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Logo: Institute of Statistics/Leibniz Universität Hannover
Logo Leibniz Universität Hannover
Logo: Institute of Statistics/Leibniz Universität Hannover
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Prof. Dr. Philipp Sibbertsen

 

 

Curriculum Vitae

  • Professor of Statistics, since 2005
  • Heisenbergstipendiat of DFG, 2004-2005
  • Research Assistant at the Department of Statistics, University of Dortmund, 1999-2004
  • PhD in Statistics, University of Dortmund, 1999

Research Interests

  • Time Series Analysis
  • Statistics of Financial Markets, Empirical Capital Market Research

Selected Publications

2017

  • Voges, M., Leschinski, C. and Sibbertsen, P.: Seasonal long memory in intraday volatility and trading volume of Dow Jones stocks, Advances in Applied Financial Econometrics (forthcoming)
  • Leschinski, C. and Sibbersten, P.: Model order selection in periodic long memory models, Econometrices and Statistics (forthcoming)
  • Wenger, K., Leschinski, C. and Sibbertsen, P.: The Memory of Volatility, Quantitative Finance and Economics (forthcoming)
  • Sibbertsen, P., Leschinski, C. and Busch, M.: A Multivariate Test Against Spurious Long Memory, Journal of Econometrics (forthcoming)

2016

  • Rinke, S. and Sibbertsen, P.: Information Criteria for Nonlinear Time Series Models. Studies of Nonlinear Dynamics and Econometrics, 20(3), 325–341 (link)

2015

  • Bertram, P., Sibbertsen, P., Stahl, G.: About the impact of Model Risk on Capital Reserves: A Quantitative Analysis. Journal of Risk, 17, 69-97 (link)

2014

  • Kaufmann, H., Heinen, F., Sibbertsen, P.: The dynamics of real exchange rates - A reconsideration. Journal of Applied Econometrics, 29, 758 - 773 (link)
  • Sibbertsen, P., Wegener, C. and Basse, T.: Testing for a Break in the Persistence in Yield Spreads of EMU Government Bonds, Journal of Banking and Finance, 41, 109 - 118 (link)

2013

  • Heinen, F., Michael, S. and Sibbertsen, P.: Weak identification in the ESTAR model and a new model, Journal of Time Series Analysis, 34, 238 - 261 (link)

2009

  • Sibbertsen, P., Kruse, R.: Testing for a break in persistence under long-range dependencies. Journal of Time Series Analysis 30, 263 - 285 (link)

2007

  • Nordman, D., Sibbertsen, P., Lahiri, S. N.: Empirical likelihood confidence intervals for the mean of a long-range dependent process. Journal of Time Series Analysis 28, 576 - 599 (link)

2005

  • Davidson, J., Sibbertsen, P.: Generating schemes for long memory processes. Journal of Econometrics 128, 253 - 282 (link)

Current Teaching

Winter term 2017/18

  • Descriptive Statistics
  • Time Series Analysis
  • Seminar on Statistics