Course Descriptions

RMI 9000E Probability Theory (3.0)*

CSP 1,2,7. Students are expected to have satisfied the mathematical and statistical foundations requirements for the RMI doctoral program. GSU equivalent course work is AS 4120, AS 4130, or Math 4751, Math 4752 for the statistical requirement.

This course offers the basics of probability theory including probability spaces, measures, F –algebra, convergence of random variables, and moments. The course highlights the probability theory concepts that arise in financial and actuarial applications. This includes an introduction to stochastic processes, conditioning, martingale theory, as well as classical results from ruin theory and more recent approaches to modeling extreme events. The course provides a solid foundation in probability theory and the ability to use its concepts as risk management tools.

RMI 9010E Computational Methods in Risk (3.0)*

Prerequisites: Admission to Ph.D. program.

Computational methods explores the intersection of risk and computation. This course will cover as the first part of a two part sequence computational econometrics and statistics, computational risk and finance, and the computational modeling of dynamic systems. The material covered in the course will be continuously updated to reflect advances in the field.

RMI 9040E Stochastic Processes (3.0)*

Prerequisites: Admission to Ph.D. program.

This Ph.D.-level course provides a rigorous introduction to stochastic processes. We introduce and rigorously analyze various types of stochastic processes; especially those that are particularly relevant in finance/economics/risk management. In the first part of the course, the basic tenets of measure-theoretic probability are reviewed and random functions are introduced. In the second part of the course, we study linear transformations of random processes and processes with independent increments. In the third part of the course, we study Jump Markov processes and diffusion processes.

RMI 9050E Introduction to Game Theory and Mechanism Design (3.0)*

Prerequisites: Admission to Ph.D. program.

The course provides an introduction to game theory. The course develops the key concepts of Nash equilibrium, subgame perfection and incomplete information. The first part of the course introduces a concept of a game. Then it discusses Nash equilibrium that allows one to make predictions about the outcome of a game. The second part the course analyzes dynamic games with perfect information and discusses the notions of commitment and time consistency. In the third part of the course the concepts of Nash equilibrium is extended to the games of incomplete information. In the forth part it discusses some topics in dynamic games with incomplete information.

RMI 9100 Theory of Risk and Insurance (3.0)

Prerequisite: consent of a graduate advisor in the Department of Risk Management & Insurance. CSP: 1, 2, 6, 7.

This course is a study of the generalized concept of risk and the alternative methods of risk accommodation from the viewpoint of the individual and businessperson. Special consideration is given to the theory of insurance and its proper utilization relative to risk.

RMI 9250 Reading Seminar in Risk and Insurance (3.0)

Prerequisite: consent of a graduate advisor in the Department of Risk Management and Insurance. CSP: 1, 2, 6, 7.