristic rules to analyze movements of the markets in extreme market conditions and estimate the behavioral risk. MTH 9814