Price Changes Research Articles

Market Integration of Domestic and International Cotton Prices

Market integration is perceived as a precondition for effective market reform in developing countries. The current study on cointegration analysis was taken up to analyse how different spatially separated cotton markets are inter-connected and impact the price change in one market on other markets. Monthly prices for the period January 2010 to February 2024 from the markets viz., Cotlook-A Index, Chinese spot market, Rajkot in Gujarat, Hinganghat in Maharashtra, Adilabad and Warangal in Telangana were used for the analysis. Augmented Dickey-Fuller (ADF), Johansen's multivariate Cointegration approach, Granger causality test, and Vector Error Correction Model (VECM) were used to test the study the long-run spatial integration. The study confirmed the presence of market cointegration. The pair-wise Granger causality test identified the bidirectional and unidirectional relationship between markets chosen. Cotlook A- Index, China and Adilabad markets affected most of the markets unidirectionally and acted as lead markets. The VECM inferred that China, Rajkot and Hinganghat markets attained equilibrium rapidly in short run. Thus, the cotton markets were highly integrated in India. Thus, Stabilizing the prices in one market would influence the other markets improving competitiveness and market efficiency.

Can Social Media Information Amplify Short-term Housing Price Changes? An Investigation in China’s Major Cities

Can Social Media Information Amplify Short-term Housing Price Changes? An Investigation in China’s Major Cities

The effect of CHINA RAILWAY Express on alleviating the Red Sea Waterway Crisis: A GTAP model-based study

In November 2023, the Red Sea Waterway Crisis broke out, which caused the goods originally shipped to Europe via the Red Sea-Suez Canal Route to be transported around the Cape of Good Hope in Africa, thus increasing the shipping cost and shipping time. Firstly, this paper uses the Supply Chain Network Model and the Optimization Model to theoretically analyze the impact of this incident. Secondly, the Global Trade Analysis Project (GTAP) Model is employed to analyze the impact of the crisis on China. At the same time, it also explains whether the impact of the incident can be alleviated by using CHINA RAILWAY Express (CR Express). Finally, this paper compares the upward pressure changes of domestic commodity prices in China, Germany, France, Netherlands and Italy before and after the adoption of CR Express. The results of the study are as follows. (1) The outbreak of the Red Sea Waterway Crisis has a great negative impact on China's macro economy. When China switches to the use of CR Express, the impact on China's GDP and terms of trade is reduced. (2) The outbreak of the Red Sea Waterway Crisis has a greater impact on China's import trade than on its export trade. (3) The outbreak of the Red Sea Waterway Crisis has a great negative impact on China's textile and clothing industry and electronic equipment industry. (4) CR Express can ease the upward pressure of domestic commodity prices. Taken together, the above results can provide reference for China and other countries to cope with similar impact events.

GEOPOLITICAL INSTABILITY AND ITS IMPACT ON FINANCIAL MARKET DYNAMICS: AN ARTIFICIAL INTELLIGENCE APPROACH AND SENTIMENT ANALYSIS

This paper investigates the impact of geopolitical instability, specifically the Russia-Ukraine conflict, on the dynamics of the Ukrainian financial market. The study focuses on key indices and companies, including the PFTS Index, Kernel Holding SA (KER), MHP SA DRC (MHPC), and Ukrnafta (UNAF). Utilizing advanced machine learning models—decision trees, Random Forest, and Long Short-Term Memory (LSTM) networks—the research predicts stock price changes in response to market volatility induced by geopolitical events. The analysis reveals a significant correlation between the onset of conflict and stock price fluctuations, particularly in the agricultural and energy sectors, with notable resilience differences across industries. The findings underscore the importance of incorporating predictive analytics for decision-making in turbulent market environments, offering valuable insights for investors and policymakers navigating uncertainty.

