Chair of Financial Econometrics & Asset Management
Research at the Chair of Financial Econometrics & Asset Management focuses on the development and estimation of econometric and financial models for banking network analysis, asset allocation, endowment management, hedge fund replication and risk management, with particular focus on operational risk.
- Asset Allocation
Our research interests are on the development of robust and reliable investment strategies, with a focus on regularization methods and volatility strategies, for index tracking and multiobjective optimization. Moreover, we develop optimization heuristics to deal with high dimensionality problems and real-world problems, typically characterized by non-convex objective functions and non-linear constraints.
- Risk Management
We focus on the development of quantitative risk-management tools to improve Value-at-Risk and tail-related risk measures in market, credit and operational risk. Moreover, tools that allow to get robust estimates and include dependence modelling are proposed to better deal with noisy data and explicitly capture dependences.
- Financial Networks
Our research aims at understanding the portfolio holdings and the interconnections among monetary financial institutions (MFI) in the financial markets, as well as to put forward regulatory suggestions to improve the resilience of the overall system. Current research projects, in cooperation with Deutsche Bundesbank, focus on identifying systemic relevant and highly concentrated MFI in the German bank equity market and on analyzing the risk and diversification of banks sovereign bond holdings both, at the level of single MFI and of the entire system.
“A Systemic Risk Indicator Derived from Optimal Portfolios” Joint project with Ben Craig (Federal Reserve Bank of Cleveland & Deutsche Bundesbank), Marcel Goreflo (Deutsche Bundesbank) and Philipp J. Kremer (EBS Phd student) [Abstract CFE see left]
“Modelling Multidimensional Extremal Dependence for Operational Risk” Joint project with Oliver Kley (Technische Universität München) and Claudia Klüppeberg (Technische Universität München)
“Sparse and robust normal and t- portfolios by penalized Lq-likelihood minimization”. Joint project with Davide Ferrari (University of Melbourne) and Margherita Giuzio (EBS Phd student) [Abstract see left]
Current Working Papers
»Bogdan, M., Kremer, P.J., & Paterlini, S., “Sorting out your Investments: Sparse Portfolio Construction via the ordered L1 – Norm.”, Working Paper
»Giuzio, M., & Paterlini, S., “Un-diversifying during crises: is it a good idea?”, Working Paper
»Talmaciu, A., Kremer, P.J., & Paterlini, S., “Achieving Diversification in Multi-Factor Portfolios”, Working Paper
»Kremer, P.J., Gorenflo, M., Craig, B., & Paterlini, S., “Understanding Banking Networks from Optimal Portfolio Choices”, Working Paper
Giuzio, M., Eichhorn-Schott, K., Paterlini, S., & Weber, V., (2016). Tracking Hedge Funds using Sparse Clones, Annals of Operational Research, Advance online publication. doi:10.1007/s10479-016-2371-5.
Giuzio, M., Ferrari D., & Paterlini, S. (2016). Sparse and robust normal and t- portfolios by penalized Lq-likelihood minimization. European Journal of Operation Research, 250(1), 251-261.
Fastrich, B., Paterlini, S., & Winker, P. (2015). Constructing optimal sparse portfolios using regularization methods. Computational Management Science, 12(3), 417-434.
Brechmann, E, Czado, C., & Paterlini, S. (2014). Flexible dependence modeling of operational risk losses and its impact on total capital requirements. Journal of Banking and Finance, 40, 271-285.
Fastrich, B., Paterlini, S., & Winker, P. (2014). Cardinality versus q-Norm Constraints for Index Tracking. Quantitative Finance, 14(11), 2019-2032.
Wang, Z., Paterlini, S., Gao, F., & Yang, Y. (2014). Adaptive Minimax Regression Estimation over Sparse lq-Hulls. Journal of Machine Learning Research, 15, 1675-1711.
Mittnik, S., Paterlini, S., & Yener, T. (2013). Operational-Risk Dependencies and the Determination of Risk Capital. The Journal of Operational Risk, 8(4), 83-104. Best Paper Award, Conference on Operational Risk, Goethe University, 22 March 2013, Frankfurt, Germany.
Brechmann, E, Czado, C., & Paterlini, S. (2013). Modeling dependence of operational loss frequencies. The Journal of Operational Risk, 8(4), 105-126.
Scozzari, A., Tardella, F., Paterlini, S., & Krink, T. (2013). Exact and heuristic approaches for the index tracking problem with UCITS constraints. Annals of Operations Research, 205(1), 235-250.
Maringer, D., Paterlini, S., & Winker, P. (2012). Editorial: The 3rd Special issue on optimization heuristics in estimation. Computational Statistics & Data Analysis, 56, 2963-2964. ISSN: 0167-9473. doi:10.1016/j.csda.2012.05.006.
Krink, T., & Paterlini, S. (2011). Multiobjective optimization using differential evolution for real-world portfolio optimization. Computational Management Science, 8(1-2), 157-179.
Lyra, M., Paha, J., Paterlini S., & Winker, P. (2010). Optimization heuristics for determining internal rating grading scales. Computational Statistics & Data Analysis, 54(11), 2693-2706.
Giamouridis, D., & Paterlini, S. (2010). Regular(ized) hedge funds. Journal of Financial Research, 33(3), 223-247.
Krink, T., Mittnik, S., & Paterlini, S. (2009). Differential evolution and combinatorial search for constrained index tracking. Annals of Operation Research, 172, 153-176.
Ferrari, D., & Paterlini, S. (2009). The maximum Lq-likelihood method: An application to extreme quantile estimation in finance. Methodology and Computing in Applied Probability, 11, 3-19.
Krink, T., Paterlini S., & Resti, A. (2008). The optimal structure of PD buckets. Journal of Banking and Finance, 32(10), 2275-2286.
Krink, T., Paterlini,S., & Resti, A. (2007). Using differential evolution to improve the accuracy of bank rating systems. Computational Statistics & Data Analysis. Elsevier, 52(1), 68-87.
Paterlini, S., & Krink, T. (2006). Differential evolution and particle swarm optimisation in partitional clustering. Computational Statistics & Data Analysis, Elsevier, 50(5), 1220-1247. CSDA Top-cited paper (2005-2011).
Pattarin, F., Paterlini, S., & Minerva, T. (2004). Clustering financial time series: An application to mutual funds style analysis. Computational Statistics & Data Analysis, Elsevier, 47(2), 353-372.
Roverato, A., & Paterlini, S. (2004). Technological modeling for graphical models: An approach based on genetic algorithms. Computational Statistics & Data Analysis, Elsevier, 47(2), 323-337.
Minerva, T., Paterlini, S., & Poli, I. (2000). GANND: A genetic algorithm for predictive neural network design - a financial application. Economics & Complexity, 4.