Modelo de valoración de contratos en Derivex

ilustraciones, diagramas, mapas

Autores:: Arango londoño, Adriana

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2023

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

id	UNACIONAL2_421d8378e50a248483b157e6625d0347
oai_identifier_str	oai:repositorio.unal.edu.co:unal/83913
network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv	Modelo de valoración de contratos en Derivex
dc.title.translated.eng.fl_str_mv	Valuation model for contracts in Derivex
title	Modelo de valoración de contratos en Derivex
spellingShingle	Modelo de valoración de contratos en Derivex 330 - Economía::333 - Economía de la tierra y de la energía Electricidad - Aspectos económicos Electricity - Industry Electricidad - Industria Derivex Electricity spot price Numerical simulation Management risk Precio spot de la electricidad Simulación numérica Gestión de riesgos
title_short	Modelo de valoración de contratos en Derivex
title_full	Modelo de valoración de contratos en Derivex
title_fullStr	Modelo de valoración de contratos en Derivex
title_full_unstemmed	Modelo de valoración de contratos en Derivex
title_sort	Modelo de valoración de contratos en Derivex
dc.creator.fl_str_mv	Arango londoño, Adriana
dc.contributor.advisor.none.fl_str_mv	Velásquez Henao, Juan David
dc.contributor.author.none.fl_str_mv	Arango londoño, Adriana
dc.contributor.researchgroup.spa.fl_str_mv	Big Data y Data Analytics
dc.contributor.orcid.spa.fl_str_mv	Arango londoño, Adriana [0000-0001-8919-7548] Velásquez
dc.subject.ddc.spa.fl_str_mv	330 - Economía::333 - Economía de la tierra y de la energía
topic	330 - Economía::333 - Economía de la tierra y de la energía Electricidad - Aspectos económicos Electricity - Industry Electricidad - Industria Derivex Electricity spot price Numerical simulation Management risk Precio spot de la electricidad Simulación numérica Gestión de riesgos
dc.subject.lemb.none.fl_str_mv	Electricidad - Aspectos económicos Electricity - Industry Electricidad - Industria
dc.subject.proposal.eng.fl_str_mv	Derivex Electricity spot price Numerical simulation Management risk
dc.subject.proposal.spa.fl_str_mv	Precio spot de la electricidad Simulación numérica Gestión de riesgos
description	ilustraciones, diagramas, mapas
publishDate	2023
dc.date.accessioned.none.fl_str_mv	2023-05-30T17:00:28Z
dc.date.available.none.fl_str_mv	2023-05-30T17:00:28Z
dc.date.issued.none.fl_str_mv	2023-05-28
dc.type.spa.fl_str_mv	Trabajo de grado - Doctorado
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/doctoralThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.coar.spa.fl_str_mv	http://purl.org/coar/resource_type/c_db06
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TD
format	http://purl.org/coar/resource_type/c_db06
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/83913
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/83913 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.indexed.spa.fl_str_mv	RedCol LaReferencia
dc.relation.references.spa.fl_str_mv	[1] N. Singh, S. R. Mohanty, and S. Dev, “Short term electricity price forecast based on environmentally adapted generalized neuron,” Energy, vol. 125, pp. 127–139, 2017, doi: 10.1016/j.energy.2017.02.094. [2] “Power System Economics: Designing Markets for Electricity \| Wiley,” Wiley.com. https://www.wiley.com/en- [3] A. Khosravi, S. Nahavandi, and D. Creighton, “A neural network-GARCH-based method for construction of Prediction Intervals,” Electr. Power Syst. Res., vol. 96, pp. 185–193, 2013, doi: 10.1016/j.epsr.2012.11.007. [4] M. Gürtler and T. Paulsen, “Forecasting performance of time series models on electricity spot markets: a quasi-meta-analysis,” Int. J. Energy Sect. Manag., vol. 12, no. 1, pp. 103–129, 2018, doi: 10.1108/IJESM-06-2017-0004. [5] A. Khosravi, S. Nahavandi, and D. Creighton, “Quantifying uncertainties of neural network-based electricity price forecasts,” Appl. Energy, vol. 112, pp. 120–129, 2013, doi: 10.1016/j.apenergy.2013.05.075. [6] P. Di and I. Perin, “An ambit stochastic approach to pricing electricity forward contracts: The case of the German Energy Market,” J. Probab. Stat., vol. 2015, 2015, doi: 10.1155/2015/626020. [7] M. Shafie-khah, M. P. Moghaddam, and M. K. Sheikh-El-Eslami, “Price forecasting of day-ahead electricity markets using a hybrid forecast method,” Energy Convers. Manag., vol. 52, no. 5, pp. 2165–2169, May 2011, doi: 10.1016/j.enconman.2010.10.047. [8] H. Xiong and R. Mamon, “A higher-order Markov chain-modulated model for electricity spot-price dynamics,” Appl. Energy, vol. 233–234, pp. 495–515, 2019, doi: 10.1016/j.apenergy.2018.09.039. [9] G. G. P. Murthy, V. Sedidi, A. K. Panda, and B. N. Rath, “Forecasting electricity prices in deregulated wholesale spot electricity market: A review,” Int. J. Energy Econ. Policy, vol. 4, no. 1, pp. 32–42, 2014. [10] J.-M. Hu and J.-Z. Wang, “Forecasting day-ahead electricity price using a hybrid improved approach,” J. Electr. Eng. Technol., vol. 12, no. 6, pp. 2166–2176, 2017, doi: 10.5370/JEET.2017.12.6.2166. [11] K. B. Sahay, “One hour ahead price forecast of Ontario electricity market by using ANN,” in 2015 International Conference on Energy Economics and Environment (ICEEE), Mar. 2015, pp. 1–6. doi: 10.1109/EnergyEconomics.2015.7235102. [12] H. Mosbah and M. El-Hawary, “Hourly Electricity Price Forecasting for the Next Month Using Multilayer Neural Network,” Can. J. Electr. Comput. Eng., vol. 39, no. 4, pp. 283–291, 2016, doi: 10.1109/CJECE.2016.2586939. [13] J.-L. Zhang, Y.-J. Zhang, D.-Z. Li, Z.-F. Tan, and J.-F. Ji, “Forecasting day-ahead electricity prices using a new integrated model,” Int. J. Electr. Power Energy Syst., vol. 105, pp. 541–548, Feb. 2019, doi: 10.1016/j.ijepes.2018.08.025. [14] A. Aghajani, R. Kazemzadeh, and A. Ebrahimi, “Short-term prediction of market-clearing price of electricity in the presence of wind power plants by a hybrid intelligent system,” Neural Comput. Appl., May 2018, doi: 10.1007/s00521-018-3544-8. [15] T. Niimura, “Forecasting Techniques for Deregulated Electricity Market Prices - Extended Survey,” in 2006 IEEE PES Power Systems Conference and Exposition, Oct. 2006, pp. 51–56. doi: 10.1109/PSCE.2006.296248. [16] M. M. G. Zapata and C. M. Ochoa, “Modelación De La Volatilidad De Los Precios De La Energía Eléctrica En Colombia,” Rev. Ing. Univ. Medellín, vol. 7, no. 12, pp. 87–114, 2008, Accessed: Jun. 05, 2019. [Online]. Available: http://www.redalyc.org/articulo.oa?id=75011517005 [17] K. Maciejowska and R. Weron, “Forecasting of daily electricity prices with factor models: utilizing intra-day and inter-zone relationships,” Comput. Stat., vol. 30, no. 3, pp. 805–819, 2015, doi: 10.1007/s00180-014-0531-0. [18] G. P. Girish and S. Vijayalakshmi, “Spot Electricity Price Dynamics of Indian Electricity Market,” in Advances in Computer Science and its Applications, H. Y. Jeong, M. S. Obaidat, N. Y. Yen, and J. J. (Jong H. Park, Eds., in Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2014, pp. 1129–1135. [19] J. Pokora, “Hybrid arima and support vector regression in short-term electricity price forecasting,” Acta Univ. Agric. Silvic. Mendel. Brun., vol. 65, no. 2, pp. 699–708, 2017, doi: 10.11118/actaun201765020699. [20] H. Izadpanah, G. R. Yousefi, and S. M. Kaviri, “Asset and risk management for a wind farm in a competitive electricity market,” in 2013 21st Iranian Conference on Electrical Engineering (ICEE), May 2013, pp. 1–6. doi: 10.1109/IranianCEE.2013.6599765. [21] J. Kettunen, A. Salo, and D. W. Bunn, “Optimization of electricity retailer’s contract portfolio subject to risk preferences,” IEEE Trans. Power Syst., vol. 25, no. 1, pp. 117–128, 2010, doi: 10.1109/TPWRS.2009.2032233. [22] M. Liu and F. F. Wu, “Framework for generation risk management in electricity markets,” Dianli Xitong ZidonghuaAutomation Electr. Power Syst., vol. 28, no. 13, pp. 1–6, 2004. [23] M. Liu and F. F. Wu, “Risk management in a competitive electricity market,” in Analytical Methods for Energy Diversity and Security, 2008, pp. 247–262. doi: 10.1016/B978-0-08-056887-4.00012-3. [24] K. He, C. Xie, S.-M. Guu, L. Yu, and K. K. Lai, “Multi scale risk measurement in electricity market: A wavelet based value at risk approach,” J. Southeast Univ. Engl. Ed., vol. 24, no. SUPPL., pp. 54–59, 2008. [25] K. Andriosopoulos and N. Nomikos, “Risk management in the energy markets and value-at-risk modelling: A hybrid approach,” Eur. J. Finance, vol. 21, no. 7, pp. 548–574, 2015, doi: 10.1080/1351847X.2013.862173. [26] J. Wang, Y. Li, and S. Zhang, “CVaR-based electricity purchase model for power supply company,” Dianli Zidonghua Shebei Electr. Power Autom. Equip., vol. 28, no. 2, pp. 19–23, 2008. [27] H. Hailun, Y. Zheng, and H. Yunhe, “A novel CVaR based portfolio optimization model for LDC electricity procurement,” presented at the 2008 Joint International Conference on Power System Technology POWERCON and IEEE Power India Conference, POWERCON 2008, 2008. doi: 10.1109/ICPST.2008.4745232. [28] A. Hatami, H. Seifi, and M. K. Sheikh-El-Eslami, “A stochastic-based decision-making framework for an electricity retailer: Time-of-use pricing and electricity portfolio optimization,” IEEE Trans. Power Syst., vol. 26, no. 4, pp. 1808–1816, 2011, doi: 10.1109/TPWRS.2010.2095431. [29] D. Feng, D. Gan, J. Zhong, and Y. Ni, “Supplier asset allocation in a pool-based electricity market,” IEEE Trans. Power Syst., vol. 22, no. 3, pp. 1129–1138, 2007, doi: 10.1109/TPWRS.2007.901282. [30] M. Liu and F. F. Wu, “Correlation matrices estimation in energy portfolio optimization,” presented at the IEEE Power and Energy Society 2008 General Meeting: Conversion and Delivery of Electrical Energy in the 21st Century, PES, 2008. doi: 10.1109/PES.2008.4596170. [31] D. Feng, D. Gan, and J. Zhong, “Supplier multi-trading strategy: A stochastic programming approach,” presented at the IEEE Power and Energy Society 2008 General Meeting: Conversion and Delivery of Electrical Energy in the 21st Century, PES, 2008. doi: 10.1109/PES.2008.4596119. [32] P. Tang and A. Callan, “Contract optimisation and market operation - A generator’s perspective,” presented at the CIGRE International Symposium Guilin 2009: Operation and Development of Power System in the New Context, 2009. [33] J. F. RodrÍguez, “Hedging swing options,” Int. J. Theor. Appl. Finance, vol. 14, no. 2, pp. 295–312, 2011, doi: 10.1142/S021902491100636X. [34] F. Martínez-álvarez, A. Troncoso, G. Asencio-Cortés, and J. C. Riquelme, “A survey on data mining techniques applied to electricity-related time series forecasting,” Energies, vol. 8, no. 11, pp. 13162–13193, 2015, doi: 10.3390/en81112352. [35] H. Daneshi and A. Daneshi, “Price forecasting in deregulated electricity markets - A bibliographical survey,” presented at the 3rd International Conference on Deregulation and Restructuring and Power Technologies, DRPT 2008, 2008, pp. 657–661. doi: 10.1109/DRPT.2008.4523487. [36] G. J. Osório, J. C. O. Matias, and J. P. S. Catalão, “Electricity prices forecasting by a hybrid evolutionary-adaptive methodology,” Energy Convers. Manag., vol. 80, pp. 363–373, 2014, doi: 10.1016/j.enconman.2014.01.063. [37] S. K. Aggarwal, L. M. Saini, and A. Kumar, “Electricity price forecasting in deregulated markets: A review and evaluation,” Int. J. Electr. Power Energy Syst., vol. 31, no. 1, pp. 13–22, 2009, doi: 10.1016/j.ijepes.2008.09.003. [38] P. M. R. Bento, J. A. N. Pombo, M. R. A. Calado, and S. J. P. S. Mariano, “A bat optimized neural network and wavelet transform approach for short-term price forecasting,” Appl. Energy, vol. 210, pp. 88–97, Jan. 2018, doi: 10.1016/j.apenergy.2017.10.058. [39] S. K. Aggarwal, L. M. Saini, and A. Kumar, “Short term price forecasting in deregulated electricity markets: A review of statistical models and key issues,” Int. J. Energy Sect. Manag., vol. 3, no. 4, pp. 333–358, 2009, doi: 10.1108/17506220911005731. [40] A. Aggarwal and M. M. Tripathi, “A novel hybrid approach using wavelet transform, time series time delay neural network, and error predicting algorithm for day-ahead electricity price forecasting,” presented at the 2017 6th International Conference on Computer Applications in Electrical Engineering - Recent Advances, CERA 2017, 2018, pp. 199–204. doi: 10.1109/CERA.2017.8343326. [41] F. Wang et al., “Daily pattern prediction based classification modeling approach for day-ahead electricity price forecasting,” Int. J. Electr. Power Energy Syst., vol. 105, pp. 529–540, Feb. 2019, doi: 10.1016/j.ijepes.2018.08.039. [42] S. S. Reddy, C.