麻豆村

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Master of Science in Computational Finance  ›  Academics  ›  Curriculum  ›  Financial Time Series Analysis

Financial Time Series Analysis

Course Number: 46929

This course introduces time series methodology to the MSCF students. Emphasis will be placed on basic time series models (AR, MA, ARMA and ARIMA) and their use in financial applications, including forecasting and the development of quantitative trading strategies. Topics studied in this course include univariate forecasting, seasonality, model identification and diagnostics. In addition, GARCH and stochastic volatility modeling will be covered as will state space models and Kalman filtering. Multivariate time series and cointegration will be introduced along with their application to trading strategies. Non-MSCF students may not take this course without written permission from the instructor. To be eligible, you must be a BSCF student, or a graduate student enrolled in an MSCF participating college/department (Stats & Data Science, Heinz, Tepper, Computer Science Dept.,or Math Sciences). PhD students with relevant research may be eligible with permission from the instructor.

Concentration: Statistics / Data Science
Semester(s): Mini 3
Required/Elective: Required
Prerequisite(s): 46921, 46923, 46926

Instructor(s):

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Reference Texts (Not Required)

  • Brockwell & Davis, Introduction to Time Series and Forecasting, 3rd edition, Springer (2016)
  • N.H. Chan, Time Series: Applications to Finance, Wiley (2002)
  • Ruppert & Matteson, Statistics and Data Analysis for Financial Engineering, 2nd Edition, Springer (2015)

  • Ruey S. Tsay, Analysis of Financial Time Series, 3rd Edition, Wiley (2010)