This course develops basic knowledge of applied statistics and applied econometrics with particular emphasis on the relationship between economic variables. The first part of the course reviews statistical measures, random variables, and probability distributions. It will examine the role of a random sample and estimation and testing of parameters. The second part will introduce estimation approaches such as simple ordinary least squares and then multiple regression. These techniques will be applied to real data for the purpose of policy analysis in areas as diverse as health, labor markets, finance, development, and taxation.

This course is an introduction to life contingencies as applied in actuarial practice. Topics include present value random variables for contingent annuities and insurance, their distributions and actuarial present values, equivalence principle, and other principles for determining premiums.

This course provides an introduction to fitting and validating actuarial models, including estimating loss distributions and applying credibility theory, tests of goodness-of-fit for frequency and severity distributions, and credibility of information obtained from various sources.

This course consists of life insurance as well as non-life insurance (health and pension) mathematics. Topics include insurance and annuity reserves, characterization of discrete and continuous multiple decrement models, joint life and survivor benefits.

This course introduces students to continuous-time financial models essential for the practice of mathematical risk management. It begins with a discussion of the fundamental mathematical tools from continuous-time stochastic processes including Ito’s formula, change of measure, and martingales. This provides a framework for financial concepts including hedging, complete markets, and incomplete markets. The mathematical tools and financial concepts are applied to the risk management and valuation of financial derivatives based on stocks and bonds, separately, and insurance company liabilities with embedded financial options. The course concludes with a consideration of models that jointly value stocks, bonds and non-traded assets.

This course is an applied modeling course which introduces students to statistical learning and predictive analytics. It covers following topics; General Strategies (data pre-processing, over fitting-model tuning), Regression Models (linear regression, partial least squares, penalized models, non-linear models, neural networks, multivariate adaptive regression splines, support vector machines, k-nearest neighbor), Regression Trees (random forest, boosting), Re-sampling Method, Principal Component Analysis and generalized Additive Models. Students will use R and/or SAS for class projects.

This course provides an introduction to fitting and validating actuarial models, including estimating loss distributions and applying credibility theory, tests of goodness-of-fit for frequency and severity distributions, and credibility of information obtained from various sources.

This course exposes the students to the principles and techniques involved in insurance ratemaking across various product lines (life insurance, health insurance and Property & Casualty insurance). Insurance ratemaking (or pricing) is a key function performed by actuaries and is fundamental to the financial viability of an insurance company. The course will incorporate policy and claims experience, pricing assumptions, profitability targets, and capital requirements in developing premium rates.