Co-movement Of Prices Research Articles

Price co-movements in decentralized financial markets

ABSTRACT In decentralized finance (Defi), market participants are allowed to have the right to manage their own funds as opposed to centralized finance (Cefi) with a central custodian, centralized exchanges (CEX). Most Defi projects provide their own service and simultaneously issue a unique token that can be traded in decentralized exchanges (DEX). However, the values of these tokens have rarely been studied. We confirm that the prices of tokens in the Defi market have a persistent tendency to move together. We also demonstrate that the correlation gradually increased from the Defi market’s inception and the price co-movement increased in a bear market, and conversely, decreased in a bull market. Specifically, we find a notable difference in the level of price co-movement between CEX cryptocurrencies and DEX tokens.

A Novel Hybrid Model for Gasoline Prices Forecasting Based on Lasso and CNN

Gasoline is the lifeblood of the national economy. The forecasting of gasoline prices is difficult because of frequent price fluctuations, its complex nature, diverse influencing factors, and low accuracy of prediction results. Previous studies mainly focus on forecasting gasoline prices in a single region by single time series analysis which ignores the daily price co-movement of different series from multiple regions. Because price co-movement may contain useful information for price forecasting, this paper proposes the Lasso-CNN ensemble model that combines statistical models and deep neural networks to forecast gasoline prices. In this model, the Least Absolute Shrinkage and Selection Operator (Lasso) screens and chooses the correlated time series to enhance the performance of forecasting and avoid overfitting, while Convolutional Neural Network (CNN) takes the selected multiple series as its input and then forecasts the gasoline prices in a certain region. Forecasting results of gasoline prices at the national level and regional levels by using the new method demonstrate that the new approach provides more accurate results for the predictions of gasoline prices than those results generated by alternative methods. Thus, the relevant series can enhance the performance of forecasting and help to gain better results.

Network Analysis of Price Comovements Among Corn Futures and Cash Prices

Abstract Due to significant implications for resource and food sectors that directly influence social well-being, commodity price comovements represent an important issue in agricultural economics. In this study, we approach this issue by concentrating on daily prices of the corn futures market and 496 cash markets from 16 states in the United States for the period of July 2006 – February 2011 through correlation based hierarchical analysis and synchronization analysis, which allow for determining interactions and interdependence among these prices, heterogeneities in price synchronization, and their changing patterns over time. As the first study of the issue focusing on prices of the futures and hundreds of spatially dispersed cash markets for a commodity of indubitable economic significance, empirical findings show that the degree of comovements is generally higher after March 2008 but no persistent increase is observed. Different groups of cash markets are identified, each of which has its members exhibit relatively stable price synchronization over time that is generally at a higher level than the synchronization among the futures and all of the 496 cash markets. The futures is not found to show stable price synchronization with any cash market. Certain cash markets have potential of serving as cash price leaders. Results here benefit resource and food policy analysis and design for economic welfare. The empirical framework has potential of being adapted to network analysis of prices of different commodities.

Regional house price co-movement in the USA: the medium cycle is not the business cycle

Regional house price co-movement in the USA: the medium cycle is not the business cycle

Toward energy finance market transition: Does China’s oil futures shake up global spots market?

China is breaking through the petrodollar system, establishing RMB-dominating crude oil futures market. The country is achieving a milestone in its transition to energy finance market internationalization. This study explores the price leadership of China’s crude oil futures and identifies its price co-movement to uncover whether it truly shakes up the global oil spots market. First, we find that for oil spots under different gravities, China’s oil futures is only a net price information receiver from light-, medium-, and heavy-gravity oil spots, but it has a relatively stronger price co-movement with these three spots. Second, for oil spots under different sulfur contents, China’s oil futures still has weak price leadership in sweet, neutral, and sour oil spots, but it has strong co-movement with them. Third, for oil spots under different geographical origins, China’s oil futures shows price leadership in East Asian and Australian oil spots at the medium- and longrun time scales and strong price co-movement with East Asian, Middle Eastern, Latin American and Australian oil spots. China’s oil futures may not have good price leadership in global spots market, but it features favorable price co-movement.

Price Trend and Integration of Cowpea Markets in Biu Local Government Area of Borno State, Nigeria

The study analysed the price trend and integration of prices of cowpea in Biu and Miringa markets of Biu Local Government Areas of Borno State, Nigeria. The specific objectives of the study were to: describe the trend of cowpea price variation between 2016– 2018; determine the degree of variation in the price of cowpea overtime in the study area and analyze integration between the markets. Secondary data were used for the study. Data on monthly prices of 100kg of cowpea for Biu and Miringa Markets were obtained from Famine Early Warning System Network (FEWSNET) office Biu Branch for the period of three (3) years (2016-2018). Both descriptive and inferential statistics were used to analyse the data. The descriptive statistics used was graphical presentation of the trend of price data. While the inferential statistics used were the unit root test and Engle and Granger co-integration analysis. This study revealed that there were upward and downward trends in prices of cowpea grain in both Biu and Miringa markets for the period of the study. The variations observed in the prices were influenced by time and space. The stationarity test (ADF unit root test) showed that all the price series were non-stationary at levels. On differencing, stationarity was attained indicating that the prices were integrated of order one, which showed co-movement of prices between the two markets. Engle and Granger co-integration tests showed the existence of two co-integration equations. There is a strong need for private organizations and the Government through the producers and marketers of cowpea to provide more and better dissemination of market intelligence and information among the farmers and traders to improve knowledge which would help make the price more stable.

