Name: Handbook of Heavy-Tailed Distributions in Asset Management and Risk Management (en Inglés)
Price: 824.71 PEN
Availability: InStock
Author: Bianchi, Michele Leonardo ; Stoyanov, Stoyan V. ; Tassinari, Gian Luca
ISBN: 9789813274914

portada Handbook of Heavy-Tailed Distributions in Asset Management and Risk Management (en Inglés)

Formato

Libro Físico

Editorial

World Scientific Publishing Company

Autor

Bianchi, Michele Leonardo ; Stoyanov, Stoyan V. ; Tassinari, Gian Luca

Idioma

Inglés

N° páginas

600

Encuadernación

Tapa Dura

Dimensiones

22.9 x 15.2 x 3.3 cm

Peso

0.96 kg.

ISBN13

9789813274914

Categorías

Finanzas
Probabilidad y estadística

Handbook of Heavy-Tailed Distributions in Asset Management and Risk Management (en Inglés)

Bianchi, Michele Leonardo ; Stoyanov, Stoyan V. ; Tassinari, Gian Luca (Autor) · World Scientific Publishing Company · Tapa Dura

Libro Físico

S/ 824,71

S/ 1.649,42

Ahorras: S/ 824,71

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Reseña del libro "Handbook of Heavy-Tailed Distributions in Asset Management and Risk Management (en Inglés)"

The study of heavy-tailed distributions allows researchers to represent phenomena that occasionally exhibit very large deviations from the mean. The dynamics underlying these phenomena is an interesting theoretical subject, but the study of their statistical properties is in itself a very useful endeavor from the point of view of managing assets and controlling risk. In this book, the authors are primarily concerned with the statistical properties of heavy-tailed distributions and with the processes that exhibit jumps. A detailed overview with a Matlab implementation of heavy-tailed models applied in asset management and risk managements is presented. The book is not intended as a theoretical treatise on probability or statistics, but as a tool to understand the main concepts regarding heavy-tailed random variables and processes as applied to real-world applications in finance. Accordingly, the authors review approaches and methodologies whose realization will be useful for developing new methods for forecasting of financial variables where extreme events are not treated as anomalies, but as intrinsic parts of the economic process.