Professoren
Prof. Sandra Paterlini, PhD

Prof. Sandra Paterlini, PhD

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Publications

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.

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