-M. Jung, and K. J. Seog, “Day-ahead electricity price forecasting using back propagation neural networks and weighted least square technique,” Front. Energy, vol. 10, no. 1, pp. 105–113, 2016, doi: 10.1007/s11708-016-0393-y. [43] M. Cerjan, I. Krželj, M. Vidak, and M. Delimar, “A literature review with statistical analysis of electricity price forecasting methods,” presented at the IEEE EuroCon 2013, 2013, pp. 756–763. doi: 10.1109/EUROCON.2013.6625068. [44] X. Yan and N. A. Chowdhury, “Mid-term electricity market clearing price forecasting utilizing hybrid support vector machine and auto-regressive moving average with external input,” Int. J. Electr. Power Energy Syst., vol. 63, pp. 64–70, Dec. 2014, doi: 10.1016/j.ijepes.2014.05.037. [45] Guang Li, Chen-Ching Liu, J. Lawarree, M. Gallanti, and A. Venturini, “State-of-the-art of electricity price forecasting,” in International Symposium CIGRE/IEEE PES, 2005., Oct. 2005, pp. 110–119. doi: 10.1109/CIGRE.2005.1532733. [46] A. Ghanbari, S. F. Ghaderi, and M. A. Azadeh, “Adaptive Neuro-Fuzzy Inference System vs. Regression based approaches for annual electricity load forecasting,” in 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE), Feb. 2010, pp. 26–30. doi: 10.1109/ICCAE.2010.5451534. [47] E. Raviv, K. E. Bouwman, and D. van, “Forecasting day-ahead electricity prices: Utilizing hourly prices,” Energy Econ., vol. 50, pp. 227–239, 2015, doi: 10.1016/j.eneco.2015.05.014. [48] J. Carpio, J. Juan, and D. López, “Multivariate Exponential Smoothing and Dynamic Factor Model Applied to Hourly Electricity Price Analysis,” Technometrics, vol. 56, no. 4, pp. 494–503, Oct. 2014, doi: 10.1080/00401706.2013.860920. [49] F. Lisi and M. M. Pelagatti, “Component estimation for electricity market data: Deterministic or stochastic?,” Energy Econ., vol. 74, pp. 13–37, Aug. 2018, doi: 10.1016/j.eneco.2018.05.027. [50] H. Ebrahimian, S. Barmayoon, M. Mohammadi, and N. Ghadimi, “The price prediction for the energy market based on a new method,” Econ. Res.-Ekon. Istraživanja, vol. 31, no. 1, pp. 313–337, Jan. 2018, doi: 10.1080/1331677X.2018.1429291. [51] C. Ballester and D. Furió, “Impact of Wind Electricity Forecasts on Bidding Strategies,” Sustainability, vol. 9, no. 8, p. 1318, Aug. 2017, doi: 10.3390/su9081318. [52] V. S, G. G p, K. Singhania, and E. N. Vincent, “Is Indian Spot Electricity Price Series Stationary?,” Energy Procedia, vol. 138, pp. 1067–1072, Oct. 2017, doi: 10.1016/j.egypro.2017.10.114. [53] H. S. Sandhu, L. Fang, and L. Guan, “Forecasting day-ahead price spikes for the Ontario electricity market,” Electr. Power Syst. Res., vol. 141, pp. 450–459, Dec. 2016, doi: 10.1016/j.epsr.2016.08.005. [54] S. K. Dash, R. Bisoi, and P. K. Dash, “A hybrid functional link dynamic neural network and evolutionary unscented Kalman filter for short-term electricity price forecasting,” Neural Comput. Appl., vol. 27, no. 7, pp. 2123–2140, 2016, doi: 10.1007/s00521-015-2011-z. [55] A. K. Rout, R. Bisoi, and P. K. Dash, “A low complexity evolutionary computationally efficient recurrent Functional link Neural Network for time series forecasting,” in 2015 IEEE Power, Communication and Information Technology Conference (PCITC), Oct. 2015, pp. 576–582. doi: 10.1109/PCITC.2015.7438230. [56] L. Zhou, B. Wang, Z. Wang, F. Wang, and M. Yang, “Seasonal classification and RBF adaptive weight based parallel combined method for day-ahead electricity price forecasting,” in 2018 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT), Feb. 2018, pp. 1–5. doi: 10.1109/ISGT.2018.8403372. [57] K. Sarada and V. Bapiraju, “Comparison of day-ahead price forecasting in energy market using Neural Network and Genetic Algorithm,” in 2014 International Conference on Smart Electric Grid (ISEG), Sep. 2014, pp. 1–5. doi: 10.1109/ISEG.2014.7005607. [58] R. Beigaite and T. Krilavičius, “Electricity price forecasting for Nord Pool data,” presented at the CEUR Workshop Proceedings, 2017, pp. 37–42. [59] R. Beigaite and T. Krilavičius, “Electricity price forecasting for nord pool data using recurrent neural networks,” presented at the CEUR Workshop Proceedings, 2018, pp. 75–78. [60] R. Bisoi, P. K. Dash, and P. P. Das, “Short-term electricity price forecasting and classification in smart grids using optimized multikernel extreme learning machine,” Neural Comput. Appl., Aug. 2018, doi: 10.1007/s00521-018-3652-5. [61] N. A. Shrivastava, A. Khosravi, and B. K. Panigrahi, “Prediction Interval Estimation of Electricity Prices Using PSO-Tuned Support Vector Machines,” IEEE Trans. Ind. Inform., vol. 11, no. 2, pp. 322–331, Apr. 2015, doi: 10.1109/TII.2015.2389625. [62] A. Bello, J. Reneses, A. Muñoz, and A. Delgadillo, “Probabilistic forecasting of hourly electricity prices in the medium-term using spatial interpolation techniques,” Int. J. Forecast., vol. 32, no. 3, pp. 966–980, 2016, doi: 10.1016/j.ijforecast.2015.06.002. [63] M. Burger, B. Klar, A. Müller, and G. Schindlmayr, “A spot market model for pricing derivatives in electricity markets,” Quant. Finance, vol. 4, no. 1, pp. 109–122, Feb. 2004, doi: 10.1088/1469-7688/4/1/010. [64] C. Redl, R. Haas, C. Huber, and B. Böhm, “Price formation in electricity forward markets and the relevance of systematic forecast errors,” Energy Econ., vol. 31, no. 3, pp. 356–364, 2009, Accessed: Jul. 03, 2019. [Online]. Available: https://econpapers.repec.org/article/eeeeneeco/v_3a31_3ay_3a2009_3ai_3a3_3ap_3a356-364.htm [65] J. D. Velásquez, C. J. Franco, and Y. Olaya, “Predicción de los precios promedios mensuales de contratos despachados en el mercado mayorista de electricidad en Colombia usando máquinas de vectores de soporte,” Cuad. Adm., vol. 23, no. 40, 2010. [66] M. Bierbrauer, C. Menn, S. T. Rachev, and S. Trück, “Spot and derivative pricing in the EEX power market,” J. Bank. Finance, vol. 31, no. 11, pp. 3462–3485, 2007, doi: 10.1016/j.jbankfin.2007.04.011. [67] J. D. Velásquez, V. Gil, and C. J. Franco, “An Overview of the Colombian Market for Standarized Derivatives of Energy Commodities,” IEEE Lat. Am. Trans., vol. 13, no. 7, pp. 2176–2182, 2015, doi: 10.1109/TLA.2015.7273774. [68] Isaac. Dyner, Carlos Jaime. Franco, and Santiago. Arango, El mercado mayorista de electricidad colombiano, Primera edición. Medellín: Centro de complejidad Ceiba, 2008. [69] XM, “Informes operación del SIN y administración del mercado.” 2021. [Online]. Available: https://www.xm.com.co/corporativo/Paginas/Nuestra-empresa/informes-anuales.aspx [70] F. Gutiérrez and J. A. Dracup, “An analysis of the feasibility of long-range streamflow forecasting for Colombia using El Niño–Southern Oscillation indicators,” J. Hydrol., vol. 246, no. 1, pp. 181–196, Jun. 2001, doi: 10.1016/S0022-1694(01)00373-0. [71] J. D. Velasquez, I. Dyner, and C. J. Franco, “Modeling the Effect of Macroclimatic Events on River Inflows in the Colombian Electricity Market,” IEEE Lat. Am. Trans., vol. 14, no. 10, pp. 4287–4292, 2016, doi: 10.1109/TLA.2016.7786307. [72] NOAA, “ENSO: Recent Evolution, Current Status and Predictions.” 2017. [Online]. Available: http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/lanina/enso_evolution-status-fcsts-web.pdf [73] N. A. Shrivastava and B. K. Panigrahi, “A hybrid wavelet-ELM based short term price forecasting for electricity markets,” Int. J. Electr. Power Energy Syst., vol. 55, pp. 41–50, Feb. 2014, doi: 10.1016/j.ijepes.2013.08.023. [74] C. A. Giraldo-Prieto, G. J. González Uribe, C. Vesga Bermejo, and D. C. Ferreira Herrera, “Coberturas financieras con derivados y su incidencia en el valor de mercado en empresas colombianas que cotizan en Bolsa,” Contad. Adm., vol. 62, no. 5, pp. 1553–1571, Dec. 2017, doi: 10.1016/j.cya.2017.04.008. [75] J. Benavides, Á. Cadena, J. J. González, C. Hidalgo, and A. Piñeros, Mercado eléctrico en Colombia: Transición hacia una arquitectura descentralizada. 2018. Accessed: Jun. 25, 2019. [Online]. Available: http://www.repository.fedesarrollo.org.co/handle/11445/3694 [76] G. S. S. Marín and J. P. Robayo, “Los precios forward sobre electricidad. ¿Determinados racionalmente por los agentes del mercado colombiano?,” AD-Minist., no. 18, pp. 77–99, 2011, Accessed: Nov. 21, 2021. [Online]. Available: https://dialnet.unirioja.es/servlet/articulo?codigo=6251514 [77] I. Dyner, E. Larsen, and C. J. Franco, “Games for electricity traders: Understanding risk in a deregulated industry,” Energy Policy, vol. 37, no. 2, pp. 465–471, 2009, doi: 10.1016/j.enpol.2008.09.075. [78] A. Pérez and J. Carabalí, “Un modelo sobre competencia en mercados de electricidad: contratos bilaterales en Colombia,” Rev. Desarro. Soc., Feb. 2021, doi: 10.13043/DYS.87.5. [79] J. Barrientos Marín, M. Toro Martínez, J. Barrientos Marín, and M. Toro Martínez, “ON THE FUNDAMENTALS OF ELECTRIC ENERGY PRICE: EMPIRICAL EVIDENCE FOR COLOMBIA,” Rev. Econ. Caribe, no. 19, pp. 33–59, Jun. 2017, Accessed: May 28, 2019. [Online]. Available: http://www.scielo.org.co/scielo.php?script=sci_abstract&pid=S2011-21062017000100033&lng=en&nrm=iso&tlng=es [80] J. Barrientos-Marín and M. T. Martínez, “Análisis de los fundamentales del precio de la energía eléctrica: evidencia empírica para Colombia,” Econ. Caribe, no. 19, pp. 33–59, 2017, Accessed: Oct. 31, 2021. [Online]. Available: https://dialnet.unirioja.es/servlet/articulo?codigo=6143169 [81] BVC and XM S.A, “Estudio de Factibilidad Nuevo Mercado de Derivados Estandarizados sobre Commodities Energéticos.” 2009. [Online]. Available: http://www.derivex.com.co/accionistas/Asamblea%20de%20Constitucion/Indice/Documentos_Asamblea/9_Estudio%20de%20Factibilidad%20DERIVEX.pdf [82] Derivex, “Circular única del Mercado de derivados de commodities energéticos Derivex.” Oct. 31, 2021. [83] Comisión de regulación de Energía y Gas, “Resolución 114 de 2018.” Aug. 02, 2018. Accessed: Nov. 06, 2021. [Online]. Available: www.http://apolo.creg.gov.co/Publicac.nsf/1c09d18d2d5ffb5b05256eee00709c02/e1aa2bca619f4f6e05258305007463d1/$FILE/Creg114-2018.pdf [84] CRCC S.A, “Reglamento de funcionamiento. Cámara de riesgo central de contraparte de Colombia S.A. CRCC S.A.” 2017. [85] R. Mariño, C. León, and C. Cadena, “Las entidades de contrapartida central en la mitigación del riesgo de contraparte y de liquidez: El caso de los derivados cambiarios en Colombia,” Borradores Econ., vol. 1101, 2020. [86] C. B. Hunzinger and C. C. A. Labuschagne, “The Cox, Ross and Rubinstein tree model which includes counterparty credit risk and funding costs,” North Am. J. Econ. Finance, vol. 29, pp. 200–217, Jul. 2014, doi: 10.1016/j.najef.2014.06.002. [87] R. Huisman and R. Mahieu, “Regime jumps in electricity prices,” Energy Econ., vol. 25, no. 5, pp. 425–434, Sep. 2003, doi: 10.1016/S0140-9883(03)00041-0. [88] Y. C. C. Martinez and L. B. Valencia, “Portfolio theory based approach to risk management in electricity markets: Colombian case study,” in IEEE Systems and Information Engineering Design Symposium, 2003, Apr. 2003, pp. 35–40. doi: 10.1109/SIEDS.2003.158001. [89] F. Azevedo, Z. A. Vale, and A. A. do Vale, “Decision-support tool for the establishment of contracts in the electricity market,” in 2003 IEEE Bologna Power Tech Conference Proceedings, Jun. 2003, p. 6 pp. Vol.1-. doi: 10.1109/PTC.2003.1304154. [90] F. Azevedo, Z. A. Vale, and A. A. Vale, “Hedging using futures and options contracts in the electricity market,” Renew. Energy Power Qual. J., vol. 1, no. 1, pp. 496–501, 2003, doi: 10.24084/repqj01.407. [91] A. Pritchard, “Statistical Bibliography or Bibliometrics?,” Journal of Documentation, vol. 25, pp. 348–349, 1969. [92] S. Morris, C. DeYong, Z. Wu, S. Salman, and D. Yemenu, “DIVA: A visualization system for exploring document databases for technology forecasting,” Comput. Ind. Eng., vol. 43, no. 4, pp. 841–862, 2002, doi: 10.1016/S0360-8352(02)00143-2. [93] T. T. Chen and L. Q. Xie, “Identifying critical focuses in research domains,” in Ninth International Conference on Information Visualisation (IV’05), Jul. 2005, pp. 135–142. doi: 