Course Descriptions

Core Courses


Prerequisites: Admission to the graduate program and permission of the program director. Review of financial economic theory using discrete-time models. Topics include: risk measurement; choices under uncertainty; portfolio selection; capital asset pricing model (CAPM); Arrow-Debreu pricing; options and market completeness; the Martingale measure; arbitrage theory; consumption based CAPM; and valuation of the firm. (Fall, Spring)

Crosslisted as ECON 6219. Prerequisites: ECON/STAT 6113. Advanced time series with financial applications. Topics covered include time series regressions (univariate and multivariate, stationary and non-stationary) and time series models (including ARMA, ARCH, GARCH, stochastic volatility and factor models). The emphasis will be on model properties, estimators, test statistics, and applications in finance. (Fall, Spring)

Prerequisite: permission of the program director.  This course introduces the advanced study of the theory and application of statistics to economic problems.  Topics include derivation of the least-squares estimator; methods with which to detect and correct for potential problems with the classical regression model; maximum likelihood estimation; instrumental variables regression; the problems with multicollinearity, heteroscedasticity, and autocorrelation; introduction to the time-series estimation, including ARIMA models and basic forecasting tools. (Fall, Spring)

Prerequisite/Corequisite: FINN 6203 or equivalent, or permission of program. Theory and practice of financial derivatives markets including forwards, futures, and options markets. Topics include the economics of derivatives markets, pricing models for instruments in these markets, strategies for hedging and speculation, as well as regulatory and governance issues. (Fall, Spring)

Prerequisite: FINN 6210 or permission of program. Risk management of fixed income portfolios as well as the theory and practice of fixed income markets. Topics include fixed income instruments, term structure models, pricing methods, portfolio management, duration and convexity, securitization, and hedging. (Fall, Spring)

An introduction to those aspects of partial differential equations and diffusion processes most relevant to finance, Random walk and first-step analysis, Markov property, martingales and semi-martingales, Brownian motion. Stochastic differential equations: Ito’s lemma, backward and forward Kolmogorov equations, the Feynman-Kac formula, stopping times, Hull and White Models, Cox-Ingersoll-Ross Model. Applications to finance including portfolio optimization and option pricing. (Fall, Spring)

Concentration Courses


FINANCIAL DATA ANALYTICS

Prerequisite: ECON 6112 or ECON 6113. Underlying assumptions regarding the population, specification, estimation, and testing of microeconometric models.  Students become acquainted with a variety of extensions of conventional linear models for crosssectional and panel data, including, but not limited to: panel data models, instrumental variables models,  and qualitative response models. (Spring)

Prerequisite: Graduate standing or permission of instructor. Analyzing algorithms and problems; data abstraction and data structures; recursion and induction; time and space complexities; searching and sorting; search trees and tries; hashing; heaps; dynamic programming; graph algorithms; string matching; NP-complete problems. (Fall, Spring)

Prerequisite: Graduate standing or permission of instructor. The modeling, programming, and implementation of database systems. Focuses on relational database systems, but may also address non-relational databases or other advanced topics. Topics include: (1) modeling: conceptual data modeling, ER diagram, relational data model, schema design and refinement; (2) programming: relational algebra and calculus, SQL, constraints, triggers, views; (3) implementation: data storage, indexing, query execution, query optimization, and transaction management; and (4) advanced: semi-structured data model, XML, and other emerging topics. (Fall, Spring)

Prerequisite: Full graduate standing or permission of department. Identification of business database needs; requirements specification; relational database model; SQL; E-R modeling; database design, implementation, and verification; distributed databases; databases replication; object-oriented databases; data warehouses; OLAP; data mining; security of databases; vendor selection; DBMS product comparison; database project management; tools for database development, integration, and transaction control. (Fall)

Prerequisite: MBAD 5121 or equivalent. An overview of the business approach to identifying, modeling, retrieving, sharing, and evaluating an enterprise's data and knowledge assets. Focuses on the understanding of data and knowledge management, data warehousing, data mining (including rule-based systems, decision trees, neural networks, etc.), and other business intelligence concepts. Covers the organizational, technological and management perspectives. (Fall, On demand)

COMPUTATIONAL FINANCE

Prerequisite: FINN 6210 or permission of program.  The course covers multi-factor derivative pricing models. Topics include the discrete-time and discrete-state models, Ito processes, relevant topics on stochastic calculus, Risk Neutral Valuation, and review of the Black-Scholes model. Additional topics include commodity pricing models, stochastic volatility models, multi-period discrete-time (GARCH) models, and the interest rate models such as the Vasicek and CIR models.  (Spring)

Prerequisite: MATH 6203 or permission of program. This course will introduce students to numerical and computational techniques for solving both European- and American-style financial derivatives. The approach will be the finite difference method and the basic theoretical concepts will be introduced. Final projects will involve implementing the techniques on computers. Some spectral and Monte Carlo methods will also be discussed. (Fall)

Prerequisite: MATH 6203 or permission of program. This course is devoted to Monte Carlo simulations in Finance. Particular topics include pricing derivatives by Monte Carlo method, simulation of stochastic differential equations, variance reduction techniques. The course teaches both theory and practical programming skills needed in computational financial applications. The current lab language is MATLAB (no preliminary acquaintance required).(Fall)

Prerequisite: MATH 6203 or permission of program. This course focuses on the applications of stochastic calculus techniques to advanced financial modeling. Topics include pricing of European, American and fixed-income derivatives in the Black-Scholes and stochastic volatility models. The Jump-diffusion model will also be introduced.(Spring)

RISK MANAGEMENT

Prerequisite: FINN 6203 or permission of Program Director. This course describes the following:  how market risk, credit risk and operational risk are quantified; Basel II regulatory framework; estimation of aggregate economical capital; calculation and use of RAROC; and recent bank risk management tools: back test, CCAR and Dodd-Frank proposals. It will also address recent big losses that have occurred in financial markets and how they can be avoided.  (Fall)

Prerequisite: FINN 6203 or permission of Program Director. This course provides students with a foundation in investments and portfolio management from the perspective of an institutional investor. Particular attention will be given to the issues associated with managing assets of an insurance company. Topics include: measuring and modeling return and risk, expected return models, information ratio, valuation theory and practice, forecasting, portfolio construction, transaction costs, turnover and trading, performance analysis, asset allocation, securities analysis, and the legal and regulatory landscape of institutional investing. (Fall)

Prerequisite: FINN 6203 permission of Program Director. This course examines the operations and risks of an insurance firm and how to evaluate and manage those operations and risks in a dynamic business environment. The following topics are covered: 1. The role of insurance firms within the financial services industry, 2. The functions of insurance firms with emphasis on operations unique to insurers, 3. Insurer financial and risk management in the complex regulatory environment and 4. Financial and strategic analysis of insurance firms. (Spring)

Prerequisite: FINN 6203 & ECON 613 or MATH 6203 or permission of Program Director. This course offers the quantitative techniques and tools for the risk management. It starts with the basic concepts and methodologies. Topics include risk measures such as VaR and Expected Shortfall, univariate and multivariate models, copulas and tail dependence in risk management framework, and back testing. This course also discusses how to estimate VaR and Expected Shortfall parametrically, semi parametrically and non-parametrically. (Spring)