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Autor Sibillo, Marilena |
Documentos disponibles escritos por este autor (3)
Crear una solicitud de compra Refinar búsquedaMathematical and Statistical Methods for Actuarial Sciences and Finance / Corazza, Marco ; Gilli, Manfred ; Perna, Cira ; Pizzi, Claudio ; Sibillo, Marilena
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 [...]
TÃtulo : Mathematical and Statistical Methods for Actuarial Sciences and Finance : MAF 2016 Tipo de documento: documento electrónico Autores: Corazza, Marco, ; Perna, Cira, ; Legros, Florence, ; Sibillo, Marilena, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2017 Número de páginas: VIII, 169 p. 19 ilustraciones, 8 ilustraciones en color. ISBN/ISSN/DL: 978-3-319-50234-2 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: ciencia actuarial Ciencias sociales EstadÃsticas Macroeconómica Finanzas Matemáticas actuariales Matemáticas en Negocios EconomÃa y Finanzas EstadÃstica en Negocios Gestión EconomÃa Seguros MacroeconomÃa y economÃa monetaria EconomÃa Financiera Clasificación: 368.01 Resumen: Este volumen reúne artÃculos seleccionados y revisados ​​por pares presentados en la conferencia internacional "MAF 2016 – Métodos matemáticos y estadÃsticos para las ciencias actuariales y las finanzas", celebrada en ParÃs (Francia) en la Université Paris-Dauphine del 30 de marzo al 1 de abril de 2016. Las contribuciones destacan nuevas ideas sobre métodos matemáticos y estadÃsticos en las ciencias actuariales y las finanzas. La cooperación entre matemáticos y estadÃsticos que trabajan en seguros y finanzas es un campo muy fructÃfero, que produce modelos teóricos únicos y aplicaciones prácticas, asà como nuevos conocimientos en la discusión de problemas de interés nacional e internacional. Este volumen está dirigido a académicos, investigadores, estudiantes de doctorado y profesionales. Nota de contenido: 1 The effects of credit rating announcements on bond liquidity: An event study -- 2 The effect of credit rating events on the emerging CDS market -- 3 A generalised linear model approach to predict the result of research evaluation -- 4 Projecting dynamic life tables using Data Cloning -- 5 Markov switching GARCH models: Filtering, approximations and duality -- 6 A network approach to risk theory and portfolio selection -- 7 A PSO-based approach for improving simple trading systems -- 8 Provisions for outstanding claims with distance-based generalized linear models -- 9 Profitability vs. attractiveness within a performance analysis of a life annuity business -- 10 Uncertainty in historical Value-at-Risk: an alternative quantile-based risk measure -- 11 Modeling volatility risk premium -- 12 Covered call writing and framing: A cumulative prospect theory approach -- 13 Optimal portfolio selection for an investor with asymmetric attitude to gains and losses. Tipo de medio : Computadora Summary : This volume gathers selected peer-reviewed papers presented at the international conference "MAF 2016 – Mathematical and Statistical Methods for Actuarial Sciences and Finance", held in Paris (France) at the Université Paris-Dauphine from March 30 to April 1, 2016. The contributions highlight new ideas on mathematical and statistical methods in actuarial sciences and finance. The cooperation between mathematicians and statisticians working in insurance and finance is a very fruitful field, one that yields unique theoretical models and practical applications, as well as new insights in the discussion of problems of national and international interest. This volume is addressed to academicians, researchers, Ph.D. students and professionals. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Mathematical and Statistical Methods for Actuarial Sciences and Finance : MAF 2016 [documento electrónico] / Corazza, Marco, ; Perna, Cira, ; Legros, Florence, ; Sibillo, Marilena, . - 1 ed. . - [s.l.] : Springer, 2017 . - VIII, 169 p. 19 ilustraciones, 8 ilustraciones en color.
