Mathematical and Statistical Methods for Actuarial Sciences and Finance: eMAF2020

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 -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., Robomanagement^TM: 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 crude oil 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 dynamic joint 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.