Retail Investors and Stock Price Synchronicity

In this study, we use the number of retail investors in China’s stock market to investigate how retail investors affect stock price synchronicity. We find that a higher number of retail investors in a firm is associated with higher stock price synchronicity. Moreover, we trace this association to two sources. One is a negative effect of the number of retail investors on the probability of informed trading (PIN), suggesting that retail investors generate arbitrage risk which discourages informed trading. The other is a positive influence of the number of retail investors on price comovement (beta), resulting from correlated trading among retail investors.

How sovereign is sovereign credit risk? Global prices, local quantities

Price fluctuations of sovereign default insurance are dominated by common risks. In contrast, fluctuations in their quantities are primarily explained by country-specific factors. Using net positions in sovereign default insurance contracts for 60 countries between 2008 and 2015, we show that a country’s debt and size explain 75% of cross-country differences in net insured interest. We develop an economic framework showing that high co-movement in prices and low co-movement in quantities can arise jointly only if insurance demand and supply shift in opposite directions in response to global shocks. Positions data support that this evidence is more salient for more intermediated assets.

Administrative Division Adjustment and Housing Price Comovement: Evidence from City‐County Mergers in China

AbstractInterregional housing price comovement is a stylized fact worldwide. This study explores how it is affected by administrative division adjustment. We exploit city‐county mergers in China as a quasi‐natural experiment to construct a difference‐in‐differences strategy for causal identification. Based on monthly housing price data for districts (counties) in China from 2010 to 2019, we find that city‐county mergers significantly improve correlations in housing prices between the merged county and the urban district. This effect is more obvious in cities with a large economic gap between merged counties and urban districts, located in the central and western regions, and with lower administrative hierarchies (non‐provincial‐capital cities). The mechanism test shows that the impact of city‐county mergers on housing price comovement results mainly from integrating housing demand rather than integrating housing supply, like the unified land supply policy that local government implements in the new administrative scope after mergers. The results are helpful in understanding housing price comovement from the view of regional integration and provide clear policy implications for housing market regulation in China.

Assessing the impact of COVID-19 on price Co-movements in China

The COVID-19 crisis has caused violent fluctuations in prices, and government responses to the pandemic further intensify the uncertainty of price changes. This study evaluates the dynamic price co-movement of main consumption categories within an Additive Bayesian Network (ABN) framework, which shows dramatically fluctuating price risks during the pandemic period. The global fears for COVID-19 affects price co-movements in China negatively with a direct linkage. By contrast, confirmed cases in China, confirmed cases around the world, and fears for the domestic pandemic situation are indirectly related with price co-movements through financial markets. The exchange rate and international hedging assets such as gold play important intermediary roles in such relationships. Meanwhile, volatile international markets including crude oil and Bitcoin are also indirectly linked with price changes in China. Comparing with the situation in China, the global pandemic appears to be a more important factor influencing the price stability in China. Overall, the impacts of COVID-19 on price co-movements are empirically demonstrated, which highlights the importance of prudent policies in response to the pandemic.

Hedge fund networks, information dissemination, and stock price comovement: Evidence from China

We define a hedge fund network as consisting of weak ties based on common holdings. By creating a filtered dataset of stocks held by hedge funds in China from 2010 to 2019, we examine the effect of the network on stock return comovement and further test the incentive-compatible conditions for honest communication between competitors (Stein, 2008). First, we find that the weak ties in a social network affect the behaviour of hedge fund managers, when formal institutions are imperfect. Second, we find that listed firms held by hedge funds are exposed to an information network that is composed of hedge funds. The higher the hedge fund network density is, the more likely information dissemination is, and the weaker the stock price comovement is. Third, our results support Stein's (2008) two major conclusions: a network effect exists only within stable relationships, and the more central a stock is in the network, the stronger the network effect is. After controlling for various external factors that might influence the hedge fund network's information diffusion mechanism, all the results meet our theoretical expectations. Overall, we contribute to the literature by determining the role of the hedge fund network as an information transmitter in weak ties and by providing empirical evidence on the theory of honest communication among competitors.

Do gasoline and diesel prices co-move? Evidence from the time-frequency domain.

