Analyzing pure contagion between crude oil and agricultural futures markets

Crude oil and agricultural product prices are important factors affecting a country's economic and social stability. The pure contagion between these two markets may lead to excessive price linkage, increasing the fragility of the financial system. This paper uses the CEEMDAN method, fine-to-coarse reconstruction method, and TVP-VAR model to study the pure contagion between crude oil and agricultural futures markets. The empirical results show that there always is significant pure contagion between agricultural futures markets. However, pure contagion between crude oil and agricultural futures markets only exists in some specific periods. The crude oil futures market has obvious pure contagion to the agricultural futures markets in most periods. Only a few periods the agricultural futures have pure contagion to the crude oil futures. It is worth noting that the COVID-19 epidemic aggravates the pure contagion between crude oil and the agricultural futures markets. Based on the research conclusions, this paper puts forward corresponding policy recommendations, hoping to provide a reference and theoretical basis for the government to formulate corresponding policies. © 2023 Elsevier Ltd

Analyzing pure contagion between crude oil and agricultural futures markets

Crude oil and agricultural product prices are important factors affecting a country's economic and social stability. The pure contagion between these two markets may lead to excessive price linkage, increasing the fragility of the financial system. This paper uses the CEEMDAN method, fine-to-coarse reconstruction method, and TVP-VAR model to study the pure contagion between oil and agricultural futures markets. The empirical results show that there always is significant pure contagion between agricultural futures markets. However, pure contagion between crude oil and agricultural futures markets only exists in some specific periods. The crude oil futures market has obvious pure contagion to the agricultural futures markets in most periods. Only a few periods the agricultural futures have pure contagion to the crude oil futures. It is worth noting that the COVID-19 epidemic aggravates the pure contagion between crude oil and the agricultural futures markets. Based on the research conclusions, this paper puts forward corresponding policy recommendations, hoping to provide reference and theoretical basis for the government to formulate corresponding policies.

Forecasting carbon dioxide emission price using a novel mode decomposition machine learning hybrid model of CEEMDAN-LSTM

Global carbon dioxide emissions have become a great threat to economic sustainability and human health. The carbon market is recognized as the most promising mean to curb carbon emissions, furthermore, carbon price forecasting will promote the role of the carbon market in emissions reduction and achieve reduction targets at lower economic costs for emission entities. However, there are still some technical problems in carbon price prediction, such as mode mixing and larger reconstruction error for the traditional empirical mode decomposition-type models. Therefore, the innovation of this paper is constructing a novel carbon price prediction model of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-long short-term memory (LSTM), that combines the advantages of CEEMDAN in decomposing the multiscale time-frequency carbon price signals and the LSTM model in fitting the financial signals. The results show the proposed CEEMDAN-LSTM model has significant accuracy in predicting the complex carbon price signals. The prediction error and expectation indicators of root mean square error, mean absolute error, mean absolute percentage error, and direction accuracy are 0.638342, 0.448695, 0.015666, and 0.687631, respectively, which is better than other benchmark models. Further evidence convince that the short-term forecasting performance is superior to the long-term and medium-term performance. That evidence concludes that the proposed model is a reliable method to reveal the carbon price-driving mechanism from the point of multiscale time-frequency characteristics. Particularly, short-term forecasting is more accurate and can provide a valuable technical reference for reduction entities and green financial companies to judge the market situation and formulate quantitative transactions.

CEEMDAN-IPSO-LSTM: A Novel Model for Short-Term Passenger Flow Prediction in Urban Rail Transit Systems.

Urban rail transit (URT) is a key mode of public transport, which serves for greatest user demand. Short-term passenger flow prediction aims to improve management validity and avoid extravagance of public transport resources. In order to anticipate passenger flow for URT, managing nonlinearity, correlation, and periodicity of data series in a single model is difficult. This paper offers a short-term passenger flow prediction combination model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and long-short term memory neural network (LSTM) in order to more accurately anticipate the short-period passenger flow of URT. In the meantime, the hyperparameters of LSTM were calculated using the improved particle swarm optimization (IPSO). First, CEEMDAN-IPSO-LSTM model performed the CEEMDAN decomposition of passenger flow data and obtained uncoupled intrinsic mode functions and a residual sequence after removing noisy data. Second, we built a CEEMDAN-IPSO-LSTM passenger flow prediction model for each decomposed component and extracted prediction values. Third, the experimental results showed that compared with the single LSTM model, CEEMDAN-IPSO-LSTM model reduced by 40 persons/35 persons, 44 persons/35 persons, 37 persons/31 persons, and 46.89%/35.1% in SD, RMSE, MAE, and MAPE, and increase by 2.32%/3.63% and 2.19%/1.67% in R and R2, respectively. This model can reduce the risks of public health security due to excessive crowding of passengers (especially in the period of COVID-19), as well as reduce the negative impact on the environment through the optimization of traffic flows, and develop low-carbon transportation.