...on the homepage of the Chair of Econometrics and Quantitative Methods at Paderborn University. The main focus of our research and teaching is "Financial and Economic Data Science" in teaching and research. Topics include semi-parametric modelling of forecasts of seasonal time series and time series with long memory; modelling of spatial time series with long memory; deep learning as well as combinations of time series and deep learning approaches.

The modules listed below are offered at the professorship of Prof. Dr. Yuanhua Feng. Detailed information can be obtained by clicking on the respective module. You can access the complete module handbook of the Faculty of Economic Sciences here.

Overview of courses by semester as of SoSe 2022

WS (from WS 2022/ 2023, modules for 1st/3rd Master's semester)

4479 Econometrics
ECTS: 10
Lecture: 4 SWS
Exercise: 2 SWS

5452 Topics in Financial and Economic Data Science
ECTS: 5
Lecture: 2 SWS
Exercise: 1 SWS

6472 Advanced Quantitative Methods in Statistics and Economotrics (WP)
ECTS: 5
Lecture: none
Exercise: none

SoSe (from SoSe 2022, modules for 2nd/4th Master's semester)

2453 Applied Time Series Analysis and Introduction to Financial Econometrics
ECTS: 5
Lecture: 2 SWS
Exercise: 1 o. 2 SWS

4451 Financial Econometrics and Quantitative Risk Management
ECTS: 5
Lecture: 2 SWS
Exercise: 1 SWS

5452 Topics in Financial and Economic Data Science
ECTS: 5
Lecture: 2 SWS
Exercise: 1 SWS

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

Subareas and details

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

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 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.

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

Subareas and details

1. subarea I: Basic time series econometrics

I.a) Parametric, semi-parametric models for economic, financial or environmental time and their applications. Forecasting economic, financial or environmental time using semiparametric ARMA models possibly with seasonal component (topics based on M.184.4451 and Chapter 2 of W5453, the main package for this subarea is "smoots", x12, DeSeaTS).

I.b) Forecasting economic, financial or environmental time using semiparametric long-memory time series models (see M.184.5453, Ch3, coming, R package is "esemifar")

2. subarea II: Advanced Financial Econometrics

II.a) Modeling of high-frequency financial data (see Chapter 5 of M.184.5453)

II.b) Parametric and semiparametric long memory GARCH models and their application in quantitative risk management (see Chapter 6 of M.184.5453)

II.c) Parametric and semiparametric multivariate GARCH models and their application in quantitative risk management (see Chapter 7 of M.184.5453)

3. subarea III: Deep Learning with application to Financial and Economic Data:

a. Combination of deep learning with shortmemory (SM) GARCH models

b. Combination of deep learning with semiparametric GARCH models

c. Combination of deep learning with longmemory (LM) GARCH models

d. Combination of machine learning with semiparametric (LM) GARCH models

e. Deep learning with application to economic, business or environmental data.

Remark 1. The prerequisite for writing a Master Thesis at our Chair is that you have visited M.184.5451. If you still not yet have visited it but are going to visit this module in SS 2023, please indicate that you have visited W5333. This will be considered as an alternative to W5451.

Remark3. Topics Subarea II are suitable for those who are visiting the advanced time series module M.184.5453. If you are going to choose topics from Subarea III, the prerequisite is M.184.5452. You can also learn those topics yourselves.

Remark 3. Topics with BWL-focuses, e.g. studies on the behaviors of important German firms on the financial market, can be provided,

Master-Theses in Financial Econometrics and Financial and Economic Data Science, WS 2023/24

Subareas and details

1. subarea I: Basic time series econometrics

I.a) Parametric, semi-parametric models for economic, financial or environmental time and their applications. Forecasting economic, financial or environmental time using semiparametric ARMA models possibly with seasonal component (topics based on M.184.4451 and Chapter 2 of W5453, the main package for this subarea is "smoots", x12, DeSeaTS).