ISBN : 978-3-319-50234-2
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: ciencia actuarial Ciencias sociales EstadÃsticas Macroeconómica Finanzas Matemáticas actuariales Matemáticas en Negocios EconomÃa y Finanzas EstadÃstica en Negocios Gestión EconomÃa Seguros MacroeconomÃa y economÃa monetaria EconomÃa Financiera Clasificación: 368.01 Resumen: Este volumen reúne artÃculos seleccionados y revisados ​​por pares presentados en la conferencia internacional "MAF 2016 – Métodos matemáticos y estadÃsticos para las ciencias actuariales y las finanzas", celebrada en ParÃs (Francia) en la Université Paris-Dauphine del 30 de marzo al 1 de abril de 2016. Las contribuciones destacan nuevas ideas sobre métodos matemáticos y estadÃsticos en las ciencias actuariales y las finanzas. La cooperación entre matemáticos y estadÃsticos que trabajan en seguros y finanzas es un campo muy fructÃfero, que produce modelos teóricos únicos y aplicaciones prácticas, asà como nuevos conocimientos en la discusión de problemas de interés nacional e internacional. Este volumen está dirigido a académicos, investigadores, estudiantes de doctorado y profesionales. Nota de contenido: 1 The effects of credit rating announcements on bond liquidity: An event study -- 2 The effect of credit rating events on the emerging CDS market -- 3 A generalised linear model approach to predict the result of research evaluation -- 4 Projecting dynamic life tables using Data Cloning -- 5 Markov switching GARCH models: Filtering, approximations and duality -- 6 A network approach to risk theory and portfolio selection -- 7 A PSO-based approach for improving simple trading systems -- 8 Provisions for outstanding claims with distance-based generalized linear models -- 9 Profitability vs. attractiveness within a performance analysis of a life annuity business -- 10 Uncertainty in historical Value-at-Risk: an alternative quantile-based risk measure -- 11 Modeling volatility risk premium -- 12 Covered call writing and framing: A cumulative prospect theory approach -- 13 Optimal portfolio selection for an investor with asymmetric attitude to gains and losses. Tipo de medio : Computadora Summary : This volume gathers selected peer-reviewed papers presented at the international conference "MAF 2016 – Mathematical and Statistical Methods for Actuarial Sciences and Finance", held in Paris (France) at the Université Paris-Dauphine from March 30 to April 1, 2016. The contributions highlight new ideas on mathematical and statistical methods in actuarial sciences and finance. The cooperation between mathematicians and statisticians working in insurance and finance is a very fruitful field, one that yields unique theoretical models and practical applications, as well as new insights in the discussion of problems of national and international interest. This volume is addressed to academicians, researchers, Ph.D. students and professionals. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Mathematical and Statistical Methods for Actuarial Sciences and Finance : MAF 2018 Tipo de documento: documento electrónico Autores: Corazza, Marco, ; Durbán, MarÃa, ; Grané, Aurea, ; Perna, Cira, ; Sibillo, Marilena, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2018 Número de páginas: XVI, 518 p. ISBN/ISSN/DL: 978-3-319-89824-7 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: EstadÃsticas EconometrÃa Probabilidades Optimización matemática EstadÃstica en Negocios Gestión EconomÃa Finanzas Seguros TeorÃa de probabilidad Mejoramiento Clasificación: 300.727 Resumen: La interacción entre matemáticos, estadÃsticos y econometristas que trabajan en ciencias actuariales y finanzas está produciendo numerosos resultados cientÃficos significativos. Este volumen presenta nuevas ideas, en forma de artÃculos de cuatro páginas, presentados en la conferencia internacional Mathematical and Statistical Methods for Actuarial Sciences and Finance (MAF), celebrada en la Universidad Carlos III de Madrid (España), del 4 al 6 de abril de 2018. El libro cubre una amplia variedad de temas en las ciencias actuariales y los campos financieros, todos discutidos en el contexto de la cooperación entre los tres enfoques cuantitativos. Los temas incluyen: modelos actuariales; análisis de datos financieros de alta frecuencia; finanzas conductuales; carbono y finanzas verdes; métodos y modelos de riesgo crediticio; optimización dinámica en finanzas; econometrÃa financiera; previsión de fenómenos dinámicos actuariales y financieros; evaluación del desempeño de fondos; análisis de riesgos de cartera de seguros; modelos