IE 522

IE 522 - Statistical Methods in Finance

Fall 2023

Statistical Methods in FinanceIE522B77432LCD40930 - 1050 T R  4101 Materials Science & Eng Bld Liming Feng
Hao Qin


Official Description

Statistical tools that are fundamental for financial modeling, analyzing financial data and further studies in financial engineering. Topics include summary statistics, statistical plots, point estimation, accuracy and precision, confidence interval, Monte Carlo simulation, maximum likelihood estimation, normal mixture, resampling, hypothesis testing, simple linear regression, multiple linear regression, variable selection, regression diagnostics, autocorrelation, moving average models, filtering, autoregressive models, ARIMA, forecasting, and selected additional topics. Implementations are done using R. Course Information: 4 graduate hours. No professional credit. Credit is not given for both IE 522 and GE 524. Prerequisite: IE 300 and MATH 461.

Course Description

Statistics is the most important tool for analyzing financial data. IE 522 covers basic probability theory and statistical methods that are fundamental for financial modeling and analysis of financial data. This course is restricted to the Master of Science in Financial Engineering students. The materials that are covered include: probability basics (probability space, probability measure, conditional probability, random variable, probability distribution, expectation, commonly used distributions, characteristic function, multivariate distribution, conditional expectation); statistical inference (descriptive statistics, R, point estimation, Monte Carlo simulation, confidence interval, hypothesis testing, maximum likelihood estimation, likelihood ratio test, resampling, normal mixture); time series models (stationarity, autocorrelation, moving averaging process, autoregressive process, ARIMA models, parameter estimation, forecasting, ARCH and GARCH models); and linear regression (simple linear regression, ordinary least squares estimation, analysis of variance, goodness of fit, multiple linear regression, statistical inference, regression diagnostics). Credit is not given for both IE 522 and GE 524. Prerequisite: MATH 415.

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