The Influences of Energy Price Variation on the Prices of Other Industries: A Study Based on Input-Output Price Model

Energy has laid material foundation for human society during its development. Meanwhile, any change of price in the energy industry may influence social production and people’s life at all levels via an input-output mechanism under which the change related to energy is surely transmitted to other industries. The price change thus incurred in all industries may adversely affect the realization of macroeconomic objective-maintaining prices at a stable level. It is, therefore, needed to conduct an empirical research related to the impact of price change in energy industry on that in other industries. According to the data coming from “China’s 2015 Input-Output Extension Table (42 Departments)” and four hypothetical basis, this article focuses on four energy sectors and analyzes how deeply the price change of them, by use of input-output model, affects that of other industrial products under five conditions where each of their price rises by 10% individually or simultaneously, and why such an influence occurs. The results show that the price rising of the energies in question leads to an upward growth in the prices of other industrial products, especially when their prices go up simultaneously. Besides, the price increase in the four energy sectors doesn’t influence other industries in an accumulation form but actually leads to a rollback in some of other industries. It is recommended to adopt diversified pricing strategies for different energy products, thus maximizing the value of each specific energy, and meanwhile achieving the goals of energy consumption reduction and price equilibrium.

Stock Price Prediction Based on the Bi-GRU-Attention Model

The stock market, as one of the hotspots in the financial field, forms a data system with a huge volume of data and complex relationships between various factors, making stock price prediction an area of keen interest for further in-depth mining and research. Mathematical statistics methods struggle to deal with nonlinear relationships in practical applications, making it difficult to explore deep information about stocks. Meanwhile, machine learning methods, particularly neural network models and composite models, which have achieved outstanding results in other fields, are being applied to the stock market with significant results. However, researchers have found that these methods do not grasp the essential information of the data as well as expected. In response to these issues, researchers are exploring better neural network models and combining them with other methods to analyze stock data. Thus, this paper proposes the ABiGRU composite model, which combines the attention mechanism and bidirectional gated recurrent unit (GRU) that can effectively extract data features for stock price prediction research. Models such as LSTM, GRU, and Bi-LSTM are selected for comparative experiments. To ensure the credibility and representativeness of the research data, daily stock price indices of BYD are chosen for closing price prediction studies across different models. The results show that the ABiGRU model has a lower prediction error and better fitting effect on three index-based stock prices, enhancing the learning efficiency of the neural network model and demonstrating good prediction stability. This suggests that the ABiGRU model is highly adaptable for stock price prediction.

A Deep Learning Ensemble Method for Forecasting Daily Crude Oil Price Based on Snapshot Ensemble of Transformer Model

The oil industries are an important part of a country’s economy.The crude oil’s price is influenced by a wide range of variables.Therefore,how accurately can countries predict its behavior and what predictors to employ are two main questions.In this view,we propose utilizing deep learning and ensemble learning techniques to boost crude oil’s price forecasting performance.The suggested method is based on a deep learning snapshot ensemble method of the Transformer model.To examine the superiority of the proposed model,this paper compares the proposed deep learning ensemble model against different machine learning and statistical models for daily Organization of the Petroleum Exporting Countries(OPEC)oil price forecasting.Experimental results demonstrated the outperformance of the proposed method over statistical and machine learning methods.More precisely,the proposed snapshot ensemble of Transformer method achieved relative improvement in the forecasting performance compared to autoregressive integrated moving average ARIMA(1,1,1),ARIMA(0,1,1),autoregressive moving average(ARMA)(0,1),vector autoregression(VAR),random walk(RW),support vector machine(SVM),and random forests(RF)models by 99.94%,99.62%,99.87%,99.65%,7.55%,98.38%,and 99.35%,respectively,according to mean square error metric.

Brent vs.West Texas Intermediate in the US petro derivatives price formation

In this paper,we apply the spatial panel model to explore the relationship between the dynamic of two types of crude oil prices(WTI and Brent crude oil)and their refined products over time.Considering the turbulent months of 2011,when Cushing Oklahoma had reached capacity and the crude oil export ban removal in 2015 as breakpoints,we apply this method both in the full sample and the three resultant regimes.First,results suggest our results show that both WTI and Brent display very similar behaviour with the refined products.Second,when attending to each regime,results derived from the first and third regimes are quite similar to the full sample results.Therefore,during the second regime,Brent crude oil became the benchmark in the petrol market,and it influenced the distillate products.Furthermore,our model can let us determine the price-setters and price-followers in the price formation mechanism through refined products.These results possess important considerations to policymakers and the market participants and the price formation.

