- 06.2019: Roxana Halbleib is co-organizing the conference on Quantitative Finance and Financial Econometrics (QFFE) held in Marseille from 5th to 7th June, 2019
- 06.2019: Roxana Halbleib is invited speaker at the Workshop on Modelling Economic and Financial Time Series (Link) held in Madrid on 7th June , 2019
- 02.2019: Roxana Halbleib has been awarded funding from the Heisenberg Program of the German Research Foundation with the project "Econometric Analysis and Forecasts of Financial Risks based on High-frequency Data" (Link)
- 12.2018: Roxana Halbleib has become the co-chair of the 13th International Conference on Computational and Financial Econometrics 2019 (CFE) held in London from 14th to 16th December, 2019
- 12.2018: Timo Dimitriadis has received his doctoral degree with the thesis: "Three Essays on Estimation, Forecasting and Evaluation of Financial Risk"
Dr. Roxana Halbleib studied Economics at the "Alexandru Ioan Cuza" University of Iasi, Romania and University of Konstanz. After obtaining her doctoral degree in Econometrics in 2010 from the University of Konstanz, she went to the Université libre de Bruxelles in Belgium for a one-year post-doc. Since 2011, she is Margarete von Wrangell Research Fellow and since 2013 Zukunftskolleg Fellow at the University of Konstanz.
Her research interests are at the junction between econometrics, data science and computational statistics. For her research results, in 2017, she was awarded with the Wolfgang Wetzel Award of the German Statistical Society. Starting with 2019 she is admitted in the Heisenberg Programme of the German Science Foundation.Curriculum Vitae
- (Ultra) High Frequency Data
- High Dimensional Data Analysis
- Risk Estimation and Forecasting
- Simulation-based Estimation Methods
"Estimating Stable Latent Factor Models by Indirect Inference", 2018, Journal of Econometrics, Volume 205, Issue 1, pages 280-301 (with Giorgio Calzolari)
"Forecasting Covariance Matrices: A Mixed Approach", 2016, Journal of Financial Econometrics, Volume 4, Issue 2, pages 383-417 (with Valeri Voev)
"Estimating GARCH-type Models with Symmetric Stable Innovations: Indirect Inference versus Maximum Likelihood", 2014, Computational Statistics and Data Analysis, Volume 76, pages 158 - 171 (with Giorgio Calzolari and Alessandro Parrini)
"Improving the Value at Risk Forecasts: Theory and Evidence from the Financial Crisis"***, 2012, Journal of Economic Dynamics and Control, Volume 36, Issue 8, Pages 1212-1228 (with Winfried Pohlmeier) ***Previous versions of the paper circulated under the title "How Risky is the Value at Risk?"
"Modelling and Forecasting Multivariate Realized Volatility", 2011, Journal of Applied Econometrics, Volume 26, pages 922-947 (with Valeri Voev)
"Forecasting Multivariate Volatility using the VARFIMA Model on Realized Covariance Cholesky Factors", 2011, Journal of Economics and Statistics (Jahrbücher für Nationalökonomie und Statistik), Vol. 231/1, pages 134-152 (with Valeri Voev), Working Paper Version
"Messen und Verstehen von Finanzrisiken - Eine Perspektive der Ökonometrie", 2017, Springer, in Messen und Verstehen in der Wissenschaft – Interdisziplinäre Ansätze, Springer Verlag, pages 135-149 (Eds: M. Schweiker, J. Hass, A. Novokhatko and R. Halbleib)
"Messen und Verstehen in der Wissenschaft", 2017, Springer (Eds: M. Schweiker, J. Hass, A. Novokhatko, R. Halbleib)
"How informative is High-Frequency Data for Tail Risk Estimation and Forecasting? An Intrinsic Time Perspective", 2018, GSDS Working Paper No. 2018-04 (with Timo Dimitriadis)
"A Latent Factor Model for Forecasting Realized Volatilities", 2017, GSDS Working Paper No. 2017-14 (with Giorgio Calzolari and Aygul Zagidullina)
"Modelling Realized Covariance Matrices with Stochastic Volatility Latent Factors: Filter, Likelihood, Forecast", 2018 (with Giorgio Calzolari)
Heidelberger Akademie der Wissenschaften, WIN-Kolleg - Junior Academy for Young Scholars and Scientists with the topic "Messen und Verstehen der Welt durch die Wissenschaft": Analyzing, Measuring and Forecasting Financial Risks by means of High-Frequency Data