Selecting bivariate copula models using image recognition

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<title>Selecting bivariate copula models using image recognition</title>
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<name type="personal" usage="primary" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20130016566">
<namePart>Tsanakas, Andreas</namePart>
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<namePart>Zhu, Rui</namePart>
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<abstract displayLabel="Summary">The choice of a copula model from limited data is a hard but important task. Motivated by the visual patterns that different copula models produce in smoothed density heatmaps, we consider copula model selection as an image recognition problem. We extract image features from heatmaps using the pre-trained AlexNet and present workflows for model selection that combine image features with statistical information. We employ dimension reduction via Principal Component and Linear Discriminant Analyses and use a Support Vector Machine classifier. Simulation studies show that the use of image data improves the accuracy of the copula model selection task, particularly in scenarios where sample sizes and correlations are low. This finding indicates that transfer learning can support statistical procedures of model selection. We demonstrate application of the proposed approach to the joint modelling of weekly returns of the MSCI and RISX indices.

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<accessCondition type="use and reproduction">La copia digital se distribuye bajo licencia "Attribution 4.0 International (CC BY 4.0)"</accessCondition>
<note type="statement of responsibility">Andreas Tsanakas, Rui Zhu</note>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080579258">
<topic>Cálculo actuarial</topic>
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<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080559373">
<topic>Matemáticas</topic>
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<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20090035034">
<topic>Modelización mediante cópulas</topic>
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<title>Astin bulletin</title>
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<publisher>Belgium : ASTIN and AFIR Sections of the International Actuarial Association</publisher>
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<identifier type="issn">0515-0361</identifier>
<identifier type="local">MAP20077000420</identifier>
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<text>05/09/2022 Volumen 52 Número 3 - septiembre 2022 , p. 707-734</text>
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