Mostrar el registro sencillo del ítem

Application of the hodrick-prescott model for the price forecast of electricity in Colombia [Aplicación del modelo de hodrick-prescott para el pronóstico del precio de la electricidad en Colombia]

dc.creatorArango M.
dc.creatorGalvis J.
dc.date2019
dc.date.accessioned2020-04-29T14:53:57Z
dc.date.available2020-04-29T14:53:57Z
dc.identifier.issn16469895
dc.identifier.urihttp://hdl.handle.net/11407/5771
dc.descriptionThe Colombian electricity market aims to offer a continuous and reliable service. However, the specific characteristics associated with its high dependence on hydroelectric generation, the technical limitations that allow its storage and the passive behavior of demand jeopardize the fulfillment of this objective. Uncertainty about future results has a significant impact on investment prospects, which consequently restricts the diversification of the energy matrix. In this context, among the mechanisms considered for the valuation of new projects and regulatory reforms, the interest to study the short and long-term behavior of the price of electricity stands out. The tool proposed in this paper develops an algorithm based on the Hodrick and Prescott filter for the Colombian market, which allows identifying the behavior of the cycle and the price trend, contributing to the decision making in the sector. © 2019, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.
dc.language.isospa
dc.publisherAssociacao Iberica de Sistemas e Tecnologias de Informacao
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85078334368&partnerID=40&md5=ecbf47ca991ea0a87540a6d732e32f70
dc.sourceRISTI - Revista Iberica de Sistemas e Tecnologias de Informacao
dc.subjectElectricity price forecasting
dc.subjectHodrick
dc.subjectLong-term seasonal component
dc.subjectPrescott filter
dc.titleApplication of the hodrick-prescott model for the price forecast of electricity in Colombia [Aplicación del modelo de hodrick-prescott para el pronóstico del precio de la electricidad en Colombia]
dc.typeArticleeng
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programIngeniería Financiera
dc.relation.citationvolume2019
dc.relation.citationissueE21
dc.relation.citationstartpage382
dc.relation.citationendpage396
dc.publisher.facultyFacultad de Ingenierías
dc.affiliationArango, M., Universidad de Medellín/Universidad Nacional de Colombia, Medellín, Colombia; Galvis, J., Universidad de Medellín, Medellín, Colombia
dc.relation.referencesAlonso, J.C., Arcila, A.M., Empleo del comportamiento estacional para mejorar el pronóstico de un commodity: El caso del mercado internacional del azúcar (2013) Estudios Gerenciales, 29 (129), pp. 406-415. , https://doi.org/10.1016/j.estger.2013.11.006
dc.relation.referencesArango, M., Botero, S., The application of real options as a tool for decision-making in the electricity market [La aplicación de opciones reales como herramienta de toma de decisiones en el mercado de electricidad] (2017) Iberian Conference on Information Systems and Technologies, CISTI, , https://doi.org/10.23919/CISTI.2017.7975807
dc.relation.referencesBuga, A., Zaborowicz, M., Boniecki, P., Janczak, D., Koszela, K., Czeka, W., Lewicki, A., (2018) Short-Term Forecast of Generation of Electric Energy in Photovoltaic Systems, 81 (November), pp. 306-312. , https://doi.org/10.1016/j.rser.2017.07.032, 2016
dc.relation.referencesFama, E.F., (1965) The Behavior of Stock-Market Prices, 38 (1), pp. 34-105. , http://links.jstor.org/sici?sici=0021-9398%2528196501%252938%253A1%253C34%253ATBOSP%253E2.0.CO%253B2-6
dc.relation.referencesGómez Sánchez, A.M., Análisis de la interdependencia de los ciclos económicos del cauca y el suroccidente colombiano: Una aproximación econométrica desde los filtros de kalman y hodrick-prescott1 (2011) Estudios Gerenciales, 27 (121), pp. 115-141. , https://doi.org/10.1016/S0123-5923(11)70184-X
