A Novel Carbon Price Fluctuation Trend Prediction Method Based on Complex Network and Classification Algorithm

Carbon price fluctuation is affected by both internal market mechanisms and the heterogeneous environment. Moreover, it is a complex dynamic evolution process. This paper focuses on carbon price fluctuation trend prediction. In order to promote the accuracy of the forecasting model, this paper proposes the idea of integrating network topology information into carbon price data; that is, carbon price data are mapped into a complex network through a visibility graph algorithm, and the network topology information is extracted. The extracted network topology structure information is used to reconstruct the data, which are used to train the model parameters, thus improving the prediction accuracy of the model. Five prediction models are selected as the benchmark model, and the price data of the EU and seven pilot carbon markets in China from June 19, 2014, to October 9, 2020, are chosen as the sample for empirical analysis. The research finds that the integration of network topology information can significantly improve the price trend prediction of the five benchmark models for the EU carbon market. However, there are great differences in the accuracy improvement effects of China’s seven pilot carbon market price forecasts. Moreover, the forecasting accuracy of the four carbon markets (i.e., Guangdong, Chongqing, Tianjin, and Shenzhen) has improved slightly, but the prediction accuracy of the carbon price trend in Beijing, Shanghai, and Hubei has not improved. We analyze the reasons leading to this result and offer suggestions to improve China’s pilot carbon market.

Transmission Mechanism of Stock Price Fluctuation in the Rare Earth Industry Chain

The transmission of stock price fluctuations of listed companies in the rare earth industry has complex characteristics. Mastering its transmission law is of great meaning to understand the relationship between the upstream and downstream of the rare earth industry chain and market investment. This article uses the time series of daily closing prices of stocks in the global rare earth industry chain in the past ten years as the research object. The Granger causality test and complex network theory were used to construct the risk transmission network of the industrial chain. We have identified the key stocks in the network of stock price fluctuation in the rare earth industry chain and obtained the transmission path of stock price fluctuation. According to the results: (1) The stocks of Chinese and Japanese listed companies considerably influence the transmission of the stock price fluctuation in the rare earth industry chain. (2) The transmission distance of the stock price fluctuation of each network is relatively small, and the transmission speed is relatively fast. (3) The fluctuation of stock price in the rare earth industry chain is mainly transmitted from the upstream and midstream links to the midstream and downstream links.

The prediction of fluctuation in the order-driven financial market

Risk prediction is one of the important issues that draws much attention from academia and industry. And the fluctuation—absolute value of the change of price, is one of the indexes of risk. In this paper, we focus on the relationship between fluctuation and order volume. Based on the observation that the price would move when the volume of order changes, the prediction of price fluctuation can be converted into the prediction of order volume. Modelling the trader’s behaviours—order placement and order cancellation, we propose an order-based fluctuation prediction model. And our model outperforms better than baseline in OKCoin and BTC-e datasets.

Study on the Volatility Relationship between the Price of China's Live-hogs Industrial Chain and CPI

This paper uses the SAVR model to study the dynamic relationship between monthly corn prices, live hog prices, pork prices, and CPI volatility from January 2011 to August 2021. It is found that: 1. live-hog prices is the cause of pork price fluctuation, and live hogs and pork prices is the cause of CPI change. 2. live hog prices has short-term positive brunt on CPI, and pork prices has short-term positive and negative impact on CPI. 3. pork prices change is mainly caused by live hog price and its own change, and CPI change is mainly caused by spontaneous factors, live hog prices and pork prices change. Based on the above research, relevant policy recommendations to smooth out the fluctuation of pork prices are proposed.

An Empirical Analysis of the Price Volatility Characteristics of China’s Soybean Futures Market Based on ARIMA-GJR-GARCH Model

As the main force in the futures market, agricultural product futures occupy an important position in the China’s market. Taking the representative soybean futures in Dalian Commodity Futures Market of China as the research object, the relationship between price fluctuation characteristics and trading volume and open position was studied. The empirical results show that the price volatility of China’s soybean futures market has a “leverage effect.” The trading volume and open interest are divided into expected parts and unexpected parts, which are added to the conditional variance equation. The expected trading volume coefficient is estimated. Also, the estimated value of the expected open interest coefficient is, respectively, smaller than the estimated value of the unexpected trading volume coefficient and the estimated value of the unexpected open interest coefficient. Therefore, the impact of expected trading volume on the price fluctuation of China’s soybean futures market is less than that of unexpected trading volume on the price of soybean futures market. This paper adds transaction volume as an information flow to the variance of the conditional equation innovatively and also observes transaction volume as the relationship between conditional variance and price fluctuations.