de tasas de interés; riesgo de longevidad; aprendizaje automático y computación blanda en finanzas; gestión en negocios de seguros; modelos y métodos para análisis de series temporales financieras, modelos para derivados financieros; técnicas multivariadas para análisis de mercados financieros; optimización en seguros; fijación de precios; probabilidad en ciencias actuariales, seguros y finanzas; finanzas del mundo real; gestión de riesgos; análisis de solvencia; riesgo soberano; selección y gestión de carteras estáticas y dinámicas; sistemas comerciales. Este libro es un recurso valioso para académicos, estudiantes de doctorado, practicantes, profesionales e investigadores, y también es de interés para otros lectores con conocimientos cuantitativos. Nota de contenido: 1 M. Caporin, G. Bonaccolto and S. Paterlini, Conditional Autoregressive Quantile-Located Value-at-Risk -- 2 M. Galeotti, G. Rabitti and E. Vannucci, The Rearrangement algorithm of Puccetti and Rüschendorf: proving the convergence -- 3 R. Cesari and V. Mosco, Optimal Management of Immunized Portfolios -- 4 E. Russo, M. Costabile and I. Massabo, Evaluating variable annuities with GMWB when exogenous factors influence the policy-holder withdrawals -- 5 A. Jokiel-Rokita and R. Magiera, Estimation and prediction for the modulated power law process -- 6 M. De La O González and F. Jareño, Extensions of Fama and French models -- 7 A. Hitaj, L. Mercuri and E. Rroji, Stochastic mortality modelling: some extensions based on Lévy CARMA models -- 8 L. Ballester, R. Fernández and A. González-Urteaga, An empirical analysis of the lead lag relationship between the CDS and stock market: Evidence in Europe and US -- 9 I.L. Amerise, Automatic detection and imputation of outliers in electricity price time series -- 10 F. Giordano, M. Niglio and M. Restaino, Variable selection in estimating bank default -- 11 F. Jareño, M.Ã. Medina, M. Tolentino and M. De La O González, European Insurers: Interest Rate Risk Management -- 12 M. Corazza and C. Nardelli, Comparing possibilistic portfolios to probabilistic ones -- 13 M. Maggi and P. Uberti, Google searches for portfolio management: a risk and return analysis -- 14 M.C. Schisani, M.P. Vitale and G. Ragozini, Financial Networks and Mechanisms of Business Capture in Southern Italy over the First Global Wave (1812-1913). A Network Approach -- 15 H. Gzyl, S. Mayoral and E. P. Gomes, Loss data analysis with maximum entropy -- 16 I.D.Fabián, P. Devolder, J. A. Herce and F. Del Olmo, A two-steps mixed pension system: An aggregate analysis -- 17 D. Atance and E. Navarro, A Single Factor Model for Constructing Dynamic Life Tables -- 18 L. Sanchis, J.M. Montero and G. Fernández-Avilés, Downside risk co-movement in commodity markets during distress periods. A Multidimensional scaling approach -- 19 G. Caivano and S. Bonini, Probability of Default Modeling: A Machine Learning Approach -- 20 S. Corsaro, V. De Simone, Z. Marino and F. Perla, Numerical solution of the regularized portfolio selection problem -- 21 N. Ahlgren and P. Catani, Practical Problems with Tests of Cointegration Rank with Strong Persistence and Heavy-Tailed Errors -- 22 M. De La O Gonzalez, F. Jareño and C. El Haddouti Ben Ali, The Islamic Financial Industry. Performance of Islamic vs. conventional sector portfolios -- 23 L. Invernizzi and V. Magatti, Could Machine Learning predict the Conversion in Motor Business? -- 24 S. Albosaily and S. Pergamenshchikov, The optimal investment and consumption for financial markets generated by the spread of risky assets for the power utility -- 25 M.E. De Giuli, M. Neffelli and M. Resta, An Integrated Approach to Explore the Complexity of Interest Rates Network Structure -- 26 I. Fuente, E. Navarro and G. Serna, Estimating regulatory capital requirements for reverse mortgages. An international comparison -- 27 L. Gómez-Valle and J. MartÃnez-RodrÃguez, Real-world versus neutral risk measures in the estimation of an interest rate model with stochastic volatility -- 28 G. Apicella, M. Dacorogna, E. Di Lorenzo and M. Sibillo, Improving Lee-Carter forecasting: methodology and some results -- 29 V. D'amato, A. Diaz, E. Di Lorenzo, E. Navarro and M. Sibillo, What if two different interest rates datasets allow for discribing the same financial product? -- 30 V. D'Amato, E. Di Lorenzo, M. Sibillo and R. Tizzano, Money purchase" pensions: contract proposals and risk analysis -- 31 K. Colaneri, S. Herzel and M. Nicolosi, The value of information for optimal portfolio management -- 32 N. Loperfido, Kurtosis Maximization for Outlier Detection in GARCH Models -- 33 A. Berti and N. Loperfido, An Extension of Multidimensional Scaling to Several Distance Matrices, and its Application to the Italian Banking Sector -- 34 C. Franceschini, Exploratory Projection Pursuit for Multivariate Financial Data -- 35 I. Albarrán Lozano, P. J. Alonso-González and A. Grané, Using deepest dependency paths to enhance life expectancy estimation -- 36 L. Rossini, M. Billio and R. Casarin, Bayesian nonparametric sparse Vector Autoregressive models -- 37 P. Angulo, V. Gallego, D. Gómez Ullate and P. Suárez, Bayesian Factorization Machines for Risk Management and Robust Decision Making -- 38 M. Coppola, M. Russolillo and R. Simone, Risk and Uncertainty for Flexible Retirement Schemes -- 39 G. Giordano, S. Haberman and M. Russolillo, Empirical Evidence from the Three-way LC model -- 40 A. Diaz and G. Garrido Sanchez, Socially Responsible Ratings and Financial Performance -- 41 M. Bernardi and M. Costola, Sparse causality networks through regularised regressions -- 42 J. Iñaki De La Peña and N. Peña-Miguel, A Basic Social Pension for Everyone? -- 43 M.C. Fernandez-Ramos, J. Iñaki De La Peña, A. T. Herrera, I. Iturricastillo and N.Peña-Miguel, HelpingLong Term Care coverage via differential on mortality? -- 44 N. Peña-Miguel, M.C. Fernández-Ramos and J. Iñaki De La Peña, A minimum pension for older people via expenses rate -- 45 S. Bonini and G. Caivano, Risk/Return analysis on credit exposure: do small banks really apply a pricing risk-based on their loans? -- 46 M. Pacella, F. Giordano and M.L. Parrella, Multiple testing for different structures of Spatial Dynamic Panel Data models -- 47 M. Billio, R. Casarin, M. Costola and L. Frattarolo, Disagreement in Signed Financial Networks -- 48 M. González-Fernández and C. González-Velasco, Do Google trends help to forecast sovereign risk in Europe? -- 49 F. Battaglia, D. Cucina and M.l Rizzo, Periodic autoregressive models with multiple structural changes by genetic algorithms -- 50 G. Albano, M. La Rocca and C. Perna, Small Sample Analysis in Diffusion Processes: a Simulation Study -- 51 M. Corazza and C. Pizzi, Some critical insights on the unbiased efficient frontier à la Bodnar& Bodnar -- 52 G. De Luca, G. Rivieccio and S. Corsaro, A copula-based quantile model -- 53 M. Billio, R. Casarin and M. Iacopini, Bayesian Tensor Binary Regression -- 54 F. Baione, D. Biancalana, P. De Angelis and I. Granito, Dynamic policyholder behaviour and surrender option evaluation for life insurance -- 55 A. Amendola, M. Braione, V. Candila and G. Storti, Combining multivariate volatility models -- 56 A. Bernardi and M. Bernardi, Two–Sided Skew and Shape Dynamic Conditional Score Models -- 57 F. Baione, D. Biancalana, P. De Angelis and I. Granito, An individual risk model for premium calculation based on quantile: a comparison between Generalized Linear Models and Quantile Regression -- 58 A. DÃaz and C. Esparcia, Time-varying risk aversion. An application to European optimal portfolios -- 59 E. Boj Del Val and T. Costa Cor, Logistic classification for new policyholders taking into account prediction error -- 60 A. Caner Turkmen and A. Taylan Cemgil, Modeling High-Frequency Price Datawith Bounded-Delay Hawkes Processes -- 61 F. Bartolucci, A. Cardinali and F. Pennoni, A generalized moving average convergence/divergence for testing semi-strong market efficiency -- 62 L. Crosato, L. Grossi and F. Nan, Forecasting the volatility of electricity prices by robust estimators: an application to the Italian market -- 63 D. Curcio, N. Borri, R. Cerrone and R. Cocozza, Life insurers' asset-liability dependency and low-interest-rate environment -- 64 M. Guillen and A. M. Pérez-MarÃn, The Contribution of Usage-based Data Analytics to benchmark Semi-autonomous Vehicle Insurance -- 65 P. Abad, A. DÃaz, A. Escribano and M.D. Robles, The effect of rating contingent guidelines and regulation around credit rating news -- 66 P. Peinado, Disability Pensions in Spain: A Factor to Compensate Life-Time Losses -- 67 D. De Gaetano and M. Braione, Transmission of prices and price volatility in Australian electricity spot markets: A MGARCH-based forecast comparison -- 68 D. Barro, Optimal portfolio selection integrating non-financial criteria -- 69 R. Cerqueti, M. Giacalone and D. Panarello, A Generalized Error Distribution-based method for Conditional Value-at-Risk evaluation -- 70 M. Bernardi and P. Stolfi, Robust time-varying undirected graphs -- 71 J.L. Vilar-Zanón and O. Peraita-Ezcurra, Pricing illiquid assets by entropy maximization through linear goal programming -- 72 R. Casarin, M.
