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.

Forecast on Price of Agricultural Futures in China Based on ARIMA Model

The forecast on price of agricultural futures is studied in this paper. We use the ARIMA model to estimate the price trends of agricultural futures,which can help the investors to optimize their investing plans. The soybean future contracts are taken as an example to simulate the forecast based on the auto-regression coefficient(p),differential times(d) and moving average coefficient(q). The results show that ARIMA model is better to simulate and forecast the trend of closing prices of soybean futures contract,and it is applicable to forecasting the price of agricultural futures.

Investigating seasonality,policy intervention and forecasting in the Indian gold futures market:a comparison based on modeling non‑constant variance using two different methods

This study focuses on the Indian gold futures market where primary participants hold sentimental value for the underlying asset and are globally ranked number two in terms of the largest private holdings in the physical form.The trade of gold futures relates to seasons,festivity,and government policy.So,the paper will discuss seasonality and intervention in the analysis.Due to non-constant variance,we will also use the standard variance stabilization transformation method and the ARIMA/GARCH modelling method to compare the forecast performance on the gold futures prices.The results from the analysis show that while the standard variance transformation method may provide better point forecast values,the ARIMA/GARCH modelling method provides much shorter forecast intervals.The empirical results of this study which rationalise the effect of seasonality in the Indian bullion derivative market have not been reported in literature.

Nonlinearities between oil spot and futures markets： Evidence from intraday data

This paper builds on the literature of the relationship between oil spot and futures prices from the NYNEX market, both in their means and in their conditional volatilities, to investigate whether the association is linear or not. The novelty of this work is based on intraday data from both markets. The empirical findings indicate the presence of nonlinearities both in means and conditional volatilities. Moreover, non-linear causality estimations both in means and in volatilities reveal the presence of bi-directional causality, a fact that provides additional support to the hypothesis that both markets are driven by the same information sets.

Theory summary on price discovery function of futures market

Price discovery is the basic function of futures market, and whether the futures market has the function of price discovery is an important research field for scholars both at home and abroad. This paper classifies the test methods and models on a basis of previous research, and introduces the applicable premise of research methods and models as well as the major research achievements of scholars at home and abroad, and also reviews the shortcomings of test methods and models.

China futures price forecasting based on online search and information transfer

The synchronicity effect between the financial market and online response for time-series forecasting is an important task with wide applications.This study combines data from the Baidu index(BDI),Google trends(GT),and transfer entropy(TE)to forecast a wide range of futures prices with a focus on China.A forecasting model based on a hybrid gray wolf optimizer(GWO),convolutional neural network(CNN),and long short-term memory(LSTM)is developed.First,Baidu and Google dual-platform search data were selected and constructed as Internetbased consumer price index(ICPI)using principal component analysis.Second,TE is used to quantify the information between online behavior and futures markets.Finally,the effective Internet-based consumer price index(ICPI)and TE are introduced into the GWO-CNN-LSTM model to forecast the daily prices of corn,soybean,polyvinyl chloride(PVC),egg,and rebar futures.The results show that the GWO-CNN-LSTM model has a significant improvement in predicting future prices.Internet-based CPI built on Baidu and Google platforms has a high degree of real-time performance and reduces the platform and language bias of the search data.Our proposed framework can provide predictive decision support for government leaders,market investors,and production activities.

Analysis on the spatial pattern and evolution of China's petroleum trade under the dual effect of international oil price and “Belt and Road” Framework

“Belt and Road” is the important origin of oil import in China. Based on social network analysis and stochastic frontier gravity model, this paper studied the characteristic evolution and influence factor of oil import network between China and “Belt and Road” countries. Then by constructing a stochastic frontier gravity model including the crude oil future price and oil importing price, it found that the international crude oil future price, the oil importing price, the political situation, the trade agreements have the effects on the China's oil import from “Belt and Road” region. It provided suggestions for improving the spatial pattern of China's petroleum trade.

PRICING OF LIBOR FUTURES BY MARTINGALE METHOD IN COX-INGERSOLL-ROSS MODEL

This paper considers the pricing of LIBOR futures in the Cox-Ingersoll-Ross(CIR)modelunder Pozdnyakov and Steele(2004)'s martingale framework for futures prices.Under the CIR modelfor short term interest rate,we prove that there exists a unique futures price process associated withthe terminal value and the standard financial market,and that this unique futures price process has amartingale representation.Moreover,a general closed-form pricing formula for LIBOR futures contractsis obtained in the CIR model.