SPATIAL MARKET INTEGRATION AND PRICE TRANSMISSION OF COWPEA (Vigna unguiculata) IN NIGERIA

The study investigated spatial market integration and price transmission of cowpea in Nigeria. Secondary data on monthly basis for wholesale cowpea price per kilogram were used for the study. Augmented Dicky Fuller Test, Johansen Co-integration model, Granger Causality Test and Variance Decomposition of Vecto Autoregressive Model and Error Correction Model were used as analytical tools. Augmented Dickey Fuller test revealed that first difference all the price series were stationary and lag one (1) is the optimum lag length appropriate for the specified variables. Cointegration test results of the markets proved that despite that these markets were spatially separated geographically, they were connected in terms of price transmission across them, both in the long and short run but the level of integration is weak. It was observed that price changes are temporary and would converge to an equilibrium within a given time span. Granger causality test results indicate that one pair showed bi-directional causation between Bauchi and Enugu States. Thirty two (32) links showed unidirectional and three (3) pairs such as Niger→Abia, Niger→Lagos and Sokoto→Cross River States pairs showed no causal relationship between them. The study showed that Gombe and River States cowpea price had a faster speed of adjustment to equilibrium than other states. It also revealed that that 1% increase in Jigawa State will result to 20.5% increase in Enugu state and 1 % increase in River State will lead to 40.6% and 64.8% increase in Gombe and Nasarawa States in the short run respectively. The study concluded that, there is integration between the cowpea prices of source and destination states, but the degree of integration is weak. It is recommended that market participants should adopt standardize trading, settlement practice and invest in technology (electronic trading platform) to reduce operational inefficiencies in different markets to support integration.

Asian Rainbow Option Pricing Problem Based on Uncertain Fractional Differential Equations

Option pricing is a core issue in option trading, which directly affects investors’ trading decisions and risk management. With the growing maturity of uncertainty theory, the research on option pricing problems based upon uncertainty theory has been gradually deepened. However, the volatility of stock price is not only related to the price at the current moment, but also related to all the previous stock price changes. Uncertain fractional differential equations can well portray the memory and history dependence of financial markets. Its nonlocal nature can more accurately describe the long-term memory effect and the phenomenon of spiky fat tails of stock market fluctuations. Thus, this paper mainly explores the pricing of Asian rainbow option in uncertain financial environments, where the underlying asset price is viewed as uncertain fractional processes. First, two models are presented for discussing the price of Asian rainbow option in two-asset cases, respectively. Then the pricing problems of different types of Asian rainbow options under [Formula: see text]-assets case are further discussed. Finally, empirical analysis is conducted on the proposed models with real stock data to verify the validity of the pricing formulas.

Wheat Price Dynamics in Hungary: Resilience to Shocks

This study investigates the impact of recent global disruptions, focusing on the Covid-19 pandemic and the ongoing Ukraine-Russia war, on Hungary's wheat-flour supply chain, with a focus on price transmission and market integration. While much research has explored food supply chain disruptions and rising prices globally, there has been limited analysis of their effects on Central and Eastern European agricultural markets, particularly Hungary. This study fills that gap by using linear and non-linear cointegration approaches to examine price relationships along the wheat supply chain. Causality tests indicate a strong bidirectional relationship between producer and consumer prices, demonstrating a tightly integrated market. Cointegration analysis reveals a long-term equilibrium between these prices, with no significant structural breaks during either the pandemic or the Ukraine war. Despite short-term price asymmetries, the study finds that producer-level price changes are ultimately transmitted to consumer prices in the long run, offering insights into Hungary’s unique agricultural market dynamics.

