The Spillover Effect between Futures and Spot Price of Agricultural Products:A Case Study of Soybean Products of China

Taking soybean products as an example and using the daily price data of 2007-2015,this paper established the error correction model and BEKK-GARCH model,and made an empirical study on the spillover effect of futures and spot price of agricultural products of China. According to this study,there were mean spillover effect and two-way volatility spillover effect in futures and spot price of soybean,soybean oil,and soybean meal; soybean futures prices significantly guided the spot price; in the price linkage between the types,the price relationship between the soybean meal and soybean was closer than between the soybean oil and soybean.

Investigation on Active and Reactive Combined Spot Price Integrated with Wind Farm

An active and reactive combined spot price model and the corresponding algorithm are introduced in this paper based on the theory of optimal power flow. Different from the traditional economic dispatch in the field of spot price, the objective function is to minimize generation costs based on pricing reactive power. Then considering the characteristics of wind turbines, processing methods of wind farms in optimal power flow is discussed. Finally, the feasibility of the model and the algorithm is verified through the simulation results of IEEE 30 system.

An Application of Decision Trees Algorithm to Project Hourly Electricity Spot Price as Support for Decision Making on Electricity Trading in Brazil

Estimating the price of a financial asset or any tradable product is a complex task that depends on the availability of a reasonable amount of data samples. In the Brazilian electricity market environment, where spot prices are centrally calculated by computational models, the projection of hourly energy prices at the spot market is essential for decision-making, and with the particularities of this sector, this task becomes even more complex due to the stochastic behavior of some variables, such as the inflow to hydroelectric power plants and the correlation between variables that affect electricity generation, traditional statistical techniques of time series forecasting present an additional complexity when one tries to project scenarios of spot prices on different time horizons. To address these complexities of traditional forecasting methods, this study presents a new approach based on Machine Learning methodology applied to the electricity spot prices forecasting process. The model’s Learning Base is obtained from public information provided by the Brazilian official computational models: NEWAVE, DECOMP, and DESSEM. The application of the methodology to real cases, using back-testing with actual information from the Brazilian electricity sector demonstrates that the research is promising, as the adherence of the projections with the realized values is significant.

Electricity Pricing with Continuous-time Commodity Model and Non-monotonic Load Curve

Under the "carbon peak and carbon neutrality"background,clean energy enters the market on a large scale,so it is very important to study the pricing theory of electricity.The bright spot of this paper is that the load curve is more practical.In the original version of electricity pricing theory based on continuous time commodity model,the load curve is supposed to be a monotonic function with time for simplicity of derivation,which is not realistic.To overcome this defect,load curves are supposed to be in shapes of a unimodal model and multimodal cases in this paper,which are more in conformity with the actual load curve in real-world power markets.Two study cases are used to demonstrate the rationality of the proposed models.These results together with the authors'previous work provide a complete pricing scheme for continuous-time electricity commodity model.

Causality of Future and Spot Grain Prices Between China and the US:Evidence from Soybean and Corn Markets Against the Surging Import Pressure

As the boom of the world grain market phases out, the challenge for Chinese government has gradually moved from retarding grain exports to restraining imports. This study tries to examine the causalities of soybean and corn price movement among the United States(US) future market, Chinese domestic future market and Chinese spot markets. We find that the daily prices of all these three types of grains belong to I(1) series, and there are long-run integrations. Also Chinese soybean future prices adjust more quickly than its spot prices, while Chinese corn future prices adjust slower. This paper finds that the soybean price movement originates from the US future market, then passes through Chinese future market, and finally reaches Chinese spot market, while the corn price movement starts in Chinese spot market, then spreads to the future markets in both China and the US.Finally, this paper also provides some policy implications on how to release the pressure from the grain imports.