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Type: Artigo
Title: Robust bootstrap forecast densities for GARCH returns and volatilities
Author: Trucíos, Carlos , Hotta, Luiz K.
Ruiz, Esther
Abstract: Bootstrap procedures are useful to obtain forecast densities for both returns and volatilities in the context of generalized autoregressive conditional heteroscedasticity models. In this paper, we analyse the effect of additive outliers on the finite sample properties of these bootstrap densities and show that, when obtained using maximum likelihood estimates of the parameters and standard filters for the volatilities, they are badly affected with dramatic consequences on the estimation of Value-at-Risk. We propose constructing bootstrap densities for returns and volatilities using a robust parameter estimator based on variance targeting implemented together with an adequate modification of the volatility filter. We show that the performance of the proposed procedure is adequate when compared with available robust alternatives. The results are illustrated with both simulated and real data.
Subject: Valores estranhos (Estatística)
Bootstrap (Estatística)
Verossimilhança (Estatística)
Volatilidade (Finanças)
Estatística robusta
Outliers (Statistics)
Bootstrap (Statistics)
Likelihood (Statistics)
Financial volatility
Robust statistics
Country: Reino Unido
Editor: Taylor & Francis
Rights: fechado
Identifier DOI: 10.1080/00949655.2017.1359601
Date Issue: 2017
Appears in Collections:IMECC - Artigos e Outros Documentos

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