Nonparametric adaptive value-at-risk quantification based on the multiscale energy distribution of asset returns
| dc.rights.license | open | en_US |
| hal.structure.identifier | Institut de Recherche en Gestion des Organisations [IRGO] | |
| dc.contributor.author | TZAGKARAKIS, Georgios | |
| hal.structure.identifier | Institut de Recherche en Gestion des Organisations [IRGO] | |
| dc.contributor.author | MAURER, Frantz | |
| dc.contributor.author | DIONYSOPOULOS, Thomas | |
| dc.date.accessioned | 2022-07-20T11:20:00Z | |
| dc.date.available | 2022-07-20T11:20:00Z | |
| dc.date.issued | 2021 | |
| dc.date.conference | 2021-01-18 | |
| dc.identifier.issn | 2219-5491 | en_US |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/140536 | |
| dc.description.abstractEn | Quantifying risk is pivotal for every financial institution, with the temporal dimension being the key aspect for all the well-established risk measures. However, exploiting the frequency information conveyed by financial data, could yield improved insights about the inherent risk evolution in a joint time-frequency fashion. Nevertheless, the great majority of risk managers make no explicit distinction between the information captured by patterns of different frequency content, while relying on the full time-resolution data, regardless of the trading horizon. To address this problem, a novel value-at-risk (VaR) quantification method is proposed, which combines nonlinearly the time-evolving energy profile of returns series at multiple frequency scales, determined by the predefined trading horizon. Most importantly, our proposed method can be coupled with any quantile-based risk measure to enhance its performance. Experimental evaluation with real data reveals an increased robustness of our method in efficiently controlling under-/overestimated VaR values. | |
| dc.language.iso | EN | en_US |
| dc.subject.en | Commerce | |
| dc.subject.en | Different frequency | |
| dc.subject.en | Experimental evaluation | |
| dc.subject.en | Financial institution | |
| dc.subject.en | Frequency information | |
| dc.subject.en | Multiple frequency | |
| dc.subject.en | Multiscale energy | |
| dc.subject.en | Quantification methods | |
| dc.subject.en | Risk assessment | |
| dc.subject.en | Signal processing | |
| dc.subject.en | Temporal dimensions | |
| dc.subject.en | Value engineering | |
| dc.title.en | Nonparametric adaptive value-at-risk quantification based on the multiscale energy distribution of asset returns | |
| dc.type | Communication dans un congrès avec actes | en_US |
| dc.identifier.doi | 10.23919/Eusipco47968.2020.9287568 | en_US |
| dc.subject.hal | Sciences de l'Homme et Société/Gestion et management | en_US |
| bordeaux.page | 2393-2397 | en_US |
| bordeaux.volume | 2021-January | en_US |
| bordeaux.hal.laboratories | IRGO (Institut de Recherche en Gestion des Organisations) - EA 4190 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.conference.title | Nonparametric adaptive value-at-risk quantification based on the multiscale energy distribution of asset returns | en_US |
| bordeaux.country | nl | en_US |
| bordeaux.title.proceeding | 28th European Signal Processing Conference (EUSIPCO) | en_US |
| bordeaux.conference.city | Amsterdam | en_US |
| bordeaux.peerReviewed | oui | en_US |
| hal.identifier | hal-03728672 | |
| hal.version | 1 | |
| hal.date.transferred | 2022-07-20T11:20:02Z | |
| hal.export | true | |
| dc.rights.cc | Pas de Licence CC | en_US |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2021&rft.volume=2021-January&rft.spage=2393-2397&rft.epage=2393-2397&rft.eissn=2219-5491&rft.issn=2219-5491&rft.au=TZAGKARAKIS,%20Georgios&MAURER,%20Frantz&DIONYSOPOULOS,%20Thomas&rft.genre=proceeding |
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