dc.relation.referencesHe, Y., Wang, B., Wang, J., Xiong, W., Xia, T., Correlation between Chinese and international energy prices based on a HP filter and time difference analysis (2013) Energy Policy, 62, pp. 898-909. , https://doi.org/10.1016/j.enpol.2013.07.136
dc.relation.referencesHodrick, R.J., Prescott, E.C., Postwar U. S. Business Cycles: An Empirical Investigation (1997) Journal of Money, Credit and Banking, 29 (1), pp. 1-16. , https://doi.org/10.2307/2953682
dc.relation.referencesKucher, O., Kurov, A., Review of Financial Economics Business cycle, storage, and energy prices (2014) Review of Financial Economics, 23 (4), pp. 217-226. , https://doi.org/10.1016/j.rfe.2014.09.001
dc.relation.referencesLisi, F., Nan, F., Component estimation for electricity prices: Procedures and comparisons (2014) Energy Economics, 44, pp. 143-159. , https://doi.org/10.1016/j.eneco.2014.03.018
dc.relation.referencesMelichar, M., Energy price shocks and economic activity: Which energy price series should we be using? (2016) Energy Economics, 54, pp. 431-443. , https://doi.org/10.1016/j.eneco.2015.12.017
dc.relation.referencesNowotarski, J., Weron, R., Recent advances in electricity price forecasting: A review of probabilistic forecasting (2018) Renewable and Sustainable Energy Reviews, 81 (March 2017), pp. 1548-1568. , https://doi.org/10.1016/j.rser.2017.05.234
dc.relation.referencesOseni, M.O., Pollitt, M.G., The prospects for smart energy prices: Observations from 50 years of residential pricing for telecoms and energy (2016) EPRG Working Paper, 70, pp. 150-160. , https://doi.org/10.1016/j.rser.2016.11.214, March
dc.relation.referencesRios, G., (2008) Series De Tiempo, p. 52
dc.relation.referencesRuiz, B.J.Ã., Rodr?, V.Ã., (2006) Renewable Energy Sources in the Colombian Energy Policy, 34, pp. 3684-3690. , https://doi.org/10.1016/j.enpol.2005.08.007, analysis and perspectives
dc.relation.referencesSatagopan, J.M., (1996) A Bayesian Approach, , (1992)
dc.relation.referencesSingh, H., Fang, L., Guan, L., Forecasting day-ahead price spikes for the Ontario electricity market (2016) Electric Power Systems Research, 141, pp. 450-459. , https://doi.org/10.1016/j.epsr.2016.08.005
dc.relation.referencesTsai, S., Dong, W., Xue, Y., Zhang, J., Chen, Q., Liu, Y., Zhou, J., Models for forecasting growth trends in renewable energy (2017) Renewable and Sustainable Energy Reviews, 77 (2015), pp. 1169-1178. , https://doi.org/10.1016/j.rser.2016.06.001, December
dc.relation.referencesUna Visión del Mercado Eléctrico Colombiano (2004) Mercado De Energía Eléctrica En Colombia-Análisis Comercial Y De Estrategias, pp. 1-110
dc.relation.references(2016) Boletín Estadístico: Minas Y energía 2012 , 2016, p. 200
dc.relation.referencesWang, D., Luo, H., Grunder, O., Lin, Y., Guo, H., Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm (2017) Applied Energy, 190, pp. 390-407. , https://doi.org/10.1016/j.apenergy.2016.12.134
dc.relation.referencesWeron, R., Zator, M., A note on using the Hodrick Prescott filter in electricity markets (2015) Energy Economics, 48, pp. 1-6. , https://doi.org/10.1016/j.eneco.2014.11.014
dc.relation.referencesYang, Z., Ce, L., Lian, L., Electricity price forecasting by a hybridmodel, combining wavelet transform, ARMA and kernel-based extreme learning machine methods (2017) Applied Energy, 190, pp. 291-305. , https://doi.org/10.1016/j.apenergy.2016.12.130
dc.relation.referencesZiel, F., Steinert, R., Electricity price forecasting using sale and purchase curves: The X-Model (2016) Energy Economics, 59, pp. 435-454. , https://doi.org/10.1016/j.eneco.2016.08.008
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.type.driverinfo:eu-repo/semantics/article


Ficheros en el ítem

FicherosTamañoFormatoVer

No hay ficheros asociados a este ítem.

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem