A DARE for VaR (notice n° 166675)

détails MARC
000 -LEADER
fixed length control field 01467cam a2200193 4500500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250112034109.0
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title fre
042 ## - AUTHENTICATION CODE
Authentication code dc
100 10 - MAIN ENTRY--PERSONAL NAME
Personal name Hamidi, Benjamin
Relator term author
245 00 - TITLE STATEMENT
Title A DARE for VaR
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Date of publication, distribution, etc. 2015.<br/>
500 ## - GENERAL NOTE
General note 80
520 ## - SUMMARY, ETC.
Summary, etc. This paper introduces a new class of models for the Value-at-Risk (VaR) and Expected Shortfall (ES), called the Dynamic AutoRegressive Expectiles (DARE) models. Our approach is based on a weighted average of expectile-based VaR and ES models, i.e. the Conditional Autoregressive Expectile (CARE) models introduced by Taylor (2008a) and Kuan et al. (2009). First, we briefly present the main non-parametric, parametric and semi-parametric estimation methods for VaR and ES. Secondly, we detail the DARE approach and show how the expectiles can be used to estimate quantile risk measures. Thirdly, we use various backtesting tests to compare the DARE approach to other traditional methods for computing VaR forecasts on the French stock market. Finally, we evaluate the impact of several conditional weighting functions and determine the optimal weights in order to dynamically select the more relevant global quantile model.
700 10 - ADDED ENTRY--PERSONAL NAME
Personal name Hurlin, Christophe
Relator term author
700 10 - ADDED ENTRY--PERSONAL NAME
Personal name Kouontchou, Patrick
Relator term author
700 10 - ADDED ENTRY--PERSONAL NAME
Personal name Maillet, Bertrand
Relator term author
786 0# - DATA SOURCE ENTRY
Note Finance | Vol.36 | 1 | 2015-05-13 | p. 7-38 | 0752-6180
856 41 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://shs.cairn.info/journal-finance-2015-1-page-7?lang=en">https://shs.cairn.info/journal-finance-2015-1-page-7?lang=en</a>

Pas d'exemplaire disponible.

PLUDOC

PLUDOC est la plateforme unique et centralisée de gestion des bibliothèques physiques et numériques de Guinée administré par le CEDUST. Elle est la plus grande base de données de ressources documentaires pour les Étudiants, Enseignants chercheurs et Chercheurs de Guinée.

Adresse

627 919 101/664 919 101

25 boulevard du commerce
Kaloum, Conakry, Guinée

Réseaux sociaux

Powered by Netsen Group @ 2025