Statistical Inference in Financial and Insurance with R examines a range of statistical inference methods in the context of finance and insurance applications-asymptotical efficiency to give the proper notion of estimation risk, computations with the provided software R, and non-classical statistical experiments. Finance and insurance companies are facing a wide range of mathematical problems, and statistical experiments with independent and identically distributed sample are relatively common. Such classical experiments are now well understood including those with some exponential family of probability measures. Two examples will be treated within this book: Generalized Linear Models (GLM) extending to non-Gaussian samples (the standard regression method), and homogeneous Markov chains, which appear naturally observing the price of an asset considered as the solution of some stochastic differential equation. Examines a range of statistical inference methods in the context of finance and insurance applicationsPresents the LAN (local asymptotic normality) property of likelihoodsCombines the proofs of LAN property for different statistical experiments that appears in financial and insurance mathematicsProvides the proper description of such statistical experiments and invites you to seek optimal estimators (performed in R) for such statistical experiments INDICE: I Introduction 1 Introduction of the valuation of derivatives in finance 2 General homogeneous diffusion processes II Statistical experiments 3 Statistical experiments 4 Classical experiments III Statistical inference for finance 5 Parametric classical setting
- ISBN: 978-1-78548-083-6
- Editorial: ISTE Press - Elsevier
- Encuadernacion: Cartoné
- Páginas: 150
- Fecha Publicación: 01/06/2016
- Nº Volúmenes: 1
- Idioma: Inglés