Lehrangebot

Bachelor-Theses in Financial Econometrics and Financial and Economic Data Science, SS 2023

 

Subareas and details

1. Subarea I: Quantitative Risk Management (QRM) according to the latest Basel-regulations:

  • General definition of QRM
  • Development of the Basel-regulations
  • Current required risk measurement regarding Basel
  • Risk calculation by means of different GARCH-Models
  • Suitable Back-Testing methods
  • Empirical study with own data

Remark: This subject area is mainly based on the content of Chapter 7 of W2453

 

2. Subarea II: Semi-parametric models and their application to time series:

  • Estimation of a deterministic trend function by means of a non-parametric smoothing method
  • Forecasting of time series by means of a semi-parametric short-memory model
  • Calculation of point forecasts for trend and error term (short-memory)
  • Calculation of forecasting intervals
  • Calculation of forecasting bounds by means of Bootstrap

Remark: This subject area is based on the content of Chapter 7a) of W2453.

 

3. Subarea III: Machine Learning with application to time series:

  • Application of different types of neural networks
  • Forecasting of volatility of return series
  • Comparative study with conventional statistical methods (e.g. ARMA, GARCH)

Remark: This subject area is based on the content of the modules W5451 and W5452 and students. It is recommended to choose this subject area only if you have advanced skills in one of the programming languages R or/and Python

 

 

 

Bachelor-Thesis in Financial Econometrics and Financial and Economic Data Science, WS 2023/24

 

Subareas and details

1. Subarea I: Quantitative Risk Management (QRM) according to the latest Basel-regulations

  • General definition of QRM
  • Development of the Basel-regulations
  • Current required risk measurement with regard to Basel
  • Risk calculation by means of different GARCH-Models
  • Suitable Back-Testing methods
  • Empirical study with own data

 

Remark: This subject area is mainly based on the content of Chapter 7 of W2453.

 

2. Subarea II: Semi-parametric models and their application to time series

  • Estimation of a deterministic trend function by means of a non-parametric smoothing method
  • Forecasting of time series by means of a semi-parametric short-memory model
  • Calculation of point forecasts for trend and error term (short-memory)
  • Calculation of forecasting intervals
  • Calculation of forecasting bounds by means of Bootstrap

 

Remark: This subject area is based on the content of Chapter 7a) of W2453.

 

Subarea III: Machine Learning with application to time series

  • Application of different types of neural networks
  • Forecasting of volatility of return series
  • Comparative study with conventional statistical methods (e.g. ARMA, GARCH)

 

Remark: This subject area is based on the content of the modules W5451 and W5452. It is recommended to choose this subject area only if you have advanced skills in one of the programming languages R or/and Python.

 

Subarea IV: Linear Model Selection with some variable selection methods

  • Choose a base model (multiple linear regression including all variables) that will be used for comparison with your selected variable selection methods
  • Possible variable selection methods:
    • Subset Selection (Best Subset Selection, Stepwise Selection).
    • Shrinkage Methods (Ridge Regression, The Lasso).
  • Model evaluation based on certain criteria, such as MSE or MAE

Remark: This subject area is based on Chapter 6 of the book "An Introduction to Statistical Learning with Applications in R.

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