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).
Highlights
Understanding the economy evolution in response to structural changes in the energy commodity network system is important to professional economists
Natural gas, coal, nuclear fuel, and renewable energy are termed as primary energy components of the energy goods network system because other sources of energy depend on them
According to the US Energy Information Administration (EIA), the major energy goods consumed in the United States are petroleum, natural gas, coal, nuclear, and renewable energy
Summary
Understanding the economy evolution in response to structural changes in the energy commodity network system is important to professional economists. Analyzing the short- and long-term relationships between the energy commodities It has been shown in Appendices A, B, C, and D of the work of Otunuga et al [16] that the LLGMM parameter estimation scheme performs better than existing nonparametric statistical methods. The usefulness of this approach is further illustrated by applying the technique to study the relationship between three energy commodity data sets: Henry Hub natural gas, crude oil, and coal data sets for state and parameter estimation problems. The usefulness of computational algorithm is illustrated by applying the procedure to test for the relationship between Henry Hub natural gas, crude oil, and coal for the state and parameter estimation problems.
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