10.1109/IV.2005.59. [94] M. J. Cobo, A. G. López‐Herrera, E. Herrera‐Viedma, and F. Herrera, “Science mapping software tools: Review, analysis, and cooperative study among tools,” J. Am. Soc. Inf. Sci. Technol., vol. 62, no. 7, pp. 1382–1402, Jul. 2011, doi: 10.1002/asi.21525. [95] N. J. van Eck and L. Waltman, “Software survey: VOSviewer, a computer program for bibliometric mapping,” Scientometrics, vol. 84, no. 2, pp. 523–538, Aug. 2010, doi: 10.1007/s11192-009-0146-3. [96] M. Aria and C. Cuccurullo, “bibliometrix: An R-tool for comprehensive science mapping analysis,” J. Informetr., vol. 11, no. 4, pp. 959–975, Nov. 2017, doi: 10.1016/j.joi.2017.08.007. [97] L. Cadavid and K. Salazar-Serna, “sustainability Mapping the Research Landscape for the Motorcycle Market Policies: Sustainability as a Trend-A Systematic Literature Review,” Sustainability, vol. 13, p. 10813, Sep. 2021, doi: 10.3390/su131910813. [98] Q. Zhang, X. Wang, and J. Wang, “Electricity purchasing and selling risk decision for power supplier under real-time pricing,” Dianli Xitong ZidonghuaAutomation Electr. Power Syst., vol. 34, no. 3, pp. 22-27+43, 2010. [99] Q. Zhang and X. Wang, “Hedge contract characterization and risk-constrained electricity procurement,” IEEE Trans. Power Syst., vol. 24, no. 3, pp. 1547–1558, 2009, doi: 10.1109/TPWRS.2009.2021233. [100] Y. Xiao, X. Wang, C. Du, and S. Liu, “Hedging against uncertainties for wind power producer with block futures contracts,” presented at the IEEE Power and Energy Society General Meeting, 2015. doi: 10.1109/PESGM.2015.7285850. [101] Z. Wu, X. Wang, Y. Xiao, Y. Zhang, T. Yang, and C. Xue, “Risk management strategy and model for photovoltaic suppliers based on bilateral contracts,” presented at the China International Conference on Electricity Distribution, CICED, 2016. doi: 10.1109/CICED.2016.7576081. [102] F. Azevedo and Z. A. Vale, “A short-term risk management tool applied to OMEL electricity market using Particle Swarm Optimization,” presented at the 2008 5th International Conference on the European Electricity Market, EEM, 2008. doi: 10.1109/EEM.2008.4579039. [103] F. Azevedo, Z. A. Vale, and P. B. de Moura Oliveira, “A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets,” IEEE Trans. Power Syst., vol. 22, no. 3, pp. 995–1003, 2007, doi: 10.1109/TPWRS.2007.901463. [104] F. Azevedo and Z. A. Vale, “Optimal short-term contract allocation using particle swarm optimization,” WSEAS Trans. Inf. Sci. Appl., vol. 2, no. 5, pp. 552–558, 2005. [105] F. Azevedo and Z. A. Vale, “Short-term price forecast from risk management point of view,” presented at the Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems, ISAP’05, 2005, pp. 111–116. doi: 10.1109/ISAP.2005.1599249. [106] F. Azevedo and Z. A. Vale, “Forecasting electricity prices with historical statistical information using neural networks and clustering techniques,” presented at the 2006 IEEE PES Power Systems Conference and Exposition, PSCE 2006 - Proceedings, 2006, pp. 44–50. doi: 10.1109/PSCE.2006.296247. [107] M. Liu and F. F. Wu, “Managing price risk in a multimarket environment,” IEEE Trans. Power Syst., vol. 21, no. 4, pp. 1512–1519, 2006, doi: 10.1109/TPWRS.2006.882455. [108] M. Liu and F. F. Wu, “Portfolio optimization in electricity markets,” Electr. Power Syst. Res., vol. 77, no. 8, pp. 1000–1009, 2007, doi: 10.1016/j.epsr.2006.08.025. [109] M. Liu and F. Wu, “Effect of Financial Hedging in Portfolio Selection in Electricity Markets,” HKIE Trans. Hong Kong Inst. Eng., vol. 17, no. 2, pp. 34–40, 2010, doi: 10.1080/1023697X.2010.10668194. [110] M. Liu and F. F. Wu, “Trading strategy of generation companies in electricity market,” Zhongguo Dianji Gongcheng XuebaoProceedings Chin. Soc. Electr. Eng., vol. 28, no. 25, pp. 111–117, 2008. [111] H. Algarvio and F. Lopes, “Risk Management and Bilateral Contracts in Multi-agent Electricity Markets,” Commun. Comput. Inf. Sci., vol. 430, pp. 279–308, 2014, doi: 10.1007/978-3-319-07767-3_27. [112] H. Algarvio, F. Lopes, and J. Santana, “Bilateral contracting in multi-agent energy markets: Forward contracts and risk management,” Commun. Comput. Inf. Sci., vol. 524, pp. 260–269, 2015, doi: 10.1007/978-3-319-19033-4_22. [113] F. Lopes, H. Algarvio, and J. Santana, “Agent-based simulation of electricity markets: Risk management and contracts for difference,” Underst. Complex Syst., no. 9783319463308, pp. 207–225, 2017, doi: 10.1007/978-3-319-46331-5_10. [114] A. Nasir, K. Shaukat, I. A. Hameed, S. Luo, T. M. Alam, and F. Iqbal, “A Bibliometric Analysis of Corona Pandemic in Social Sciences: A Review of Influential Aspects and Conceptual Structure,” IEEE Access, vol. 8, pp. 133377–133402, 2020, doi: 10.1109/ACCESS.2020.3008733. [115] P. Mathuria and R. Bhakar, “Large consumer’s purchase portfolio optimization in electricity market,” presented at the 2016 IEEE 6th International Conference on Power Systems, ICPS 2016, 2016. doi: 10.1109/ICPES.2016.7584115. [116] J. Hanly, “Risk management and hedging approaches in energy markets,” in Handbook of Energy Finance: Theories, Practices and Simulations, 2020, pp. 651–667. doi: 10.1142/9789813278387_0026. [117] Y. Cai, Q. Chen, and W. Zhang, “Credit and risk management of electricity transaction: A real case based on Guangdong electricity market rules,” presented at the Proceedings - 2020 5th Asia Conference on Power and Electrical Engineering, ACPEE 2020, 2020, pp. 994–999. doi: 10.1109/ACPEE48638.2020.9136388. [118] A.-S. Chen, M. T. Leung, S. Pan, and C.-Y. Chou, “Financial hedging in energy market by cross-learning machines,” Neural Comput. Appl., vol. 32, no. 14, pp. 10321–10335, 2020, doi: 10.1007/s00521-019-04572-4. [119] H. Liu, X. Chen, and J. Xie, “Optimal Bidding Strategy for Electricity Sales Company Considering Contract for Difference and Risk,” presented at the 2019 3rd IEEE Conference on Energy Internet and Energy System Integration: Ubiquitous Energy Network Connecting Everything, EI2 2019, 2019, pp. 2155–2160. doi: 10.1109/EI247390.2019.9062253. [120] T. L. D. Huynh, M. Shahbaz, M. A. Nasir, and S. Ullah, “Financial modelling, risk management of energy instruments and the role of cryptocurrencies,” Ann. Oper. Res., 2020, doi: 10.1007/s10479-020-03680-y. [121] A. Varnavskiy, U. Gruzina, A. Rot, A. Buryakova, E. Sebechenko, and V. Trubnikov, “Prospects and Limitations of the Use of Blockchain-Options for the Supply of Electricity,” Stud. Comput. Intell., vol. 887, pp. 95–110, 2020, doi: 10.1007/978-3-030-40417-8_6. [122] M. R. Cogollo and J. D. Velasquez, “Methodological Advances in Artificial Neural Networks for Time Series Forecasting,” IEEE Lat. Am. Trans., vol. 12, no. 4, pp. 764–771, Jun. 2014, doi: 10.1109/TLA.2014.6868881. [123] K. Ignatieva and S. Trück, “Modeling spot price dependence in Australian electricity markets with applications to risk management,” Comput. Oper. Res., vol. 66, pp. 415–433, 2016, doi: 10.1016/j.cor.2015.07.019. [124] M. Maenhoudt and G. Deconinck, “Strategic Offering to Maximize Day-Ahead Profit by Hedging Against an Infeasible Market Clearing Result,” IEEE Trans. Power Syst., vol. 29, no. 2, pp. 854–862, Mar. 2014, doi: 10.1109/TPWRS.2013.2286005. [125] T. Pinto and F. Falcão-Reis, “Strategic participation in competitive electricity markets: Internal versus sectorial data analysis,” Int. J. Electr. Power Energy Syst., vol. 108, pp. 432–444, 2019, doi: 10.1016/j.ijepes.2019.01.011. [126] E. J. Anderson, X. Hu, and D. Winchester, “Forward contracts in electricity markets: The Australian experience,” Energy Policy, vol. 35, no. 5, pp. 3089–3103, May 2007, doi: 10.1016/j.enpol.2006.11.010. [127] S. Bhattacharya, A. Gupta, K. Kar, and A. Owusu, “Hedging strategies for risk reduction through weather derivatives in renewable energy markets,” in 2015 International Conference on Renewable Energy Research and Applications (ICRERA), Nov. 2015, pp. 1190–1195. doi: 10.1109/ICRERA.2015.7418597. [128] S. Adamson, T. Noe, and G. Parker, “Efficiency of financial transmission rights markets in centrally coordinated periodic auctions,” Energy Econ., vol. 32, no. 4, pp. 771–778, Jul. 2010, doi: 10.1016/j.eneco.2010.03.006. [129] C. Pirrong and M. Jermakyan, “The price of power: The valuation of power and weather derivatives,” J. Bank. Finance, vol. 32, no. 12, pp. 2520–2529, 2008, doi: 10.1016/j.jbankfin.2008.04.007. [130] C. L. Anderson and M. Davison, “The Application of Cash-Flow-at-Risk to Risk Management in a Deregulated Electricity Market,” Hum. Ecol. Risk Assess. Int. J., vol. 15, no. 2, pp. 253–269, Apr. 2009, doi: 10.1080/10807030902761148. [131] J. I. Peña and R. Rodriguez, “Default supply auctions in electricity markets: Challenges and proposals,” Energy Policy, vol. 122, pp. 142–151, 2018, doi: 10.1016/j.enpol.2018.07.031. [132] D. Xiao and H. Chen, “Stochastic up to Congestion Bidding Strategy in the Nodal Electricity Markets Considering Risk Management,” IEEE Access, vol. 8, pp. 202428–202438, 2020, doi: 10.1109/ACCESS.2020.3015025. [133] F. Azevedo, Z. A. Vale, P. B. M. Oliveira, and H. M. Khodr, “A long-term risk management tool for electricity markets using swarm intelligence,” Electr. Power Syst. Res., vol. 80, no. 4, pp. 380–389, Apr. 2010, doi: 10.1016/j.epsr.2009.10.002. [134] N. Jovanović, J. García-González, J. Barquín, and S. Cerisola, “Electricity market short-term risk management via risk-adjusted probability measures,” IET Gener. Transm. Distrib., vol. 11, no. 10, pp. 2599–2607, 2017, doi: 10.1049/iet-gtd.2016.1731. [135] F.-J. Heredia, J. Cifuentes-Rubiano, and C. Corchero, “Stochastic optimal generation bid to electricity markets with emissions risk constraints,” J. Environ. Manage., vol. 207, pp. 432–443, 2018, doi: 10.1016/j.jenvman.2017.11.010. [136] S. S. Oren and Y. Oum, “Managing risk under a fixed price load following obligation for electricity service,” presented at the IEEE PES General Meeting, PES 2010, 2010. doi: 10.1109/PES.2010.5589689. [137] B. A. Souhir, B. Heni, and B. Lotfi, “Price risk and hedging strategies in Nord Pool electricity market evidence with sector indexes,” Energy Econ., vol. 80, pp. 635–655, 2019, doi: 10.1016/j.eneco.2019.02.001. [138] B. Tranberg, R. T. Hansen, and L. Catania, “Managing volumetric risk of long-term power purchase agreements,” Energy Econ., vol. 85, 2020, doi: 10.1016/j.eneco.2019.104567. [139] M. J. Stevenson, L. F. M. Do Amaral, and M. Peat, “Risk management and the role of spot price predictions in the Australian retail electricity market,” Stud. Nonlinear Dyn. Econom., vol. 10, no. 3, 2006, doi: 10.2202/1558-3708.1383. [140] J. Huang, Y. Xue, Z. Y. Dong, and K. P. Wong, “An efficient probabilistic assessment method for electricity market risk management,” IEEE Trans. Power Syst., vol. 27, no. 3, pp. 1485–1493, 2012, doi: 10.1109/TPWRS.2012.2183900. [141] C. Klüppelberg, T. Meyer-Brandis, and A. Schmidt, “Electricity spot price modelling with a view towards extreme spike risk,” Quant. Finance, vol. 10, no. 9, pp. 963–974, 2010, doi: 10.1080/14697680903150496. [142] R. I. Brik and A. Roncoroni, “Static mitigation of volumetric risk,” J. Energy Mark., May 2016, Accessed: Nov. 23, 2021. [Online]. Available: https://www.risk.net/node/2457758 [143] F. E. Benth, L. Di Persio, and S. Lavagnini, “Stochastic modeling of wind derivatives in energy markets,” Risks, vol. 6, no. 2, 2018, doi: 10.3390/risks6020056. [144] K. W. Park, “Risk management and derivatives in korea electricity markets,” presented at the IFAC Proceedings Volumes (IFAC-PapersOnline), 2003, pp. 915–920. doi: 10.1016/S14746670(17)34590-1. [145] W. Liu, J. Shang, W. Zhou, F. Wen, and Z. Lin, “Evaluation of value-at-risk in electricity markets based on multifractal theory,” Dianli Xitong ZidonghuaAutomation Electr. Power Syst., vol. 37, no. 7, pp. 48–54, 2013, doi: 10.7500/AEPS201206172. [146] A. T. Carrasquilla, J. F. R. García, and J. O. P. Robayo, “Sub-optimal allocation under VaR constraints in electricity markets,” Rev. Econ. Rosario, vol. 19, no. 2, pp. 201–220, 2016, doi: 10.12804/revistas.urosario.edu.co/economia/a.5625. [147] Q. Zhang and X. Wang, “Customer electricity purchasing risk decision under real-time pricing,” presented at the 2009 IEEE/PES Power Systems Conference and Exposition, PSCE 2009, 2009. doi: 10.1109/PSCE.2009.4839934. [148] J. Cabero, M. J. Ventosa, S. Cerisola, and Á. Baíllo, “Modeling risk management in oligopolistic electricity markets: A benders decomposition approach,” IEEE Trans. Power Syst., vol. 25, no. 1, pp. 263–271, 2010, doi: 10.1109/TPWRS.2009.2036788. [149] D. Chin and A. Siddiqui, “Capacity expansion and forward contracting in a duopolistic power sector,” Comput. Manag. Sci., vol. 11, no. 1–2, pp. 57–86, 2014, doi: 10.1007/s10287-013-0166-6. [150] F. Gökgöz and M. E. Atmaca, “Financial optimization in the Turkish electricity market: Markowitz’s mean-variance approach,” Renew. Sustain. Energy Rev., vol. 16, no. 1, pp. 357–368, 2012, doi: 10.1016/j.rser.2011.06.018. [151] Y.