I.b) Forecasting economic, financial or environmental time using semiparametric long-memory time series models (see M.184.5453, Ch3, coming, R package is "esemifar")

2. subarea II: Advanced Financial Econometrics

II.a) Modeling of high-frequency financial data (see Chapter 5 of M.184.5453)

II.b) Parametric and semiparametric long memory GARCH models and their application in quantitative risk management (see Chapter 6 of M.184.5453)

II.c) Parametric and semiparametric multivariate GARCH models and their application in quantitative risk management (see Chapter 7 of M.184.5453)

II.d) Application of some most recently proposed long memory volatility models

II.e) Stationery and trend-stationary dual long memory processes in finance and economics

Subarea III: Deep Learning with application to Financial and Economic Data:

a. Combination of deep learning with short-memory (SM) GARCH models

b. Combination of deep learning with semiparametric (SM) GARCH models

c. Combination of deep learning with long-memory (LM) GARCH models

d. Combination of machine learning with semiparametric (LM) GARCH models

e. Deep learning with application to economic, business or environmental data.

Remark1. The prerequisite for writing a Master Thesis at our Chair is that you have at least visited one of M.184.4451, M.184.5451 (or equivalently M.184.5333 by Prof. Dr. Oliver Müller), M.184.5452 or M.184.5453. If you still not yet have visited one of those modules, but are visiting M.184.4451 or M.184.5452 just now, please indicate this in your application for the allocation of your Master thesis on the central allocation system.

Remark3. Topics Subarea II are suitable for those who are visiting the advanced time series module M.184.5453. If you are going to choose topics from Subarea III, the prerequisite is M.184.5452. You can also learn those topics yourselves.

Remark3. Topics with BWL-focuses, e.g. studies on the behaviors of important German firms on the financial market, can be provided,

Remark4. The choice of further advanced Econometric/Data Science topics, e.g. those in reinforcement learning or modeling of spatial data, is encouraged, provided that you have the previous knowledge on those topics. If this is your case, please discuss it with me in advance.

Conference Presentations and Selected Seminar Talks (since 2001)

2014: "Recent development of the iterative plug-in bandwidth selection rule and its further application". German Statistical Week 2014, 16 - 19 September 2014, Hannover.

2014: "Non- and semiparametric modeling of volatility and correlation components in
nancial returns". International Workshop `Non- and Semiparametric Volatility and Correlation models, 24 - 26 July 2014, Paderborn, Germany.

2014: "The deviation ARCH(∞), the deviation ACD(∞) and their relationship to the linear ARCH model". The 2014 International Indian Statistical Association Conference. 11 - 13 July 2014, Riverside, CA, USA.

2013: "Data-driven estimation of realized volatility under independent microstructure noise". Invited talk, 24 September 2013, at the Center for Economic Research, Shandong University, Jinan, China.

2013: "Double-conditional smoothing of high-frequency volatility surface in a spatial multiplicative component GARCH with random e
ects". Contributed talk at the 29th European Meeting of Statistics, Budapest, 20-25 July 2013.

2013: "Modelling and forecasting
nancial market activity using the semiparametric fractionally integrated Log-ACD model". Invited talk at the Finance Colloquium, University of Hannover, 12 June 2013.

2012: "Data-driven estimation of smooth correlation changes in a semiparametric dynamic conditional correlation model", organized talk at the 6th CSDA Intern. Confer. on Computitional and Financial Econometrics, 1-3 Dec 2012, Oviedo, Spain.

2012: "The exponential FARIMA and the exponential SEMIFAR models, and their applications", invited talk at the Conference Program and Abstracts of the International Conference on Advanced Interdisciplinary Statistics and Combinatorics, 5-7 October 2012, Greensboro, USA.

2010: "Estimation of the memory parameter in fractionally diferencing processes". Invited talk at the Advanced Seminar `Complex Systems', Institute of Mathematics, University of Paderborn, 24 June 2010.

2010: "Filtered log-periodogram regression of long memory processes". Special Invited Talk at the International Conference on Statistics, Probability, Operations Research, Computer Science and Allied Areas, 4-8 January 2010, Visakahapatnam, India.

2009: "Modelling local and conditional volatility under long memory". Public Lecture, Faculty of Business Administration and Economics, University of Paderborn. 28 October 2009.

2009: "Modelling fnancial time series with SEMIFAR-GARCH models". Talk at the Statistische Woche 2009, 5-8 October 2009, Wuppertal, Germany.

