• This course focuses on the ways in which risks are quantified and managed by financial institutions by covering the following topics:

• Introduction to financial institutions, such as banks, insurance companies, mutual funds and hedge funds, and their risk management issues. (The credit crisis of 2007 will be covered in this part as well.)
• The market risk, including topics on interest risk, Value-at-Risk, volatility, correlations and copulas.
• Credit risk, including default probability estimation, CVA, DVA and credit value at risk.
• (If time permits) Other topics, such as operational risk, liquidity risk, model risk, etc.

• Rapid application development (RAD) tools to design and implement database-based applications including:

• SQL query language
• VBA in database application construction
• a standard RAD environment and how to access information in a database
• entity/attribute modeling of the database structure
• anomalies of database structures that create problems for applications
• modeling of application system’s functionality
• and integrating these tools together to design and implement engineering applications
• examples from manufacturing and production systems.

• Expose students to techniques all types of organizations are implementing to manage
  the ever-increasing portfolio of risks threatening the organization’s business model and strategic plan
• Begin with obtaining an understanding of the growing expectations being placed on boards of directors and senior executives for more effective oversight of risks
• Walk through the core elements of an ERM process entities use to identify, assess, manage, and monitor its most important risks to their business model

• Fundamentals of corporate risk management from a strategic decision-making perspective
• Emphasis on how exposures to financial risks (foreign currency, credit, interest rate, etc.) affect the firm, and how risk exposures can be re-engineered to enhance shareholder value
• Topics include the major sources of risk, the measurement of risk exposures, methods, and strategies for managing and controlling risk
• Introduce tools of the financial engineer–futures, options, swaps, and other derivatives

• Introduction, TVM, Bond and Stock valuation
• Capital budgeting, Estimating incremental FCF, NPV
• Estimating cost of debt, beta, cost of equity
• Bond and Stock valuation (DDM)
• Introduction to WRDS. Capital Structure ‐ Ideal mode
• Capital Structure ‐ Taxes, bankruptcy costs
• Capital Structure – In Practice
• How Firms Raise External Capital
• FCF valuation in practice
• Leasing
• Mergers and Acquisitions
• Options ‐ Valuation, Real Options
• Derivatives valuation and risk management

• Rapid application development (RAD) tools to design and implement database-based applications, including different method and programming language:

• SQL query language
• Visual Basic for Applications in database application
• Standard RAD environment and how to access information in a database construction
• Entity/attribute modeling of the database structure
• Anomalies of database structures that create problems for applications
• Modeling of application system’s functionality
• Integrating these tools together to design and implement engineering applications

• Estimation and testing in full and non-full rank linear models
• Normal theory distributional properties
• Least squares principle and the Gauss-Markov theorem
• Estimability, analysis of variance and co variance in a unified manner
• Practical model-building in linear regression including residual analysis, regression diagnostics, and variable selection
• Emphasis on use of the computer to apply methods with data sets

• General statistical concepts and techniques useful to research workers in engineering, textiles, wood technology, etc
• Probability distributions, measurement of precision, simple and multiple regression
• Tests of significance, analysis of variance, enumeration data and experimental designs

• Introduction to Bayesian concepts of statistical inference
• Bayesian learning; Markov chain Monte Carlo methods using existing software (SAS and OpenBUGS)
• Linear and hierarchical models
• model selection and diagnostics

• Exploratory analysis of time series
• Time domain methods, such as ARIMA models
• Frequency domain methods (periodogram, spectrum,…) analysis, filtering, and transfer functions
• Transfer function modeling in the time domain
• Further topics, such as long memory and conditional heteroscedasticity models, and nonparametric time series methods, as time permits

• This is a hands-on course using modeling techniques designed mostly for large observational studies
• Estimation topics include recursive splitting, ordinary and logistic regression, neural networks, and discriminant analysis
• Clustering and association analysis are covered under the topic “unsupervised learning,” and the use of training and validation data sets are emphasized
• Model evaluation alternatives to statistical significance include lift charts and receiver operating characteristic curves
• SAS Enterprise Miner is used in the demonstrations, and some knowledge of basic SAS programming is helpful

To be added…

• A survey course in the theory and methods of mathematical programming to meet the needs of students from a variety of backgrounds
• A wide array of topics and applications in linear and nonlinear programming comprise the course
• The major prerequisite is familiarity with vector and matrix manipulations
• Some differential calculus is required for the discussion of nonlinear programming

• Provide the fundamental understanding to the theory and algorithms of linear optimization. It involves mathematical analysis, theorem proving, algorithm design and numerical methods:

• Introduction to LP
• Geometric Interpretation of LP
• Simplex Method
• Duality and Sensitivity Analysis
• Interior Point Method
• Robust Optimization

• Introduction to methods for obtaining approximate solutions to unconstrained and constrained minimization problems of moderate size
• Emphasis on geometrical interpretation and actual coordinate descent, steepest descent, Newton and quasi-Newton methods
• Conjugate gradient search, gradient projection and penalty function methods for constrained problems
• Specialized problems and algorithms treated as time permits

• Advanced topics in investments with a focus on underlying theory and practical application using real world data
• Stock valuation models
• Bond valuation
• Derivatives, portfolio performance evaluation
• Investment strategies, efficient market theory
• Other current issues in investment finance

• Advanced quantitative course on applied equity valuation.Students conduct stock valuation analysis which is then used to select stocks for the student-managed SunTrust MBA fund. Topics include:

• The investment decision making process
• Empirical evidence on securities returns
• Forecasting financial statements
• Industry and macro-economic analysis
• Valuation models
• Portfolio performance evaluation and performance attribution