Discussion on Inheritance Valuation Methods When Listed Company Stocks Experience Drastic Price Changes under Estate Tax (II)

Discussion on Inheritance Valuation Methods When Listed Company Stocks Experience Drastic Price Changes under Estate Tax (II)

Unveiling Extreme Dependencies between Oil Price Shocks and Inflation in Tunisia: Insights from a Copula Approach

We follow a non-linear dynamic correlation approach using a combination of a DCC-GARCH model and a copula model to capture the dependence between oil price changes and inflation in Tunisia. The case of Tunisia is particularly instructive since after having been an exporter and a major producer, it became a net oil importer in the 2000s. The study, based on monthly data spanning decades, selects a Gumbel copula and shows that beyond weak average dependencies, there is a strong correlation between extreme values, suggesting that inflation in Tunisia is more sensitive to extreme (positive) variations in oil prices than to average variations. The implications of these empirical results for economic policy are crucial for the Tunisian economy.Copyright© 2024 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.

Applying Fractal Theory: Solving the Geometric Challenge of Price Change and Scaling in Economics

<i>Background</i>: Price changes in economics present significant geometric challenges due to sharp discontinuities, which cannot be efficiently described by continuous processes like Brownian motion. Traditional models often rely on linear assumptions, yet financial data frequently exhibit irregular, complex patterns. Fractal theory, a mathematical framework, offers a more accurate way to describe these fluctuations by revealing the underlying self-similar structures in price changes and scaling phenomena. This study explores the use of fractal geometry to gain deeper insights into market behavior. <i>Objective</i>: The objective is to demonstrate that an alternative model, constructed based on geometric scaling assumptions, offers a more accurate description of price changes in competitive markets. <i>Method</i>: The study combined the scaling principle from fractal geometry with a stable Levy model to formulate an integrated model. The logarithmic transformation of the model was applied over successive price changes to observe the behavior of market prices. <i>Result</i>: The scaling principle asserts that no specific time interval (such as a day or a week) holds inherent significance in competitive markets. Instead, these time features are compensated or arbitrated away, supporting the idea that market behavior is self-similar across different time scales. <i>Conclusion</i>: The scaling principle provides a more reliable framework for modeling price changes and is recommended for consideration in economic analyses.

Dynamic Price Monitoring in E-Commerce: Leveraging Web Scraping, Data Analysis, and Email Automation for Dynamic Pricing Insights

This paper focuses on the development of a web application that leverages web scraping, data storage, and email automation technologies to track real-time price changes for specific products on e-commerce platforms. Built using Next.js for the frontend, MongoDB as the database, and Nodemailer for email notifications, the system aims to provide users with a comprehensive and user-friendly interface to monitor price fluctuations. By scraping data from product pages, the application gathers key pricing metrics, including the average, lowest, highest, and current prices, and presents this information visually to help users make informed purchasing decisions. By utilizing the combination of web scraping and real-time data analysis, this project provides a dynamic solution for both consumers and businesses, offering valuable insights into price changes and trends in the competitive e-commerce landscape.

Growth, Poverty, and Food Policy in the Philippines: Lessons for the Post-COVID-19 Era

The Philippine economy’s growth has accelerated since 2010, outperforming its Asian peers in the current decade. However, poverty reduction is comparatively weak in response to growth, suggesting that growth has been less inclusive than expected. Poverty in the Philippines is still largely a rural phenomenon despite the country’s rapid urbanization. Our primary objective is to reexamine how much the national food policy has influenced the poverty-reducing effects of economic growth using the more recent national household-level data from 2000 to 2021. The longer-term data, including a period of mobility relaxation following long lockdowns upon the onset of the COVID-19 pandemic, allow us to revisit the growth-poverty conundrum in the Philippines. We focus on the more recent decade of relatively sustained growth compared to earlier studies on the subject. We use Engel food shares as a proxy for household welfare and consider the differential welfare effects of food price changes across segments of the population. We show that economic growth in recent years would have been strongly pro-poor if not for the misguided set of policy tools chosen to achieve the food self-sufficiency goal. The government’s move to dismantle the quantitative restriction regime on rice imports in favor of tariffs is a step in the right direction.