Flexible Load Participation in Peaking Shaving and Valley Filling Based on Dynamic Price Incentives

Considering the widening of the peak-valley difference in the power grid and the difficulty of the existing fixed time-of-use electricity price mechanism in meeting the energy demand of heterogeneous users at various moments or motivating users,the design of a reasonable dynamic pricing mechanism to actively engage users in demand response becomes imperative for power grid companies.For this purpose,a power grid-flexible load bilevel model is constructed based on dynamic pricing,where the leader is the dispatching center and the lower-level flexible load acts as the follower.Initially,an upper-level day-ahead dispatching model for the power grid is established,considering the lowest power grid dispatching cost as the objective function and incorporating the power grid-side constraints.Then,the lower level comprehensively considers the load characteristics of industrial load,energy storage,and data centers,and then establishes a lower-level flexible load operation model with the lowest user power-consuming cost as the objective function.Finally,the proposed method is validated using the IEEE-118 system,and the findings indicate that the dynamic pricing mechanism for peaking shaving and valley filling can effectively guide users to respond actively,thereby reducing the peak-valley difference and decreasing users’purchasing costs.

Carbon price impacts on sector cost:based on an input-output model of Beijing

Under the pressure of sustained growth in energy consumption in China,the implementation of a carbon pricing mechanism is an effective economic policy measure for promoting emission reduction,as well as a hotspot of research among scholars and policy makers.In this paper,the effects of carbon prices on Beijing's economy are analyzed using input-output tables.The carbon price costs are levied in accordance with the products'embodied carbon emission.By calculation,given the carbon price rate of 10 RMB/t-CO_2,the total carbon costs of Beijing account for approximately 0.22-0.40%of its gross revenue the same year.Among all industries,construction bears the largest carbon cost Among export sectors,the coal mining and washing industry has much higher export carbon price intensity than other industries.Apart from traditional energy-intensive industries,tertiary industry,which accounts for more than 70%of Beijing's economy,also bears a major carbon cost because of its large economic size.However,from 2007 to 2010,adjustment of the investment structure has reduced the emission intensity in investment sectors,contributing to the reduction of overall emissions and carbon price intensity.

The Prediction for the Consumer Price Index of Residents in Perspective of Time Series Method in Case of Chongqing

The consumer price index (CPI) measures the relative number of changes in the price level of consumer goods and services over time, reflecting the trend and degree of changes in the price level of goods and services purchased by residents. This article uses the ARMA model to analyze the fluctuation trend of the CPI (taking Chongqing as an example) and make short-term predictions. To test the predictive performance of the model, the observation values from January to December 2023 were retained as the reference object for evaluating the predictive accuracy of the model. Finally, through trial predictions of the data from May to August 2023, it was found that the constructed model had good fitting performance.

The Effects of Infrastructure Projects on House Prices and Rents:Evidence from the HS2 Extension Cancellation in the UK

This paper uses the HS2 extension cancellation in November 2021 as a quasi-experiment to study its impact on house prices and rents in Leeds.Using a DiD approach on repeat sales and monthly rents,I compare property values near the HS2 station and proposed construction site before and after the announcement.Results show a 3.6%decrease in house prices and a 3.9%decline in rents near the station,while properties near the construction site experienced a 2.4%increase in prices and a 2.1%rise in rents.This is the first paper to analyse the HS2 cancellation effect using panel data methods.

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.

SOLVABILITY RESULTS OF A CONDITIONAL INPUT-OUTPUT EQUATION BASED ON A TYPE OF NONLINEAR LEONTIEF MODEL

A class of nonlinear and continuous type Leontief model and its corresponding conditional input-output equation are introduced, and two basic problems under the so called positive or negative boundary assumption are presented. By approaches of nonlinear analysis some solvability results of this equation and continuous perturbation properties of the relative solution sets are obtained, and some economic significance are illustrated by the remark.

