Hedging Electricity Price Volatility Applying Seasonal and Trend Decomposition

The Wholesale Electricity Market (MEM) has allowed participants to trade electricity at Local Marginal Price (LMP); therefore, developing hedging models to face high volatility electricity prices and avoid financial losses has become essential. This work proposes a methodology based on the Seasonal...

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Detalles Bibliográficos
Autores: Alfredo Ramírez-García, Eduardo Saucedo
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:México
Institución:Instituto Tecnológico y de Estudios Superiores de Monterrey
Repositorio:Redalyc-ITESM
OAI Identifier:oai:redalyc.org:41370350009
Acceso en línea:https://www.redalyc.org/articulo.oa?id=41370350009
https://www.redalyc.org/journal/413/41370350009/
https://www.redalyc.org/journal/413/41370350009/html/
https://www.redalyc.org/journal/413/41370350009/41370350009.epub
https://www.redalyc.org/journal/413/41370350009/movil
Access Level:acceso abierto
Palabra clave:Economía y Finanzas
C15
G10
O13
P18
Q47
Descripción
Sumario:The Wholesale Electricity Market (MEM) has allowed participants to trade electricity at Local Marginal Price (LMP); therefore, developing hedging models to face high volatility electricity prices and avoid financial losses has become essential. This work proposes a methodology based on the Seasonal and Trend Decomposition Model (STL) to the LMP returns series, which is fitted into NIG distribution by obtaining empirical NIG parameters from LMP returns using Maximum Likelihood Estimation (MLE) to generate a simulated NIG distributed series. Finally, the goodness-of-fit test is estimated to demonstrate that empirical data can be fitted into NIG Distribution. This work should be considered the first Electricity Hedging Valuation Methodology for the MEM. Results obtained show that electricity price returns can be fitted and simulated by NIG distribution even through economic crisis periods. The analysis period is from 29/01/2016 to 09/07/2021.