Seminar Financial Time Series Analysis

Content

This seminar provides a comprehensive introduction into current tech-
niques of the analysis of nancial data. The most popular models will
be presented and investigated and their statistical estimation will be
addressed including the analysis of real-world nancial data sets (using
the open-source statistical software package R).

Registration

To register for the seminar,  please write an email to eva.nacca@uni-ulm.de until 31st March 2020.

Please give your name, matriculation number, and your courses of studies and subjects you have taken in the area of Financial Mathematics, Probability Theory, Statistics
or Stochastic Processes. 

The number of participants is limited to 15 students.

Topics covered

  • Financial Time Series and Their Characteristics
  • Linear Time Series Analysis & Applications
  • GARCH Models
  • Non-linear Models and Their Applications
  • High-frequency Financial Data and Market Microstructure E ects
  • Extremes of Time Series and Estimation of Risk Measures
  • Multivariate Time Series Analysis & Applications (Incl. Cointegration
  • and Pairs Trading)
  • Principal Component Analysis and Factor Models
  • MCMC Methods and Applications

Literature

R. S. Tsay Analysis of Financial Time Series, Wiley, 3rd edn., 2010

Lecturer

Robert Stelzer

Farid Mohamed

Time and Venue of the meetings

Due to the current closure of Ulm University the first meeting is postponed until further notice. As soon as there is reliable information how and when teaching will continue, you will be informed about an (online) meeting.
Assignment of topics: Tuesday, 7th April 2020, 15:00 - 16:00, Room: tba (see website)

Weekly during summer term (or en bloc).

Type

Master (also possible Bachelor)

Prerequisites

  • Master Wima/Mathe/MaBi students:
     -  Required: Elementary Probability and Statistics, Stochastik I
     -  Helpful: Financial Mathematics I or Time Series Analysis
  •  Master in Finance:
    -  Required: Financial Mathematics I
    -  Helpful: Time Series Analysis