2009: "Semiparametric modelling of diferent volatility components in high-frequency fnancial time series", invited talk, Center of Statistics, University of Bielefeld, 14 July 2009.

2009: "GTR -- ein neuer Ansatz der empirischen Wirtschaftsforschung". Lecture at the Research Seminar of the Faculty of Economics, Paderborn University, 10 February 2009, Braunlage.

2008: "Semiparametrische Volatilit¨atsmodelle f¨ur Finanzzeitreihen", invited talk, Private Hanseuniversit¨at Rostock, 04 July 2008.

2008: "Modelling of slowly changing scale and correlation functions in financial time series", invited talk, Department of Statistics, University of Bristol, 08 May 2008.

2007: "Modelling of local and conditional changes in the mean, variance and cross correlations", invited talk, Department of Mathematics, University of Liverpool, 28 August 2007.

2007: "Generalized Trimmed Regression", invited talk, Department of Economics, University of Duisburg-Essen, 10 July 2007.

2007: "Semiparametric modelling of local and conditional correlations", invited talk, The ICSA International Conference, 25 to 27 June 2007, Taipei.

2007: "Time series smoothing and change point detection", invited talk, Department of Mathematics, Hong Kong University of Science and Technology, 22 June 2007.

2007: "Nonparametric estimation of trends in mean, variance and cross-correlations", invited talk, School of Mathematics, University of Birmingham, 24 April 2007.

2007: "Local and conditional changes in the mean, variance and correlation of financial time series", invited talk, Department of Mathematics, University of York, 8 March 2007.

2006: "Least squares, least checks, trimmed least squares & generalized trimmed least squares", invited talk, Department of Mathematics, University of York, 23 November 2006.

2006: "Modelling local and dynamic conditional correlations in financial returns", invited talk, Department of Mathematical Sciences, Brunel University, 23 October 2006, London.

2006: Discussion on "Nonparametric regression quantiles: thou shalt not cross?" given by Ivan Mizera, at the ICMS workshop: Quantile Regression, LMS and Robust Methods, 19-23 June 2006, Edinburgh.

2006: "A local dynamic conditional correlation model", contributed talk, International Conference on High Frequency Finance, 19-20 May 2006, Konstanz.

2005: "A slowly changing vector random walk model", Maxwell Institute Statistics Seminar, Heriot-Watt University, 2 December 2005.

2005: "Einige semiparametrische ¨okonometrische Zeitreihenmodelle" (in German), invited talk, German Institute for Economic Research (DIW), 22 June 2005, Berlin.

2005: "Semiparametric Modelling of Financial Time Series" (in German), invited talk, Department of Economics, University of Innsbruck, 25 May 2005.

2005: "Semiparametric Modelling of Financial Time Series" (in German), public lecture (for receiving the title of German PD), University of Konstanz, 11 May 2005.

2004: "Optimal combinations of different (in-vitro) tests" (in German), seminar for the final Habilitation examination, University of Konstanz, 20 Oct 2004.

2004: "Semiparametric Regression with Strongly Dependent and Heteroscedastic Errors", invited talk, School of Mathematical Sciences, Queen Mary College, University of London, 24 June 2004.

2004: "Non- and Semiparametric Approaches for Modelling Financial Time Series", invited talk, Department of Mathematics and Statistics, University of Limerick, 11 June 2004.

2004: "Some Semiparametric Models for Time Series", invited talk, Department of Mathematics and Statistics, Imperial College, 11 February 2004, London.

2003: Discussion in the invited session "Computer Intensive Methods for Semiparametrics", invited discussion, the 54th ISI-Session, 13-20 August 2003, Berlin.

2003: "A New Data-Driven Semiparametric Approach for Decomposing Seasonal Time Series", contributed talk, the 54th ISI-Session, 13-20 August 2003, Berlin.

2003: "Modellierung verschiedener Volatilit¨atskomponenten in Finanzrenditen" (in German), invited talk, University of Magdeburg, 11 February 2003.

2002: "Simultaneously Modelling Local and Conditional Heteroskedasticities", contributed talk, German Statistical Week, 7-10 October 2002, Konstanz.

2002: "Bandwidth selection in nonparametric regression with fractional time series errors", invited talk, International Conference on Current Advances and Trends in Nonparametric Statistics, 15-19 July 2002, Crete, Greece.