Impacts of CME Changing Mechanism for Allowing Negative Oil Prices on Prices and Trading Activities in the Crude Oil Futures Market

This study investigates and compares the effects of the Coronavirus disease 2019(COVID-19)pandemic,the Chicago mercantile exchange(CME)'s negative price suggestion on prices and trading activities in the crude oil futures market to discuss the cause of negative crude oil futures prices.Through event studies,the empirical results show that the COVID-19 pandemic no longer impacts crude oil futures prices in April,2020 after controlled market risk,while the CME's negative prices suggestion can explain the crude oil futures price changes around and even after April 8,2020 to some degree.Moreover,this study uncovers anomalies in prices and trading activities by analyzing returns,trading volume,open interest,and illiquidity measures using vector autoregressive(VAR)models.The results imply that CME's allowing negative prices strengthens the price impact on trading volume and makes illiquidity risk matter.This study's results coincide with the following lawsuit evidence of market manipulation.

How did high temperature extremes in southern Xinjiang,China induce the repaid rise in jujube futures prices in the summer of 2021?

In the summer of 2021,southern Xinjiang,China,experienced a temporary period of high temperature extremes.Because of this weather event,jujube futures prices rose by more than 50%in a short time.To clarify the influence mechanism of these two events,we investigated the current status of jujube farming and collected investors’online comments.We analysed these comments specifically using textual analysis tools,such as co-word networks.Results showed that the concerns of investors about the reduction in jujube production triggered by high temperature extremes were the primary reason for the rapid rise in jujube futures prices.Especially in combination with the cultivation density of jujube and their adaptability to the growing environment,a new understanding can be obtained.That is to say,when a crop is excessively densely cultivated in a region and is highly sensitive to a meteorological variable anomaly at a certain growth stage,a less destructive local extreme weather event could induce severe panic among investors regarding production reduction and thus influence the normal changes in futures price.In response to the impact mechanisms revealed in this study,we proposed policy recommendations,such as strengthening the degree of crop damage disclosure and designing weather futures derivatives,to address similar situations in the future.This study not only fills the gap in the research on the impact paths of high temperature extremes on jujube futures prices but also has a reference value for securing the stability of futures prices of related agricultural products in the future.

The Hope Project:the Future Has a Price

The Hope Project has made a difference to the lives of many chldren in China..This magazine has also received a large number of inquiries from overseas readers.and,to answer their questions,we bring more informa-tion on the Hope Project in the following outline.Readers can contact the Hope Project directly,and China I oday will also be happy to answer reader inquiries.

Exploring Long-Memory Process in the Prediction of Interval-Valued Financial Time Series and Its Application

Long-memory process has been widely studied in classical financial time series analysis,which has merely been reported in the field of interval-valued financial time series.The aim of this paper is to explore long-memory process in the prediction of interval-valued time series(IvTS).To model the long-memory process,two novel interval-valued time series prediction models named as interval-valued vector autoregressive fractionally integrated moving average(IV-VARFIMA)and ARFIMAX-FIGARCH were established.In the developed long-memory pattern,both of the short term and long-term influences contained in IvTS can be included.As an application of the proposed models,interval-valued form of WTI crude oil futures price series is predicted.Compared to current IvTS prediction models,IV-VARFIMA and ARFIMAX-FIGARCH can provide better in-sample and out-of-sample forecasts.

The effect of nighttime-trading of futures markets on information flows:Evidence from China

In the past couple of years,China’s futures exchanges have launched nighttime trading sessions.We use daily data from 23 commodity futures to investigate the impact of this important policy change.Our findings suggest that the launching of nighttime trading effectively improved the efficiency of futures prices and reduced the volatility of prices.The normality of returns improves during the post-nighttime trading period.As documented in the literature,the interactions between trading activities(i.e.,trading volume and open interest)and volatility conform better to the observed patterns in developed markets.This study provides sound evidence that China has taken steady steps toward its goal of establishing price-setting power in key commodities on world financial markets.