Time Series Analysis with Applications in Finance (2020 fall).

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• Exam topics
• Course requirements
• Topic description
• Midterm assignments
• Lessons 1-2: Stationary time series and spectral representation
• Lessons 3-4: Estimating parameters of weakly stationary time series, periodograms and spectra
• Lesson 5-6: ARMA processes in 1D, Wold decomposition and classification of 1D time series
• Lesson 7-8: VARMA processes in MD, Wold decomposition and classification of MD time series
• Lesson 9-10: Prediction of multivariate time series, Kalman's filtering, and dynamic PCA
• Lesson 11: Non-stationary processes, financial time series, ARCH and GARCH processes
• Illustration to the Lessons: simulated and real life data (many examples and figures)
• Basics: Complex matrices
• Basics: Complex functions
• Basics: Prediction in Hilbert spaces
• For C.R. Rao's 100th birthday
• Dynamic Factor Analysis
• GDFM