-K. Wu, “Intelligent management and hedging strategy on energy contracts in competitive electricity markets,” Int. J. Power Energy Syst., vol. 30, no. 3, pp. 213–218, 2010, doi: 10.2316/Journal.203.2010.3.203-4616. [152] D. Feng, D. Gan, J. Zhong, and Y. Ni, “Efficient frontier analysis for the GenCo multi-trading strategy: Part one Model analysis,” Dianli Xitong ZidonghuaAutomation Electr. Power Syst., vol. 31, no. 13, pp. 29–35, 2007. [153] F. Azevedo, Z. A. Vale, and P. B. de Moura Oliveira, “A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets,” IEEE Trans. Power Syst., vol. 22, no. 3, pp. 995–1003, 2007, doi: 10.1109/TPWRS.2007.901463. [154] F. Azevedo, Z. A. Vale, and P. B. M. Oliveira, “Long-term Price Range Forecast Applied to Risk Management Using Regression Models,” in 2007 International Conference on Intelligent Systems Applications to Power Systems, Nov. 2007, pp. 1–6. doi: 10.1109/ISAP.2007.4441656. [155] J. A. Díaz Contreras, G. I. Macías Villalba, and E. Luna González, “Estrategia de cobertura con productos derivados para el mercado energético colombiano,” Estud. Gerenciales, vol. 30, no. 130, pp. 55–64, Jan. 2014, doi: 10.1016/j.estger.2014.02.008. [156] A. T. Carrasquilla, J. F. R. García, and J. Pantoja, “Estrategia de cobertura a través de contratos a plazo en mercados eléctricos,” Acad. Rev. Latinoam. Adm., no. 50, pp. 148–157, 2012, Accessed: Jun. 25, 2019. [Online]. Available: http://www.redalyc.org/articulo.oa?id=71624352010 [157] K. Maradey-Angarita, J. O. Pantoja-Robayo, A. Trespalacios-Carrasquilla, K. Maradey-Angarita, J. O. Pantoja-Robayo, and A. Trespalacios-Carrasquilla, “Analysis of the financial margins required to hedge risks in electric power futures markets,” Ecos Econ., vol. 21, no. 45, pp. 67–105, Dec. 2017, doi: 10.17230/ecos.2017.45.4. [158] J. Barrientos, E. Rodas, E. Velilla, M. Lopera, and F. Villada, “A model for forecasting electricity prices in Colombia,” Lect. Econ., no. 77, pp. 91–127, Dec. 2012, Accessed: May 28, 2019. [Online]. Available: http://www.scielo.org.co/scielo.php?script=sci_abstract&pid=S0120-25962012000200003&lng=en&nrm=iso&tlng=es [159] E. U. Gaviria and A. T. Carrasquilla, “Contraste de modelos estocásticos para el precio de la energía en Colombia,” Rev. Fac. Cienc., vol. 3, no. 1, pp. 41–55, Jan. 2014, Accessed: May 29, 2019. [Online]. Available: https://revistas.unal.edu.co/index.php/rfc/article/view/49524 [160] O. H. N, J. D. Velásquez, and I. Dyner, “Modelos ARIMA y estructural de la serie de precios promedio de los contratos en el Mercado Mayorista de Energía Eléctrica en Colombia,” Energética, no. 34, pp. 5–10, 2005, Accessed: Jun. 05, 2019. [Online]. Available: http://www.redalyc.org/articulo.oa?id=147019373001 [161] A. A. A. Monica, S. B. Botero, and H. H. B. Jaime, “Financial anomalies in the electricity market: Empirical analysis of spot prices,” in 2018 13th Iberian Conference on Information Systems and Technologies (CISTI), Jun. 2018, pp. 1–7. doi: 10.23919/CISTI.2018.8398640. [162] A. Muñoz-Santiago, J. Urquijo-Vanstrahlengs, A. Castro-Otero, and J. Lombana, “Pronóstico del precio de la energía en Colombia utilizando modelos ARIMA con IGARCH,” Rev. Econ. Rosario, vol. 20, no. 1, p. 36, Oct. 2017, doi: 10.12804/revistas.urosario.edu.co/economia/a.6152. [163] F. Villada, D. R. Cadavid, and J. D. Molina, “Electricity price forecasting using artificial neural networks,” Rev. Fac. Ing., no. 44, pp. 111–118, 2008. [164] F. Villada, E. García, and J. D. Molina, “Electricity price forecasting using neurofuzzy networks,” Inf. Tecnol., vol. 22, no. 6, pp. 111–120, 2011, doi: 10.4067/S0718-07642011000600012. [165] A. P. Agudelo, J. M. López-Lezama, and E. Velilla, “Forecasting Electricity Stock Price by Means of a Nonlinear Autoregressive Neural Model with Exogenous Inputs,” Inf. Tecnológica, vol. 26, no. 6, pp. 99–108, 2015, doi: 10.4067/S0718-07642015000600012. [166] H. Juan David Velásquez, C. Carlos Jaime Franco, and M. Yris Olaya, “Forecasting average monthly prices for colombian wholesale electricity market contracts,” Cuad. Adm., vol. 23, no. 40, pp. 321–337, 2010. [167] J. B. Marín, E. T. Orozco, and E. Velilla, “Forecasting electricity price in Colombia: A comparison between neural network, ARMA process and hybrid models,” Int. J. Energy Econ. Policy, vol. 8, no. 3, pp. 97–106, 2018. [168] N. J. H. Bueno, M. de los Á. P. Calderón, Y. A. M. Maldonado, and A. O. Castro, “Determination of Models of Simple Regression and Multivariate Analysis for the Forecast of the Electricity Price in Colombia at 2030,” Int. J. Energy Econ. Policy, vol. 8, no. 5, pp. 202–211, Sep. 2018, Accessed: Jun. 05, 2019. [Online]. Available: https://www.econjournals.com/index.php/ijeep/article/view/6415 [169] D. Tobón-Orozco et al., “LONG-TERM SEASONAL FORWARDS IN ELECTRICITY GENERATION MARKETS: AN APPLICATION TO COLOMBIA,” Cuad. Econ., vol. 37, no. 74, pp. 287–313, Dec. 2018, doi: 10.15446/cuad.econ.v37n74.54299. [170] J. C. H. Jhon C. Hull, Introducción a los Mercados futuros y opciones, 6Ed. Prentice, Hall, 2008. [171] F. Black and M. Scholes, “The Pricing of Options and Corporate Liabilities,” J. Polit. Econ., vol. 81, no. 3, pp. 637–654, May 1973, doi: 10.1086/260062. [172] F. Venegas, Riesgos financieros y económicos, Segunda. CENGAGE Learning, 2008. [173] J. C. Cox, S. A. Ross, and M. Rubinstein, “Option pricing: A simplified approach,” J. Financ. Econ., vol. 7, no. 3, pp. 229–263, Sep. 1979, doi: 10.1016/0304-405X(79)90015-1. [174] A. M. Calle Fernández and V. M. Tamayo Bustamante, “Decisiones de inversión a través de opciones reales,” Estud. Gerenciales, vol. 25, no. 111, pp. 107–126, Apr. 2009, doi: 10.1016/S0123-5923(09)70073-7. [175] A. Bello, J. Reneses, and A. Muñoz, “Medium-Term Probabilistic Forecasting of Extremely Low Prices in Electricity Markets: Application to the Spanish Case,” Energies, vol. 9, no. 3, p. 193, Mar. 2016, doi: 10.3390/en9030193. [176] E. S. Schwartz, “The Stochastic Behavior of Commodity Prices: Implications for Valuation and Hedging,” J. Finance, vol. 52, no. 3, pp. 923–73, 1997, Accessed: Jun. 24, 2019. [Online]. Available: https://econpapers.repec.org/article/blajfinan/v_3a52_3ay_3a1997_3ai_3a3_3ap_3a923-73.htm [177] D. Pilipovic, Energy Risk: Valuing and Managing Energy Derivatives, Second. McGraw-Hill, 2007. [178] V. Canazza, Guang Li, Chen Ching Liu, D. Lucarella, and A. Venturini, “An Intelligent System For Price Forecasting Accuracy Assessment,” in Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems, Nov. 2005, pp. 92–99. doi: 10.1109/ISAP.2005.1599246. [179] V. Gonzalez, J. Contreras, and D. W. Bunn, “Forecasting Power Prices Using a Hybrid Fundamental-Econometric Model,” IEEE Trans. Power Syst., vol. 27, no. 1, pp. 363–372, Feb. 2012, doi: 10.1109/TPWRS.2011.2167689. [180] M. G. Khajeh, A. Maleki, M. A. Rosen, and M. H. Ahmadi, “Electricity price forecasting using neural networks with an improved iterative training algorithm,” Int. J. Ambient Energy, vol. 39, no. 2, pp. 147–158, 2018, doi: 10.1080/01430750.2016.1269674. [181] B. Uniejewski and R. Weron, “Efficient Forecasting of Electricity Spot Prices with Expert and LASSO Models,” Energies, vol. 11, no. 8, p. 2039, Aug. 2018, doi: 10.3390/en11082039. [182] R. Weron, “Electricity price forecasting: A review of the state-of-the-art with a look into the future,” Int. J. Forecast., vol. 30, no. 4, pp. 1030–1081, 2014, doi: 10.1016/j.ijforecast.2014.08.008. [183] X. Yan and N. A. Chowdhury, “Mid-term electricity market clearing price forecasting: A multiple SVM approach,” Int. J. Electr. Power Energy Syst., vol. 58, pp. 206–214, Jun. 2014, doi: 10.1016/j.ijepes.2014.01.023. [184] P. Mandal, T. Senjyu, N. Urasaki, T. Funabashi, and A. K. Srivastava, “Short-Term Price Forecasting for Competitive Electricity Market,” in 2006 38th North American Power Symposium, Sep. 2006, pp. 137–141. doi: 10.1109/NAPS.2006.360135. [185] L. Wu and M. Shahidehpour, “A Hybrid Model for Day-Ahead Price Forecasting,” IEEE Trans. Power Syst., vol. 25, no. 3, pp. 1519–1530, Aug. 2010, doi: 10.1109/TPWRS.2009.2039948. [186] I. Shah, H. Bibi, S. Ali, L. Wang, and Z. Yue, “Forecasting One-Day-Ahead Electricity Prices [186] I. Shah, H. Bibi, S. Ali, L. Wang, and Z. Yue, “Forecasting One-Day-Ahead Electricity Prices for Italian Electricity Market Using Parametric and Nonparametric Approaches,” IEEE Access, vol. 8, pp. 123104–123113, 2020, doi: 10.1109/ACCESS.2020.3007189. [187] M. A. Boyacioglu and D. Avci, “An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: The case of the Istanbul stock exchange,” Expert Syst. Appl., vol. 37, no. 12, pp. 7908–7912, 2010, doi: 10.1016/j.eswa.2010.04.045. [188] Z. Yang, L. Ce, and L. Lian, “Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods,” Appl. Energy, vol. 190, pp. 291–305, 2017, doi: 10.1016/j.apenergy.2016.12.130. [189] R. T. Clement, Making Hard Decisions: An Introduction to Decision Analysis, 2nd ed. United States of America: Duxbury, 1997. [190] J. Barrientos, E. Rodas, E. Velilla, M. Lopera, and F. Villada, “Modelo para el pronóstico del precio de la energía eléctrica en Colombia,” Lect. Econ., no. 77, pp. 91–127, Dec. 2012, Accessed: Aug. 30, 2018. [Online]. Available: http://www.scielo.org.co/scielo.php?script=sci_abstract&pid=S0120-25962012000200003&lng=en&nrm=iso&tlng=es [191] G. Poveda and Ó. J. Mesa, “Las fases extremas del fenómeno ENSO (El Niño y La Niña) y su influencia sobre la hidrología de Colombia,” Tecnol. Cienc. Agua, vol. 11, no. 1, Art. no. 1, 1996, Accessed: Nov. 02, 2021. [Online]. Available: http://www.revistatyca.org.mx/index.php/tyca/article/view/765 [192] UPME, “Plan de Expansión de Referencia Generación-Transimisión 2015-2029.” 2015. [Online]. Available: https://www1.upme.gov.co/Paginas/Plan-Expansion-2015-2029.aspx
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.extent.spa.fl_str_mv	xiv, 89 páginas
dc.format.mimetype.spa.fl_str_mv	application/pdf
dc.coverage.country.none.fl_str_mv	Colombia
dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Medellín - Minas - Doctorado en Ingeniería - Sistemas Energéticos
dc.publisher.faculty.spa.fl_str_mv	Facultad de Minas
dc.publisher.place.spa.fl_str_mv	Medellín, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Medellín
institution	Universidad Nacional de Colombia
bitstream.url.fl_str_mv	https://repositorio.unal.edu.co/bitstream/unal/83913/2/1.036.598.990.2023.pdf https://repositorio.unal.edu.co/bitstream/unal/83913/1/license.txt https://repositorio.unal.edu.co/bitstream/unal/83913/3/1.036.598.990.2023.pdf.jpg
bitstream.checksum.fl_str_mv	a0b7ab11c169a5e83e5f0ae265ef048f eb34b1cf90b7e1103fc9dfd26be24b4a 0eae51ad88ab8893a917c194d7c1f475
bitstream.checksumAlgorithm.fl_str_mv	MD5 MD5 MD5
repository.name.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv	repositorio_nal@unal.edu.co
_version_	1814089609005498368
spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Velásquez Henao, Juan David7b16d4a5377f0f1b1f90d3c8c6fd9f8bArango londoño, Adriana6924c3d5f4bee5f1c41c2be9f49968bdBig Data y Data AnalyticsArango londoño, Adriana [0000-0001-8919-7548]Velásquez2023-05-30T17:00:28Z2023-05-30T17:00:28Z2023-05-28https://repositorio.unal.edu.co/handle/unal/83913Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, mapasDesde el año 1994 el mercado eléctrico colombiano opera bajo una nueva estructura que permitió la apertura al Mercado Eléctrico Mayorista en Colombia (MEM) y la creación de la Bolsa de Energía. Esta reestructuración creó un ambiente competitivo en el que los agentes se enfrentan a nuevos desafíos por las nuevas reglas del mercado y se exponen a la incertidumbre asociada al precio futuro de la electricidad que se caracteriza por tener una alta volatilidad. Con el fin de mitigar la exposición al riesgo de los agentes del mercado se crean los mercados de derivados eléctricos que cuentan con una estructura similar a la de los mercados financieros. Colombia no es ajeno a esta situación y en el 2010 se crea el Mercado de Derivados Estandarizados de Commodities Energéticos Derivex, en el cual se pueden negociar contratos de futuros de electricidad que comprometen a las partes a cumplir sus obligaciones de compra o venta en una fecha futura a un precio establecido. Diferentes aproximaciones se han desarrollado en la teoría financiera para valorar los contratos de futuros; sin embargo, estas metodologías no pueden ser aplicadas en los mercados eléctricos por la complejidad que exhibe la serie de precios de la electricidad la cual determina la decisión de comprar o vender el contrato. En consecuencia, los agentes del mercado no cuentan con un soporte teórico para construir una estrategía de cobertura que les permita mitigar los riesgos asociados a las fluctuaciones del precio de la electricidad y maximizar sus beneficios económicos. Este trabajo presenta un modelo de valoración para los contratos de futuros de Derivex compuesto por un modelo de árboles de decisión y un modelo de simulación del precio de la electricidad en la Bolsa de Energía. Los resultados obtenidos demuestran que es posible realizar el cubrimiento del riesgo de la Bolsa de energía a partir del modelo propuesto. El modelo desarrollado permite incorporar las expectativas del analista sobre el crecimiento de la demanda y la evolución de la hidrología. (Texto tomado de la fuente)Since 1994, the electricity market in Colombia operates under a new structure that allowed the opening of the Wholesale Electricity Market (WEM) and the creation of the spot market. This restructure created a competitive environment in which agents face new challenges due to new market rules, and are exposed to the uncertainty associated with the future price of electricity, which is characterized by high volatility. In order to mitigate the risk exposure of market agents, electricity derivatives markets are created, counting with a structure similar to that of financial markets. Colombia is not unfamiliar with this situation and in 2010 the Standardized Derivatives Market of Energy Commodities DERIVEX was created, in which electricity futures contracts can be negotiated, committing the parties to fulfill their purchase and sale obligations on an agreed date and price. Different approaches were developed in financial theory to value futures contracts; however, these methodologies cannot be applied in the electricity markets due to the complexity exhibited by the series of electricity prices, which determines the decision to buy or sell the contract. Consequently, market agents do not have theoretical support to build a hedging strategy that allows them to mitigate the risks associated with fluctuations in the price of electricity and maximize their economic benefits. This paper presents a valuation model for Derivex futures contracts composed of a decision tree model and a simulation model of the price of electricity on the spot market. The results obtained show that it is possible to hedge the risk of the electricity spot market from the proposed model. The developed model allows incorporating the analyst's expectations regarding demand growth and the evolution of hydrology.DoctoradoDoctor en IngenieríaAnalítica en mercados de energíaÁrea curricular de Ingeniería Química e Ingeniería de Petróleosxiv, 89 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Doctorado en Ingeniería - Sistemas EnergéticosFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín330 - Economía::333 - Economía de la tierra y de la energíaElectricidad - Aspectos económicosElectricity - IndustryElectricidad - IndustriaDerivexElectricity spot priceNumerical simulationManagement riskPrecio spot de la electricidadSimulación numéricaGestión de riesgosModelo de valoración de contratos en DerivexValuation model for contracts in DerivexTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDColombiaRedColLaReferencia[1] N. Singh, S. R. Mohanty, and S. Dev, “Short term electricity price forecast based on environmentally adapted generalized neuron,” Energy, vol. 125, pp. 127–139, 2017, doi: 10.1016/j.energy.2017.02.094.[2] “Power System Economics: Designing Markets for Electricity \| Wiley,” Wiley.com. https://www.wiley.com/en-[3] A. Khosravi, S. Nahavandi, and D. Creighton, “A neural network-GARCH-based method for construction of Prediction Intervals,” Electr. Power Syst. Res., vol. 96, pp. 185–193, 2013, doi: 10.1016/j.epsr.2012.11.007.[4] M. Gürtler and T. Paulsen, “Forecasting performance of time series models on electricity spot markets: a quasi-meta-analysis,” Int. J. Energy Sect. Manag., vol. 12, no. 1, pp. 103–129, 2018, doi: 10.1108/IJESM-06-2017-0004.[5] A. Khosravi, S. Nahavandi, and D. Creighton, “Quantifying uncertainties of neural network-based electricity price forecasts,” Appl. Energy, vol. 112, pp. 120–129, 2013, doi: 10.1016/j.apenergy.2013.05.075.[6] P. Di and I. Perin, “An ambit stochastic approach to pricing electricity forward contracts: The case of the German Energy Market,” J. Probab. Stat., vol. 2015, 2015, doi: 10.1155/2015/626020.[7] M. Shafie-khah, M. P. Moghaddam, and M. K. Sheikh-El-Eslami, “Price forecasting of day-ahead electricity markets using a hybrid forecast method,” Energy Convers. Manag., vol. 52, no. 5, pp. 2165–2169, May 2011, doi: 10.1016/j.enconman.2010.10.047.[8] H. Xiong and R. Mamon, “A higher-order Markov chain-modulated model for electricity spot-price dynamics,” Appl. Energy, vol. 233–234, pp. 495–515, 2019, doi: 10.1016/j.apenergy.2018.09.039.[9] G. G. P. Murthy, V. Sedidi, A. K. Panda, and B. N. Rath, “Forecasting electricity prices in deregulated wholesale spot electricity market: A review,” Int. J. Energy Econ. Policy, vol. 4, no. 1, pp. 32–42, 2014.[10] J.-M. Hu and J.-Z. Wang, “Forecasting day-ahead electricity price using a hybrid improved approach,” J. Electr. Eng. Technol., vol. 12, no. 6, pp. 2166–2176, 2017, doi: 10.5370/JEET.2017.12.6.2166.[11] K. B. Sahay, “One hour ahead price forecast of Ontario electricity market by using ANN,” in 2015 International Conference on Energy Economics and Environment (ICEEE), Mar. 2015, pp. 1–6. doi: 10.1109/EnergyEconomics.2015.7235102.[12] H. Mosbah and M. El-Hawary, “Hourly Electricity Price Forecasting for the Next Month Using Multilayer Neural Network,” Can. J. Electr. Comput. Eng., vol. 39, no. 4, pp. 283–291, 2016, doi: 10.1109/CJECE.2016.2586939.[13] J.-L. Zhang, Y.-J. Zhang, D.-Z. Li, Z.-F. Tan, and J.-F. Ji, “Forecasting day-ahead electricity prices using a new integrated model,” Int. J. Electr. Power Energy Syst., vol. 105, pp. 541–548, Feb. 2019, doi: 10.1016/j.ijepes.2018.08.025.[14] A. Aghajani, R. Kazemzadeh, and A. Ebrahimi, “Short-term prediction of market-clearing price of electricity in the presence of wind power plants by a hybrid intelligent system,” Neural Comput. Appl., May 2018, doi: 10.1007/s00521-018-3544-8.[15] T. Niimura, “Forecasting Techniques for Deregulated Electricity Market Prices - Extended Survey,” in 2006 IEEE PES Power Systems Conference and Exposition, Oct. 2006, pp. 51–56. doi: 10.1109/PSCE.2006.296248.[16] M. M. G. Zapata and C. M. Ochoa, “Modelación De La Volatilidad De Los Precios De La Energía Eléctrica En Colombia,” Rev. Ing. Univ. Medellín, vol. 7, no. 12, pp. 87–114, 2008, Accessed: Jun. 05, 2019. [Online]. Available: http://www.redalyc.org/articulo.oa?id=75011517005[17] K. Maciejowska and R. Weron, “Forecasting of daily electricity prices with factor models: utilizing intra-day and inter-zone relationships,” Comput. Stat., vol. 30, no. 3, pp. 805–819, 2015, doi: 10.1007/s00180-014-0531-0.[18] G. P. Girish and S. Vijayalakshmi, “Spot Electricity Price Dynamics of Indian Electricity Market,” in Advances in Computer Science and its Applications, H. Y. Jeong, M. S. Obaidat, N. Y. Yen, and J. J. (Jong H. Park, Eds., in Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2014, pp. 1129–1135.[19] J. Pokora, “Hybrid arima and support vector regression in short-term electricity price forecasting,” Acta Univ. Agric. Silvic. Mendel. Brun., vol. 65, no. 2, pp. 699–708, 2017, doi: 10.11118/actaun201765020699.[20] H. Izadpanah, G. R. Yousefi, and S. M. Kaviri, “Asset and risk management for a wind farm in a competitive electricity market,” in 2013 21st Iranian Conference on Electrical Engineering (ICEE), May 2013, pp. 1–6. doi: 10.1109/IranianCEE.2013.6599765.[21] J. Kettunen, A. Salo, and D. W. Bunn, “Optimization of electricity retailer’s contract portfolio subject to risk preferences,” IEEE Trans. Power Syst., vol. 25, no. 1, pp. 117–128, 2010, doi: 10.1109/TPWRS.2009.2032233.[22] M. Liu and F. F. Wu, “Framework for generation risk management in electricity markets,” Dianli Xitong ZidonghuaAutomation Electr. Power Syst., vol. 28, no. 13, pp. 1–6, 2004.[23] M. Liu and F. F. Wu, “Risk management in a competitive electricity market,” in Analytical Methods for Energy Diversity and Security, 2008, pp. 247–262. doi: 10.1016/B978-0-08-056887-4.00012-3.[24] K. He, C. Xie, S.-M. Guu, L. Yu, and K. K. Lai, “Multi scale risk measurement in electricity market: A wavelet based value at risk approach,” J. Southeast Univ. Engl. Ed., vol. 24, no. SUPPL., pp. 54–59, 2008.[25] K. Andriosopoulos and N. Nomikos, “Risk management in the energy markets and value-at-risk modelling: A hybrid approach,” Eur. J. Finance, vol. 21, no. 7, pp. 548–574, 2015, doi: 10.1080/1351847X.2013.862173.[26] J. Wang, Y. Li, and S. Zhang, “CVaR-based electricity purchase model for power supply company,” Dianli Zidonghua Shebei Electr. Power Autom. Equip., vol. 28, no. 2, pp. 19–23, 2008.[27] H. Hailun, Y. Zheng, and H. Yunhe, “A novel CVaR based portfolio optimization model for LDC electricity procurement,” presented at the 2008 Joint International Conference on Power System Technology POWERCON and IEEE Power India Conference, POWERCON 2008, 2008. doi: 10.1109/ICPST.2008.4745232.[28] A. Hatami, H. Seifi, and M. K. Sheikh-El-Eslami, “A stochastic-based decision-making framework for an electricity retailer: Time-of-use pricing and electricity portfolio optimization,” IEEE Trans. Power Syst., vol. 26, no. 4, pp. 1808–1816, 2011, doi: 10.1109/TPWRS.2010.2095431.[29] D. Feng, D. Gan, J. Zhong, and Y. Ni, “Supplier asset allocation in a pool-based electricity market,” IEEE Trans. Power Syst., vol. 22, no. 3, pp. 1129–1138, 2007, doi: 10.1109/TPWRS.2007.901282.[30] M. Liu and F. F. Wu, “Correlation matrices estimation in energy portfolio optimization,” presented at the IEEE Power and Energy Society 2008 General Meeting: Conversion and Delivery of Electrical Energy in the 21st Century, PES, 2008. doi: 10.1109/PES.2008.4596170.[31] D. Feng, D. Gan, and J. Zhong, “Supplier multi-trading strategy: A stochastic programming approach,” presented at the IEEE Power and Energy Society 2008 General Meeting: Conversion and Delivery of Electrical Energy in the 21st Century, PES, 2008. doi: 10.1109/PES.2008.4596119.[32] P. Tang and A. Callan, “Contract optimisation and market operation - A generator’s perspective,” presented at the CIGRE International Symposium Guilin 2009: Operation and Development of Power System in the New Context, 2009.[33] J. F. RodrÍguez, “Hedging swing options,” Int. J. Theor. Appl. Finance, vol. 14, no. 2, pp. 295–312, 2011, doi: 10.1142/S021902491100636X.[34] F. Martínez-álvarez, A. Troncoso, G. Asencio-Cortés, and J. C. Riquelme, “A survey on data mining techniques applied to electricity-related time series forecasting,” Energies, vol. 8, no. 11, pp. 13162–13193, 2015, doi: 10.3390/en81112352.[35] H. Daneshi and A. Daneshi, “Price forecasting in deregulated electricity markets - A bibliographical survey,” presented at the 3rd International Conference on Deregulation and Restructuring and Power Technologies, DRPT 2008, 2008, pp. 657–661. doi: 10.1109/DRPT.2008.4523487.[36] G. J. Osório, J. C. O. Matias, and J. P. S. Catalão, “Electricity prices forecasting by a hybrid evolutionary-adaptive methodology,” Energy Convers. Manag., vol. 80, pp. 363–373, 2014, doi: 10.1016/j.enconman.2014.01.063.