| TÃtulo : |
Mathematical and Statistical Methods for Actuarial Sciences and Finance : eMAF2020 |
| Tipo de documento: |
documento electrónico |
| Autores: |
Corazza, Marco, ; Gilli, Manfred, ; Perna, Cira, ; Pizzi, Claudio, ; Sibillo, Marilena, |
| Mención de edición: |
1 ed. |
| Editorial: |
[s.l.] : Springer |
| Fecha de publicación: |
2021 |
| Número de páginas: |
XIV, 401 p. 67 ilustraciones, 29 ilustraciones en color. |
| ISBN/ISSN/DL: |
978-3-030-78965-7 |
| Nota general: |
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. |
| Idioma : |
Inglés (eng) |
| Palabras clave: |
Industria de servicios financieros Finanzas ciencia actuarial Ciencias sociales EstadÃsticas Servicios financieros EconomÃa Financiera Matemáticas actuariales Matemáticas en Negocios EconomÃa y Finanzas EstadÃstica en Negocios Gestión EconomÃa Seguros |
| Clasificación: |
332.17 |
| Resumen: |
La cooperación y la interacción entre matemáticos, estadÃsticos y econometristas que trabajan en ciencias actuariales y finanzas están mejorando la investigación sobre estos temas y produciendo numerosos resultados cientÃficos significativos. Este volumen presenta nuevas ideas, en forma de artÃculos de cuatro a seis páginas, presentados en la Conferencia Internacional eMAF2020 – Métodos matemáticos y estadÃsticos para las ciencias actuariales y las finanzas. Debido a la ya tristemente famosa pandemia de COVID-19, la conferencia se celebró de forma remota a través de la plataforma Zoom ofrecida por el Departamento de EconomÃa de la Universidad Ca'' Foscari de Venecia los dÃas 18, 22 y 25 de septiembre de 2020. eMAF2020 es la novena edición de una serie bienal internacional de reuniones cientÃficas, iniciada en 2004 por iniciativa del Departamento de EconomÃa y EstadÃstica de la Universidad de Salerno. La eficacia de esta idea ha quedado demostrada por la amplia participación en todas las ediciones, que se han celebrado en Salerno (2004, 2006, 2010 y 2014), Venecia (2008, 2012 y 2020), ParÃs (2016) y Madrid (2018). Este libro cubre una amplia variedad de temas: inteligencia artificial y aprendizaje automático en finanzas y seguros, finanzas conductuales, métodos y modelos de riesgo crediticio, optimización dinámica en finanzas, análisis de datos financieros, dinámica de pronóstico de fenómenos actuariales y financieros, mercados de divisas, seguros. modelos, modelos de tasas de interés, riesgo de longevidad, modelos y métodos para análisis de series de tiempo financieras, técnicas multivariadas para análisis de mercados financieros, sistemas de pensiones, selección y gestión de carteras, finanzas del mundo real, análisis y gestión de riesgos, sistemas de negociación, y otros. Este volumen es un recurso valioso para académicos, estudiantes de doctorado, practicantes, profesionales e investigadores. Además, también es de interés para otros lectores con conocimientos cuantitativos previos. |
| Nota de contenido: |
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. |
| Tipo de medio : |
Computadora |
| Summary : |
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 papers, presented at the International Conference eMAF2020 – Mathematical and Statistical Methods for Actuarial Sciences and Finance. Due to the now sadly famous COVID-19 pandemic, the conference was held remotely through the Zoom platform offered by the Department of Economics of the Ca' Foscari University of Venice on September 18, 22 and 25, 2020. eMAF2020 is the ninth edition of an international biennial series of scientific meetings, started in 2004 at the initiative of the Department of Economics and Statistics of the University of Salerno. The effectiveness of this idea has been proven by wide participation in all editions, which have been held in Salerno (2004, 2006, 2010 and 2014), Venice (2008, 2012 and 2020), Paris (2016) and Madrid (2018). This book covers a wide variety of subjects: artificial intelligence and machine learning in finance and insurance, behavioral finance, credit risk methods and models, dynamic optimization in finance, financial data analytics, forecasting dynamics of actuarial and financial phenomena, foreign exchange markets, insurance models, interest rate models, longevity risk, models and methods for financial time series analysis, multivariate techniques for financial markets analysis, pension systems, portfolio selection and management, real-world finance, risk analysis and management, trading systems, and others. This volume is a valuable resource for academics, PhD students, practitioners, professionals and researchers. Moreover, it is also of interest to other readers with quantitative background knowledge. |
| Enlace de acceso : |
