Document Type
Article
Publication Date
2020
Digital Object Identifier (DOI)
https://doi.org/10.1155/2020/2075258
Abstract
In this work, we examine the relationship between different energy commodity spot prices. To do this, multivariate stochastic models with and without external random interventions describing the price of energy commodities are developed. Random intervention process is described by a continuous jump process. The developed mathematical model is utilized to examine the relationship between energy commodity prices. The time-varying parameters in the stochastic model are estimated using the recently developed parameter identification technique called local lagged adapted generalized method of moment (LLGMM). The LLGMM method provides an iterative scheme for updating statistic coefficients in a system of generalized method of moment/observation equations. The usefulness of the LLGMM approach is illustrated by applying to energy commodity data sets for state and parameter estimation problems. Moreover, the forecasting and confidence interval problems are also investigated (U.S. Patent Pending for the LLGMM method described in this manuscript).
Rights Information
This work is licensed under a Creative Commons Attribution 4.0 License.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Journal of Energy, v. 2020, art. 2075258
Scholar Commons Citation
Otunuga, Olusegun M. and Ladde, Gangaram, "Two-Scale Network Dynamic Model for Energy Commodity Processes" (2020). Mathematics and Statistics Faculty Publications. 76.
https://digitalcommons.usf.edu/mth_facpub/76
