Mathematical and Statistical Methods for Actuarial Sciences and Finance

Marco Corazza, PhD in "Mathematics for the Analysis of Financial Markets", is an associate professor at the Department of Economics of the Ca' Foscari University of Venice. Among his main research interests are static and dynamic portfolio management theories; trading system models; machine learning applications in finance; bioinspired metaheuristics for optimization; multicriteria methods for economic decision support; nonstandard probability distributions in finance; and port scheduling models and algorithms. He has participated and participates in several research projects, both at the national and international levels. He is an author/coauthor of approximately one hundred thirty scientific publications; some of them have received national and international awards. He is also editor-in-chief of the international scientific journal “Mathematical Methods in Economics and Finance”, editor of Springer books, and has been and is member of the scientific committees of several conferences and of some private companies. His combined academic activity with consulting services.Manfred Gilli is Professor emeritus at the Geneva School of Economics and Management at the University of Geneva, where he has taught numerical methods in economics and finance. He is also a faculty member of the Swiss Finance Institute, a member of the Advisory Board of Computational Statistics and Data Analysis and a member of the editorial board of Computational Economics. He formerly served as president of the Society for Computational Economics.Cira Perna is a full professor of statistics at the Department of Economics and Statistics of the University of Salerno (Italy). Since 2018, she has been elected a member of the Steering Committee of the Italian Statistical Society; since 2019, she has been a member of the Board of Directors of the University of Salerno; since the first edition of the Conference, in 2004, she has been a chair of the international conference MAF and guest editor of the associated international journals; and since 2006, she has been an Editor of the Springer books MAF. Her research work mainly focuses on nonlinear time series, artificial neural network models and resampling techniques. On these topics, she has published numerous papers in national and international journals. She has participated in several research projects, both at the national and international levels, and she has been a member of several scientific committees of national and international conferences.Claudio Pizzi is an associate professor at the Department of Economics of the Ca' Foscari University of Venice, where he teaches statistical methods for financial and monetary markets and business statistics. His research is focused mainly on statistical analysis of financial time series, linear and nonlinear models for time series, technical analysis, trading system models, bioinspired metaheuristics for optimization, and systemic risk. Hehas participated in both national and international research projects. He is a member of the editorial board of “Statistical Method and Applications”.Marilena Sibillo is a full professor of Mathematical Methods for Economics, Finance and Actuarial Sciences at the University of Salerno and is currently a contract professor of Financial Mathematics in the 2020/2021 academic year at Luiss University in Rome. In 2012, she was awarded a Highly Commended Award Winner at the Literati Network Awards for Excellence, and since 2013, she has been a Paul Harris Fellow. She had national and international awards related to teaching. Since 2006, she has been an editor of the Springer books MAF and Finance and a guest editor of international journals. Since 2004, she has been chair of the international conference MAF, and since 2016, she has been chair of the UNISActuarial School. She is an author of more than 100 papers mostly published in international journals and books. Her scientific activity mainly deals with risk theory, analysis and control of the interactions between financial and demographic risks, variable annuities, stochastic mortality, and innovative pension contracts.

