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Master-Theses in Financial Econometrics and Financial and Economic Data Science, WS 2022/2023

Subareas and details

 

  1. Subarea I: Parametric, semi-parametric models for economic, financial or environmental time and their application
    1. One-side smoothing for detecting different kinds of financial crises or similar structural breaks economic or environmental time series
    2. Forecasting economic, financial or environmental time using semiparametric long-memory time series models
  2. Subarea II: Machine Learning with application to Financial and Economic Data:
    1. Random forests, in particular generalized and local linear random forests with application to financial, economic or environmental data
    2. Deep learning with application to financial, economic environmental data
  1. Subarea III: Hybrid models defined based on the combination of Subareas I and II
    1. Combination of machine learning with short-memory (SM) GARCH models
    2. Combination of machine learning with semiparametric SM GARCH models
    3. Combination of machine learning with long-memory (LM) GARCH models
    4. Combination of machine learning with semiparametric LM GARCH models

 

Topics in Ia) are mainly designed based on Chapter 8 in W4451 (a new chapter in SS 2022, wait for the slides for further info). If you are not visiting W4451 in this semester, you can ask us to get those additional slides.

 

Topics in Ib) are designed based those in the two modules W4451 and W5453. The latter (W5453: Advanced Time Series Analysis and Forecasting) will be provided in WS 2022/2023. Further info will be announced briefly in the lecture of W4451 in this semester. It is possible that are visiting W5453 in WS 2022/2023 as a required preparation for a Master-Thesis on those topics. Hereby, piecewise models under the consideration of structural breaks in Ib) can also be studied and applied.

 

W5451 “Statistical Learning for Data Science with R and Python” is required for topics in Subarea IIa. This Seminar will be provided in WS 2022/2023. It is possible that are visiting W5451 in WS 2022/2023 as a required preparation for a Master-Thesis on those topics. In addition, either W4479 “Econometrics” or, particularly, W4451 is required for a Master-Thesis on those topics.

 

Topics in IIb) are designed based those in the two modules W5452 and one of W4479 or W4451. Thus, W5452 is required for a Master-Thesis on those topics. It will be very helpful, if you are going to visit W5453 “Advanced Time Series Analysis and Forecasting” in WS 2022/2023.

 

Topics in III combine those in Ib) and IIb). Thus, W4451 and W5452 are required for a Master-Thesis on those topics. Here, knowledge on long-memory time series in W5453 will be helpful but unnecessary.

 

Remark 1. Topics from Ib), IIa) and IIb) can be on modeling of cross-sectional, i.e. non-time series, data. Hence, you are welcome to write your Master-Theses on those topics, if you have only visited W4479 “Econometrics” and W5451.

 

Remark 2. If you have visited W5333. This will be considered as an alternative to W5451.

 

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