IE 522 A - Statistical Methods in Finance
Methods of statistical modeling of signals and systems with an emphasis on finance applications. Review of linear algebra, probability theory, and spectral analysis; Linear Time Invariant (LTI) and ARX models; least-squares, maximum-likelihood, non-parametric, and frequency-domain methods; convergence, consistency and identifiability of linear models; asymptotic distribution of parameter estimates; techniques of model validation; Principle Component Analysis (PCA) for dimension reduction; ARCH and GARCH processes and their related models; implementation, application, and case-studies of recursive identification; Monte Carlo simulation. Course Information: Credit is not given for both IE 522 and GE 524. Prerequisite: MATH 415.
Academic Program Restrictions:
MS: Financial Engineering
Option 1Number of Required Visit(s): 0
Course Level: Graduate