Asymmetric Price Transmission Analysis of the International Soybean Market

This study analyzed the asymmetric price transmission in the international soybean market, using data from the US (Chicago Futures), European (Rotterdam), Brazilian (Paranaguá), Argentinian (Rosario Futures and Rosario Spot), and Chinese (Spot and Futures) markets. The study looked at the price transmission between these markets over a period of almost 10 years, from September 2009 to May 2019. The Phillips-Perron unit root test was used to determine the order of integration of the time series. The Engle-Granger cointegration test failed to find any evidence of cointegration between the Chinese and Argentinian markets with any others of the international markets. The lack of cointegration was associated with highly government intervened markets. The cointegration and threshold test proposed by Enders and Siklos, succeeded in rejecting the Null hypothesis and finding cointegration among the series after structural breaks had been taken into account. The BDS test for nonlinearity showed that most of the time series were nonlinear, which prompted the investigation to look into nonlinear modelling. To evaluate asymmetric price transmission, the study used the Threshold autoregressive (TAR) model and the momentum threshold model (MTAR). The Argentine and Chinese markets were primarily suspected of exhibiting asymmetric price transmission due to structural government intervention. However, the test results failed to reject the null hypothesis and revealed asymmetric price transmission between these markets and the international market. As expected, the results found no evidence of asymmetric price transmission in the Paranaguá, Rotterdam, and Chicago markets. Hence, it can be concluded that symmetric price transmission is more prevalent in the global soybean market than asymmetric price transmission.

A Short-Term Electricity Price Forecasting Scheme for Power Market

Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent to contribute more efforts on developing appropriate price forecasting scheme to maximize their profits. This paper introduces a time series method developed by Box-Jenkins that applies autoregressive integrated moving average (ARIMA) model to address a best-fitted time-domain model based on a time series of historical price data. Using the model’s parameters determined from the stationarized time series of prices, the price forecasts in UK electricity market for 1 step ahead are estimated in the next day and the next week. The most suitable models are selected for them separately after comparing their prediction outcomes. The data of historical prices are obtained from UK three-month Reference Price Data from April 1st to July7th 2010.

Review of the Global Upstream Oil and Gas Market in 2023 and 2024 Outlook

Review of the global upstream oil and gas market in 2023,International oil prices fluctuated significantly throughout theyear,In 2023,the international oil prices overall exhibited "box fluctuation"within the range of USD 65-95/barrel,presenting a short-term significant high-level volatility trend of"two steady phases,two increases,and three decreases",which is rare in recent years.This also fully reflects the evident "tug of war"between bulls and bears in the global crude oil market.

Maximizing Supermarket Profits:Data-Driven Strategies for Pricing,Sales,and Forecasting

The actual circumstances of daily life are crucial for the purchasing and pricing strategies of supermarkets.Developing strategies based on these circumstances can assist businesses in ensuring profits and fostering win-win cooperation.This paper explores methods to maximize profit through purchasing and sales strategies.Initially,the relevant data for various categories of vegetables is integrated.Through histograms,their sales patterns are directly understood,highlighting the most popular vegetables.Upon analyzing each vegetable category,it becomes evident that their sales data do not conform to normal distributions.Therefore,Spearman correlation coefficients are calculated,revealing strong correlations between certain categories,such as aquatic roots and edible fungi.A line chart depicting the top ten selling vegetables indicates a noticeable periodicity.Traditional fitting methods struggle to adequately model the sales of each vegetable category and their relationship with cost-plus pricing.To address this,additional factors such as holidays,weeks,and months are incorporated using techniques like random forest regression.This approach yields cost-plus pricing dependence curves that better capture the relationship,while effectively managing noise.Regarding sales volume prediction,the original data displays significant volatility,necessitating the handling of outliers using the threshold method.For missing data,linear interpolation is employed to mitigate the impact of continuous missing values on prediction accuracy.Subsequently,Adam-optimized long short-term memory(LSTM)networks are utilized to forecast incoming quantities for the next seven days.By extrapolating from normal sales volume,market capacity is estimated,allowing for additional sales through discount strategies.This framework has the potential to increase original income by 1.1 times.