Research on the Factors Affecting House Price using Multiple Linear Regression Model

This article aims to identify the factors that affect the change in house prices. In this paper, the Multiple Linear Regression Model is used to analyze the factors with 1000 random samples from the USA which was collected from the 2nd of May in 2014 to the 10th of July in 2014. Based on the datasets, 13 variables (bedrooms, bathrooms, sqft_living, sqft_lot, floors, waterfront, view, condition, sqft-above, sqft_basement, yr_built, yr_renovated, street, city) are selected as independent variables, and used in scatter diagram to show the correlation with the dependent variable price. Turns out that 3 variables have not shown a significant correlation with the price (yr_built, yr_renovate, city). To find which of the factors have the most significant impact on the change in house prices, VIF value is used to compare the collinearity of those 10 variables. Finally turns out that only 5 variables (bedrooms, view, condition, waterfront, sqft_lot) show the most significant effect on the price of housing. Overall, the main factors affecting the price of housing in the USA were the number of bedrooms and views next to the house, as well as the conditions needed for buying the house.

House Prices and Rents

Abstract Variation in monthly metro area house prices unrelated to expected rents clouds the information about future rents in price-rent ratios and lagged changes in house prices. The variation in house prices unrelated to expected rents is, however, correlated across areas, and the problem is mitigated by measuring rent growth regression variables net of their monthly cross-section (across-area) means. This control for price variation unrelated to expected rents substantially enhances the information about future rents that we extract from price-rent ratios and lagged changes in house prices. (JEL C51, C58, D53, G14)

Changes in price, income and e-cigarette affordability for young people in the USA from 2015 to 2021

BackgroundE-cigarette products are the most popular tobacco/nicotine product used among youth and young adults in the USA. While emerging research has shown that e-cigarette taxes increase their price, no study to date has examined e-cigarette tax burdens nor their affordability for youth and young adults.MethodsUsing real (2021 US dollars) prices per mL of e-liquid data from NielsenIQ and annual real (2021 US dollars) personal income data from Integrated Public Use Microdata Series, we calculate relative income prices and examine average annual percentage changes in affordability using Joinpoint trend analysis from 2015 to 2021. In addition, we use tax data to calculate e-cigarette tax burdens as a percentage of price per 1 mL of e-liquid.ResultsIn all states analysed, tax burdens increased from 2015 to 2021. E-cigarette prices decreased considerably from 2019 to 2021; in 2021 the real (2021 US dollars) average price of 1 mL of e-liquid was US$4.45. E-cigarettes on average became more affordable for all age groups and states; however, e-cigarettes tended to be less affordable in states with ad-valorem tax structures. On average, 16–17-year-olds needed 31% of their annual income to purchase 100 mL of e-liquid, whereas 18–19-year-olds, 20–24-year-olds and 25–34-year-olds needed 9%, 3% and 1%, respectively.ConclusionsE-cigarettes have become more affordable for young people, but less so in states with ad-valorem tax structures. Policy efforts should focus on reducing e-cigarette affordability, especially for youth, through tax-induced increases in e-cigarette prices to levels high enough to outpace income growth.

Research on the Prediction of the Housing Pricing in China

As an important cornerstone of industrial development, real estate has been widely concerned by people. A house can not only provide a place of peace for people, but also provide a certain source of income for many people. A stable house price will give people confidence in their economy. After 2007, there have been obvious changes in house prices, which further increase the difficulty of forecasting and reduce the accuracy of forecasting. As a typical forecasting model, linear regression is favored by scholars all over the world for its accuracy and adaptability. This paper will use this model to forecast the house price in the next year. The conclusion is as follows: In the coming year, the overall trend will show a more obvious upward trend. Therefore, it is recommended to invest in real estate in the short term. At the same time, this paper believes that ensuring the stability of domestic housing prices can improve people’s quality of life and enhance the status of the country in the hearts of the people.