[37] S. K. Aggarwal, L. M. Saini, and A. Kumar, “Electricity price forecasting in deregulated markets: A review and evaluation,” Int. J. Electr. Power Energy Syst., vol. 31, no. 1, pp. 13–22, 2009, doi: 10.1016/j.ijepes.2008.09.003.[38] P. M. R. Bento, J. A. N. Pombo, M. R. A. Calado, and S. J. P. S. Mariano, “A bat optimized neural network and wavelet transform approach for short-term price forecasting,” Appl. Energy, vol. 210, pp. 88–97, Jan. 2018, doi: 10.1016/j.apenergy.2017.10.058.[39] S. K. Aggarwal, L. M. Saini, and A. Kumar, “Short term price forecasting in deregulated electricity markets: A review of statistical models and key issues,” Int. J. Energy Sect. Manag., vol. 3, no. 4, pp. 333–358, 2009, doi: 10.1108/17506220911005731.[40] A. Aggarwal and M. M. Tripathi, “A novel hybrid approach using wavelet transform, time series time delay neural network, and error predicting algorithm for day-ahead electricity price forecasting,” presented at the 2017 6th International Conference on Computer Applications in Electrical Engineering - Recent Advances, CERA 2017, 2018, pp. 199–204. doi: 10.1109/CERA.2017.8343326.[41] F. Wang et al., “Daily pattern prediction based classification modeling approach for day-ahead electricity price forecasting,” Int. J. Electr. Power Energy Syst., vol. 105, pp. 529–540, Feb. 2019, doi: 10.1016/j.ijepes.2018.08.039.[42] S. S. Reddy, C.-M. Jung, and K. J. Seog, “Day-ahead electricity price forecasting using back propagation neural networks and weighted least square technique,” Front. Energy, vol. 10, no. 1, pp. 105–113, 2016, doi: 10.1007/s11708-016-0393-y.[43] M. Cerjan, I. Krželj, M. Vidak, and M. Delimar, “A literature review with statistical analysis of electricity price forecasting methods,” presented at the IEEE EuroCon 2013, 2013, pp. 756–763. doi: 10.1109/EUROCON.2013.6625068.[44] X. Yan and N. A. Chowdhury, “Mid-term electricity market clearing price forecasting utilizing hybrid support vector machine and auto-regressive moving average with external input,” Int. J. Electr. Power Energy Syst., vol. 63, pp. 64–70, Dec. 2014, doi: 10.1016/j.ijepes.2014.05.037.[45] Guang Li, Chen-Ching Liu, J. Lawarree, M. Gallanti, and A. Venturini, “State-of-the-art of electricity price forecasting,” in International Symposium CIGRE/IEEE PES, 2005., Oct. 2005, pp. 110–119. doi: 10.1109/CIGRE.2005.1532733.[46] A. Ghanbari, S. F. Ghaderi, and M. A. Azadeh, “Adaptive Neuro-Fuzzy Inference System vs. Regression based approaches for annual electricity load forecasting,” in 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE), Feb. 2010, pp. 26–30. doi: 10.1109/ICCAE.2010.5451534.[47] E. Raviv, K. E. Bouwman, and D. van, “Forecasting day-ahead electricity prices: Utilizing hourly prices,” Energy Econ., vol. 50, pp. 227–239, 2015, doi: 10.1016/j.eneco.2015.05.014.[48] J. Carpio, J. Juan, and D. López, “Multivariate Exponential Smoothing and Dynamic Factor Model Applied to Hourly Electricity Price Analysis,” Technometrics, vol. 56, no. 4, pp. 494–503, Oct. 2014, doi: 10.1080/00401706.2013.860920.[49] F. Lisi and M. M. Pelagatti, “Component estimation for electricity market data: Deterministic or stochastic?,” Energy Econ., vol. 74, pp. 13–37, Aug. 2018, doi: 10.1016/j.eneco.2018.05.027.[50] H. Ebrahimian, S. Barmayoon, M. Mohammadi, and N. Ghadimi, “The price prediction for the energy market based on a new method,” Econ. Res.-Ekon. Istraživanja, vol. 31, no. 1, pp. 313–337, Jan. 2018, doi: 10.1080/1331677X.2018.1429291.[51] C. Ballester and D. Furió, “Impact of Wind Electricity Forecasts on Bidding Strategies,” Sustainability, vol. 9, no. 8, p. 1318, Aug. 2017, doi: 10.3390/su9081318.[52] V. S, G. G p, K. Singhania, and E. N. Vincent, “Is Indian Spot Electricity Price Series Stationary?,” Energy Procedia, vol. 138, pp. 1067–1072, Oct. 2017, doi: 10.1016/j.egypro.2017.10.114.[53] H. S. Sandhu, L. Fang, and L. Guan, “Forecasting day-ahead price spikes for the Ontario electricity market,” Electr. Power Syst. Res., vol. 141, pp. 450–459, Dec. 2016, doi: 10.1016/j.epsr.2016.08.005.[54] S. K. Dash, R. Bisoi, and P. K. Dash, “A hybrid functional link dynamic neural network and evolutionary unscented Kalman filter for short-term electricity price forecasting,” Neural Comput. Appl., vol. 27, no. 7, pp. 2123–2140, 2016, doi: 10.1007/s00521-015-2011-z.[55] A. K. Rout, R. Bisoi, and P. K. Dash, “A low complexity evolutionary computationally efficient recurrent Functional link Neural Network for time series forecasting,” in 2015 IEEE Power, Communication and Information Technology Conference (PCITC), Oct. 2015, pp. 576–582. doi: 10.1109/PCITC.2015.7438230.[56] L. Zhou, B. Wang, Z. Wang, F. Wang, and M. Yang, “Seasonal classification and RBF adaptive weight based parallel combined method for day-ahead electricity price forecasting,” in 2018 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT), Feb. 2018, pp. 1–5. doi: 10.1109/ISGT.2018.8403372.[57] K. Sarada and V. Bapiraju, “Comparison of day-ahead price forecasting in energy market using Neural Network and Genetic Algorithm,” in 2014 International Conference on Smart Electric Grid (ISEG), Sep. 2014, pp. 1–5. doi: 10.1109/ISEG.2014.7005607.[58] R. Beigaite and T. Krilavičius, “Electricity price forecasting for Nord Pool data,” presented at the CEUR Workshop Proceedings, 2017, pp. 37–42.[59] R. Beigaite and T. Krilavičius, “Electricity price forecasting for nord pool data using recurrent neural networks,” presented at the CEUR Workshop Proceedings, 2018, pp. 75–78.[60] R. Bisoi, P. K. Dash, and P. P. Das, “Short-term electricity price forecasting and classification in smart grids using optimized multikernel extreme learning machine,” Neural Comput. Appl., Aug. 2018, doi: 10.1007/s00521-018-3652-5.[61] N. A. Shrivastava, A. Khosravi, and B. K. Panigrahi, “Prediction Interval Estimation of Electricity Prices Using PSO-Tuned Support Vector Machines,” IEEE Trans. Ind. Inform., vol. 11, no. 2, pp. 322–331, Apr. 2015, doi: 10.1109/TII.2015.2389625.[62] A. Bello, J. Reneses, A. Muñoz, and A. Delgadillo, “Probabilistic forecasting of hourly electricity prices in the medium-term using spatial interpolation techniques,” Int. J. Forecast., vol. 32, no. 3, pp. 966–980, 2016, doi: 10.1016/j.ijforecast.2015.06.002.[63] M. Burger, B. Klar, A. Müller, and G. Schindlmayr, “A spot market model for pricing derivatives in electricity markets,” Quant. Finance, vol. 4, no. 1, pp. 109–122, Feb. 2004, doi: 10.1088/1469-7688/4/1/010.[64] C. Redl, R. Haas, C. Huber, and B. Böhm, “Price formation in electricity forward markets and the relevance of systematic forecast errors,” Energy Econ., vol. 31, no. 3, pp. 356–364, 2009, Accessed: Jul. 03, 2019. [Online]. Available: https://econpapers.repec.org/article/eeeeneeco/v_3a31_3ay_3a2009_3ai_3a3_3ap_3a356-364.htm[65] J. D. Velásquez, C. J. Franco, and Y. Olaya, “Predicción de los precios promedios mensuales de contratos despachados en el mercado mayorista de electricidad en Colombia usando máquinas de vectores de soporte,” Cuad. Adm., vol. 23, no. 40, 2010.[66] M. Bierbrauer, C. Menn, S. T. Rachev, and S. Trück, “Spot and derivative pricing in the EEX power market,” J. Bank. Finance, vol. 31, no. 11, pp. 3462–3485, 2007, doi: 10.1016/j.jbankfin.2007.04.011.[67] J. D. Velásquez, V. Gil, and C. J. Franco, “An Overview of the Colombian Market for Standarized Derivatives of Energy Commodities,” IEEE Lat. Am. Trans., vol. 13, no. 7, pp. 2176–2182, 2015, doi: 10.1109/TLA.2015.7273774.[68] Isaac. Dyner, Carlos Jaime. Franco, and Santiago. Arango, El mercado mayorista de electricidad colombiano, Primera edición. Medellín: Centro de complejidad Ceiba, 2008.[69] XM, “Informes operación del SIN y administración del mercado.” 2021. [Online]. Available: https://www.xm.com.co/corporativo/Paginas/Nuestra-empresa/informes-anuales.aspx[70] F. Gutiérrez and J. A. Dracup, “An analysis of the feasibility of long-range streamflow forecasting for Colombia using El Niño–Southern Oscillation indicators,” J. Hydrol., vol. 246, no. 1, pp. 181–196, Jun. 2001, doi: 10.1016/S0022-1694(01)00373-0.[71] J. D. Velasquez, I. Dyner, and C. J. Franco, “Modeling the Effect of Macroclimatic Events on River Inflows in the Colombian Electricity Market,” IEEE Lat. Am. Trans., vol. 14, no. 10, pp. 4287–4292, 2016, doi: 10.1109/TLA.2016.7786307.[72] NOAA, “ENSO: Recent Evolution, Current Status and Predictions.” 2017. [Online]. Available: http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/lanina/enso_evolution-status-fcsts-web.pdf[73] N. A. Shrivastava and B. K. Panigrahi, “A hybrid wavelet-ELM based short term price forecasting for electricity markets,” Int. J. Electr. Power Energy Syst., vol. 55, pp. 41–50, Feb. 2014, doi: 10.1016/j.ijepes.2013.08.023.[74] C. A. Giraldo-Prieto, G. J. González Uribe, C. Vesga Bermejo, and D. C. Ferreira Herrera, “Coberturas financieras con derivados y su incidencia en el valor de mercado en empresas colombianas que cotizan en Bolsa,” Contad. Adm., vol. 62, no. 5, pp. 1553–1571, Dec. 2017, doi: 10.1016/j.cya.2017.04.008.[75] J. Benavides, Á. Cadena, J. J. González, C. Hidalgo, and A. Piñeros, Mercado eléctrico en Colombia: Transición hacia una arquitectura descentralizada. 2018. Accessed: Jun. 25, 2019. [Online]. Available: http://www.repository.fedesarrollo.org.co/handle/11445/3694[76] G. S. S. Marín and J. P. Robayo, “Los precios forward sobre electricidad. ¿Determinados racionalmente por los agentes del mercado colombiano?,” AD-Minist., no. 18, pp. 77–99, 2011, Accessed: Nov. 21, 2021. [Online]. Available: https://dialnet.unirioja.es/servlet/articulo?codigo=6251514[77] I. Dyner, E. Larsen, and C. J. Franco, “Games for electricity traders: Understanding risk in a deregulated industry,” Energy Policy, vol. 37, no. 2, pp. 465–471, 2009, doi: 10.1016/j.enpol.2008.09.075.[78] A. Pérez and J. Carabalí, “Un modelo sobre competencia en mercados de electricidad: contratos bilaterales en Colombia,” Rev. Desarro. Soc., Feb. 2021, doi: 10.13043/DYS.87.5.[79] J. Barrientos Marín, M. Toro Martínez, J. Barrientos Marín, and M. Toro Martínez, “ON THE FUNDAMENTALS OF ELECTRIC ENERGY PRICE: EMPIRICAL EVIDENCE FOR COLOMBIA,” Rev. Econ. Caribe, no. 19, pp. 33–59, Jun. 2017, Accessed: May 28, 2019. [Online]. Available: http://www.scielo.org.co/scielo.php?script=sci_abstract&pid=S2011-21062017000100033&lng=en&nrm=iso&tlng=es[80] J. Barrientos-Marín and M. T. Martínez, “Análisis de los fundamentales del precio de la energía eléctrica: evidencia empírica para Colombia,” Econ. Caribe, no. 19, pp. 33–59, 2017, Accessed: Oct. 31, 2021. [Online]. Available: https://dialnet.unirioja.es/servlet/articulo?codigo=6143169[81] BVC and XM S.A, “Estudio de Factibilidad Nuevo Mercado de Derivados Estandarizados sobre Commodities Energéticos.” 2009. [Online]. Available: http://www.derivex.com.co/accionistas/Asamblea%20de%20Constitucion/Indice/Documentos_Asamblea/9_Estudio%20de%20Factibilidad%20DERIVEX.pdf[82] Derivex, “Circular única del Mercado de derivados de commodities energéticos Derivex.” Oct. 31, 2021.[83] Comisión de regulación de Energía y Gas, “Resolución 114 de 2018.” Aug. 02, 2018. Accessed: Nov. 06, 2021. [Online]. Available: www.http://apolo.creg.gov.co/Publicac.nsf/1c09d18d2d5ffb5b05256eee00709c02/e1aa2bca619f4f6e05258305007463d1/$FILE/Creg114-2018.pdf[84] CRCC S.A, “Reglamento de funcionamiento. Cámara de riesgo central de contraparte de Colombia S.A. CRCC S.A.” 2017.[85] R. Mariño, C. León, and C. Cadena, “Las entidades de contrapartida central en la mitigación del riesgo de contraparte y de liquidez: El caso de los derivados cambiarios en Colombia,” Borradores Econ., vol. 1101, 2020.[86] C. B. Hunzinger and C. C. A. Labuschagne, “The Cox, Ross and Rubinstein tree model which includes counterparty credit risk and funding costs,” North Am. J. Econ. Finance, vol. 29, pp. 200–217, Jul. 2014, doi: 10.1016/j.najef.2014.06.002.[87] R. Huisman and R. Mahieu, “Regime jumps in electricity prices,” Energy Econ., vol. 25, no. 5, pp. 425–434, Sep. 2003, doi: 10.1016/S0140-9883(03)00041-0.[88] Y. C. C. Martinez and L. B. Valencia, “Portfolio theory based approach to risk management in electricity markets: Colombian case study,” in IEEE Systems and Information Engineering Design Symposium, 2003, Apr. 2003, pp. 35–40. doi: 10.1109/SIEDS.2003.158001.