Billio and M.
Iacopini, Bayesian Tensor Regression Models -- 73 M. Bernardi and P. Stolfi, Approximate EM algorithm for sparse estimation of multivariate location--scale mixture of normal. 74 I. Albarrán Lozano, P. J. Alonso-González and J. De Vicente Maldonado, Links between mortality rates and economic activity: a DFM approach -- 75 C. De Rosa, E. Luciano and L. Regis, Geographic diversification in annuity portfolios -- 76 U. Fiore, Z. Marino, F. Perla, S. Scognamiglio and P. Zanetti, Tuning a Deep Learning Network on Solvency II: Preliminary Results -- 77 G. Albano and V. Giorno,Inference in a Non-Homogeneous Vasicek-Type Model -- 78 D. Arzu and G M. Mantovani, Research Project MAF: A Bank Specific Integrated Rating -- 79 G. Piscopo, A comparative analysis of neuro fuzzy infer-ence systems for mortality prediction -- 80 F. Gannon, F. Legros and V. Touze, Automatic Balancing Mechanisms in Practice: What lessons for pension policy makers? -- 81 A.R. Bacinello and I. Zoccolan, Variable Annuities with State-Dependent Fees -- 82 A. Masson, The challenges of wealth and its intergenerational transmission in an aging society -- 83 L. Catania, F. Ravazzolo and S. Grassi, Quantitative Risk Management for Cryptocurrencies -- 84 J. Lledo Benito, J. M. PavÃa Miralles and F. G. Morillas Jurado, The Level Mortality in Insured Population -- 85 I. Chatterjee, M. Hao, A. Macdonald, P. Tapadar and R. Guy Thomas, When is utilitarian welfare higher under insurance risk pooling? -- 86 D. Cortes-Sanc.Tipo de medio : Computadora Summary : The interaction between mathematicians, statisticians and econometricians working in actuarial sciences and finance is producing numerous meaningful scientific results. This volume introduces new ideas, in the form of four-page papers, presented at the international conference Mathematical and Statistical Methods for Actuarial Sciences and Finance (MAF), held at Universidad Carlos III de Madrid (Spain), 4th-6th April 2018. The book covers a wide variety of subjects in actuarial science and financial fields, all discussed in the context of the cooperation between the three quantitative approaches. The topics include: actuarial models; analysis of high frequency financial data; behavioural finance; carbon and green finance; credit risk methods and models; dynamic optimization in finance; financial econometrics; forecasting of dynamical actuarial and financial phenomena; fund performance evaluation; insurance portfolio risk analysis; interest rate models; longevity risk; machine learning and soft-computing in finance; management in insurance business; models and methods for financial time series analysis, models for financial derivatives; multivariate techniques for financial markets analysis; optimization in insurance; pricing; probability in actuarial sciences, insurance and finance; real world finance; risk management; solvency analysis; sovereign risk; static and dynamic portfolio selection and management; trading systems. This book is a valuable resource for academics, PhD students, practitioners, professionals and researchers, and 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 : MAF 2018 [documento electrónico] / Corazza, Marco, ; Durbán, MarÃa, ; Grané, Aurea, ; Perna, Cira, ; Sibillo, Marilena, . - 1 ed. . - [s.l.] : Springer, 2018 . - XVI, 518 p.
