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High quantiles estimation with Quasi-PORT and DPOT: An application to value-at-risk for financial variables
Journal article   Peer reviewed

High quantiles estimation with Quasi-PORT and DPOT: An application to value-at-risk for financial variables

Paulo Araújo Santos, Isabel Fraga Alves and Shawkat Hammoudeh
The North American journal of economics and finance, v 26, pp 487-496
Dec 2013
DOI: https://doi.org/10.1016/j.najef.2013.02.017
Featured in Collection :   UN Sustainable Development Goals @ Drexel

Abstract

Financial time series High quantiles Quantitative risk management Statistics of extremes
Recurrent ⿿black swans⿿ financial events are a major concern for both investors and regulators because of the extreme price changes they cause, despite their very low probability of occurrence. In this paper, we use unconditional and conditional methods, such as the recently proposed high quantile (HQ) extreme value theory (EVT) models of DPOT (Duration-based Peak Over Threshold) and quasi-PORT (peaks over random threshold), to estimate the Value-at-Risk with very small probability values for an adequately long and major financial time series to obtain a reasonable number of violations for backtesting. We also compare these models and other alternative strategies through an out-of-sample accuracy investigation to determine their relative performance within the HQ context. Policy implications relevant to estimation of risk for extreme events are also provided.

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11 citations in Web of Science
15 citations in Scopus

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Business, Finance
Economics
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