• Introduction to modeling, analysis and control of linear discrete-time and continuous-time dynamical systems
• State space representations and transfer methods
• Applications to biological, chemical, economic, electrical, mechanical and sociological systems

• Rapid application development (RAD) tools to design and implement database-based applications, including different method and programming language:

• SQL query language
• Visual Basic for Applications in database application
• Standard RAD environment and how to access information in a database construction
• Entity/attribute modeling of the database structure
• Anomalies of database structures that create problems for applications
• Modeling of application system’s functionality
• Integrating these tools together to design and implement engineering applications

• Theory of consumer behavior
• Primal-dual relationships in consumer theory including indirect utility functions and consumer expenditure functions
• Properties of consumer demand functions
• Consumer welfare measurement
• Long-run market equilibrium in a competitive market environment
• Market equilibrium with upward sloping input supply equations. The theory of monopoly
• General equilibrium
• Economics of information and uncertainty
• Game theory
• Mechanism design and social choice

• Introduction to principles of estimation of linear regression models, such as ordinary least squares and generalized least squares
• Extensions to time series and panel data
• Consideration of endogeneity and instrumental variables estimation
• Limited dependent variable and sample selection models
• Attention to implementation of econometric methods using a statistical package and microeconomic and macroeconomic data sets

• Discussion of important concepts in the asymptotic statistical analysis of vector process with application to the inference procedures based on the aforementioned estimation methods. Introduction to important econometric methods of estimation such as:

• Least Squares, instrumentatl Variables
• Maximum Likelihood, and Generalized Method of Moments
• Their application to the estimation of linear models for cross-sectional ecomomic data

• The characteristics of macroeconomic and financial time series data
• Discussion of stationarity and non-stationarity as they relate to economic time series
• Linear models for stationary economic time series: autoregressive moving average (ARMA) models; vector autoregressive (VAR) models
• Linear models for nonstationary data: deterministic and stochastic trends
• Methods for capturing volatility of financial time series such as autoregressive conditional heteroscedasticity (ARCH) models
• Generalized Method of Moments estimation of nonlinear dynamic models

• The characteristics of microeconomic data. Limited dependent variable models for cross-sectional microeconomic data:

• Logit/probit models
• Tobit models
• Methods for accounting for sample selection
• Count data models
• Duration analysis
• Non-parametricmethods
• Panel data models
• Limited dependent variables and panel data analysis

• Conditional expectation, Martingales, submartingales, supermartingales
• Doob’s decomposition, Doob’s inequality, Uniform integrability
• Convergence theorems, Optional stopping theorems
• Markov chains: Discrete-time, examples of Markov chains (queueing, birth-death, etc.) properties of Markov chains (recurrence, transient, etc.) and stationary measures
• Brownian motion: Probability spaces for continuous-time processes (E.g. “path space”), definition and some properties of Brownian motion and applications with Brownian motion models

• Expose students to techniques all types of organizations are implementing to manage the ever-increasing portfolio of risks threatening the organization’s business model and strategic plan
• Begin with obtaining an understanding of the growing expectations being placed on boards of directors and senior executives for more effective oversight of risks
• Walk through the core elements of an ERM process entities use to identify, assess, manage, and monitor its most important risks to their business model

• Provide the fundamental understanding to the theory and algorithms of linear optimization. It involves mathematical analysis, theorem proving, algorithm design and numerical methods:

• Introduction to LP
• Geometric Interpretation of LP
• Simplex Method
• Duality and Sensitivity Analysis
• Interior Point Method
• Robust Optimization

• Discussion of important concepts in the asymptotic statistical analysis of vector process with application to the inference procedures based on the aforementioned estimation methods. Introduction to important econometric methods of estimation such as:

• Least Squares, instrumental Variables
• Maximum Likelihood, and Generalized Method of Moments
• Their application to the estimation of linear models for cross-sectional economic data

• The characteristics of macroeconomic and financial time series data
• Discussion of stationarity and non-stationarity as they relate to economic time series
• Linear models for stationary economic time series: autoregressive moving average (ARMA) models; vector autoregressive (VAR) models
• Linear models for nonstationary data: deterministic and stochastic trends
• Methods for capturing volatility of financial time series such as autoregressive conditional heteroscedasticity (ARCH) models
• Generalized Method of Moments estimation of nonlinear dynamic models

• Vector spaces, linear transformations and matrices
• Orthogonality, orthogonal transformations with emphasis on rotations and reflections
• Matrix norms, projectors
• Least squares
• Generalized inverses
• Definite matrices and ingular values

• Motivating applications and prototypical models
• Fundamental aspects of probability, random processes and statistics
• Representation of random inputs
• Parameter selection techniques
• Frequentist and Bayesian model calibration
• Uncertainty propagation in models
• Stochastic spectral methods and sparse grid techniques
• Prediction in the presence of model discrepancy
• Surrogate models
• Global sensitivity analysis

• Foundation of probability theory including random variables, conditioning, independence
• Limit theorems in the context of independent random variables/vectors
• Probability distributions and conditional expectations
• Characteristic functions, Gaussian processes, sums of independent random variables
• Laws of large numbers and central limit theorem

To be updated…

• Introduction to Bayesian inference
• Specifying prior distributions
• Conjugate priors, summarizing posterior information, predictive distributions, hierachical models, asymptotic consistency and asymptotic normality
• Markov Chain Monte Carlo (MCMC) methods and the use of exising software(e.g., WinBUGS)