Autore	Recht Peter
Pubbl/distr/stampa	Berlin/München/Boston : , : Walter de Gruyter GmbH, , 2021
Descrizione fisica	1 online resource (332 pages)
Altri autori (Persone)	SchadePhilipp
Collana	De Gruyter Studium
Soggetto topico	BUSINESS & ECONOMICS / Insurance / Life
Soggetto non controllato	Insurance mathematics calculating policies calculating rates commutation values insurance and linear algebra life insurance calculations life insurance techniques orthogonality principle of equivalence in insurance mathematics
ISBN	9783110740905 3110740907
Classificazione	QP 890
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	ger
Nota di contenuto	Intro -- Inhalt -- Einleitung -- Teil I: Technische Kalkulation von Lebensversicherungsverträgen -- Einführung -- 1 Profile als technische Grundlage der Kalkulation -- 2 Bewertung von Beitrags- und Leistungsprofilen -- das Äquivalenzprinzip -- 3 Erstkalkulation eines Versicherungsvertrages -- 4 Kalkulationen während der Laufzeit eines Versicherungsvertrages -- 5 Klassische Kommutationswerte und Barwertfaktoren -- Teil II: Ein strukturelles Fundament der Kalkulation -- Einführung -- 6 Algebraische Grundlagen -- 7 φ-Orthogonalität -- 8 Algebraische Verallgemeinerung des Äquivalenzprinzips -- 9 Unterjährige Bewertung von Profilen -- m-Expansionen von φ -- 10 Verallgemeinerte Kommutationswerte und Barwertfaktoren -- Epilog -- Literatur -- Stichwortverzeichnis.
Record Nr.	UNINA-9910554227803321

Autore	Huynh Huu Tue
Pubbl/distr/stampa	Chichester, England ; ; Hoboken, N.J., : John Wiley & Sons, c2008
Descrizione fisica	1 online resource (356 p.)
Disciplina	332.01/51923 332.0151923
Altri autori (Persone)	LaiVan Son SoumaréIssouf
Collana	Wiley finance
Soggetto topico	Finance - Mathematical models Stochastic models
ISBN	1-283-37237-1 9786613372376 1-118-46737-X 0-470-72213-4
Classificazione	DAT 306f MAT 605f QP 890 ST 601 M35 WIR 160f
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Stochastic Simulation and Applications in Finance with MATLAB® Programs; Contents; Preface; 1 Introduction to Probability; 1.1 Intuitive Explanation; 1.1.1 Frequencies; 1.1.2 Number of Favorable Cases Over The Total Number of Cases; 1.2 Axiomatic Definition; 1.2.1 Random Experiment; 1.2.2 Event; 1.2.3 Algebra of Events; 1.2.4 Probability Axioms; 1.2.5 Conditional Probabilities; 1.2.6 Independent Events; 2 Introduction to Random Variables; 2.1 Random Variables; 2.1.1 Cumulative Distribution Function; 2.1.2 Probability Density Function 2.1.3 Mean, Variance and Higher Moments of a Random Variable2.1.4 Characteristic Function of a Random Variable; 2.2 Random vectors; 2.2.1 Cumulative Distribution Function of a Random Vector; 2.2.2 Probability Density Function of a Random Vector; 2.2.3 Marginal Distribution of a Random Vector; 2.2.4 Conditional Distribution of a Random Vector; 2.2.5 Mean, Variance and Higher Moments of a Random Vector; 2.2.6 Characteristic Function of a Random Vector; 2.3 Transformation of Random Variables; 2.4 Transformation of Random Vectors 2.5 Approximation of the Standard Normal Cumulative Distribution Function3 Random Sequences; 3.1 Sum of Independent Random Variables; 3.2 Law of Large Numbers; 3.3 Central Limit Theorem; 3.4 Convergence of Sequences of Random Variables; 3.4.1 Sure Convergence; 3.4.2 Almost Sure Convergence; 3.4.3 Convergence in Probability; 3.4.4 Convergence in Quadratic Mean; 4 Introduction to Computer Simulation of Random Variables; 4.1 Uniform Random Variable Generator; 4.2 Generating Discrete Random Variables; 4.2.1 Finite Discrete Random Variables 4.2.2 Infinite Discrete Random Variables: Poisson Distribution4.3 Simulation of Continuous Random Variables; 4.3.1 Cauchy Distribution; 4.3.2 Exponential Law; 4.3.3 Rayleigh Random Variable; 4.3.4 Gaussian Distribution; 4.4 Simulation of Random Vectors; 4.4.1 Case of a Two-Dimensional Random Vector; 4.4.2 Cholesky Decomposition of the Variance-Covariance Matrix; 4.4.3 Eigenvalue Decomposition of the Variance-Covariance Matrix; 4.4.4 Simulation of a Gaussian Random Vector with MATLAB; 4.5 Acceptance-Rejection Method; 4.6 Markov Chain Monte Carlo Method (MCMC) 4.6.1 Definition of a Markov Process4.6.2 Description of the MCMC Technique; 5 Foundations of Monte Carlo Simulations; 5.1 Basic Idea; 5.2 Introduction to the Concept of Precision; 5.3 Quality of Monte Carlo Simulations Results; 5.4 Improvement of the Quality of Monte Carlo Simulations or Variance Reduction Techniques; 5.4.1 Quadratic Resampling; 5.4.2 Reduction of the Number of Simulations Using Antithetic Variables; 5.4.3 Reduction of the Number of Simulations Using Control Variates; 5.4.4 Importance Sampling; 5.5 Application Cases of Random Variables Simulations 5.5.1 Application Case: Generation of Random Variables as a Function of the Number of Simulations
Record Nr.	UNINA-9910139741303321

Autore	Huynh Huu Tue
Edizione	[1st ed.]
