Figure - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Different augmented volume models with cokurtosis and coskewness for daily returns of Colombian electricity spot prices. Panel A-OLS Regression-Augmented Volume Models
Source publication
This paper explores the empirical validity of an augmented volume model for Colombian electricity price returns (in the present study, the definition of returns is simply the “rate of change” of observed prices for different periods). Of particular interest is the impact of coskewness and cokurtosis when modeling Colombian electricity price returns...
Contexts in source publication
Context 1
... results for the models in Equation (3) for daily and monthly returns based on the sample are given in Tables 1 and 2 To test the consistency of the results, we also ran the models of Equation (3) using the generalized method of moments (GMM), which is widely used in asset pricing. Since our data deviate from what is expected from a normal distribution, GMM addresses the problems of non-normality in our data by correcting for serial correlation, heteroscedasticity, and leptokurtosis [24]. ...Context 2
... results for the models in Equation (3) for daily and monthly returns based on the sample are given in Tables 1 and 2, wherein Panel A shows the results obtained running single and augmented volume models with OLS, and Panel B shows the same results for the same models with GMM. The results show that the volume lambda (λ v,t ) is significant at the daily and monthly level and with the expected negative sign. ...Citations
... This statement is also supported by the heatmap in Figure 10, where a very different behavior of this indicator is observed, compared to the others. Kurtosis end skewness have been used as indicators in the analysis of electricity markets [21][22][23]. However, their descriptive power of load profile features remains as a research topic. ...
The construction of daily electricity consumption profiles is a common practice for user characterization and segmentation tasks. As in any data analysis project, to obtain these load profiles, a stage of data preparation is necessary. This article explores to what extent does the selection of the data preparation technique impacts load profiling. The techniques discussed are used in the following tasks: standardization, construction of data, dimensionality reduction and data enrichment. The analysis reveals a great incidence of the data preparation on the result. The need to make the data preparation process explicit in each report is identified. In particular, it is highlighted that the most usual default standardization process, column standardization, is not adequate in the preparation of energy consumption profiles.