ISBN : 978-3-319-89824-7
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: EstadÃsticas EconometrÃa Probabilidades Optimización matemática EstadÃstica en Negocios Gestión EconomÃa Finanzas Seguros TeorÃa de probabilidad Mejoramiento Clasificación: 300.727 Resumen: La interacción entre matemáticos, estadÃsticos y econometristas que trabajan en ciencias actuariales y finanzas está produciendo numerosos resultados cientÃficos significativos. Este volumen presenta nuevas ideas, en forma de artÃculos de cuatro páginas, presentados en la conferencia internacional Mathematical and Statistical Methods for Actuarial Sciences and Finance (MAF), celebrada en la Universidad Carlos III de Madrid (España), del 4 al 6 de abril de 2018. El libro cubre una amplia variedad de temas en las ciencias actuariales y los campos financieros, todos discutidos en el contexto de la cooperación entre los tres enfoques cuantitativos. Los temas incluyen: modelos actuariales; análisis de datos financieros de alta frecuencia; finanzas conductuales; carbono y finanzas verdes; métodos y modelos de riesgo crediticio; optimización dinámica en finanzas; econometrÃa financiera; previsión de fenómenos dinámicos actuariales y financieros; evaluación del desempeño de fondos; análisis de riesgos de cartera de seguros; modelos de tasas de interés; riesgo de longevidad; aprendizaje automático y computación blanda en finanzas; gestión en negocios de seguros; modelos y métodos para análisis de series temporales financieras, modelos para derivados financieros; técnicas multivariadas para análisis de mercados financieros; optimización en seguros; fijación de precios; probabilidad en ciencias actuariales, seguros y finanzas; finanzas del mundo real; gestión de riesgos; análisis de solvencia; riesgo soberano; selección y gestión de carteras estáticas y dinámicas; sistemas comerciales. Este libro es un recurso valioso para académicos, estudiantes de doctorado, practicantes, profesionales e investigadores, y también es de interés para otros lectores con conocimientos cuantitativos. Nota de contenido: 1 M. Caporin, G. Bonaccolto and S. Paterlini, Conditional Autoregressive Quantile-Located Value-at-Risk -- 2 M. Galeotti, G. Rabitti and E. Vannucci, The Rearrangement algorithm of Puccetti and Rüschendorf: proving the convergence -- 3 R. Cesari and V. Mosco, Optimal Management of Immunized Portfolios -- 4 E. Russo, M. Costabile and I. Massabo, Evaluating variable annuities with GMWB when exogenous factors influence the policy-holder withdrawals -- 5 A. Jokiel-Rokita and R. Magiera, Estimation and prediction for the modulated power law process -- 6 M. De La O González and F. Jareño, Extensions of Fama and French models -- 7 A. Hitaj, L. Mercuri and E. Rroji, Stochastic mortality modelling: some extensions based on Lévy CARMA models -- 8 L. Ballester, R. Fernández and A. González-Urteaga, An empirical analysis of the lead lag relationship between the CDS and stock market: Evidence in Europe and US -- 9 I.L. Amerise, Automatic detection and imputation of outliers in electricity price time series -- 10 F. Giordano, M. Niglio and M. Restaino, Variable selection in estimating bank default -- 11 F. Jareño, M.Ã. Medina, M. Tolentino and M. De La O González, European Insurers: Interest Rate Risk Management -- 12 M. Corazza and C. Nardelli, Comparing possibilistic portfolios to probabilistic ones -- 13 M. Maggi and P. Uberti, Google searches for portfolio management: a risk and return analysis -- 14 M.C. Schisani, M.P. Vitale and G. Ragozini, Financial Networks and Mechanisms of Business Capture in Southern Italy over the First Global Wave (1812-1913). A Network Approach -- 15 H. Gzyl, S. Mayoral and E. P. Gomes, Loss data analysis with maximum entropy -- 16 I.D.Fabián, P. Devolder, J. A. Herce and F. Del Olmo, A two-steps mixed pension system: An aggregate analysis -- 17 D. Atance and E. Navarro, A Single Factor Model for Constructing Dynamic Life Tables -- 18 L. Sanchis, J.M. Montero and G. Fernández-Avilés, Downside risk co-movement in commodity markets during distress periods. A Multidimensional scaling approach -- 19 G. Caivano and S. Bonini, Probability of Default Modeling: A Machine Learning Approach -- 20 S. Corsaro, V. De Simone, Z. Marino and F. Perla, Numerical solution of the regularized portfolio selection problem -- 21 N. Ahlgren and P. Catani, Practical Problems with Tests of Cointegration Rank with Strong Persistence and Heavy-Tailed Errors -- 22 M. De La O Gonzalez, F. Jareño and C. El Haddouti Ben Ali, The Islamic Financial