https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] |
Mathematical and Statistical Methods for Actuarial Sciences and Finance : eMAF2020 [documento electrónico] / Corazza, Marco, ; Gilli, Manfred, ; Perna, Cira, ; Pizzi, Claudio, ; Sibillo, Marilena, . - 1 ed. . - [s.l.] : Springer, 2021 . - XIV, 401 p. 67 ilustraciones, 29 ilustraciones en color. ISBN : 978-3-030-78965-7 Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés ( eng)
| Palabras clave: |
Industria de servicios financieros Finanzas ciencia actuarial Ciencias sociales EstadÃsticas Servicios financieros EconomÃa Financiera Matemáticas actuariales Matemáticas en Negocios EconomÃa y Finanzas EstadÃstica en Negocios Gestión EconomÃa Seguros |
| Clasificación: |
332.17 |
| Resumen: |
La cooperación y la interacción entre matemáticos, estadÃsticos y econometristas que trabajan en ciencias actuariales y finanzas están mejorando la investigación sobre estos temas y produciendo numerosos resultados cientÃficos significativos. Este volumen presenta nuevas ideas, en forma de artÃculos de cuatro a seis páginas, presentados en la Conferencia Internacional eMAF2020 – Métodos matemáticos y estadÃsticos para las ciencias actuariales y las finanzas. Debido a la ya tristemente famosa pandemia de COVID-19, la conferencia se celebró de forma remota a través de la plataforma Zoom ofrecida por el Departamento de EconomÃa de la Universidad Ca'' Foscari de Venecia los dÃas 18, 22 y 25 de septiembre de 2020. eMAF2020 es la novena edición de una serie bienal internacional de reuniones cientÃficas, iniciada en 2004 por iniciativa del Departamento de EconomÃa y EstadÃstica de la Universidad de Salerno. La eficacia de esta idea ha quedado demostrada por la amplia participación en todas las ediciones, que se han celebrado en Salerno (2004, 2006, 2010 y 2014), Venecia (2008, 2012 y 2020), ParÃs (2016) y Madrid (2018). Este libro cubre una amplia variedad de temas: inteligencia artificial y aprendizaje automático en finanzas y seguros, finanzas conductuales, métodos y modelos de riesgo crediticio, optimización dinámica en finanzas, análisis de datos financieros, dinámica de pronóstico de fenómenos actuariales y financieros, mercados de divisas, seguros. modelos, modelos de tasas de interés, riesgo de longevidad, modelos y métodos para análisis de series de tiempo financieras, técnicas multivariadas para análisis de mercados financieros, sistemas de pensiones, selección y gestión de carteras, finanzas del mundo real, análisis y gestión de riesgos, sistemas de negociación, y otros. Este volumen es un recurso valioso para académicos, estudiantes de doctorado, practicantes, profesionales e investigadores. Además, también es de interés para otros lectores con conocimientos cuantitativos previos. |
| Nota de contenido: |
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. |
| Tipo de medio : |
Computadora |
| Summary : |
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 papers, presented at the International Conference eMAF2020 – Mathematical and Statistical Methods for Actuarial Sciences and Finance. Due to the now sadly famous COVID-19 pandemic, the conference was held remotely through the Zoom platform offered by the Department of Economics of the Ca' Foscari University of Venice on September 18, 22 and 25, 2020. eMAF2020 is the ninth edition of an international biennial series of scientific meetings, started in 2004 at the initiative of the Department of Economics and Statistics of the University of Salerno. The effectiveness of this idea has been proven by wide participation in all editions, which have been held in Salerno (2004, 2006, 2010 and 2014), Venice (2008, 2012 and 2020), Paris (2016) and Madrid (2018). This book covers a wide variety of subjects: artificial intelligence and machine learning in finance and insurance, behavioral finance, credit risk methods and models, dynamic optimization in finance, financial data analytics, forecasting dynamics of actuarial and financial phenomena, foreign exchange markets, insurance models, interest rate models, longevity risk, models and methods for financial time series analysis, multivariate techniques for financial markets analysis, pension systems, portfolio selection and management, real-world finance, risk analysis and management, trading systems, and others. This volume is a valuable resource for academics, PhD students, practitioners, professionals and researchers. Moreover, it is also of interest to other readers with quantitative background knowledge. |
| Enlace de acceso : |
https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] |
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