Economic Peaks and Value-at-Risk Analysis: A Novel Approach Using the Laplace Distribution for House Prices
| dc.contributor.affiliation | Dibrugarh University | |
| dc.contributor.affiliation | Persian Gulf University | |
| dc.contributor.affiliation | Universidad de Las Americas - Chile | |
| dc.contributor.affiliation | Princess Nourah bint Abdulrahman University | |
| dc.contributor.affiliation | Egyptian Knowledge Bank (EKB) | |
| dc.contributor.affiliation | Benha University | |
| dc.contributor.author | Das, Jondeep | |
| dc.contributor.author | Hazarika, Partha Jyoti | |
| dc.contributor.author | Alizadeh, Morad | |
| dc.contributor.author | Contreras-Reyes, Javier E. | |
| dc.contributor.author | Mohammad, Hebatallah H. | |
| dc.contributor.author | Yousof, Haitham M. | |
| dc.date.accessioned | 2025-06-15T00:29:34Z | |
| dc.date.available | 2025-06-15T00:29:34Z | |
| dc.date.issued | 2025-01-07 | |
| dc.description.abstract | In this article, a new extension of the standard Laplace distribution is introduced for house price modeling. Certain important properties of the new distribution are deducted throughout this study. We used the new extension of the Laplace model to conduct a thorough economic risk assessment utilizing several metrics, including the value-at-risk (VaR), the peaks over a random threshold value-at-risk (PORT-VaR), the tail value-at-risk (TVaR), the mean of order-P (MOP), and the peaks over a random threshold based on the mean of order-P (PORT-MOP). These metrics capture different facets of the tail behavior, which is essential for comprehending the extreme median values in the Boston house price data. Notably, PORT-VaR improves the risk evaluations by incorporating randomness into the selection of the thresholds, whereas VaR and TVaR focus on measuring the potential losses at specific confidence levels, with TVaR offering insights into significant tail risks. The MOP method aids in balancing the reliability goals while optimizing the performance in the face of uncertainty. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Mathematical and Computational Applications, 30(1), 4. https://doi.org/10.3390/mca30010004 | |
| dc.identifier.doi | https://doi.org/10.3390/mca30010004 | |
| dc.identifier.issn | 2297-8747 | |
| dc.identifier.orcid | https://orcid.org/0000-0001-9246-0597 | |
| dc.identifier.orcid | https://orcid.org/0000-0002-8370-9028 | |
| dc.identifier.orcid | https://orcid.org/0000-0001-6638-2185 | |
| dc.identifier.orcid | https://orcid.org/0000-0003-1172-5456 | |
| dc.identifier.orcid | https://orcid.org/0000-0002-7017-8336 | |
| dc.identifier.orcid | https://orcid.org/0000-0003-4589-4944 | |
| dc.identifier.researcherid | MIT-0407-2025 | |
| dc.identifier.researcherid | G-8831-2018 | |
| dc.identifier.researcherid | GSI-5693-2022 | |
| dc.identifier.researcherid | AAW-3899-2020 | |
| dc.identifier.researcherid | K-4854-2019 | |
| dc.identifier.ror | https://ror.org/045kfbt16 | |
| dc.identifier.ror | https://ror.org/03n2mgj60 | |
| dc.identifier.ror | https://ror.org/0166e9x11 | |
| dc.identifier.ror | https://ror.org/05b0cyh02 | |
| dc.identifier.ror | https://ror.org/03tn5ee41 | |
| dc.identifier.scopusauthorid | 57849362100 | |
| dc.identifier.scopusauthorid | 54919059600 | |
| dc.identifier.scopusauthorid | 56097103200 | |
| dc.identifier.scopusauthorid | 55022896200 | |
| dc.identifier.scopusauthorid | 59210682000 | |
| dc.identifier.scopusauthorid | 56607387300 | |
| dc.identifier.uri | https://repositorio.udla.cl/handle/udla/1920 | |
| dc.language.iso | eng | |
| dc.publisher | MDPI AG | |
| dc.relation.isindexedby | Web of Science | |
| dc.relation.issn | 2297-8747 | |
| dc.rights | Creative Commons Attribution 4.0 International | |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.source | MATHEMATICAL AND COMPUTATIONAL APPLICATIONS | |
| dc.source.uri | https://doi.org/10.3390/mca30010004 | |
| dc.subject | Laplace | |
| dc.subject | odd log-logistic | |
| dc.subject | economic risk | |
| dc.subject | extreme house price data | |
| dc.subject | mean of order-P | |
| dc.subject | peaks over a random threshold | |
| dc.subject | value-at-risk | |
| dc.subject | tail behavior | |
| dc.subject.oecd1 | 1 Ciencias Naturales | |
| dc.subject.oecd2 | 1.1 Matemáticas | |
| dc.subject.oecd3 | 1.1.2 Matemáticas Aplicadas | |
| dc.title | Economic Peaks and Value-at-Risk Analysis: A Novel Approach Using the Laplace Distribution for House Prices | |
| dc.type | journal article | |
| dc.type.coar | http://purl.org/coar/resource_type/c_6501 | |
| dc.type.driver | info:eu-repo/semantics/article | |
| oaire.citation.issue | 1 | |
| oaire.citation.title | MATHEMATICAL AND COMPUTATIONAL APPLICATIONS | |
| oaire.citation.volume | 30 | |
| udla.campus | Providencia | |
| udla.campus.adscripcion | CC | |
| udla.carrera.adscripcion | INGENIERÍA EN PREVENCIÓN DE RIESGOS Y MEDIO AMBIENTE | |
| udla.curacion.control | jmvg | |
| udla.escuela.adscripcion | Biotecnología y Medio Ambiente | |
| udla.facultad | Facultad de Ingeniería y Negocios | |
| udla.facultad.adscripcion | FINE | |
| udla.facultad.codigo | FINE | |
| udla.oecd.area | 1 Ciencias Naturales | |
| udla.oecd.discipline | 1.1.2 Matemáticas Aplicadas | |
| udla.oecd.subarea | 1.1 Matemáticas |
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