Mathematical and Statistical Methods for Actuarial Sciences and Finance MAF 2018 (Cham : Springer International Publishing, 2018)
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.
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. Pavía 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-.
Mathematics and Statistics
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Keywords: Statistics .; Econometrics; Probabilities; Mathematical optimization