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Course Criteria
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0.00 - 4.00 Credits
Students conduct a one-semester project. Topics chosen by students with approval of the faculty. A written report is required at the end of the term.
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0.00 - 4.00 Credits
Econometric and statistical methods as applied to finance. Topics include: Overview of Statistical Methods; Predictability of asset returns; Discrete time volatility models; Efficient Portfolio and CAPM; Multifactor Pricing Models; Intertemporal Equilibrium and Stochastic Discount Models; Expectation and present value relation; Simulation methods for financial derivatives; Econometrics of financial derivatives; Forecast and Management of Market Risks; Multivariate time series in finance; Nonparametric methods in financial econometrics
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0.00 - 4.00 Credits
Linear and mixed effect models. Nonlinear regression. Nonparametricegression and classification. Time series analysis: stationarity and classical linear models (AR, MA, ARMA, ..). Nonlinear and nonstationary time series models. State space systems, hidden Markov models and filtering.
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0.00 - 4.00 Credits
Under the direction of a faculty member, each student carries out research and presents the results. Directed Research is normally taken during the first year of study.
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0.00 - 4.00 Credits
This seminar is a continuation of ORF 509. Each student writes a report and presents research results. For doctoral students, the course must be completed one semester prior to taking the general examinations.
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0.00 - 4.00 Credits
Course covers the pricing and hedging of advanced derivatives, including topics such as exotic options, greeks, interest rate derivatives and credit derivatives, as well as covering the basics of stochastic calculus necessary for finance. Designed for Masters students.
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0.00 - 4.00 Credits
Topics discussed include: the simplex method and its complexity, degeneracy, duality, the revised simplex method, convex analysis, game theory, network flows, primal-dual interior point methods, first order optimality conditions, Newton's method, KKT conditions, quadratic programming, and convex optimization. A broad spectrum of applications are presented.
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0.00 - 4.00 Credits
The aim of this course is to convey a solid understanding of the theoretical and practical fundamentals of nonlinear optimization. Topics covered include convex optimization problems, duality, firsts and second order optimalilty conditions, unconstrained methods and constrained methods
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0.00 - 4.00 Credits
A graduate-level introduction to statistical theory and methods and some of the most important and commonly-used principles of statistical inference. Covers the statistical theory and methods for point estimation, confidence intervals, and hypothesis testing, and the applications of the fundamental theory to generalized linear models.
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0.00 - 4.00 Credits
A theoretical introduction to statistical learning problems related to regression and classification. Topics covered include generalized linear models, nonparametric estimation (splines, kernel smoothing), penalization (ridge, lasso), aggregation (adaptation, boosting), and Vapnik-Chervonenkis theory. Emphasis placed on deriving precise error bounds for the proposed estimators or classifiers.
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