Industry. Performance of Islamic vs. conventional sector portfolios -- 23 L. Invernizzi and V. Magatti, Could Machine Learning predict the Conversion in Motor Business? -- 24 S. Albosaily and S. Pergamenshchikov, The optimal investment and consumption for financial markets generated by the spread of risky assets for the power utility -- 25 M.E. De Giuli, M. Neffelli and M. Resta, An Integrated Approach to Explore the Complexity of Interest Rates Network Structure -- 26 I. Fuente, E. Navarro and G. Serna, Estimating regulatory capital requirements for reverse mortgages. An international comparison -- 27 L. Gómez-Valle and J. MartÃnez-RodrÃguez, Real-world versus neutral risk measures in the estimation of an interest rate model with stochastic volatility -- 28 G. Apicella, M. Dacorogna, E. Di Lorenzo and M. Sibillo, Improving Lee-Carter forecasting: methodology and some results -- 29 V. D'amato, A. Diaz, E. Di Lorenzo, E. Navarro and M. Sibillo, What if two different interest rates datasets allow for discribing the same financial product? -- 30 V. D'Amato, E. Di Lorenzo, M. Sibillo and R. Tizzano, Money purchase" pensions: contract proposals and risk analysis -- 31 K. Colaneri, S. Herzel and M. Nicolosi, The value of information for optimal portfolio management -- 32 N. Loperfido, Kurtosis Maximization for Outlier Detection in GARCH Models -- 33 A. Berti and N. Loperfido, An Extension of Multidimensional Scaling to Several Distance Matrices, and its Application to the Italian Banking Sector -- 34 C. Franceschini, Exploratory Projection Pursuit for Multivariate Financial Data -- 35 I. Albarrán Lozano, P. J. Alonso-González and A. Grané, Using deepest dependency paths to enhance life expectancy estimation -- 36 L. Rossini, M. Billio and R. Casarin, Bayesian nonparametric sparse Vector Autoregressive models -- 37 P. Angulo, V. Gallego, D. Gómez Ullate and P. Suárez, Bayesian Factorization Machines for Risk Management and Robust Decision Making -- 38 M. Coppola, M. Russolillo and R. Simone, Risk and Uncertainty for Flexible Retirement Schemes -- 39 G. Giordano, S. Haberman and M. Russolillo, Empirical Evidence from the Three-way LC model -- 40 A. Diaz and G. Garrido Sanchez, Socially Responsible Ratings and Financial Performance -- 41 M. Bernardi and M. Costola, Sparse causality networks through regularised regressions -- 42 J. Iñaki De La Peña and N. Peña-Miguel, A Basic Social Pension for Everyone? -- 43 M.C. Fernandez-Ramos, J. Iñaki De La Peña, A. T. Herrera, I. Iturricastillo and N.Peña-Miguel, HelpingLong Term Care coverage via differential on mortality? -- 44 N. Peña-Miguel, M.C. Fernández-Ramos and J. Iñaki De La Peña, A minimum pension for older people via expenses rate -- 45 S. Bonini and G. Caivano, Risk/Return analysis on credit exposure: do small banks really apply a pricing risk-based on their loans? -- 46 M. Pacella, F. Giordano and M.L. Parrella, Multiple testing for different structures of Spatial Dynamic Panel Data models -- 47 M. Billio, R. Casarin, M. Costola and L. Frattarolo, Disagreement in Signed Financial Networks -- 48 M. González-Fernández and C. González-Velasco, Do Google trends help to forecast sovereign risk in Europe? -- 49 F. Battaglia, D. Cucina and M.l Rizzo, Periodic autoregressive models with multiple structural changes by genetic algorithms -- 50 G. Albano, M. La Rocca and C. Perna, Small Sample Analysis in Diffusion Processes: a Simulation Study -- 51 M. Corazza and C. Pizzi, Some critical insights on the unbiased efficient frontier à la Bodnar& Bodnar -- 52 G. De Luca, G. Rivieccio and S. Corsaro, A copula-based quantile model -- 53 M. Billio, R. Casarin and M. Iacopini, Bayesian Tensor Binary Regression -- 54 F. Baione, D. Biancalana, P. De Angelis and I. Granito, Dynamic policyholder behaviour and surrender option evaluation for life insurance -- 55 A. Amendola, M. Braione, V. Candila and G. Storti, Combining multivariate volatility models -- 56 A. Bernardi and M. Bernardi, Two–Sided Skew and Shape Dynamic Conditional Score Models -- 57 F. Baione, D. Biancalana, P. De Angelis and I. Granito, An individual risk model for premium calculation based on quantile: a comparison between Generalized Linear Models and Quantile Regression -- 58 A. DÃaz and C. Esparcia, Time-varying risk aversion. An application to European optimal portfolios -- 59 E. Boj Del Val and T. Costa Cor, Logistic classification for new policyholders taking into account prediction error -- 60 A. Caner Turkmen and A. Taylan Cemgil, Modeling High-Frequency Price Datawith