A comparison of the forecasting performances of multivariate volatility models

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Soggetto
Volatility; Multivariate GARCH; Loss functionAbstract
The consistent ranking of multivariate volatility models by means of statistical
loss function is a challenging research field, because it concerns the quality of the proxy
chosen to replace the unobserved volatility, the set of competing models to be ranked and
the kind of loss function. The existent works only consider the ranking of multivariate
GARCH (MGARCH) models, based on daily frequency of the returns. Less is known
about the behaviour of the models that directly use the realized covariance (RCOV), the
proxy that generally provides a consistent estimate of the unobserved volatility. The aim
of this paper is to evaluate which model has the best forecast volatility accuracy, from a
statistical and economic point of view. For the first point, we empirically rank a set of
MGARCH and RCOV models by means of four consistent statistical loss functions. For
the second point, we evaluate if these rankings are coherent with those resulting from the
use of an economic loss function. The evaluation of the volatility models through the
economic loss function is usually done by looking at the Value at Risk (VaR) measures
and its violations. A violation occurs every time the portfolio losses exceed the VaR. To
assess the performances of the volatility models from an economic point of view, different
tests regarding the violations have been proposed. In this work, the unconditional and
conditional tests are considered. The analysis is based on a Monte Carlo experiment that
samples from a trivariate continuoustime stochastic process a vector of observation each
five minutes per two years.
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Data
2013Autore
Candila, Vincenzo
Metadata
Mostra tutti i dati dell'itemAutori  Candila, Vincenzo  
Data Realizzazione  20160713T10:20:43Z  
Date Disponibilità  20160713T10:20:43Z  
Data di Pubblicazione  2013  
Identificatore (Citazione)  Candila, V. (2013). “A comparison of the forecasting performances of multivariate volatility models”. DISES Working Paper 3.228, Università degli Studi di Salerno, Dipartimento di Scienze Economiche e Statistiche.  it_IT 
ISSN  19713029  it_IT 
Identificatore (URI)  http://hdl.handle.net/10556/2117  
Abstract  The consistent ranking of multivariate volatility models by means of statistical loss function is a challenging research field, because it concerns the quality of the proxy chosen to replace the unobserved volatility, the set of competing models to be ranked and the kind of loss function. The existent works only consider the ranking of multivariate GARCH (MGARCH) models, based on daily frequency of the returns. Less is known about the behaviour of the models that directly use the realized covariance (RCOV), the proxy that generally provides a consistent estimate of the unobserved volatility. The aim of this paper is to evaluate which model has the best forecast volatility accuracy, from a statistical and economic point of view. For the first point, we empirically rank a set of MGARCH and RCOV models by means of four consistent statistical loss functions. For the second point, we evaluate if these rankings are coherent with those resulting from the use of an economic loss function. The evaluation of the volatility models through the economic loss function is usually done by looking at the Value at Risk (VaR) measures and its violations. A violation occurs every time the portfolio losses exceed the VaR. To assess the performances of the volatility models from an economic point of view, different tests regarding the violations have been proposed. In this work, the unconditional and conditional tests are considered. The analysis is based on a Monte Carlo experiment that samples from a trivariate continuoustime stochastic process a vector of observation each five minutes per two years.  it_IT 
Descrizione fisica  23 p.  it_IT 
Lingua  en  it_IT 
Collezione Digitale  Working Papers ; 3.228  it_IT 
Fonte  UniSa. Sistema Bibliotecario di Ateneo  it_IT 
Soggetto  Volatility  it_IT 
Soggetto  Multivariate GARCH  it_IT 
Soggetto  Loss function  it_IT 
Titolo  A comparison of the forecasting performances of multivariate volatility models  it_IT 
Tipo  Working Paper  it_IT 