Artículos (Gifyb)

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Examinando Artículos (Gifyb) por Autor "B. Del Brio, Esther"

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    Semi-nonparametric VaR forecasts for hedge funds during the recent crisis
    (Elsevier, 2014) B. Del Brio, Esther; Mora-Valencia, Andrés; Perote, Javier; Faculty of Economics and Business, Department of Business, University of Salamanca, Spain; School of Economics and Finance, Department of Finance, EAFIT University, Colombia; Faculty of Economics and Business, Department of Economics, University of Salamanca, Spain; Economía y Finanzas; Finanzas; Grupo de Investigación Finanzas y Banca
    The need to provide accurate value-at-risk (VaR) forecasting measures has triggered an important literature in econophysics. Although these accurate VaR models and methodologies are particularly demanded for hedge fund managers, there exist few articles specifically devoted to implement new techniques in hedge fund returns VaR forecasting. This article advances in these issues by comparing the performance of risk measures based on parametric distributions (the normal, Student’s t and skewed-t), semi-nonparametric (SNP) methodologies based on Gram–Charlier (GC) series and the extreme value theory (EVT) approach. Our results show that normal-, Student’s t- and Skewed t- based methodologies fail to forecast hedge fund VaR, whilst SNP and EVT approaches accurately success on it. We extend these results to the multivariate framework by providing an explicit formula for the GC copula and its density that encompasses the Gaussian copula and accounts for non-linear dependences. We show that the VaR obtained by the meta GC accurately captures portfolio risk and outperforms regulatory VaR estimates obtained through the meta Gaussian and Student’s tdistributions.
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    VaR performance during the subprime and sovereign debt crises: An application to emerging markets
    (Elsevier, 2014) B. Del Brio, Esther; Mora-Valencia, Andrés; Perote, Javier; aculty of Economics and Business, Department of Business, University of Salamanca, Spain; School of Economics and Finance, Department of Finance, EAFIT University, Colombia; Faculty of Economics and Business, Department of Economics, University of Salamanca, Spain; Economía y Finanzas; Finanzas; Grupo de Investigación Finanzas y Banca
    Highly volatile scenarios, such as those provoked by the recent subprime and sovereign debt crises, have questioned the accuracy of current risk forecasting methods. This paper adds fuel to this debate by comparing the performance of alternative specifications for modeling the returns filtered by an ARMA-GARCH: Parametric distributions (Student's t and skewed-t), the extreme value theory (EVT), semi-nonparametric methods based on the Gram–Charlier (GC) expansion and the normal (benchmark). We implement backtesting techniques for the pre-crisis and crisis periods for stock index returns and a hedge fund of emerging markets. Our results show that the Student's t fails to forecast VaR during the crisis, while the EVT and GC accurately capture market risk, the latter representing important savings in terms of efficient regulatory capital provisions.