Dynamic Models for Volatility and Heavy Tails (Paperback)

Andrew C. Harvey

ISBN 10: 1107630029 / ISBN 13: 9781107630024

Edité par Cambridge University Press, Cambridge, 2013

A propos de cet article

Paperback. The volatility of financial returns changes over time and, for the last thirty years, Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models have provided the principal means of analyzing, modeling and monitoring such changes. Taking into account that financial returns typically exhibit heavy tails - that is, extreme values can occur from time to time - Andrew Harvey's new book shows how a small but radical change in the way GARCH models are formulated leads to a resolution of many of the theoretical problems inherent in the statistical theory. The approach can also be applied to other aspects of volatility. The more general class of Dynamic Conditional Score models extends to robust modeling of outliers in the levels of time series and to the treatment of time-varying relationships. The statistical theory draws on basic principles of maximum likelihood estimation and, by doing so, leads to an elegant and unified treatment of nonlinear time-series modeling. This book presents a statistical theory for a class of nonlinear time-series models. It has particular relevance for the modeling of volatility in financial time series but the overall approach will be of interest to econometricians and statisticians in a variety of disciplines. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

N° de réf. du vendeur 9781107630024

Détails bibliographiques

• Titre: Dynamic Models for Volatility and Heavy Tails (Paperback)
• Éditeur: Cambridge University Press, Cambridge
• Date de parution: 2013
• Langue: anglais
• ISBN à 10 chiffres: 1107630029
• ISBN à 13 chiffres: 9781107630024
• Reliure: Paperback
• État de l'article: new

A propos de ce titre

Synopsis

The volatility of financial returns changes over time and, for the last thirty years, Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models have provided the principal means of analyzing, modeling and monitoring such changes. Taking into account that financial returns typically exhibit heavy tails - that is, extreme values can occur from time to time - Andrew Harvey's new book shows how a small but radical change in the way GARCH models are formulated leads to a resolution of many of the theoretical problems inherent in the statistical theory. The approach can also be applied to other aspects of volatility. The more general class of Dynamic Conditional Score models extends to robust modeling of outliers in the levels of time series and to the treatment of time-varying relationships. The statistical theory draws on basic principles of maximum likelihood estimation and, by doing so, leads to an elegant and unified treatment of nonlinear time-series modeling.

À propos de l?auteur

Andrew Harvey is Professor of Econometrics at the University of Cambridge and a Fellow of Corpus Christi College. He is a Fellow of the Econometric Society and of the British Academy. He has published more than one hundred articles in journals and edited volumes and is the author of three books, The Econometric Analysis of Time Series, Time Series Models, and Forecasting and Structural Time Series Models and the Kalman Filter (Cambridge University Press, 1989). He is one of the developers of the STAMP computer package.