[89] F. Azevedo, Z. A. Vale, and A. A. do Vale, “Decision-support tool for the establishment of contracts in the electricity market,” in 2003 IEEE Bologna Power Tech Conference Proceedings, Jun. 2003, p. 6 pp. Vol.1-. doi: 10.1109/PTC.2003.1304154.[90] F. Azevedo, Z. A. Vale, and A. A. Vale, “Hedging using futures and options contracts in the electricity market,” Renew. Energy Power Qual. J., vol. 1, no. 1, pp. 496–501, 2003, doi: 10.24084/repqj01.407.[91] A. Pritchard, “Statistical Bibliography or Bibliometrics?,” Journal of Documentation, vol. 25, pp. 348–349, 1969.[92] S. Morris, C. DeYong, Z. Wu, S. Salman, and D. Yemenu, “DIVA: A visualization system for exploring document databases for technology forecasting,” Comput. Ind. Eng., vol. 43, no. 4, pp. 841–862, 2002, doi: 10.1016/S0360-8352(02)00143-2.[93] T. T. Chen and L. Q. Xie, “Identifying critical focuses in research domains,” in Ninth International Conference on Information Visualisation (IV’05), Jul. 2005, pp. 135–142. doi: 10.1109/IV.2005.59.[94] M. J. Cobo, A. G. López‐Herrera, E. Herrera‐Viedma, and F. Herrera, “Science mapping software tools: Review, analysis, and cooperative study among tools,” J. Am. Soc. Inf. Sci. Technol., vol. 62, no. 7, pp. 1382–1402, Jul. 2011, doi: 10.1002/asi.21525.[95] N. J. van Eck and L. Waltman, “Software survey: VOSviewer, a computer program for bibliometric mapping,” Scientometrics, vol. 84, no. 2, pp. 523–538, Aug. 2010, doi: 10.1007/s11192-009-0146-3.[96] M. Aria and C. Cuccurullo, “bibliometrix: An R-tool for comprehensive science mapping analysis,” J. Informetr., vol. 11, no. 4, pp. 959–975, Nov. 2017, doi: 10.1016/j.joi.2017.08.007.[97] L. Cadavid and K. Salazar-Serna, “sustainability Mapping the Research Landscape for the Motorcycle Market Policies: Sustainability as a Trend-A Systematic Literature Review,” Sustainability, vol. 13, p. 10813, Sep. 2021, doi: 10.3390/su131910813.[98] Q. Zhang, X. Wang, and J. Wang, “Electricity purchasing and selling risk decision for power supplier under real-time pricing,” Dianli Xitong ZidonghuaAutomation Electr. Power Syst., vol. 34, no. 3, pp. 22-27+43, 2010.[99] Q. Zhang and X. Wang, “Hedge contract characterization and risk-constrained electricity procurement,” IEEE Trans. Power Syst., vol. 24, no. 3, pp. 1547–1558, 2009, doi: 10.1109/TPWRS.2009.2021233.[100] Y. Xiao, X. Wang, C. Du, and S. Liu, “Hedging against uncertainties for wind power producer with block futures contracts,” presented at the IEEE Power and Energy Society General Meeting, 2015. doi: 10.1109/PESGM.2015.7285850.[101] Z. Wu, X. Wang, Y. Xiao, Y. Zhang, T. Yang, and C. Xue, “Risk management strategy and model for photovoltaic suppliers based on bilateral contracts,” presented at the China International Conference on Electricity Distribution, CICED, 2016. doi: 10.1109/CICED.2016.7576081.[102] F. Azevedo and Z. A. Vale, “A short-term risk management tool applied to OMEL electricity market using Particle Swarm Optimization,” presented at the 2008 5th International Conference on the European Electricity Market, EEM, 2008. doi: 10.1109/EEM.2008.4579039.[103] F. Azevedo, Z. A. Vale, and P. B. de Moura Oliveira, “A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets,” IEEE Trans. Power Syst., vol. 22, no. 3, pp. 995–1003, 2007, doi: 10.1109/TPWRS.2007.901463.[104] F. Azevedo and Z. A. Vale, “Optimal short-term contract allocation using particle swarm optimization,” WSEAS Trans. Inf. Sci. Appl., vol. 2, no. 5, pp. 552–558, 2005.[105] F. Azevedo and Z. A. Vale, “Short-term price forecast from risk management point of view,” presented at the Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems, ISAP’05, 2005, pp. 111–116. doi: 10.1109/ISAP.2005.1599249.[106] F. Azevedo and Z. A. Vale, “Forecasting electricity prices with historical statistical information using neural networks and clustering techniques,” presented at the 2006 IEEE PES Power Systems Conference and Exposition, PSCE 2006 - Proceedings, 2006, pp. 44–50. doi: 10.1109/PSCE.2006.296247.[107] M. Liu and F. F. Wu, “Managing price risk in a multimarket environment,” IEEE Trans. Power Syst., vol. 21, no. 4, pp. 1512–1519, 2006, doi: 10.1109/TPWRS.2006.882455.[108] M. Liu and F. F. Wu, “Portfolio optimization in electricity markets,” Electr. Power Syst. Res., vol. 77, no. 8, pp. 1000–1009, 2007, doi: 10.1016/j.epsr.2006.08.025.[109] M. Liu and F. Wu, “Effect of Financial Hedging in Portfolio Selection in Electricity Markets,” HKIE Trans. Hong Kong Inst. Eng., vol. 17, no. 2, pp. 34–40, 2010, doi: 10.1080/1023697X.2010.10668194.[110] M. Liu and F. F. Wu, “Trading strategy of generation companies in electricity market,” Zhongguo Dianji Gongcheng XuebaoProceedings Chin. Soc. Electr. Eng., vol. 28, no. 25, pp. 111–117, 2008.[111] H. Algarvio and F. Lopes, “Risk Management and Bilateral Contracts in Multi-agent Electricity Markets,” Commun. Comput. Inf. Sci., vol. 430, pp. 279–308, 2014, doi: 10.1007/978-3-319-07767-3_27.[112] H. Algarvio, F. Lopes, and J. Santana, “Bilateral contracting in multi-agent energy markets: Forward contracts and risk management,” Commun. Comput. Inf. Sci., vol. 524, pp. 260–269, 2015, doi: 10.1007/978-3-319-19033-4_22.[113] F. Lopes, H. Algarvio, and J. Santana, “Agent-based simulation of electricity markets: Risk management and contracts for difference,” Underst. Complex Syst., no. 9783319463308, pp. 207–225, 2017, doi: 10.1007/978-3-319-46331-5_10.[114] A. Nasir, K. Shaukat, I. A. Hameed, S. Luo, T. M. Alam, and F. Iqbal, “A Bibliometric Analysis of Corona Pandemic in Social Sciences: A Review of Influential Aspects and Conceptual Structure,” IEEE Access, vol. 8, pp. 133377–133402, 2020, doi: 10.1109/ACCESS.2020.3008733.[115] P. Mathuria and R. Bhakar, “Large consumer’s purchase portfolio optimization in electricity market,” presented at the 2016 IEEE 6th International Conference on Power Systems, ICPS 2016, 2016. doi: 10.1109/ICPES.2016.7584115.[116] J. Hanly, “Risk management and hedging approaches in energy markets,” in Handbook of Energy Finance: Theories, Practices and Simulations, 2020, pp. 651–667. doi: 10.1142/9789813278387_0026.[117] Y. Cai, Q. Chen, and W. Zhang, “Credit and risk management of electricity transaction: A real case based on Guangdong electricity market rules,” presented at the Proceedings - 2020 5th Asia Conference on Power and Electrical Engineering, ACPEE 2020, 2020, pp. 994–999. doi: 10.1109/ACPEE48638.2020.9136388.[118] A.-S. Chen, M. T. Leung, S. Pan, and C.-Y. Chou, “Financial hedging in energy market by cross-learning machines,” Neural Comput. Appl., vol. 32, no. 14, pp. 10321–10335, 2020, doi: 10.1007/s00521-019-04572-4.[119] H. Liu, X. Chen, and J. Xie, “Optimal Bidding Strategy for Electricity Sales Company Considering Contract for Difference and Risk,” presented at the 2019 3rd IEEE Conference on Energy Internet and Energy System Integration: Ubiquitous Energy Network Connecting Everything, EI2 2019, 2019, pp. 2155–2160. doi: 10.1109/EI247390.2019.9062253.[120] T. L. D. Huynh, M. Shahbaz, M. A. Nasir, and S. Ullah, “Financial modelling, risk management of energy instruments and the role of cryptocurrencies,” Ann. Oper. Res., 2020, doi: 10.1007/s10479-020-03680-y.[121] A. Varnavskiy, U. Gruzina, A. Rot, A. Buryakova, E. Sebechenko, and V. Trubnikov, “Prospects and Limitations of the Use of Blockchain-Options for the Supply of Electricity,” Stud. Comput. Intell., vol. 887, pp. 95–110, 2020, doi: 10.1007/978-3-030-40417-8_6.[122] M. R. Cogollo and J. D. Velasquez, “Methodological Advances in Artificial Neural Networks for Time Series Forecasting,” IEEE Lat. Am. Trans., vol. 12, no. 4, pp. 764–771, Jun. 2014, doi: 10.1109/TLA.2014.6868881.[123] K. Ignatieva and S. Trück, “Modeling spot price dependence in Australian electricity markets with applications to risk management,” Comput. Oper. Res., vol. 66, pp. 415–433, 2016, doi: 10.1016/j.cor.2015.07.019.[124] M. Maenhoudt and G. Deconinck, “Strategic Offering to Maximize Day-Ahead Profit by Hedging Against an Infeasible Market Clearing Result,” IEEE Trans. Power Syst., vol. 29, no. 2, pp. 854–862, Mar. 2014, doi: 10.1109/TPWRS.2013.2286005.[125] T. Pinto and F. Falcão-Reis, “Strategic participation in competitive electricity markets: Internal versus sectorial data analysis,” Int. J. Electr. Power Energy Syst., vol. 108, pp. 432–444, 2019, doi: 10.1016/j.ijepes.2019.01.011.[126] E. J. Anderson, X. Hu, and D. Winchester, “Forward contracts in electricity markets: The Australian experience,” Energy Policy, vol. 35, no. 5, pp. 3089–3103, May 2007, doi: 10.1016/j.enpol.2006.11.010.[127] S. Bhattacharya, A. Gupta, K. Kar, and A. Owusu, “Hedging strategies for risk reduction through weather derivatives in renewable energy markets,” in 2015 International Conference on Renewable Energy Research and Applications (ICRERA), Nov. 2015, pp. 1190–1195. doi: 10.1109/ICRERA.2015.7418597.[128] S. Adamson, T. Noe, and G. Parker, “Efficiency of financial transmission rights markets in centrally coordinated periodic auctions,” Energy Econ., vol. 32, no. 4, pp. 771–778, Jul. 2010, doi: 10.1016/j.eneco.2010.03.006.[129] C. Pirrong and M. Jermakyan, “The price of power: The valuation of power and weather derivatives,” J. Bank. Finance, vol. 32, no. 12, pp. 2520–2529, 2008, doi: 10.1016/j.jbankfin.2008.04.007.[130] C. L. Anderson and M. Davison, “The Application of Cash-Flow-at-Risk to Risk Management in a Deregulated Electricity Market,” Hum. Ecol. Risk Assess. Int. J., vol. 15, no. 2, pp. 253–269, Apr. 2009, doi: 10.1080/10807030902761148.[131] J. I. Peña and R. Rodriguez, “Default supply auctions in electricity markets: Challenges and proposals,” Energy Policy, vol. 122, pp. 142–151, 2018, doi: 10.1016/j.enpol.2018.07.031.[132] D. Xiao and H. Chen, “Stochastic up to Congestion Bidding Strategy in the Nodal Electricity Markets Considering Risk Management,” IEEE Access, vol. 8, pp. 202428–202438, 2020, doi: 10.1109/ACCESS.2020.3015025.[133] F. Azevedo, Z. A. Vale, P. B. M. Oliveira, and H. M. Khodr, “A long-term risk management tool for electricity markets using swarm intelligence,” Electr. Power Syst. Res., vol. 80, no. 4, pp. 380–389, Apr. 2010, doi: 10.1016/j.epsr.2009.10.002.[134] N. Jovanović, J. García-González, J. Barquín, and S. Cerisola, “Electricity market short-term risk management via risk-adjusted probability measures,” IET Gener. Transm. Distrib., vol. 11, no. 10, pp. 2599–2607, 2017, doi: 10.1049/iet-gtd.2016.1731.[135] F.-J. Heredia, J. Cifuentes-Rubiano, and C. Corchero, “Stochastic optimal generation bid to electricity markets with emissions risk constraints,” J. Environ. Manage., vol. 207, pp. 432–443, 2018, doi: 10.1016/j.jenvman.2017.11.010.[136] S. S. Oren and Y. Oum, “Managing risk under a fixed price load following obligation for electricity service,” presented at the IEEE PES General Meeting, PES 2010, 2010. doi: 10.1109/PES.2010.5589689.[137] B. A. Souhir, B. Heni, and B. Lotfi, “Price risk and hedging strategies in Nord Pool electricity market evidence with sector indexes,” Energy Econ., vol. 80, pp. 635–655, 2019, doi: 10.1016/j.eneco.2019.02.001.[138] B. Tranberg, R. T. Hansen, and L. Catania, “Managing volumetric risk of long-term power purchase agreements,” Energy Econ., vol. 85, 2020, doi: 10.1016/j.eneco.2019.104567.[139] M. J. Stevenson, L. F. M. Do Amaral, and M. Peat, “Risk management and the role of spot price predictions in the Australian retail electricity market,” Stud. Nonlinear Dyn. Econom., vol. 10, no. 3, 2006, doi: 10.2202/1558-3708.1383.[140] J. Huang, Y. Xue, Z. Y. Dong, and K. P. Wong, “An efficient probabilistic assessment method for electricity market risk management,” IEEE Trans. Power Syst., vol. 27, no. 3, pp. 1485–1493, 2012, doi: 10.1109/TPWRS.2012.2183900.[141] C. Klüppelberg, T. Meyer-Brandis, and A. Schmidt, “Electricity spot price modelling with a view towards extreme spike risk,” Quant. Finance, vol. 10, no. 9, pp. 963–974, 2010, doi: 10.1080/14697680903150496.[142] R. I. Brik and A. Roncoroni, “Static mitigation of volumetric risk,” J. Energy Mark., May 2016, Accessed: Nov. 23, 2021. [Online]. Available: https://www.risk.net/node/2457758[143] F. E. Benth, L. Di Persio, and S. Lavagnini, “Stochastic modeling of wind derivatives in energy markets,” Risks, vol. 6, no. 2, 2018, doi: 10.3390/risks6020056.