Pubbl/distr/stampa	Chichester, England ; ; Hoboken, N.J., : John Wiley & Sons, c2008
Descrizione fisica	1 online resource (356 p.)
Disciplina	332.01/51923 332.0151923
Altri autori (Persone)	LaiVan Son SoumaréIssouf
Collana	Wiley finance
Soggetto topico	Finance - Mathematical models Stochastic models
ISBN	9786613372376 9781283372374 1283372371 9781118467374 111846737X 9780470722138 0470722134
Classificazione	DAT 306f MAT 605f QP 890 ST 601 M35 WIR 160f
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Stochastic Simulation and Applications in Finance with MATLAB® Programs; Contents; Preface; 1 Introduction to Probability; 1.1 Intuitive Explanation; 1.1.1 Frequencies; 1.1.2 Number of Favorable Cases Over The Total Number of Cases; 1.2 Axiomatic Definition; 1.2.1 Random Experiment; 1.2.2 Event; 1.2.3 Algebra of Events; 1.2.4 Probability Axioms; 1.2.5 Conditional Probabilities; 1.2.6 Independent Events; 2 Introduction to Random Variables; 2.1 Random Variables; 2.1.1 Cumulative Distribution Function; 2.1.2 Probability Density Function 2.1.3 Mean, Variance and Higher Moments of a Random Variable2.1.4 Characteristic Function of a Random Variable; 2.2 Random vectors; 2.2.1 Cumulative Distribution Function of a Random Vector; 2.2.2 Probability Density Function of a Random Vector; 2.2.3 Marginal Distribution of a Random Vector; 2.2.4 Conditional Distribution of a Random Vector; 2.2.5 Mean, Variance and Higher Moments of a Random Vector; 2.2.6 Characteristic Function of a Random Vector; 2.3 Transformation of Random Variables; 2.4 Transformation of Random Vectors 2.5 Approximation of the Standard Normal Cumulative Distribution Function3 Random Sequences; 3.1 Sum of Independent Random Variables; 3.2 Law of Large Numbers; 3.3 Central Limit Theorem; 3.4 Convergence of Sequences of Random Variables; 3.4.1 Sure Convergence; 3.4.2 Almost Sure Convergence; 3.4.3 Convergence in Probability; 3.4.4 Convergence in Quadratic Mean; 4 Introduction to Computer Simulation of Random Variables; 4.1 Uniform Random Variable Generator; 4.2 Generating Discrete Random Variables; 4.2.1 Finite Discrete Random Variables 4.2.2 Infinite Discrete Random Variables: Poisson Distribution4.3 Simulation of Continuous Random Variables; 4.3.1 Cauchy Distribution; 4.3.2 Exponential Law; 4.3.3 Rayleigh Random Variable; 4.3.4 Gaussian Distribution; 4.4 Simulation of Random Vectors; 4.4.1 Case of a Two-Dimensional Random Vector; 4.4.2 Cholesky Decomposition of the Variance-Covariance Matrix; 4.4.3 Eigenvalue Decomposition of the Variance-Covariance Matrix; 4.4.4 Simulation of a Gaussian Random Vector with MATLAB; 4.5 Acceptance-Rejection Method; 4.6 Markov Chain Monte Carlo Method (MCMC) 4.6.1 Definition of a Markov Process4.6.2 Description of the MCMC Technique; 5 Foundations of Monte Carlo Simulations; 5.1 Basic Idea; 5.2 Introduction to the Concept of Precision; 5.3 Quality of Monte Carlo Simulations Results; 5.4 Improvement of the Quality of Monte Carlo Simulations or Variance Reduction Techniques; 5.4.1 Quadratic Resampling; 5.4.2 Reduction of the Number of Simulations Using Antithetic Variables; 5.4.3 Reduction of the Number of Simulations Using Control Variates; 5.4.4 Importance Sampling; 5.5 Application Cases of Random Variables Simulations 5.5.1 Application Case: Generation of Random Variables as a Function of the Number of Simulations
Record Nr.	UNINA-9910814678603321