Article (Scientific journals)
A robust statistical approach to select adequate error distributions for financial returns
Hambuckers, julien; Heuchenne, Cédric
2017In Journal of Applied Statistics, 44 (1), p. 137-161
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Keywords :
error distribution; nonparametric volatility; model misspecification; goodness-of-fit; skewed-t distribution; NIG distribution; hyperbolic distribution
Abstract :
[en] In this article, we propose a robust statistical approach to select an appropriate error distribution, in a classical multiplicative heteroscedastic model. In a first step, unlike to the traditional approach, we don't use any GARCH-type estimation of the conditional variance. Instead, we propose to use a recently developed nonparametric procedure (Mercurio and Spokoiny, 2004): the Local Adaptive Volatility Estimation (LAVE). The motivation for using this method is to avoid a possible model misspecification for the conditional variance. In a second step, we suggest a set of estimation and model selection procedures (Berk-Jones tests, kernel density-based selection, censored likelihood score, coverage probability) based on the so-obtained residuals. These methods enable to assess the global fit of a set of distributions as well as to focus on their behavior in the tails, giving us the capacity to map the strengths and weaknesses of the candidate distributions. A bootstrap procedure is provided to compute the rejection regions in this semiparametric context. Finally, we illustrate our methodology throughout a small simulation study and an application on three time series of daily returns (UBS stock returns, BOVESPA returns and EUR/USD exchange rates)
Disciplines :
Quantitative methods in economics & management
Author, co-author :
Hambuckers, julien ;  Université de Liège > HEC-Ecole de gestion : UER > Statistique appliquée à la gestion et à l'économie
Heuchenne, Cédric ;  Université de Liège > HEC-Ecole de gestion : UER > Statistique appliquée à la gestion et à l'économie
Language :
English
Title :
A robust statistical approach to select adequate error distributions for financial returns
Publication date :
2017
Journal title :
Journal of Applied Statistics
ISSN :
0266-4763
eISSN :
1360-0532
Publisher :
Routledge
Volume :
44
Issue :
1
Pages :
137-161
Peer reviewed :
Peer Reviewed verified by ORBi
Funders :
F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]
Available on ORBi :
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