Prof. Sandra Paterlini, PhD
Doktor - aktiv
Position / Amtsbezeichnung
Lehrstuhlinhaber
Lehrstuhlinhaber
Universität
EBS Universität für Wirtschaft und Recht
EBS Business School
EBS Universität für Wirtschaft und Recht
EBS Business School
Fachbereich
Department of Finance, Accounting and Real Estate
Department of Finance, Accounting and Real Estate
Arbeitsbereiche
Financial Econometrics & Asset Management
Financial Econometrics & Asset Management
Forschungsbereiche
Asset Management
Financial Econometrics
Risk Management
Optimization Heuristics
Asset Management
Financial Econometrics
Risk Management
Optimization Heuristics
Land
Deutschland
Deutschland
Ort / PLZ
65189 Wiesbaden
65189 Wiesbaden
Strasse
Gustav-Stresemann-Ring 3
Gustav-Stresemann-Ring 3
Telefon
+49 611 7102 1227
+49 611 7102 1227
FAX
+49 611 7102 10 1227
+49 611 7102 10 1227
Tätigkeit an Business Schools
Veröffentlichungen
Publications in Refereed Journals
Fastrich, B., Paterlini, S., & Winker, P. (forthcoming).
Cardinality versus q-Norm Constraints for Index Tracking, Quantitative Finance, ISSN: 1469-7696, doi: 10.1080/14697688.2012.691986.
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.
Mittnik, S., Paterlini, S, & Yener, T.(2013). Operational-Risk ependencies 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), 05-126.
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 or
predictive neural network design -afinancial application. Economics & Complexity,4.
Working Papers
Mittnik, S., Paterlini S., & Yener, T. (submitted). Operational-risk dependencies and the determination of risk capital. Available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1905600
Ferrari, D., & Paterlini, S. (submitted). Robust and efficient estimation of multivariate location and scale via HCT entropy minimization. Available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1906819
Wang, Z., Paterlini, S., Gao, F., & Yang, Y. (submitted). Adaptive minimax estimation over sparse lq-Hulls. Available at http://arxiv.org/abs/1108.1961
Edited Books and Conference Proceedings in English (peer reviewed)
Scozzari, A., Tardella, F., Paterlini, S., & Krink, T. (2012). Exact and heuristic approaches for the index tracking problem with UCITS constraints. In L. Suhl, G. Mitra, C. Lucas, A. Koberstein, & L. Beckmann (Eds.), Applied Mathematical Optimization and Modelling: APMOD 2012 Extended Abstracts (1st ed., pp. 451-458). Paderborn, Germany: Books on Demand. ISBN: 9783844817942
Paterlini, S., Minerva, T. (2010). Genetic algorithms in partitional clustering: A comparison. In V.Monteanu et al. (Eds.), Recent Advances in Neural Networks, Fuzzy Systems & Evolutionary Computing (pp. 28-36). Stevens Point, Wisconsin, USA: WSEAS Press.
ISBN: 9789604741953
Paterlini, S., & Minerva, T. (2010). Regression model selection using genetic algorithms. In V. Monteanu et al. (Eds.), Recent Advances in Neural Networks, Fuzzy Systems & Evolutionary Computing (pp.19-28).
Stevens Point, Wisconsin: WSEAS Press. ISBN: 9789604741953
Mittnik, S., Paterlini, S., & Yener, T. (2010, August). Modeling operational risk: Estimation and effects of dependencies. In Y. Lechevallier, & G. Saporta (Eds.), Proceedings of COMPSTAT 2010, Paris (pp. 541-548). Heidelberg, Germany: Physica-Verlag. ISBN: 9783790826043
Paterlini, S. (2010, August). Evolutionary computation for modelling and optimization in finance. In Y. Lechevallier, & G. Saporta (Eds.), Proceedings of COMPSTAT 2010, Paris (pp. 265-275). Heidelberg, Germany: Physica-Verlag. ISBN: 9783790826043
Ferrari, D., & Paterlini, S. (2007). The maximum Lq-likelihood estimator in extreme value theory. Italian Statistical Society Conference Proceedings – Risk and Forecasting (pp. 571-572), Venice, Italy.
