A deep learning approach to forecasting EUA future contracts
This directed research project explores the use of deep learning techniques to forecast the price of European Union Allowance (EUA) future contracts. In order to achieve this, a comprehensive review of the literature surrounding emission markets, trading schemes and long short-term memory neural net...
- Autores:
-
Villamizar Díaz, Jorge Hernando
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/51454
- Acceso en línea:
- http://hdl.handle.net/1992/51454
- Palabra clave:
- Comercio de derechos de emisión
Mercado de futuros
Aprendizaje automático (Inteligencia artificial)
Administración
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Al consultar y hacer uso de este recurso, está aceptando las condiciones de uso establecidas por los autores.http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Ottonello, Giorgiof3724ce2-f63c-4821-89cf-e326be435932500Prado, Melissabb71dab7-cd48-4e15-a6d5-c0b325349269500Ter Horst, Enrique Alejandrovirtual::14321-1Villamizar Díaz, Jorge Hernando7a2123fa-4327-49a6-9abe-8344fdb1569c500Arcila Barrero, Carlos Alfredo2021-08-10T18:25:46Z2021-08-10T18:25:46Z2020http://hdl.handle.net/1992/5145423806.pdfinstname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/This directed research project explores the use of deep learning techniques to forecast the price of European Union Allowance (EUA) future contracts. In order to achieve this, a comprehensive review of the literature surrounding emission markets, trading schemes and long short-term memory neural networks is made. Possible explanatory variables for the EUA futures price are selected, curated, and the methodology used to get a forecast is described. Additionally, under the Anatolyev-Gerko (EP) test statistic and the Pesaran-Timmermann (DA) test, there is evidence that a long-short trading strategy using the forecasted values beats the random walk, in other words, the strategy generates value by skill other than luck.Este proyecto de investigación dirigida explora el uso de técnicas de aprendizaje profundo para predecir el precio de los futuros contratos sobre derechos de emisión de la Unión Europea (EUA). Para ello, se realiza una revisión exhaustiva de la literatura en torno a los mercados de emisiones, los regímenes de comercio y las redes neuronales de memoria a corto plazo. Se seleccionan posibles variables explicativas del precio de los futuros EUA, se curan y se describe la metodología utilizada para obtener una predicción. Además, con el estadístico de prueba de Anatolyev-Gerko (EP) y la prueba de Pesaran-Timmermann (DA), se comprueba que una estrategia de negociación a corto y largo plazo que utiliza los valores pronosticados supera al random walk, es decir, que la estrategia genera valor por habilidad y no por suerte.Magíster Internacional en FinanzasMaestría26 hojasapplication/pdfengUniversidad de los AndesMaestría Internacional en FinanzasFacultad de AdministraciónA deep learning approach to forecasting EUA future contractsTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesishttp://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/TMComercio de derechos de emisiónMercado de futurosAprendizaje automático (Inteligencia artificial)Administración201424605Publicationb4dbb5e2-384d-41ba-a1b0-728efa1713d0virtual::14321-1b4dbb5e2-384d-41ba-a1b0-728efa1713d0virtual::14321-1ORIGINAL23806.pdfapplication/pdf1047407https://repositorio.uniandes.edu.co/bitstreams/e88f4798-13c6-4ead-ac49-9c86bffaab46/download06822c95e4bbf8c290fc0a56d4e0e4aaMD51TEXT23806.pdf.txt23806.pdf.txtExtracted texttext/plain36142https://repositorio.uniandes.edu.co/bitstreams/50ee97ea-ea14-4f7b-85cb-b05fc329b591/download5d3ba0cc5c5495c208e3a024311163a0MD54THUMBNAIL23806.pdf.jpg23806.pdf.jpgIM Thumbnailimage/jpeg5523https://repositorio.uniandes.edu.co/bitstreams/da422197-5495-4c06-9a5a-3d03a444b823/download9b2d4ca62d935ac5039b0232fdda21daMD551992/51454oai:repositorio.uniandes.edu.co:1992/514542024-05-15 10:08:08.367http://creativecommons.org/licenses/by-nc-nd/4.0/open.accesshttps://repositorio.uniandes.edu.coRepositorio institucional Sénecaadminrepositorio@uniandes.edu.co |
| dc.title.spa.fl_str_mv |
A deep learning approach to forecasting EUA future contracts |
| title |
A deep learning approach to forecasting EUA future contracts |
| spellingShingle |
A deep learning approach to forecasting EUA future contracts Comercio de derechos de emisión Mercado de futuros Aprendizaje automático (Inteligencia artificial) Administración |
| title_short |
A deep learning approach to forecasting EUA future contracts |
| title_full |
A deep learning approach to forecasting EUA future contracts |
| title_fullStr |
A deep learning approach to forecasting EUA future contracts |
| title_full_unstemmed |
A deep learning approach to forecasting EUA future contracts |
| title_sort |
A deep learning approach to forecasting EUA future contracts |
| dc.creator.fl_str_mv |
Villamizar Díaz, Jorge Hernando |
| dc.contributor.advisor.none.fl_str_mv |
Ottonello, Giorgio Prado, Melissa Ter Horst, Enrique Alejandro |
| dc.contributor.author.none.fl_str_mv |
Villamizar Díaz, Jorge Hernando |
| dc.contributor.jury.none.fl_str_mv |
Arcila Barrero, Carlos Alfredo |
| dc.subject.armarc.none.fl_str_mv |
Comercio de derechos de emisión Mercado de futuros Aprendizaje automático (Inteligencia artificial) |
| topic |
Comercio de derechos de emisión Mercado de futuros Aprendizaje automático (Inteligencia artificial) Administración |
| dc.subject.themes.none.fl_str_mv |
Administración |
| description |
This directed research project explores the use of deep learning techniques to forecast the price of European Union Allowance (EUA) future contracts. In order to achieve this, a comprehensive review of the literature surrounding emission markets, trading schemes and long short-term memory neural networks is made. Possible explanatory variables for the EUA futures price are selected, curated, and the methodology used to get a forecast is described. Additionally, under the Anatolyev-Gerko (EP) test statistic and the Pesaran-Timmermann (DA) test, there is evidence that a long-short trading strategy using the forecasted values beats the random walk, in other words, the strategy generates value by skill other than luck. |
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2020 |
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2020 |
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2021-08-10T18:25:46Z |
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2021-08-10T18:25:46Z |
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Trabajo de grado - Maestría |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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info:eu-repo/semantics/masterThesis |
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Text |
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http://purl.org/redcol/resource_type/TM |
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http://hdl.handle.net/1992/51454 |
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23806.pdf |
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instname:Universidad de los Andes |
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reponame:Repositorio Institucional Séneca |
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repourl:https://repositorio.uniandes.edu.co/ |
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eng |
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eng |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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26 hojas |
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application/pdf |
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Universidad de los Andes |
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Maestría Internacional en Finanzas |
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Facultad de Administración |
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Universidad de los Andes |
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