Cobertura de Volatilidad del Precio de la Electricidad Aplicando la Descomposición Estacional y de Tendencia

Autores/as

  • Alfredo Ramirez Garcia Instituto Tecnológico y de Estudios Superiores de Monterrey
  • Eduardo Saucedo de la Fuente Instituto Tecnológico y de Estudios Superiores de Monterrey

DOI:

https://doi.org/10.24275/uam/azc/dcsh/ae/2022v37n94/Ramirez

Palabras clave:

Descomposición Estacional y de Tendencia usando Loess (STL), Distribución Normal Inversa Gaussiana (NIG), Pronóstico del Precio de la Electricidad (EPF), Wholesale Electricity Market (MEM), Valuación de Cobertura de la Electricidad

Resumen

El Mercado Eléctrico Mayorista (MEM) ha permitido a los participantes comercializar electricidad al Precio Marginal Local (LMP) por lo que, se requiere desarrollar modelos de cobertura para enfrentar la alta volatilidad de los precios y así evitar pérdidas financieras. Este trabajo propone una metodología basada en el Modelo de Descomposición Estacional y de Tendencias (STL) aplicado a las series de retornos PML, su ajuste a la distribución NIG obteniendo los parámetros empíricos por Estimación de Máxima Verosimilitud (MLE), para simular una serie distribuida NIG, y por medio de pruebas de bondad de ajuste demostrar el ajuste de los datos a la distribución NIG. Este trabajo debe considerarse la primera Metodología de Valuación de Coberturas Eléctricas para el MEM. Los resultados muestran que los retornos del precio de la electricidad pueden ajustarse a la distribución NIG incluso en períodos de crisis económica. El periodo de estudio contempla del 29/01/2016 al 09/07/2021.

Clasificación JEL: C15; G10; O13; P18; Q47.

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Biografía del autor/a

Alfredo Ramirez Garcia, Instituto Tecnológico y de Estudios Superiores de Monterrey

EGADE Business School, Tecnológico de Monterrey, Mexico City

Eduardo Saucedo de la Fuente, Instituto Tecnológico y de Estudios Superiores de Monterrey

GADE Business School, Tecnologico de Monterrey, Mexico City

Citas

Abdeen, A., Kharvari, F., O'Brien, W., & Gunay, B. (2021). The impact of the COVID-19 on households’ hourly electricity consumption in Canada. Energy and Buildings, Volume 250, 111280. https://doi.org/10.1016/j.enbuild.2021.111280

Andersen, T. G., Bollerslev, T., & Diebold, F. X. (2007). Roughing it up: Including jump components in the measurement, modelling and forecasting of return volatility. Review of Economics and Statistics, 89, 701−720. https://doi.org/10.3386/w11775

Anderson, T. W., & Darling, D. A. (1954). A Test of Goodness of Fit. Journal of the American Statistical Association, 49, 765-769. https://doi.org/10.1080/01621459.1954.10501232

Barndorff-Nielsen, O. E. (1977). Exponentially Decreasing Distributions for the Logarithm of Particle Size. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 353, 401-419. https://doi.org/10.1098/rspa.1977.0041

Barndorff-Nielsen, O. E. (1995). Normal Inverse Gaussian Processes and the Modelling of Stock Returns, Research Report 300, Department of Theoretical Statistics, Institute of Mathematics, University of Aarhus.

Barndorff‐Nielsen, O. E. (1997). Normal Inverse Gaussian Distributions and Stochastic Volatility Modelling. Scandinavian Journal of Statistics, 24, 1-13. https://doi.org/10.1111/1467-9469.00045

Brager, O., Reichman, O., & Wobben, M. (2010). Pricing electricity derivatives on an hourly basis. The Journal of Energy Markets, 3(3), 51–89. https://doi.org/10.21314/JEM.2010.044

CENACE (2021). Reference nodes for LMP estimation. (2021, September 5). https://www.cenace.gob.mx/DocsMEM/OpeMdo/Notas/2019-03-20%20Nodos%20de%20referencia_20190320124953_329415.pdf

Diario Oficial de la Federación (2014). DECRETO por el que se expiden la Ley de la Industria Eléctrica, la Ley de Energía Geotérmica y se adicionan y reforman diversas disposiciones de la Ley de Aguas Nacionales. 11 de agosto.

Energy Generated by Technology Type. (2021, July). In CENACE. https://www.cenace.gob.mx/Paginas/SIM/Reportes/EnergiaGeneradaTipoTec.aspx

Cleveland, R. B., Cleveland, W. S., McRae, J. E., & Terpenning, I. (1990). STL: A Seasonal-Trend Decomposition Procedure Based on Loess. Journal of Official Statistics, 6(1), 3–33.