[144] K. W. Park, “Risk management and derivatives in korea electricity markets,” presented at the IFAC Proceedings Volumes (IFAC-PapersOnline), 2003, pp. 915–920. doi: 10.1016/S14746670(17)34590-1.[145] W. Liu, J. Shang, W. Zhou, F. Wen, and Z. Lin, “Evaluation of value-at-risk in electricity markets based on multifractal theory,” Dianli Xitong ZidonghuaAutomation Electr. Power Syst., vol. 37, no. 7, pp. 48–54, 2013, doi: 10.7500/AEPS201206172.[146] A. T. Carrasquilla, J. F. R. García, and J. O. P. Robayo, “Sub-optimal allocation under VaR constraints in electricity markets,” Rev. Econ. Rosario, vol. 19, no. 2, pp. 201–220, 2016, doi: 10.12804/revistas.urosario.edu.co/economia/a.5625.[147] Q. Zhang and X. Wang, “Customer electricity purchasing risk decision under real-time pricing,” presented at the 2009 IEEE/PES Power Systems Conference and Exposition, PSCE 2009, 2009. doi: 10.1109/PSCE.2009.4839934.[148] J. Cabero, M. J. Ventosa, S. Cerisola, and Á. Baíllo, “Modeling risk management in oligopolistic electricity markets: A benders decomposition approach,” IEEE Trans. Power Syst., vol. 25, no. 1, pp. 263–271, 2010, doi: 10.1109/TPWRS.2009.2036788.[149] D. Chin and A. Siddiqui, “Capacity expansion and forward contracting in a duopolistic power sector,” Comput. Manag. Sci., vol. 11, no. 1–2, pp. 57–86, 2014, doi: 10.1007/s10287-013-0166-6.[150] F. Gökgöz and M. E. Atmaca, “Financial optimization in the Turkish electricity market: Markowitz’s mean-variance approach,” Renew. Sustain. Energy Rev., vol. 16, no. 1, pp. 357–368, 2012, doi: 10.1016/j.rser.2011.06.018.[151] Y.-K. Wu, “Intelligent management and hedging strategy on energy contracts in competitive electricity markets,” Int. J. Power Energy Syst., vol. 30, no. 3, pp. 213–218, 2010, doi: 10.2316/Journal.203.2010.3.203-4616.[152] D. Feng, D. Gan, J. Zhong, and Y. Ni, “Efficient frontier analysis for the GenCo multi-trading strategy: Part one Model analysis,” Dianli Xitong ZidonghuaAutomation Electr. Power Syst., vol. 31, no. 13, pp. 29–35, 2007.[153] F. Azevedo, Z. A. Vale, and P. B. de Moura Oliveira, “A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets,” IEEE Trans. Power Syst., vol. 22, no. 3, pp. 995–1003, 2007, doi: 10.1109/TPWRS.2007.901463.[154] F. Azevedo, Z. A. Vale, and P. B. M. Oliveira, “Long-term Price Range Forecast Applied to Risk Management Using Regression Models,” in 2007 International Conference on Intelligent Systems Applications to Power Systems, Nov. 2007, pp. 1–6. doi: 10.1109/ISAP.2007.4441656.[155] J. A. Díaz Contreras, G. I. Macías Villalba, and E. Luna González, “Estrategia de cobertura con productos derivados para el mercado energético colombiano,” Estud. Gerenciales, vol. 30, no. 130, pp. 55–64, Jan. 2014, doi: 10.1016/j.estger.2014.02.008.[156] A. T. Carrasquilla, J. F. R. García, and J. Pantoja, “Estrategia de cobertura a través de contratos a plazo en mercados eléctricos,” Acad. Rev. Latinoam. Adm., no. 50, pp. 148–157, 2012, Accessed: Jun. 25, 2019. [Online]. Available: http://www.redalyc.org/articulo.oa?id=71624352010[157] K. Maradey-Angarita, J. O. Pantoja-Robayo, A. Trespalacios-Carrasquilla, K. Maradey-Angarita, J. O. Pantoja-Robayo, and A. Trespalacios-Carrasquilla, “Analysis of the financial margins required to hedge risks in electric power futures markets,” Ecos Econ., vol. 21, no. 45, pp. 67–105, Dec. 2017, doi: 10.17230/ecos.2017.45.4.[158] J. Barrientos, E. Rodas, E. Velilla, M. Lopera, and F. Villada, “A model for forecasting electricity prices in Colombia,” Lect. Econ., no. 77, pp. 91–127, Dec. 2012, Accessed: May 28, 2019. [Online]. Available: http://www.scielo.org.co/scielo.php?script=sci_abstract&pid=S0120-25962012000200003&lng=en&nrm=iso&tlng=es[159] E. U. Gaviria and A. T. Carrasquilla, “Contraste de modelos estocásticos para el precio de la energía en Colombia,” Rev. Fac. Cienc., vol. 3, no. 1, pp. 41–55, Jan. 2014, Accessed: May 29, 2019. [Online]. Available: https://revistas.unal.edu.co/index.php/rfc/article/view/49524[160] O. H. N, J. D. Velásquez, and I. Dyner, “Modelos ARIMA y estructural de la serie de precios promedio de los contratos en el Mercado Mayorista de Energía Eléctrica en Colombia,” Energética, no. 34, pp. 5–10, 2005, Accessed: Jun. 05, 2019. [Online]. Available: http://www.redalyc.org/articulo.oa?id=147019373001[161] A. A. A. Monica, S. B. Botero, and H. H. B. Jaime, “Financial anomalies in the electricity market: Empirical analysis of spot prices,” in 2018 13th Iberian Conference on Information Systems and Technologies (CISTI), Jun. 2018, pp. 1–7. doi: 10.23919/CISTI.2018.8398640.[162] A. Muñoz-Santiago, J. Urquijo-Vanstrahlengs, A. Castro-Otero, and J. Lombana, “Pronóstico del precio de la energía en Colombia utilizando modelos ARIMA con IGARCH,” Rev. Econ. Rosario, vol. 20, no. 1, p. 36, Oct. 2017, doi: 10.12804/revistas.urosario.edu.co/economia/a.6152.[163] F. Villada, D. R. Cadavid, and J. D. Molina, “Electricity price forecasting using artificial neural networks,” Rev. Fac. Ing., no. 44, pp. 111–118, 2008.[164] F. Villada, E. García, and J. D. Molina, “Electricity price forecasting using neurofuzzy networks,” Inf. Tecnol., vol. 22, no. 6, pp. 111–120, 2011, doi: 10.4067/S0718-07642011000600012.[165] A. P. Agudelo, J. M. López-Lezama, and E. Velilla, “Forecasting Electricity Stock Price by Means of a Nonlinear Autoregressive Neural Model with Exogenous Inputs,” Inf. Tecnológica, vol. 26, no. 6, pp. 99–108, 2015, doi: 10.4067/S0718-07642015000600012.[166] H. Juan David Velásquez, C. Carlos Jaime Franco, and M. Yris Olaya, “Forecasting average monthly prices for colombian wholesale electricity market contracts,” Cuad. Adm., vol. 23, no. 40, pp. 321–337, 2010.[167] J. B. Marín, E. T. Orozco, and E. Velilla, “Forecasting electricity price in Colombia: A comparison between neural network, ARMA process and hybrid models,” Int. J. Energy Econ. Policy, vol. 8, no. 3, pp. 97–106, 2018.[168] N. J. H. Bueno, M. de los Á. P. Calderón, Y. A. M. Maldonado, and A. O. Castro, “Determination of Models of Simple Regression and Multivariate Analysis for the Forecast of the Electricity Price in Colombia at 2030,” Int. J. Energy Econ. Policy, vol. 8, no. 5, pp. 202–211, Sep. 2018, Accessed: Jun. 05, 2019. [Online]. Available: https://www.econjournals.com/index.php/ijeep/article/view/6415[169] D. Tobón-Orozco et al., “LONG-TERM SEASONAL FORWARDS IN ELECTRICITY GENERATION MARKETS: AN APPLICATION TO COLOMBIA,” Cuad. Econ., vol. 37, no. 74, pp. 287–313, Dec. 2018, doi: 10.15446/cuad.econ.v37n74.54299.[170] J. C. H. Jhon C. Hull, Introducción a los Mercados futuros y opciones, 6Ed. Prentice, Hall, 2008.[171] F. Black and M. Scholes, “The Pricing of Options and Corporate Liabilities,” J. Polit. Econ., vol. 81, no. 3, pp. 637–654, May 1973, doi: 10.1086/260062.[172] F. Venegas, Riesgos financieros y económicos, Segunda. CENGAGE Learning, 2008.[173] J. C. Cox, S. A. Ross, and M. Rubinstein, “Option pricing: A simplified approach,” J. Financ. Econ., vol. 7, no. 3, pp. 229–263, Sep. 1979, doi: 10.1016/0304-405X(79)90015-1.[174] A. M. Calle Fernández and V. M. Tamayo Bustamante, “Decisiones de inversión a través de opciones reales,” Estud. Gerenciales, vol. 25, no. 111, pp. 107–126, Apr. 2009, doi: 10.1016/S0123-5923(09)70073-7.[175] A. Bello, J. Reneses, and A. Muñoz, “Medium-Term Probabilistic Forecasting of Extremely Low Prices in Electricity Markets: Application to the Spanish Case,” Energies, vol. 9, no. 3, p. 193, Mar. 2016, doi: 10.3390/en9030193.[176] E. S. Schwartz, “The Stochastic Behavior of Commodity Prices: Implications for Valuation and Hedging,” J. Finance, vol. 52, no. 3, pp. 923–73, 1997, Accessed: Jun. 24, 2019. [Online]. Available: https://econpapers.repec.org/article/blajfinan/v_3a52_3ay_3a1997_3ai_3a3_3ap_3a923-73.htm[177] D. Pilipovic, Energy Risk: Valuing and Managing Energy Derivatives, Second. McGraw-Hill, 2007.[178] V. Canazza, Guang Li, Chen Ching Liu, D. Lucarella, and A. Venturini, “An Intelligent System For Price Forecasting Accuracy Assessment,” in Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems, Nov. 2005, pp. 92–99. doi: 10.1109/ISAP.2005.1599246.[179] V. Gonzalez, J. Contreras, and D. W. Bunn, “Forecasting Power Prices Using a Hybrid Fundamental-Econometric Model,” IEEE Trans. Power Syst., vol. 27, no. 1, pp. 363–372, Feb. 2012, doi: 10.1109/TPWRS.2011.2167689.[180] M. G. Khajeh, A. Maleki, M. A. Rosen, and M. H. Ahmadi, “Electricity price forecasting using neural networks with an improved iterative training algorithm,” Int. J. Ambient Energy, vol. 39, no. 2, pp. 147–158, 2018, doi: 10.1080/01430750.2016.1269674.[181] B. Uniejewski and R. Weron, “Efficient Forecasting of Electricity Spot Prices with Expert and LASSO Models,” Energies, vol. 11, no. 8, p. 2039, Aug. 2018, doi: 10.3390/en11082039.[182] R. Weron, “Electricity price forecasting: A review of the state-of-the-art with a look into the future,” Int. J. Forecast., vol. 30, no. 4, pp. 1030–1081, 2014, doi: 10.1016/j.ijforecast.2014.08.008.[183] X. Yan and N. A. Chowdhury, “Mid-term electricity market clearing price forecasting: A multiple SVM approach,” Int. J. Electr. Power Energy Syst., vol. 58, pp. 206–214, Jun. 2014, doi: 10.1016/j.ijepes.2014.01.023.[184] P. Mandal, T. Senjyu, N. Urasaki, T. Funabashi, and A. K. Srivastava, “Short-Term Price Forecasting for Competitive Electricity Market,” in 2006 38th North American Power Symposium, Sep. 2006, pp. 137–141. doi: 10.1109/NAPS.2006.360135.[185] L. Wu and M. Shahidehpour, “A Hybrid Model for Day-Ahead Price Forecasting,” IEEE Trans. Power Syst., vol. 25, no. 3, pp. 1519–1530, Aug. 2010, doi: 10.1109/TPWRS.2009.2039948. [186] I. Shah, H. Bibi, S. Ali, L. Wang, and Z. Yue, “Forecasting One-Day-Ahead Electricity Prices[186] I. Shah, H. Bibi, S. Ali, L. Wang, and Z. Yue, “Forecasting One-Day-Ahead Electricity Prices for Italian Electricity Market Using Parametric and Nonparametric Approaches,” IEEE Access, vol. 8, pp. 123104–123113, 2020, doi: 10.1109/ACCESS.2020.3007189.[187] M. A. Boyacioglu and D. Avci, “An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: The case of the Istanbul stock exchange,” Expert Syst. Appl., vol. 37, no. 12, pp. 7908–7912, 2010, doi: 10.1016/j.eswa.2010.04.045.[188] Z. Yang, L. Ce, and L. Lian, “Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods,” Appl. Energy, vol. 190, pp. 291–305, 2017, doi: 10.1016/j.apenergy.2016.12.130.[189] R. T. Clement, Making Hard Decisions: An Introduction to Decision Analysis, 2nd ed. United States of America: Duxbury, 1997.[190] J. Barrientos, E. Rodas, E. Velilla, M. Lopera, and F. Villada, “Modelo para el pronóstico del precio de la energía eléctrica en Colombia,” Lect. Econ., no. 77, pp. 91–127, Dec. 2012, Accessed: Aug. 30, 2018. [Online]. Available: http://www.scielo.org.co/scielo.php?script=sci_abstract&pid=S0120-25962012000200003&lng=en&nrm=iso&tlng=es[191] G. Poveda and Ó. J. Mesa, “Las fases extremas del fenómeno ENSO (El Niño y La Niña) y su influencia sobre la hidrología de Colombia,” Tecnol. Cienc. Agua, vol. 11, no. 1, Art. no. 1, 1996, Accessed: Nov. 02, 2021. [Online]. Available: http://www.revistatyca.org.mx/index.php/tyca/article/view/765[192] UPME, “Plan de Expansión de Referencia Generación-Transimisión 2015-2029.” 2015. [Online]. Available: https://www1.upme.gov.co/Paginas/Plan-Expansion-2015-2029.aspxInvestigadoresORIGINAL1.036.598.990.2023.pdf1.036.598.990.2023.pdfTesis de doctorado en Ingenieríaapplication/pdf2520501https://repositorio.unal.edu.co/bitstream/unal/83913/2/1.036.598.990.2023.pdfa0b7ab11c169a5e83e5f0ae265ef048fMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/83913/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51THUMBNAIL1.036.598.990.2023.pdf.jpg1.036.598.990.2023.pdf.jpgGenerated Thumbnailimage/jpeg4202https://repositorio.unal.edu.co/bitstream/unal/83913/3/1.036.598.990.2023.pdf.jpg0eae51ad88ab8893a917c194d7c1f475MD53unal/83913oai:repositorio.unal.edu.co:unal/839132024-08-08 23:11:50.716Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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

Modelo de valoración de contratos en Derivex

Publicaciones similares