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Quantitative Finance - Analysing Financial Data

Seminar

taught by Prof. Dr. Eva Lütkebohmert-Holtz and Miguel Herculano

Content

In this course, we will study di fferent statistical methods for analysing large data sets and apply these to di fferent practical problems in finance and economics. Topics may include:

  • regression models (linear and logistic regression, estimation methods)
  • model selection (in- and out-of-sample performance, regularization, cross validation)
  • treatment eff ects (natural experiments, uncertainty quanti fication)
  • classifi cation methods (binary classi fication, multinomial logistic regression)
  • networks (directed graphs, connectivity, page rank, Bayesian networks)
  • dependence modelling (vine copulas, tail copulas)
  • factor models (dimension reduction, latent variables, PCA)
                                                                                   
Dates

Please find the current dates on ILIAS.

 

LanguageEnglish
 
Recommendet
Prerequisite         
 Intermediate Econometrics
 
 
ECTS6
 
Application & Registrantion

The number of participants for this seminar is limited. Applications for the seminar can be send until March, 31st 2020. Please send your application attached with:

  • Overview of credit points,
  • student ID number,
  • Course of studies/Year of study

here.
 

At the first meeting you will receive the registration form. The deadline for submitting the registration form is May, 4th 2020.

ILIASCourse materials will be available on ILIAS. The password necessary to register for the course on ILIAS will be given in the first lecture!
 
Credit

The seminar is open for students of  M.Sc. Economics,  M.Sc. VWL as well as M.Sc. Mathematik.

In M.Sc. Economics, the seminar can be credited in the profile "Finance" and "ISNE".

In M.Sc. VWL the seminar can be credited in Accounting, Finance, and Taxation (new examination regulations, valid from winter term 2014/15 onwards).

Students of M.Sc. Mathematik can credit this seminar as an elective in economics (wirtschaftswissenschaftliches Wahlpflichtmodul) within the profile "Finanzmathematik.

Literature
  •  James, Witten, Hastie, and Tibshirani, An Introduction to Statistical Learning. Springer, 2017.
  •  Hastie, Tibshirani, and Friedman, Elements of Statistical Learning. 2nd edition, Springer Series in Statistics, Springer, 2009.
  •  Bishop, Pattern Recognition & Machine Learning. Springer, 2011.
  • Murphy, Machine Learning. MIT Press, 2012.

 

The University Library offers scanning services for articles as well as postal delivery for books for the duration of the closure of our building.

Due to the Corona virus, our stock of print media remains inaccessible to the public. In order to supply the members of Freiburg University (staff and students) with essential literature, the University Library temporarily offers the following services:

Article delivery service scans of articles in periodicals and anthologies (free of charge)

Postal service for books (fee required)

The University Library reserves the right to restrict or suspend these services if the advance of the pandemic should compromise their sustainability.

  

 

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