Chan, K. F., Gray, P., & van Campen, B. (2008). A new approach to characterizing and forecasting electricity price volatility. International Journal of Forecasting, 24(4), 728–743. https://doi.org/10.1016/j.ijforecast.2008.08.002

Corpus-Mendoza, A. N., Ruiz-Segoviano, H. S., Rodríguez-Contreras, S. F., Yañez-Dávila, D., & Hernández-Granados, A. (2021). Decrease of mobility, electricity demand, and NO2 emissions on COVID-19 times and their feedback on prevention measures. Science of The Total Environment, 760, 143382. https://doi.org/10.1016/j.scitotenv.2020.143382

Dickey, D. A., & Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time Series With a Unit Root. Journal of the American Statistical Association, 74(366), 427-431. https://doi.org/10.2307/2286348

Escribano, A., Pena, J., & Villaplana, P. (2011). Modelling electricity prices: International evidence. Oxford Bulletin of Economics and Statistics, 73(5), 622-650. https://doi.org/10.1111/j.1468-0084.2011.00632.x

Geman, H., & Roncoroni, A. (2006). Understanding the fine structure of electricity prices. Journal of Business, 79(3), 1225−1261. https://doi.org/10.1086/500675

Hernández Ibarzábal, J.A., & Bonilla, D. (2020). Examining Mexico's energy policy under the 4T. The Extractive Industries and Society, 7(2), 669-675. https://doi.org/10.1016/j.exis.2020.03.002

Janczura, J., & Weron, R. (2013). Goodness-of-fit testing for the marginal distribution of regime-switching models with an application to electricity spot prices. AStA Advances in Statistical Analysis, 97, 239–270. https://doi.org/10.1007/s10182-012-0202-9

Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals, Economics Letters, 6(3), 255-259. https://doi.org/10.1016/0165-1765(80)90024-5

Jiang, G. J. (1999). Stochastic volatility and jump diffusion Implications on option pricing. International Journal of Theoretical and Applied Finance, 2(4), 409−440. https://doi.org/10.1142/S0219024999000212

Johnson, B., & Barz, G. (1999). Selecting stochastic processes for modelling electricity prices. In Energy management and the management of uncertainty. London Risk Publications, 3−22.

Kruskal, W. H., & Wallis, W. A. (1952). Use of Ranks in One-Criterion Variance Analysis. Journal of the American Statistical Association, 47(260), 583-621. https://doi.org/10.2307/2280779

Lilliefors, H. W. (1967). On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown. Journal of the American Statistical Association, 62(318), 399-402. https://doi.org/10.1080/01621459.1967.10482916

Massa, R., & Rosellón, J. (2020). Linear and nonlinear Granger causality between electricity production and economic performance in Mexico. Energy Policy, 142, 111476. https://doi.org/10.1016/j.enpol.2020.111476

Massey, F. J. (1951). The Kolmogorov-Smirnov Test for Goodness of Fit. Journal of the American Statistical Association, 46(253), 68-78. https://doi.org/10.2307/2280095

Micheli, L., Solas, A. F., Soria-Moya, A., Almonacid, F., & Fernández, E. F. (2021). Short-term impact of the COVID-19 lockdown on the energy and economic performance of photovoltaics in the Spanish electricity sector. Journal of Cleaner Production, 308, 127045. https://doi.org/10.1016/j.jclepro.2021.127045

Núñez, J. A., Contreras-Valdez, M. I., & Franco-Ruiz, C. A. (2019). Statistical analysis of bitcoin during explosive behavior periods. PLoS ONE, 14(3), 1–22. https://doi.org/10.1371/journal.pone.0213919

Núñez, J. A., Contreras-Valdez, M. I., Ramírez-García, A. & Sánchez-Ruenes, E. (2018) Underlying Assets Distribution in Derivatives: The BRIC Case. Theoretical Economics Letters, 8(3), 502-513. https://doi.org/10.4236/tel.2018.83035

Pettitt, A. N. (1976). Cramer-von Mises Statistics for Testing Normality with Censored Samples. Biometrika, 63(3), 475-481. https://doi.org/10.2307/2335724

Ramírez, J. C., Ortiz-Arango, F., & Rosellón, J. (2021). Impact of Mexico's energy reform on consumer welfare. Utilities Policy, 70, 101191. https://doi.org/10.1016/j.jup.2021.101191

Sarmiento, L., Molar-Cruz, A., Avraam, C., Brown, M., Rosellón, J., Siddiqui, S., & Solano Rodríguez, B. (2021). Mexico and U.S. power systems under variations in natural gas prices. Energy Policy, 156, 112378. https://doi.org/10.1016/j.enpol.2021.112378

Shapiro, S. S., & Francia, R. S. (1972). An Approximate Analysis of Variance Test for Normality. Journal of the American Statistical Association, 67(337), 215-216. https://doi.org/10.1080/01621459.1972.10481232

Stephens, M.A., (1979). The Anderson-Darling statistics. Technical Report No.39. Department of Statistics, Stanford University.

Sun, T., Zhang, T., Teng, Y., Chen, Z., & Fang. J. (2019). Monthly Electricity Consumption Forecasting Method Based onX12 and STL Decomposition Model in an Integrated Energy System. Mathematical Problems in Engineering, 2019, 9012543. https://doi.org/10.1155/2019/9012543

Trejo, B., Nunez, J. A., & Lorenzo, A. (2006). Distribución de los rendimientos del mercado mexicano accionario. Estudios Económicos, 21(1), 85-118.

Vehviläinen, I. (2002). Basics of electricity derivative pricing in competitive markets. Applied Mathematical Finance, 9(1), 45–60. https://doi.org/10.1080/13504860210132879

Weron, R. (2014). Electricity price forecasting: A review of the state-of-the-art with a look into the future. International Journal of Forecasting, 30(4), 1030-1081. https://doi.org/10.1016/j.ijforecast.2014.08.008

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2022-01-06 — Actualizado el 2022-03-18

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Ramirez Garcia, A., & Saucedo de la Fuente, E. (2022). Cobertura de Volatilidad del Precio de la Electricidad Aplicando la Descomposición Estacional y de Tendencia. Análisis Económico, 37(94), 143–166. https://doi.org/10.24275/uam/azc/dcsh/ae/2022v37n94/Ramirez (Original work published 6 de enero de 2022)

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