Template-Type: ReDIF-Paper 1.0
Author-Name: Caporin, M.
Author-Name-Last: Caporin
Author-Name-First: Massimiliano
Author-Person: pca441
Author-Name: McAleer, M.J.
Author-Name-Last: McAleer
Author-Name-First: Michael
Author-Person: pmc90
Title: Model Selection and Testing of Conditional and Stochastic Volatility Models
Abstract: This paper focuses on the selection and comparison of alternative non-nested volatility models. We review the traditional in-sample methods commonly applied in the volatility framework, namely diagnostic checking procedures, information criteria, and conditions for the existence of moments and asymptotic theory, as well as the out-of-sample model selection approaches, such as mean squared error and Model Confidence Set approaches. The paper develops some innovative loss functions which are based on Value-at-Risk forecasts. Finally, we present an empirical application based on simple univariate volatility models, namely GARCH, GJR, EGARCH, and Stochastic Volatility that are widely used to capture asymmetry and leverage.
Creation-Date: 2010-10-12
File-URL: https://repub.eur.nl/pub/20940/EI2010-57.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: EI 2010-57
Keywords: Value-at-Risk forecasts, asymmetry, leverage, model confidence set, non-nested models, volatility model comparison, volatility model selection
Handle: RePEc:ems:eureir:20940