Zhenpeng Tang

The stability of agricultural futures market is of great significance to social economy and agri-cultural development. In view of the complexity of the fluctuation of agricultural futures prices, it is challenging to make up for the shortcomings of the existing data preprocessing technology so as to improve the prediction accuracy of the model. This paper puts forward a new VMD-SGMD-LSTM model based on improved quadratic decomposition technology and artificial intelligence model. First of all, in the data preprocessing part, VMD is used to decompose the original futures price data, and SGMD is used to further process the remaining components. Secondly, the LSTM model is used to predict a series of modal components, and the final result is obtained by synthesizing the predicted values of different components. Furthermore, based on the futures trading data of wheat, corn and sugar in China agricultural futures market, this paper makes an empirical study in the 1-step, 2-step and 4-step ahead forecasting scenarios, respectively. The results show that compared with other benchmark models, the VMD-SGMD-LSTM hybrid model proposed in this paper has better forecasting ability and robustness for different agricultural futures, which effectively makes up for the shortcomings of existing research.

Understanding the risk spillover of the oil market in economic uncertainty is of great importance. However, it is difficult to take on a traditional single perspective in describing the risk spillover law of economic uncertainty in the crude oil market on different timescales. In order to fill the research gap resulting from such difficulty, this paper incorporates empirical mode decomposition into the time-varying Copula-CoVaR model, and for the first time explores the risk spillover path of economic uncertainty on the two international crude oil pricing benchmarks—Brent and West Texas Intermediate crude oil prices—using different timescales. The empirical results not only verify the necessity of research from the perspective of different timescales, but also reveal the heterogeneity of the risk spillover paths of different types of economic uncertainty on crude oil prices. The research in this paper provides a multi-perspective interpretation for understanding the complex risk spillovers between various economic uncertainties and the crude oil market, as well as providing meaningful information to support stakeholders in making rational decisions.

The effective identification of systemically important financial institutions (SIFIs) is key to preventing and resolving systemic financial risks; thus, it is of great research significance for emerging countries to supervise SIFIs and manage systemic financial risks. Since traditional research on identifying SIFIs does not consider emerging machine learning models, it is difficult to properly fit the characteristics of actual financial institutions’ asset distribution. This paper proposes a new method for measuring SIFIs, integrating the PSO-SVM model into the Copula-CoVaR model. This new PSO-SVM-Copula-CoVaR model is meant to evaluate China’s SIFIs based on the publicly traded price data of Chinese listed financial institutions. The empirical results show that, compared with the traditional parameter method (GARCH model) and the nonparametric method (kernel density estimation), the marginal distribution estimation method using the PSO-SVM method can better fit the distribution of an institution’s financial asset return sequence. That is, the model proposed in this paper helps regulatory authorities improve the list of SIFIs more reasonably and implement effective regulatory measures.