• 1 Albano G. et al., A comparison among alternative parameters estimators in the Vasicek process: a small sample analysis.- 2 Amendola A. et al., On the use of mixed sampling in modelling realized volatility: The MEM–MIDAS.- 3 Amerise I. L. and Tarsitano A., Simultaneous prediction intervals for forecasting EUR/USD exchange rate.- 4 Andria J. and di Tollo G., An empirical investigation of heavy tails in emerging markets and robust estimation of the Pareto tail index.- 5 Anisa R. et al., Potential of reducing crop insurance subsidy based on willingness to pay and Random Forest analysis.- 6 Arfan A. and Johnson P., A stochastic volatility model for optimal market-making.- 7 Atance D. et al., Method for forecasting mortality based on Key Rates.- 8 Atance D. et al., Resampling Methods to assess the forecasting ability of mortality models.- 9 Avellone A. et al., Portfolio optimization with nonlinear loss aversion and transaction costs.- 10 Bacinello A. R. et al., Monte Carlo valuation of future annuity contracts.- 11 Baione F. et al., A risk based approach for the Solvency Capital requirement for Health Plans.- 12 Baione F. et al., An application of Zero-One Inflated Beta regression models for predicting health insurance reimbursement.- 13 Baragona R. et al., Periodic autoregressive models for stochastic seasonality.- 14 Barro D. et al., Behavioral aspects in portfolio selection.- 15 Bianchi S. et al., Stochastic dominance in the outer distributions of the α-efficiency domain.- 16 Boccia M., Formal and informal microfinance in Nigeria. Which of them works?.- 17 Candila V. and Petrella L., Conditional quantile estimation for linear ARCH models with MIDAS components.- 18 Cantaluppi G. and Zappa D., Modelling topics of car accidents events: A Text Mining approach.- 19 Carallo G. et al., A Bayesian generalized Poisson model for cyber risk analysis.- 20 Carracedo P. and Debón A., Implementation in R and Matlab of econometric models applied to ages after retirement in Europe.-21 Castellani G. et al., Machine Learning in nested simulations under actuarial uncertainty.- 22 Corazza M. et al., Comparing RL approaches for applications to financial trading systems.- 23 Corazza M. et al., MFG-based trading model with information costs.- 24 Corazza M. et al., Trading System mixed-integer optimization by PSO.- 25 Coretto P. et al., A GARCH–type model with cross-sectional volatility clusters.- 26 Costabile M. et al., A lattice approach to evaluate participating policies in a stochastic interest rate framework.- 27 De Giuli E. et al., Multidimensional visibility for describing the market dynamics around Brexit announcements.- 28 Di Lorenzo E. et al., Risk assessment in the Reverse Mortgage contract.- 29 di Tollo et al., Neural Networks to determine the relationships between business innovation and gender aspects.- 30 Donati R. and Corazza M., RobomanagementTM: Virtualizing the Asset Management Team through software objects.- 31 Fassino C. et al., Numerical stability of optimal Mean Variance portfolios.- 32 Flori A. and Regoli D., Pairs-trading strategies with Recurrent Neural Networks market predictions.- 33 Gannon F. et al., Automatic balancing mechanism and discount rate: towards an optimal transition to balance Pay-as-You-Go pension scheme without intertemporal dictatorship?.- 34 Garvey A. M. et al., The importance of reporting a pension system’s income statement and budgeted variances in a fair and sustainable scheme.- 35 Giacomelli J. and Passalacqua L., Improved precision in calibrating CreditRisk+ model for Credit Insurance applications.- 36 Giordano F. et al., A model-free screening selection approach by local derivative estimation.- 37 Giordano F. and Niglio M., Markov Switching predictors under asymmetric loss functions.- 38 Giordano F. et al., Screening covariates in presence of unbalanced binary dependent variable.- 39 Grané A. et al., Health and wellbeing profiles across Europe.- 40 He P. et al., On modelling of crudeoil futures in a bivariate State-Space framework.- 41 Jach A., A general comovement measure for time series.- 42 Kusumaningrum D. et al., Alternative area yield index based Crop Insurance policies in Indonesia.- 43 La Rocca M. and Vitale L., Clustering time series by nonlinear dependence.- 44 Laporta A. G. et al., Quantile Regression Neural Network for quantile claim amount estimation.- 45 Levantesi S. and Menzietti M., Modelling health transitions in Italy: a generalized linear model with disability duration.- 46 Lledó J. et al., Mid-year estimators in life table construction.- 47 Loperfido N., Representing Koziol’s kurtoses.- 48 Mancuso D. A. and Zappa D., Optimal portfolio for basic DAGs.- 49 Marino M. and Levantesi S., The Neural Network Lee-Carter model with parameter uncertainty: The case of Italy.-  50 Mercuri L. et al., Pricing of futures with a CARMA(p,q) model driven by a Time Changed Brownian motion.- 51 Merlo L. et al., Forecasting multiple VaR and ES using a dynamicjoint quantile regression with an application to portfolio optimization.- 52 Molina J.-E. et al., Financial market crash prediction through analysis of Stable and Pareto distributions.- 53 Neffelli M. et al., Precision matrix estimation for the Global Minimum Variance portfolio.- 54 Ojea-Ferreiro J., Deconstructing systemic risk: A reverse stress testing approach.- 55 Oyenubi A., Stochastic dominance and portfolio performance under heuristic optimization.- 56 Santos A. A. F., Big-data for high-frequency volatility analysis with time-deformed observations.- 57 Ungolo F. et al., Parametric bootstrap estimation of standard errors in survival models when covariates are missing.- 58 Zedda S. et al., The role of correlation in systemic risk: Mechanisms, effects, and policy implications.