Short-Term and Long-Term Price Forecasting Models for the Future Exchange of Mongolian Natural Sea Buckthorn Market

Sea buckthorn market floated uncertainly within a narrow range. The market situation provided upward pressure on prices, and producer and consumer interest were poor, coupled with weak prices in the regional markets. The objectives of the study are: 1) to estimate the relationship between wild Sea buckthorn (SB) price and Supply, Demand, while some other factors of crude oil price and exchange rate by using simultaneous Supply-Demand and Price system equation and Vector Error Correction Method (VECM);2) to forecast the short-term and long-term SB price;3) to compare and evaluate the price forecasting models. Firstly, the data was analyzed by Ferris and Engle-Granger’s procedure;secondly, both price forecasting methodologies were tested by Pindyck-Rubinfeld and Makridakis’s procedure. The result shows that the VECM model is more efficient using yearly data;a short-term price forecast decreases, and a long-term price forecast is predicted to increase the Mongolian Sea buckthorn market.

Price linkage between Chinese and international nonferrous metals commodity markets based on VAR-DCC-GARCH models

Using VAR-DCC-GARCH model,the literature on commodity price was extended by exploring the co-movement between Chinese nonferrous metal prices and global nonferrous metal prices represented by the nonferrous metal prices from London Metal Exchange(LME).The results show that LME nonferrous metals prices still have a greater impact on Chinese nonferrous metals prices.However,the impact of Chinese nonferrous metals prices on LME nonferrous metals prices is still weak except for lead price.The co-movement of nonferrous metal prices between LME and China presents hysteretic nature,and it lasts for 7-8trading days.Furthermore,the co-movement between LME nonferrous metals prices and Chinese nonferrous metals prices has the characteristics of time-varying,and the correlation of lead prices between LME and China is the more stable than all other nonferrous metals prices.

The Empirical Analysis of the Dynamic Prices Relationship between Cotton Spot Market and Futures Market in Xinjiang

The thesis analyzes the causal relationship between the cotton spot,and the tendency and impact of prices of futures markets in Xinjiang by using ADF test,co-integration analysis,Granger causality test and other econometric methods in order to discuss the interacted relationship between futures market prices of cotton and spot market prices since the futures of cotton in Xinjiang go public.The results of empirical analysis show that the spot market prices of cotton and the futures market prices in Xinjiang fluctuate prominently in the short run and tend to counterpoise in the long run;the futures market of cotton plays the role of leading the spot market prices of cotton in Xinjiang,while the spot market prices of cotton in Xinjiang impacts little on the futures market prices.The corresponding countermeasures are put forward.The government should continuously perfect the construction of the futures market of cotton in Xinjiang,so as to exert the function of price discovery and the function of hedging,and promote the development of cotton industry in Xinjiang.

NECESSARY AND SUFFICIENT CONDITION FOR THE EXISTENCE OF A NONNEGATIVE EQUILIBRIUM PRICE VECTOR IN THE CAPITAL MARKET WITH SHORT-SELLING

For the capital market satisfying standard assumptions that are widely adopted in the equilibrium analysis,a necessary and sufficient condition for the existence and uniqueness of a nonnegative equilibrium price vector that clears the mean-variance capital market with short sale allowed is derived.Moreover,the given explicit formula for the equilibrium price shows clearly the relationship between prices of assets and statistical properties of the rate of return on assets,the desired rates of return of individual investors as well as other economic quantities.The economic implication of the derived condition is briefly discussed.These results improve the available results about the equilibrium analysis of the mean-variance market.

The institution efficiency of stock price limits： An experimental analysis on the two stocks market with continuous bid

Price limits in product market have been discarded in classic microeconomics. However, price limits affect the trade behavior of the same agent, since agents in the stock market change their trade status frequently. Due to the controversy on the institution effectiveness of the price limits in stock market, this paper design an experimental market with two stocks with continuous bid, in which we investigate the impact of the price limits on the stock market. The results show that the price limits moderate the price volatility within and between the trades periods, thus can stabilize stock price. In addition, price limits, when measured by traditional approach, restrain the fluidity of the market, reduce the volatility of transaction shares and maintain durative of fluidity. While volatility-based fluidity measurements show that the fluidity is enhanced significantly in stock market with price limits.

