A Nonparametric approach to calculating value-at-risk

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<title>Nonparametric approach to calculating value-at-risk</title>
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<namePart>Alemany, Ramon</namePart>
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<abstract displayLabel="Summary">A method to estimate an extreme quantile that requires no distributional assumptions is presented. The approach is based on transformed kernel estimation of the cumulative distribution function (cdf). The proposed method consists of a double transformation kernel estimation. We derive optimal bandwidth selection methods that have a direct expression for the smoothing parameter. The bandwidth can accommodate to the given quantile level. The procedure is useful for large data sets and improves quantile estimation compared to other methods in heavy tailed distributions. Implementation is straightforward and R programs are available</abstract>
<note type="statement of responsibility">Ramon Alemany, Catalina Bolancé, Montserrat Guillén</note>
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<title>Insurance : mathematics and economics</title>
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<publisher>Oxford : Elsevier, 1990-</publisher>
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<identifier type="issn">0167-6687</identifier>
<identifier type="local">MAP20077100574</identifier>
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<text>04/03/2013 Volumen 52 Número 2 - marzo 2013 </text>
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