Notice bibliographique
- Notice
Type(s) de contenu et mode(s) de consultation : Texte noté : électronique
Titre(s) : Handbook of financial time series [Texte électronique] / [edited by] Torben G. Andersen [and others]
Publication : Berlin : Springer, cop. 2009
Description matérielle : 1 ressource dématérialisée
Note(s) : Includes bibliographical references and index
Offers an overview of the field financial time series and covers various relevant
topics from a statistical and an econometrical point of view. This handbook presents
among others various aspects of the important GARCH and Stochastic Volatility classes,
like for example distribution properties, estimation, forecasting and extreme value
theory
Autre(s) auteur(s) : Andersen, Torben Gustav. Fonction indéterminée
Sujet(s) : Finances -- Statistiques
Séries chronologiques
Mathématiques financières
Statistique
Modèles mathématiques
Identifiants, prix et caractéristiques : ISBN 9783540712978
Identifiant de la notice : ark:/12148/cb44694945b
Notice n° :
FRBNF44694945
(notice reprise d'un réservoir extérieur)
Table des matières : Recent developments in GARCH modeling. ; An introduction to univariate GARCH models
/Timo Teräsvirta ; Stationarity, mixing, distributional properties and moments of
GARCH (p, q) processes /Alexander M. Lindner ; ARCH [infinity symbol] models and long
memory properties /Liudas Giraitis, Remigijus Leipus and Donatas Surgailis ; A tour
in the asymptotic theory of GARCH estimation /Christian Franq and Jean-Michel Zakoïan
; Practical issues in the analysis of univariate GARCH models /Eric Zivot ; Semiparametric
and nonparametric ARCH modeling /Oliver B. Linton ; Varying coefficient GARCH models
/Pavel Čížek and Vladimir Spokoiny ; Extreme value theory for GARCH processes /Richard
A. Davis and Thomas Mikosch ; Multivariate GARCH models /Annastiina Silvennoinen and
Timo Teräsvirta ; Recent developments in stochastic volatility modeling. ; Stochastic
volatility : ; origins and overview /Neil Shephard and Torben G. Andersen ; Probabilistic
properties of stochastic volatility models /Rich