Currency exposures of the oil and natural gas stock prices in the Hushen-300 stock market： A nonlinear model approach

The paper embarks to investigate the relationship between currency risk and stock prices of the oil and natural gas exploitation industry in the value-weighted Hushen-300 stock market, by applying the standard Capital Asset Pricing Model （CAPM） and nonlinear exchange rate exposure model to the Renminbi against US dollar. The results show that the currency exposure does vary in the oil-gas stock prices throughout the bull and bear market. The study suggests that the models of the equilibrium exchange rate exposure must be extended to considering the nonlinear exchange rate exposure, the regime periods of bull and bear market, and the industry types that is sensitive to the currency exposures. The nonlinear dynamic relationship between the exchange rate changes and the Chinese energy stock prices throughout the bull and bear market add to the recent empirical evidences that foreign exchange markets and stock markets are closely correlated.

Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive data.Data security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing mechanisms.In this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary market.Moreover,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and users.Specifically,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic bidding.The incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading scenario.With temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation strategies.Furthermore,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

Comparison of ARIMA and ANN Models Used in Electricity Price Forecasting for Power Market

In power market, electricity price forecasting provides significant information which can help the electricity market participants to prepare corresponding bidding strategies to maximize their profits. This paper introduces the models of autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) which are applied to the price forecasts for up to 3 steps 8 weeks ahead in the UK electricity market. The half hourly data of historical prices are obtained from UK Reference Price Data from March 22nd to July 14th 2010 and the predictions are derived from a sliding training window with a length of 8 weeks. The ARIMA with various AR and MA orders and the ANN with different numbers of delays and neurons have been established and compared in terms of the root mean square errors (RMSEs) of price forecasts. The experimental results illustrate that the ARIMA (4,1,2) model gives greater improvement over persistence than the ANN (20 neurons, 4 delays) model.

Short-Term Electricity Price Forecasting Using a Combination of Neural Networks and Fuzzy Inference

This paper presents an artificial neural network, ANN, based approach for estimating short-term wholesale electricity prices using past price and demand data. The objective is to utilize the piecewise continuous na-ture of electricity prices on the time domain by clustering the input data into time ranges where the variation trends are maintained. Due to the imprecise nature of cluster boundaries a fuzzy inference technique is em-ployed to handle data that lies at the intersections. As a necessary step in forecasting prices the anticipated electricity demand at the target time is estimated first using a separate ANN. The Australian New-South Wales electricity market data was used to test the system. The developed system shows considerable im-provement in performance compared with approaches that regard price data as a single continuous time se-ries, achieving MAPE of less than 2% for hours with steady prices and 8% for the clusters covering time pe-riods with price spikes.

Duration Dependence in Housing Price Market: A Metro Level Test in United States

Duration dependence affects the dynamics of multi sate time to event outcomes. In this paper we are testing if a contraction or an expansion state for the housing price is duration dependent on previous states lengths. This test has implications for explaining the dynamics and the predictability of the housing prices in subsequent spells of contraction/expansion. The test is carried on using a discrete time duration model. This research shows that federal fund rate has strong effect on duration of both expansion and contraction. The analysis is also showing that while for both contraction and expansion spells we observe duration dependence, the risk of exiting from either spell at the beginning of the spell is practically flat for the first five to six years in the expansion spells and between seven and eight years in the contraction spells. After these periods the risk of exiting an expansion spell is increasing but in a non-monotone way, while for the contraction spell the risk of exiting the state is increasing in a monotone way, making the contraction periods easier to predict than the expansion periods.

Cassava sector development in Cameroon: Production and marketing factors affecting price

Regular and available supply is the prerequisite of an effective and efficient commercialization process. Using multivariate regression analysis on field data, this research appraises the production and marketing factors that influence cassava market price. The production factors include cultivated area, planting material, yield, and farmers’ field schools;while farmers access to a paved road, having a telephone, the transportation costs of fresh roots, the level of root perishability, and the prices of rice and maize stand as marketing factors. The results show that farmers who attended farmers’ field school adopted improved planting materials, propagated them in their localities and the yields in these communities increased significantly. The farm size also has a significant influence on the availability of fresh roots. On the marketing side, transportation costs, access to a paved road, the prices of rice and maize significantly affect cassava’s market price and tighten the relationship between producers and marketers. We conclude that to increase fresh roots supply, roads leading to cultivating areas should be paved, better transportation provided, communication costs reduced, even distribution of planting materials and appropriate warehouses.