Frederic, P., & Paterlini, S. (2007). Additive modelling for location, scale, and shape parameters of the skew normal distribution. Italian Statistical Society Conference Proceedings – Risk and Forecasting (pp. 573-574). Venice, Italy.
Paterlini, S., & Krink, T. (2004). High performance clustering with differential evolution. Proceedings of Congress on Evolutionary Computation (CEC-2004) (vol. 2). Piscataway, NJ: IEEE PRESS. ISBN: 9780780385153
Lalla, M., & Paterlini, S. (2004). Duration models and differential volution in the analysis of large data set. AIEL-XIX Conference on Labour Economics, Modena, Italy.
Minerva, T., & Paterlini, S. (2002). Evolutionary approaches for statistical modelling. In D. B. Fogel, M. A. El-Sharkawi, X. Yao, G. Greenwood, H. Iba, P. Marrow, & M. Shackleton (Eds.), Proceedings of the Fourth Congress on Evolutionary Computation (CEC-2002) (vol. 2, pp. 2023-2028). Piscataway, NJ: IEEE Press. ISBN: 9780780372825
Paterlini, S., & Minerva, T. (2002). Evolutionary a pproaches for data clustering and feature selection. In S.Klinke, P. Ahrend, & L. Richter (Eds.), Proceedings of the Conference CompStat 2002 - Short Communications and Posters. Berlin, Germany.
Paterlini, S. and T. Minerva, 2002, Evolutionary Model Selection: methodology and applications. In: Klinke, S., P. Ahrend and L. Richter, 2002, Proceedings of the Conference CompStat 2002 - Short Communications and Posters. Berlin, Germany.
Paterlini, S., Favaro, S., & Minerva, T. (2001). Genetic approaches for
data clustering. Book of Short Paper CLADAG 2001, SIS Scientific Meeting: Classification and Data Analysis, Palermo, Italy.
Minerva, T., Paterlini, S., & Poli, I. (1998, November). GANND: A genetic
A lgorithm for predictive neural network design - afinancial application.
In Proceedings of International seminar on new techniques and technologies for statistics NTTS 1998. Sorrento, Italy.
Book Review
Paterlini S. (2012). Review of the book:Jahrbuecher fuer Nationaloekonomie und Statistik, 23/23. ISSN: 0021-4027
Publications in Italian (Peer Reviewed) Paterlini S., Pirani, E., & Russo, M. (2008).
Differenze territoriali e specializzazioninell’industria meccanica in Italia. Un’analisi cluster dei dati censuari 1991 e 2001. In L’industria meccanica in Italia. Analisi spaziale delle specializzazioni produttive 1951-2001, acura di Margherita Russo. Carocci, Roma.
Minerva T., Paterlini, S., & Poli,I. (2000). Algoritmi ibridi per l’analisi di serie storiche finanziarie. In Scienza e Business, I(3-4), 55-77. Interscience press. Paterlini S., & Minerva,
T. (2001). Nuovi algoritmi evolutivi per il raggruppamento di dati e di serie storiche. In Proceedings SMDM 2001-Metodi statistici per le applicazioni di data mining. Pavia, Italia. Nutzungshinweise: Jede natürliche Person darf sich nur mit einer E-Mail Adresse bei WiWi-Online registrieren lassen. Die Nutzung der Daten die WiWi-Online bereitstellt ist nur für den privaten Gebrauch bestimmt - eine gewerbliche Nutzung ist verboten. Eine automatisierte Nutzung von WiWi-Online und dessen Inhalte, z.B. durch Offline-Browser, Download-Manager oder Webseiten etc. ist ausdrücklich strengstens untersagt. Zuwiderhandlungen werden straf- und zivilrechtlich verfolgt.