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An introductory course in derivative securities that includes pricing as well as the use of derivative securities in portfolio management and structured transactions.

Extensions of advanced topics beyond those covered BUS 802 and 805. Prerequisite: BUS 814 (co-requisite acceptable).

An introduction to stochastic models for the rate of return. Time series. Stochastic differential equations. Covariance equivalence principle. Applications. Prerequisite: Permission of the Department. Students with credit for ACMA 820 may not take this course for further credit.

Life insurance models. Interest rate models for life insurance: time series, stochastic differential equations, estimation. Portfolios of identical policies. Diversified portfolios. Prerequisite: ACMA 320.

Study the distribution of aggregate claims and introduce stochastic claims reserving methods in insurance. Individual versus collective models. Standard distribution-free methods. Other models. Prerequisite: Permission of the Department. Students with credit for ACMA 821 may not take this course for further credit.

The statistical theory that supports modern statistical methodologies. Distribution theory, methods for construction of tests, estimators, and confidence intervals with special attention to likelihood and Bayesian methods. Properties of the procedures including large sample theory will be considered. Consistency and asymptotic normality for maximum likelihood and related methods (e.g., estimating equations, quasi-likelihood), as well as hypothesis testing and p-values. Additional topics may include: nonparametric models, the bootstrap, causal inference, and simulation. Prerequisite: STAT 450 or permission of the instructor. Students with credit for STAT 801 may not take this course for further credit.

Advanced mathematical statistics for PhD students. Topics in probability theory including densities, expectation and random vectors and matrices are covered. The theory of point estimation including unbiased and Bayesian estimation, conditional distributions, variance bounds and information. The theoretical framework of hypothesis testing is covered. Additional topics that may be covered include modes of convergence, central limit theorems for averages and medians, large sample theory and empirical processes. Prerequisite: STAT 830 or permission from the instructor.

Application of stochastic processes: queues, inventories, counters, etc. Reliability and life testing. Point processes. Simulation. Students with credit for STAT 870 may not take this course for further credit.

An introduction to smoothing and modelling of functional data. Basis expansion methods, functional regression models and derivative estimation are covered. Prerequisite: STAT 830 or permission of the instructor.

An introduction to computational methods in applied statistics. Topics can include: the bootstrap, Markov Chain Monte Carlo, EM algorithm, as well as optimization and matrix decompositions. Statistical applications will include frequentist and Bayesian model estimation, as well as inference for complex models. The theoretical motivation and application of computational methods will be addressed. Prerequisite: STAT 830 or equivalent or permission of instructor.

A survey of asset pricing models including linear factor models, CAPM, and arbitrage models. Multi-period consumption, portfolio choice, and asset pricing models; continuous-time consumption and portfolio choice; behavioral finance and asset pricing; asset pricing with differential information.

The term structure of interest rates, fixed income returns, yield-spread analysis, sources of risk in fixed income securities, and embedded options.

Computational tools for financial analysis, financial engineering and risk management.

Credit risk management with emphasis on portfolio models, including probability of default and loss given default models, credit capital allocation, active portfolio management, credit derivatives, and structured transactions.

Value at risk, advanced market risk models, statistical models, stress testing, scenario analysis, and risk-adjusted performance measurement.