Research on the Impact of Market Concern for Real Estate Policy on Housing Prices: Evidence from Internet Search and Hedonic Price Theory

To avoid the effects of systemic financial risks caused by extreme fluctuations in housing price,the Chinese government has been exploring the most effective policies for regulating the housing market.Measuring the effect of real estate regulation policies has been a challenge for present studies.This study innovatively employs big data technology to obtain Internet search data(ISD)and construct market concern index(MCI)of policy,and hedonic price theory to construct hedonic price index(HPI)based on building area,age,ring number,and other hedonic variables.Then,the impact of market concerns for restrictive policy,monetary policy,fiscal policy,security policy,and administrative supervision policy on housing prices is evaluated.Moreover,compared with the common housing price index,the hedonic price index considers the heterogeneity of houses and could better reflect the changes in housing prices caused by market supply and demand.The results indicate that(1)a long-term interaction relationship exists between housing prices and market concerns for policy(MCP);(2)market concerns for restrictive policy and administrative supervision policy effectively restrain rising housing prices while those for monetary and fiscal policy have the opposite effect.The results could serve as a useful reference for governments aiming to stabilize their real estate markets.

Analysis of Volatility Spillover Effect of Soybean Price between Domestic and International Markets

Sharp fluctuation of soybean prices in international and domestic markets has caused big risks for both domestic soybean producers and processing enterprises in recent years. It also increases the difficulties in implementing price stabilization policy for the government. This paper analyzes the volatility spillovers in soybean prices between international and domestic markets using the multivariate VAR-BEKK-GARCH model based on the data set from December 22,2004 to December 19,2014. The estimate results indicate that there are volatility spillover effects from domestic futures market to spot market and bilateral spillover between international futures market and domestic spot market. In order to prevent market manipulation and to reduce the impacts of price volatility in international soybean market on Chinese market,this paper proposes the following policy measures such as establishing early warning mechanism for soybean price fluctuations,improving soybean futures contract design and strengthening trading risk management mechanism,amplifying information disclosure system,and regularizing speculation activities of big traders.

Regression Analysis of Electric Power Market Price of JEPX

The deregulation of electric power industries is introducing around the world and electric power markets are operated under the deregulation. Also in Japan, JEPX (Japan Electric Power Exchange) operates a day-ahead market since April 2005. We have already analyzed the electric power market prices in the several markets including JEPX by autoregressive models. We compose simple regression equations, of which a dependent variable is the market price and explanatory variables are the demand and the market price of 24 hours ago. In this paper, we analyze JEPX price data in summer seasons from 2007 to 2013. In this period, the JEPX price is affected by many social phenomena such as Lehman Shock and East Japan earthquake disasters. We compare the determination coefficient and the coefficients of regression and investigate the change of coefficients over the years.

Market Share Model of Price and Advertising Combined Game in Duopoly

The marketing share model of price and advertising in a duopoly market was studied in this paper. Market response curves of price and advertising were presented to calculate the reasonable range based on the market forecast results. The interaction effect of price and advertising was considered,and the game theory was applied to a two-stage price and advertising competition which involved the market share model. A marketing decision support system (MDSS) was developed and simulation data was provided to give the solutions. The operation results show that the leading enterprise makes higher price,spends more on advertising,and earns more profit,while the small-scale enterprise has to lower the price,spend less on advertising,and has slightly higher profit rate. The system is shown to be adaptable to a wide variety of realistic situations.

To What Extent Does the Market Price of Imported Fishmeal Affect the Market Price of Pisciculture Products in Japan？ The Case of Yellowtail and Sea Bream

This study investigates the extent of the influence of imported fishmeal price changes on the market price of pisciculture products. To date, there have been only a few insufficient researches on this topic in Japan. This paper aims to reveal the causality relationship between the market price of imported fishmeal and the market price of pisciculture products using the granger causality test, and to simulate the market price of pisciculture products using impulse response functions as the price of imported fishmeal increases. The results of the granger causality test and impulse response function analyses were as follows： （1） there is a market linkage from the price of imported fishmeal to the market price of sea bream, but no causality with the market price yellowtail; and （2） this has a positive impact on the market price of sea bream when the price of imported fishmeal changes. Moreover, spillover effects were noticed in this simple scenario （at a market price of 800 yen/kg and one unit shock of 1 yen） of about 3 yen/kg.