Bachelor of Technology and M.S. in Financial Engineering - Accelerated Master's Program

FE 621 Computational Methods in Finance

This course provides computational tools used in industry by the modern financial analyst. The current financial models and algorithms are further studied and numerically analyzed using regression and time series analysis, decision methods, and simulation techniques. The results are applied to forecasting involving asset pricing, hedging, portfolio and risk assessment, some portfolio and risk management models, investment strategies, and other relevant financial problems. Emphasis will be placed on using modern software.

FE 630 Portfolio Theory and Applications

This course introduces the modern portfolio theory and optimal portfolio selection using optimization techniques such as linear programming. Topics include contingent investment decisions, deferral options, combination options and mergers and acquisitions. The course then focuses on financial risk management with emphasis on Value-at-Risk (VAR) methods using general and parametric distributions and VAR as a risk measure. Real world scenarios are studied.

FE 680 Advanced Derivatives

This course deals with fixed-income securities and interest-rate sensitive instruments. Topics include term structure of interest rates, treasury securities, strips, swaps, swaptions, one-factor, two-factor interest rate models, Heath-Jarrow-Merton (HJM) models and credit derivatives: credit default swaps (CDS), collateralized debt obligations (CDOs), and Mortgage- backed securities (MGS).

Choose either FE 800 or FE 900

FE 800 Project in Financial Engineering

Three credits for the degree of Master of Science (Financial Engineering). This course is typically conducted as a one-on-one course between a faculty member and a student. A student may take up to two special problems courses in a master’s degree program. A department technical report is required as the final product for this course.

FE 900 Master's Thesis in Financial Engineering

For the degree of Master of Science (Financial Engineering). A minimum of six credit hours is required for the thesis. Hours and credits to be arranged.

Students can select any course above 500 level with the prefixes BIA, CS, FE, FIN, MA, MGT or MIS provided the prerequisites for the courses are met.

Examples:

FE 511 Introduction to Bloomberg & Thomson-Reuters

This course is designed to teach students the nature and availability of the financial data available at Stevens. The focus of the course will be on equity, futures, FX, options, swaps, CDS’s, interest rate swaps etc. They will learn to how use a Bloomberg terminal. As part of the course the students will be certified in the 4 areas that Bloomberg offers certification. We will cover the Thomson–Reuters Tick history data and basics of using this data. The course also introduces basics of applied statistics. Bloomberg terminal access will be required for any student taking the course on the web.

FE 515 Introduction to R

In this course the students will learn the basics of the open source programming language R. The language will be introduced using financial data and applications. Basic statistical knowledge is required to complete the course. The course is designed so that upon completion the students will be able to use R for assignments and research using data particularly in finance.

FE 514 Financial Lab: VBA in Finance

This course is an introduction to programming with VBA - the Visual Basic for Applications language. In particular, we will be using VBA within MS Excel, and time permitting, MS Access as well. Excel is used everywhere in finance, and VBA allows practitioners to go beyond standard spreadsheet calculation and modeling. Programming with VBA (and using macros) enhances the versatility and power of Excel. The goal of this course is to teach our students Excel usage at a high level using VBA, for front office applications in financial institutions. Financial and mathematical applications will be presented and studied throughout the course.

FE 541 Applied Statistics with Applications in Finance

The course prepares students to employ essential ideas and reasoning of applied statistics. Topics include data analysis, data production, maximum likelihood, method of moments, Bayesian estimators, hypothesis testing, tests of population, multivariate analysis, categorical data analysis, multiple regression, analysis of variance, nonlinear regression, risk measures, bootstrap methods and permutation tests. The course is designed to familiarize students with statistical software needed for analysis of the data. Financial applications are emphasized but the course serves areas of science and engineering where statistical concepts are needed. This course is a graduate course and is covering topics for a deeper understanding than undergraduate courses such as MA331 and BT221. Furthermore, the course will cover fundamental statistical topics which are the basis of any advanced course applying statistical notions such as MGT718, BT652 as well as courses on machine learning, knowledge discovery, big data, time series, etc.

FE 550 Data Visualization Application

Effective visualization of complex data allows for useful insights, more effective communication, and making decisions. This course investigates methods for visualizing financial datasets from a variety of perspectives in order to best identify the right tool for a given task. Students will use a number of tools to refine their data and create visualizations, including: R and associated visualization libraries, Ruby on Rails visualization tools, ManyEyes, HTML5 & CSS 3, D3.js and related javascript libraries, Google Chart Tools, Google Refine, and image-editing programs.

FE 595 Financial Technology

This course deals with financial technology underlying activities of markets, institutions and participants. The overriding purpose is to develop end-to-end business decision making data analytics tools along with enterprise level systems thinking. Statistical learning algorithms will be connected to financial objects identification and authentication along with the appropriate databases to create enterprise level financial services analytics systems.

FE 635 Financial Enterprise Risk Engineering

This course deals with risk assessment and engineering in financial systems. It covers credit risk, market risk, operational risk, liquidity risk, and model risk. Topics include classical measures of risk such as VaR, methods for monitoring volatilities and correlations, copulas, credit derivatives, the calculation of economic capital, and risk-adjusted return on capital (RAROC). The nature of bank regulation and the Basel II capital requirements for banks are examined. Case studies illustrate risk engineering successes and failures in financial enterprises.

FE 646 Optimization Models and Methods in Finance

This course concerns making sound financial decisions in an uncertain world. Increasingly, financial decision-makers are depending on optimization techniques to guide them in their decisions. This course introduces the approach of modeling financial decisions as optimization problems and then developing appropriate optimization methodologies to solve these problems. The course discusses the main classes of optimization problems encountered in financial engineering: linear and nonlinear programming, integer programming, dynamic programming, stochastic programming, and robust optimization. Recent topics about portfolio optimization arising in behavior finance will also be discussed in the later part of the course.The course will also emphasize effective modeling, the use of modeling languages, such as AMPL1, and the use of commercial solvers for solving financial optimization problems.

FE 655 Systemic Risk and Financial Regulation

This course deals with aspects of systemic risk in financial systems. It covers a review of classical risk measures and introduces non-classical risk measures such as Extreme Value Theory. It also covers the study of financial systems as a system of complex adaptive systems, agent-based modeling, history and analysis of bubble formations as a systemic risk, the role of rating agencies, the financial systems ecosystem, risk and regulatory environment, risk and the socio-political environment. It also studies international financial inter-system risk propagation and containment and its impact on international financial systems, the International Monetary Fund assessments and the effect of extreme risk on poverty, international instability and globalization.