Crude Price Changes and Nigeria’s Foreign Reserves, 2008-2023

The study investigated the correlational association between crude price and foreign reserves using the Nigerian economy as the jurisdiction of interest. The study covered the period 2008 to 2023 representing a 25year period. Bivariate pairwise correlational matrix was used to find that a highly significant positive linear association exists between crude price and foreign reserves. Following the findings arising from this study, it is recommended that the country should take advantage of crude price movement in building its foreign reserves given that increase in crude price will likely lead to a rise in revenue from crude sale. A high foreign reserve can act as stabilization fund for exchange rate, debt management and other shocks from international financial flows. Building foreign reserves can be beneficial in managing such exposures to the advantage of the Nigerian economy.

Maintaining Agricultural Production Profitability—A Simulation Approach to Wheat Market Dynamics

Profitability is a complex notion, intertwined with theoretical, socio-economic, and modern economic considerations. While traditionally linked to monopoly advantages, contemporary views broaden this to include shareholder value creation. In agriculture, profitability faces impediments such as rising input costs, market volatility, and economic disparities, emphasizing technological innovation, market conditions, and policy as crucial determinants. This study presents an econometric simulation model to analyze and forecast wheat production profitability in Poland from 2023 to 2027, providing insights into production efficiencies, market dynamics, and policy impacts. Using a system of recursive equations, the model forecasts profitability, integrating econometric techniques with expert insights. It examines variables like resource use, production levels, and price changes, validated through FADN data. The findings reveal that price relations and production efficiencies are the key to profitability. The model highlights market volatility, particularly through “price scissors”, as the main factor influencing profitability. Favorable price conditions significantly boost profitability and incentivize production. The model is a critical tool for analyzing wheat profitability, highlighting the influence of market and policy changes. Future work could extend the model’s use to other regions or crops, incorporating advanced technologies to improve accuracy. This study offers valuable insights for agricultural economics, aiding stakeholders in strategic decision-making.

Family Social Support Can Reduce Work Stress In Farmers

Background: Farmers often work alone in remote environments with little time for rest or socialization, leading to mental health problems and poor psychological well-being. The imbalance between physical and psychological aspects leads to physical responses such as difficulty concentrating, and psychological responses such as anxiety. Social support, self-efficacy, and coping mechanisms play a role in addressing farmers' work-related stress. Objective: This study aims to analyze family social support can reduce work stress in farmers Methods: The method used in this research is a literature study using 3 databases, namely Google Scholar, Science Direct, and PubMed with a range of publication years 2019-2024. Results: The results showed that farmers' work stress can be caused by various factors, including heavy workload, long working hours, as well as pressure from weather, pests, and changes in market prices. Furthermore, it has been found that high family social support can reduce farmers' work-related stress. However, it has also been found that family social support has a significant impact on farmers' stress but not on farmers' psychological well-being. Conclusion: It is important for nurses to provide education to farmers to address work-related stress by first observing the causes of farmers' work-related stress and their experiences in coping with it. Family social support is one alternative that can be provided as education to farmers to address their work-related stress. Therefore, it is necessary to further understand how family social support affects farmers' work stress and how effective coping strategies can reduce this pressure.

Investor demand for stocks in Initial Public Offerings and their price behaviour after debut. Evidence from Poland

This article investigates the relationship between investor demand during an IPO and the long-term behaviour of share prices following a company’s stock market debut. Specifically, the study examines the impact of the demand observed during the public offering and the size of the offering on abnormal return rates for a sample of 116 IPOs on the Warsaw Stock Exchange between 2012 and mid-2022. The primary research hypothesis tests whether the medians of abnormal return rates, influenced by these factors, are statistically significant over several time intervals ranging from 3 to 12 months after the debut. The findings confirm that certain information from the completed public offering, particularly the relatively low number of new shares offered, plays a significant role in predicting abnormal changes in share prices during these periods. This research offers valuable insights for investors, emphasising the key factors within the IPO process that can influence the trajectory of share prices following a company’s market debut.