to be edited
↧
Andrew Lesniewski
↧
MTH 9893 Time Series Analysis
1.5 Hours; 1.5 Credits This course covers univariate and multivariate time series analysis, conditional heteroscedastic models, principal component analysis, and factor models. Students will learn about implementing univariate and multivariate volatility models. Note: Students cannot receive credit for both MTH 9867 and MTH 9893. MTH 9814, MTH 9815, MTH 9831
↧
↧
MTH 9894 ALGORITHMIC TRADING
1.5 Hours; 1.5 Credits This course covers implementing algorithmic trading strategies and evaluating their performance. The risk management of algorithmic trading strategies, as well as their execution and monitoring are also covered. Note: Students cannot receive credit for both MTH 9867 and MTH 9893. MTH 9814, MTH 9815, MTH 9831
↧
MTH 9896 BEHAVIORAL FINANCE
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
↧
MTH 9898 Data Science in Finance I: Big Data in Finance
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++. […]
↧
↧
MTH 9899 Data Science in Finance II: Machine Learning
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 […]
↧
Baruch MFE student wins the “Solve-a-thon at MIT” trading competition
Our Baruch MFE student Song Wang (Baruch MFE December’16) won the Solve-a-thon at MIT trading competition organized by WorldQuant and a cash prize of $10,000 for achieving a score of over 100,000 (calculated based on alpha generation) in less than two months. Out of over 700 participants, the next highest score was 55,000, and only […]
↧
Baruch MFE’s Team Wins First Place in 2016 Rotman International Trading Competition
The Baruch College’s Master of Financial Engineering (MFE) program won 1th place (out of 52 teams) at 13th Rotman International Trading Competition held in February 2016 in Toronto. This was a record-breaking year and a unique win in the history of the competition: – we won three of the six events (commodities trading, credit risk, […]
↧
MTH 9901 Independent Study – Internship
1-3 Variable Credits Students register for this course if they are doing an internship in a financial institution. Advisor approval required for registration. Prerequisite: MTH 9814, MTH 9843.
↧
↧
MTH 9816 Fundamentals of Trading
1.5 (7 Weeks; 3.0 Hours per week) Hours; 1.5 Credits This course familiarizes students with how financial markets operate and the roles of market participants. Students are introduced to various securities and derivatives, order types, and investment objectives. Emphasis is placed on building financial models and applications to help recognize the potential returns and risks […]
↧
MTH 9886 Emerging Markets and Inflation Modeling
1.5 (7 Weeks; 3.0 Hours per week) Hours; 1.5 Credits The course introduces mathematical models used to price and risk manage financial derivatives in Inflation and Emerging Markets. The emphasis is on practical aspects of modeling in special conditions and conventions across Emerging Markets. Modeling of the inflation derivatives covers rarely discussed practical aspects of […]
↧
MTH 9903 Capstone Project and Presentation
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.
↧
MTH 9841 Statistics for Finance
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 […]
↧
↧
MTH 9842 Linear and Quadratic Optimization Techniques
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
↧
MTH 9845 Market and Credit Risk Management
3.0 Hours; 3.0 Credits This course covers qualitative and quantitative aspects of the financial risk associated to managing financial portfolios and to credit default. Topics include: market risk, Var and stress testing, model risk, spot and forward risk, credit default risk and credit derivatives. Prerequisite: MTH 9814
↧
MTH 9852 Numerical Methods for Finance
3.0 Hours; 3.0 Credits This course covers Monte Carlo methods, their convergence properties and variance reduction techniques, tree pricers and Greeks estimators, implied binomial trees and implied volatility trees, numerical integration techniques, and finite difference methods for pricing derivative securities, including their convergence properties. Prerequisite: MTH 9814 and MTH 9821
↧
MTH 9855 Asset Allocation and Portfolio Management
3.0 Hours; 3.0 Credits The course introduces the quantitative techniques and models commonly used in the asset management industry. The emphasis is on practical aspects of modeling, and specific techniques for portfolio construction and risk management. Topics include classic subjects such as Markowitz’s mean-variance optimization, CAPM and APT models, the Black- Litterman model, as well […]
↧
↧
MTH 9862 Stochastic Processes for Finance II
3.0 Hours; 3.0 Credits This course covers the basic stochastic processes and probabilistic techniques used in finance, for example: random walks, Markov chains, martingales, Brownian Motion, stochastic integration, and Ito’s formula. The Black-Scoles formula is presented from the standpoint of expectation in an appropriate probability space. Prerequisite: MTH 9814 and MTH 9831
↧
MTH 9863 Volatility Filtering and Estimation
1.5 Hours; 1.5 Credits This course covers various filtering techniques such as Kalman filter, particle filtering,and chaos based filtering. Applications include estimation of stochastic volatility parameters from timeseries of underlying asset prices and the use of stochastic volatility in derivative pricing. It also compares the cross-sectional and time-series based estimated parameters and applies the results […]
↧
The Baruch MFE team won the 5th IAQF Student Competition
The Baruch MFE team won the 5th IAQF Student Competition, the second year in a row our students win the competition. In a competition of 27 teams from 17 programs, our students worked on estimating industry sensitivities to oil prices and the effectiveness of hedges of oil price risks. Teams from UC Berkeley and University […]
↧