2002: "Simultaneous Modelling of Different Volatility Components in High-Frequency Financial Data", invited talk, Research Seminar 'Mathematical Statistics', Weierstrass-Institute and Humboldt University Berlin, 6 November 2002.

2001: "Recent Developments in Non- and semiparametric Models with Fractional Time Series Errors", invited talk, the 2nd Euroworkshop on Statistical Modelling - Nonparametric Models, 1-4 November 2001, Bernried (near Munich).

2001: "Semiparametric fractional autoregressive model", invited talk, the 23rd European Meeting of Statistics, 13-18 August, 2001, Madeira, Portugal.

Doktorand*innen

Sebastian Letmathe
Promotion: 20.02.23
„New Developments of Parametric and Semiparametric Volatility Models With Application to Quantitative Risk Management“

Bastian Schäfer
Promotion: 04.03.22
„Non- and Semiparametric Regression Methods for Functional Time Series on a Lattice“

Xuehai Zhang
Promotion: 22.10.18
„Further Development of Semiparametrick Volatility Models and their Applications to Value at Risk and Expected Shortfall“

Sarah Forstinger
Promotion: 25.09.2018
„Modelling and forecasting fingancial and economic time series using different semiparametric ACD models“

Chen Zhou
Promotion: 20.07.2018
„Data-driven Realized Kernels and Further Analysis Using a Semi-FI-Log-ACD Model“

Christian Peitz
Promotion: 15.06.2015
„Die parametrische und semiparametrische Analyse von Finanzzeitreihen - Neue Methoden, Modelle und Anwendungsmöglichkeiten“.

 

Jiangtao Wang (2014, ehemalige WHK, 2013-2014) mit Prof. H. Liu
"Research on ACD Model and Its Application" (Zhongnan University of Economics and Law, China, in collaboration with the University of Paderborn)

Mark Cathcart, PhD (2012) mit Prof. A. J. McNeil, PhD
"Monte Carlo Simulation Approaches to the Valuation
and Risk Management of Unit-Linked Insurance Products with Guarantees" (Heriot-Watt University, Edinburgh)

Zhichao Guo (2012, ehemalige WHK, 2009-2011) mit Prof. X. Tan, PhD
"Quantitative Study of the Change of China- Germany Trade in Agri-food Products" (China Agricultural University, in collaboration with the University of Paderborn)

Dr. Jens Bies (2011) mit Prof. Dr. B. Schiller
"Die Flow Analyse - Ein alternativer Kapitalmarktanalyseansatz zur Optimierung der Portfoliomanagement-Prozesse"

Prof. Xiaohong Liu PHD
Promotion: 2008 mit Prof. A. C. McKinnon, PhD

"The Compretitiveness of Logistics Service Providers: An Investigationin China and the UK" (Heriot-Watt University, Edinburgh)

Highly Commended Award for the Emerald/EFMD Outstanding Doctoral Research Awards in the Logistics and Supply Chain Management

WHK/ WHB

Jim-Luca Brand (11/ 2018 bis 3/ 2019, vorher SHK)

Zchichao Guo (2009 bis 2011, auch eine ehemalige Doktorandin)

Angela Christine Krause (2017 – 2018)

Shujie Li (2020, ab 1.10.20 Wissenschaftl. Mitarbeiterin/ Doktorandin)

Ma Lin (2001 bis 2012)

Sarah Christin Malangone (2017)

Claudia Nieboj (2018)

Nico Schlottmann (2017)

Kan Wang (20011 – 2012)

Semiramis Yigit (4/ 2017 bis 9/ 2017 WHB, 10/ 2017 bis 3/2018 WHK)

Xuehai Zhang (8/ 2015 bis 3/ 2017 WHK, ab 10/ 2017 Wissenschaftl. Mitarbeiter bis zur Promotion)

SHK/ Tutor*innen

Mehtap Aydinci

Jim-Luca Brand

Jessica Bronk

Sandra Caase

Benjamin Victor Carreras Painter

Marlon Fitz

Michael Gollmick

Lisa Görlach

Sebastia Sagemüller

Kristina Tautrims

Meike Lesniak

Ying Liu

Miriam Linke

Alexandra Mahler