Abstract
Threshold methods, based on fitting a stochastic model to the excesses over a threshold, were developed under the acronym POT (peaks over threshold). To eliminate the tendency to clustering of violations, a model-based approach within the POT framework, which uses the durations between excesses as covariates, is presented. Based on this approach we suggest models to forecast one-day-ahead Value-at-Risk and apply these models to the Standard & Poor’s 500 Index. Out of sample results provide evidence that they can perform better than state-of-the art risk models.
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Acknowledgements
This research was partially supported by National Funds through FCT - Fundação para a Ciência e a Tecnologia, FCT//PTDC/MAT/101736/2008, EXTREMA project.
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Alves, M.I.F., Santos, P.A. (2013). DPOT Methodology: An Application to Value-at-Risk. In: Oliveira, P., da Graça Temido, M., Henriques, C., Vichi, M. (eds) Recent Developments in Modeling and Applications in Statistics. Studies in Theoretical and Applied Statistics(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32419-2_9
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