Mathematical and statistical methods for actuarial sciences and finance : eMAF2020 / Marco Corazza, Manfred Gilli, Cira Perna, Claudio Pizzi, Marilena Sibillo, editors.

The cooperation and contamination between mathematicians, statisticians and econometricians working in actuarial sciences and finance is improving the research on these topics and producing numerous meaningful scientific results. This volume presents new ideas, in the form of four- to six-page paper...

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Bibliographic Details
Corporate Author:	MAF (Conference) Online)
Other Authors:	Corazza, Marco, 1962- (Editor), Gilli, Manfred, 1942- (Editor), Perna, Cira (Editor), Pizzi, Claudio, 1944- (Editor), Sibillo, Marilena (Editor)
Format:	eBook
Language:	English
Published:	Cham : Springer, [2021]
Subjects:	Insurance > Mathematical models > Congresses. Insurance > Statistical methods > Congresses. Finance > Mathematical models > Congresses. Finance > Statistical methods > Congresses. Finance > Mathematical models Finance > Statistical methods Insurance > Mathematical models Insurance > Statistical methods Electronic books. proceedings (reports) Conference papers and proceedings Conference papers and proceedings. Actes de congrès.
Online Access:	Click for online access

Table of Contents:

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 MEMMIDAS
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 Debon 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 GARCHtype 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 systems 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 Grane 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 Lledo J. et al., Mid-year estimators in life table construction
47 Loperfido N., Representing Koziols 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.

Mathematical and statistical methods for actuarial sciences and finance : eMAF2020 / Marco Corazza, Manfred Gilli, Cira Perna, Claudio Pizzi, Marilena Sibillo, editors.

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