The relationship between international crude oil prices and China＇s refined oil prices based on a structural VAR model

With the frequent fluctuations of international crude oil prices and China＇s increasing dependence on foreign oil in recent years, the volatility of international oil prices has significantly influenced China domestic refined oil price. This paper aims to investigate the transmission and feedback mechanism between international crude oil prices and China＇s refined oil prices for the time span from January 2011 to November 2015 by using the Granger causality test, vector autoregression model, impulse response function and variance decomposition methods. It is demonstrated that variation of international crude oil prices can cause China domestic refined oil price to change with a weak feedback effect. Moreover, international crude oil prices and China domestic refined oil prices are affected by their lag terms in positive and negative directions in different degrees. Besides, an international crude oil price shock has a signif- icant positive impact on domestic refined oil prices while the impulse response of the international crude oil price variable to the domestic refined oil price shock is negatively insignificant. Furthermore, international crude oil prices and domestic refined oil prices have strong historical inheri- tance. According to the variance decomposition analysis, the international crude oil price is significantly affected by its own disturbance influence, and a domestic refined oil price shock has a slight impact on international crude oil price changes. The domestic refined oil price variance is mainly caused by international crude oil price disturbance, while the domestic refined oil price is slightly affected by its own disturbance. Generally, domestic refined oil prices do not immediately respond to an international crude oil price change, that is, there is a time lag.

Critical Exercise Price for American Floating Strike Lookback Option in a Mixed Jump-Diffusion Model

This paper studies the critical exercise price of American floating strike lookback options under the mixed jump-diffusion model. By using It formula and Wick-It-Skorohod integral, a new market pricing model established under the environment of mixed jumpdiffusion fractional Brownian motion. The fundamental solutions of stochastic parabolic partial differential equations are estimated under the condition of Merton assumptions. The explicit integral representation of early exercise premium and the critical exercise price are also given, then the American floating strike lookback options factorization formula is obtained, the results is generalized the classical Black-Scholes market pricing model.

Analysis on the influence factors of Bitcoin’s price based on VEC model

Background:Bitcoin,the most innovate digital currency as of now,created since 2008,even through experienced its ups and downs,still keeps drawing attentions to all parts of society.It relies on peer-to-peer network,achieved decentralization,anonymous and transparent.As the most representative digital currency,people curious to study how Bitcoin’price changes in the past.Methods:In this paper,we use monthly data from 2011 to 2016 to build a VEC model to exam how economic factors such as Custom price index,US dollar index,Dow jones industry average,Federal Funds Rate and gold price influence Bitcoin price.Result:From empirical analysis we find that all these variables do have a long-term influence.US dollar index is the biggest influence on Bitcoin price while gold price influence the least.Conclusion:From our result,we conclude that for now Bitcoin can be treated as a speculative asset,however,it is far from being a proper credit currency.

African swine fever and meat prices fluctuation:An empirical study in China based on TVP-VAR model

African swine fever(ASF),a fatal disease outbroken in China in August 2018,has widely attracted social concern especially in the information era.The occurrence of ASF led to an imbalance between supply and demand in pork and other meat markets.As a result,meat prices fluctuated greatly during the past year in 2019.To measure ASF quantitatively,the internet public concern index about ASF was created using web crawler methods.The relationships between ASF and meat prices were analyzed based on time-varying parameter vector auto-regressive(TVP-VAR)model.The results showed that there were some differences in the impact size,direction and duration of ASF on the prices of pork,chicken,beef and mutton,and the characteristics of time variability and heterogeneity were obvious.At the same time,the impact of ASF on meat prices is not consistent with the trend and degree of ASF.The impulse intensity is strongly correlated with the strength and duration of ASF,and it is generally weak in the early stage and much stronger in the middle and late periods.The results indicate that macro regulations,monitoring and early-warning system,standardizing production and circulation,and the public opinion monitoring and guidance about ASF should be given more attention in future to stabilize the market expectations and to promote a smooth functioning of the livestock markets.

Research on Hybrid Model of Garlic Short-term Price Forecasting based on Big Data

Garlic prices fluctuate dramatically in recent years and it is very difficult to predict garlic prices.The autoregressive integrated moving average(ARIMA)model is currently the most important method for predicting garlic prices.However,the ARIMA model can only predict the linear part of the garlic prices,and cannot predict its nonlinear part.Therefore,it is urgent to adopt a method to analyze the nonlinear characteristics of garlic prices.After comparing the advantages and disadvantages of several major prediction models which used to forecast nonlinear time series,using support vector machine(SVM)model to predict the nonlinear part of garlic prices and establish ARIMA-SVM hybrid forecast model to predict garlic prices.The monthly average price data of garlic in 2010-2017 was used to test the effect of ARIMA model,SVM model and ARIMA-SVM model.The experimental results show that:(1)Garlic price is affected by many factors but the most is the supply and demand relationship;(2)The SVM model has a good effect in dealing with the nonlinear relationship of garlic prices;(3)The ARIMA-SVM hybrid model is better than the single ARIMA model and SVM model on the accuracy of garlic price prediction,it can be used as an effective method to predict the short-term price of garlic.

Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model

This paper studies the relationship between accessibility and housing prices in Dalian by using an improved geographically weighted regression model and house prices, traffic, remote sensing images, etc. Multi-source data improves the accuracy of the spatial differentiation that reflects the impact of traffic accessibility on house prices. The results are as follows: first, the average house price is 12 436 yuan(RMB)/m^2, and reveals a declining trend from coastal areas to inland areas. The exception was Guilin Street, which demonstrates a local peak of house prices that decreases from the center of the street to its periphery. Second, the accessibility value is 33 minutes on average, excluding northern and eastern fringe areas, which was over 50 minutes. Third, the significant spatial correlation coefficient between accessibility and house prices is 0.423, and the coefficient increases in the southeastern direction. The strongest impact of accessibility on house prices is in the southeastern coast, and can be seen in the Lehua, Yingke, and Hushan communities, while the weakest impact is in the northwestern fringe, and can be seen in the Yingchengzi, Xixiaomo, and Daheishi community areas.

Model for Bidding and Tendering with Bill of Quantities Based on Bid-Winning Estimate at Reasonable Low Price

The possibility and rationality of introducing an bid-winning estimate based on a reasonable low price into construction bidding mode with bill of quantities were analyzed by setting up a model for bidding and tendering, and the functions of the estimate of reasonable low price in the bidding were revealed. On this basis, a new bidding mode of the project with bill of quantities was pro- posed. The application of the new mode will be advantageous to the promotion of the bill of quantities in China.

Empirical Study on the Impact of Rural Labor Force Flow on the Price of Agricultural Products——A Method Based on VEC Model

Based on the research introduction of domestic and foreign scholars,dynamic equilibrium between the rural labor force flow and the price of agricultural product is analyzed by VEC model,according to the data of the rural labor force flow and the price of agricultural products in the years 1990-2007.Chows breakpoint test is used to measure the stage characteristics of the impact of rural labor force flow on the price of agricultural product.Result shows that there is a long-term and stationary relationship between the flow quantity of rural labor force and the price of agricultural product.Rural labor force flow,as an exogenous force,affects the agricultural production,and further influences the price fluctuation of agricultural products.Impact of rural labor force flow on the price of agricultural product is from weak to strong,then grows gradually weaker,and reaches its peak value at the year 1998.With the development of rural society and economy and the market process,rural labor force flow endogenously affects the price of agricultural product,which has periodic characteristics.In order to achieve a dual stabilization of the rural labor force flow and the price of agricultural products,the following countermeasures are put forward:vigorously developing vocational education,increasing the support for agricultural production,and making active employment measures.

Balance of Datum Land Prices Among Cities Based on the City Gravitation Model and Stochastic Diffusion Equation

A balance of urban datum land prices is achieved to harmonize regional land prices and make the prices truly reflect different economic development levels and land prices among cities. The current piecewise linear interpolation balance method widely used has two drawbacks that always lead to an unsatisfactory balance among some cities. When the excess of land price in the central city to the surrounding zone reaches a certain degree, land price in the circumjacent city is not only consistent with the local land grade and land use level, but also influenced by the diffusion of land price in the central city. Thus, a new balanced scheme of datum land prices based on the city gravitation model and stochastic diffusion equation is brought forward. Finally, the new method is examined in the practice of datum land price balance in Hubei Province, China.

Construction of Two-Region Input-output Model——A Case Study of Henan Province

On the basis of input-output table of Henan Province and China in 2007, this paper advances a simple method of constructing two-region input-output model using MRIO model, to research the economic link between the industries of Henan Province and the industries of other regions. I summarize the characteristics of this method based on this as follows: when researching inter-regional economic link, the multi-region or two-region input-output model has prominent superiority, and we can conduct preliminary estimation on the multi-region input-output model using location quotient approach.