Bounded-Delay Hawkes Processes -- 61 F. Bartolucci, A. Cardinali and F. Pennoni, A generalized moving average convergence/divergence for testing semi-strong market efficiency -- 62 L. Crosato, L. Grossi and F. Nan, Forecasting the volatility of electricity prices by robust estimators: an application to the Italian market -- 63 D. Curcio, N. Borri, R. Cerrone and R. Cocozza, Life insurers' asset-liability dependency and low-interest-rate environment -- 64 M. Guillen and A. M. Pérez-MarÃn, The Contribution of Usage-based Data Analytics to benchmark Semi-autonomous Vehicle Insurance -- 65 P. Abad, A. DÃaz, A. Escribano and M.D. Robles, The effect of rating contingent guidelines and regulation around credit rating news -- 66 P. Peinado, Disability Pensions in Spain: A Factor to Compensate Life-Time Losses -- 67 D. De Gaetano and M. Braione, Transmission of prices and price volatility in Australian electricity spot markets: A MGARCH-based forecast comparison -- 68 D. Barro, Optimal portfolio selection integrating non-financial criteria -- 69 R. Cerqueti, M. Giacalone and D. Panarello, A Generalized Error Distribution-based method for Conditional Value-at-Risk evaluation -- 70 M. Bernardi and P. Stolfi, Robust time-varying undirected graphs -- 71 J.L. Vilar-Zanón and O. Peraita-Ezcurra, Pricing illiquid assets by entropy maximization through linear goal programming -- 72 R. Casarin, M.
Billio and M.
Iacopini, Bayesian Tensor Regression Models -- 73 M. Bernardi and P. Stolfi, Approximate EM algorithm for sparse estimation of multivariate location--scale mixture of normal. 74 I. Albarrán Lozano, P. J. Alonso-González and J. De Vicente Maldonado, Links between mortality rates and economic activity: a DFM approach -- 75 C. De Rosa, E. Luciano and L. Regis, Geographic diversification in annuity portfolios -- 76 U. Fiore, Z. Marino, F. Perla, S. Scognamiglio and P. Zanetti, Tuning a Deep Learning Network on Solvency II: Preliminary Results -- 77 G. Albano and V. Giorno,Inference in a Non-Homogeneous Vasicek-Type Model -- 78 D. Arzu and G M. Mantovani, Research Project MAF: A Bank Specific Integrated Rating -- 79 G. Piscopo, A comparative analysis of neuro fuzzy infer-ence systems for mortality prediction -- 80 F. Gannon, F. Legros and V. Touze, Automatic Balancing Mechanisms in Practice: What lessons for pension policy makers? -- 81 A.R. Bacinello and I. Zoccolan, Variable Annuities with State-Dependent Fees -- 82 A. Masson, The challenges of wealth and its intergenerational transmission in an aging society -- 83 L. Catania, F. Ravazzolo and S. Grassi, Quantitative Risk Management for Cryptocurrencies -- 84 J. Lledo Benito, J. M. PavÃa Miralles and F. G. Morillas Jurado, The Level Mortality in Insured Population -- 85 I. Chatterjee, M. Hao, A. Macdonald, P. Tapadar and R. Guy Thomas, When is utilitarian welfare higher under insurance risk pooling? -- 86 D. Cortes-Sanc.Tipo de medio : Computadora Summary : The interaction between mathematicians, statisticians and econometricians working in actuarial sciences and finance is producing numerous meaningful scientific results. This volume introduces new ideas, in the form of four-page papers, presented at the international conference Mathematical and Statistical Methods for Actuarial Sciences and Finance (MAF), held at Universidad Carlos III de Madrid (Spain), 4th-6th April 2018. The book covers a wide variety of subjects in actuarial science and financial fields, all discussed in the context of the cooperation between the three quantitative approaches. The topics include: actuarial models; analysis of high frequency financial data; behavioural finance; carbon and green finance; credit risk methods and models; dynamic optimization in finance; financial econometrics; forecasting of dynamical actuarial and financial phenomena; fund performance evaluation; insurance portfolio risk analysis; interest rate models; longevity risk; machine learning and soft-computing in finance; management in insurance business; models and methods for financial time series analysis, models for financial derivatives; multivariate techniques for financial markets analysis; optimization in insurance; pricing; probability in actuarial sciences, insurance and finance; real world finance; risk management; solvency analysis; sovereign risk; static and dynamic portfolio selection and management; trading systems. This book is a valuable resource for academics, PhD students, practitioners, professionals and researchers, and 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 [...]