This study investigates the co-movements of gasoline and diesel prices in three European countries (i.e. Germany, France, and Italy) with different fuel tax systems in place. The methodology follows a time–frequency approach, allowing us to analyse the co-movements at different frequencies and moments in time. As a novelty, we study the impact of fuel tax systems and international oil price dynamics on gasoline and diesel price co-movement. Using weekly data spanning the period from January 2005 to June 2021, the wavelet coherence analysis shows co-movements between gasoline and diesel at all frequencies, as well as during specific periods, but stronger in the long run. This evidence is recorded across all three countries, regardless of their tax systems. However, in decoupling the effect of international oil prices, the partial wavelet coherence analysis shows co-movements emerging also in the short run, with them being stronger around the global financial crisis (2008–2009). Although gasoline taxes are generally higher than diesel taxes, the analysis highlights that fuel tax systems do not influence the co-movements of fuel prices. Thus, shedding new light on the co-movement between commodity prices is fundamental, particularly in light of the current international geopolitical scene.

Political Networks and Stock Price Comovement: Evidence from Network-Connected Firms in China

Abstract In this article, we examine whether comovement in the stock prices of pairs of Chinese firms connected to the same political network are systematically shaped by the prevailing coordination versus competition incentives of that network’s politicians. We find strong evidence from 2000 to 2012 (Jiang’s and Hu’s regimes) that stock price comovement is affected by the embeddedness of the firm–politician ties within the network. Among pairs of firms connected to a network through a common politician, we document an increase in stock price comovement. For those pairs of firms connected to a common network via separate politicians (rather than a common politician), we document a relative decrease in stock price comovement. This negative effect suggests that politicians’ relationships within these political networks are generally adversarial rather than cooperative in nature. These results become significantly weaker during Xi’s regime from 2013 to 2017, suggesting that Xi’s anti-corruption campaign and state-owned enterprise reforms may have attenuated these political network effects on the firms. Our additional tests also show that stock price comovement becomes even more positive (negative) in settings which are expected to increase the coordination or decrease the competition (decrease the coordination or increase the competition) of the politicians.

Administrative division adjustments and stock price comovement: Evidence from the city–county mergers in China

Under the background of Chinese market segmentation, whether government-led administrative division adjustments can promote regional economic integration is a practical issue. Taking interregional firms’ stock price comovement as a micro measurement of regional integration, this paper investigates the regional integration effect of administrative division adjustments, i.e., city–county mergers. We find that stock price comovement between county-level and municipal district-level firms in the merged counties and municipal districts significantly improve after city–county mergers, particularly in regions with a higher degree of market segmentation and lower degree of marketization. We further find that the increase in stock price comovement caused by city–county mergers emerges from the increase in comovement of real activities between firms in the merged counties and municipal districts. Taken together, our results suggest that government-led administrative division adjustments effectively promote regional integration.

Circularly Projected Common Factors for Grouped Data

To extract the common factors from grouped data, multilevel factor models have been put forward in the literature, and methods based on iterative principal component analysis (PCA) and canonical correlation analysis (CCA) have been proposed for estimation purpose. While iterative PCA requires iteration and is hence time-consuming, CCA can only deal with two groups of data. Herein, we develop two new methods to address these problems. We first extract the factors within groups and then project the estimated group factors into the space spanned by them in a circular manner. We propose two projection processes to estimate the common factors and determine the number of them. The new methods do not require iteration and are thus computationally efficient. They can estimate the common factors for multiple groups of data in a uniform way, regardless of whether the number of groups is large or small. They not only overcome the drawbacks of CCA but also nest the CCA method as a special case. Finally, we theoretically and numerically study the consistency properties of these new methods and apply them to studying international business cycles and the comovements of retail prices.

Return and volatility linkages between international energy markets and Chinese commodity market

This research investigates the return and volatility linkages between international energy markets and Chinese commodity market at the micro and macro level by employing the Vector Autoregressive combined with BEKK Autoregressive Conditional Heteroskedasticity model (VAR-BEKK-GARCH). Furthermore, this paper also analyzes the changes in return and volatility linkages stepping across the China's exchange rate regime reform in 2015 by splitting the sample into 2 stages: the Pre-reform period (19 June 2010-10 August 2015), and the Post-reform period (11 August 2015-31 December 2021). The empirical results show significant return and volatility spillovers between the international energy markets and China's commodity market, nevertheless, the return spillover effects has been weakened significantly and the vaolatility spillovers have been strengthened significantly after the 2015 China's exchange rate regime reform. The empirical results also provide evidence that the international energy market can effectively hedge the risks of China's commodity market, but the portfolio diversification strategies should be modified across time. Due to the decline in price co-movement, investors have been able to hedge risk in China's commodity market at a lower cost since the reform of the RMB exchange rate system in 2015. However, the efficiency of risk diversification has also declined due to the increased risk linkage between domestic and international markets. In the future, if another reform of the exchange rate system is ought to be initiated, price shocks coming from the external energy markets as well as the risk linkages are all factors needed to be taken into control. Finally, our research also provides a clear way for investors to hedge domestic commodity market risk through international energy markets.

NETWORK ANALYSIS OF HOUSING PRICE COMOVEMENTS OF A HUNDRED CHINESE CITIES

Housing price comovements are an important issue in economics. This study focuses on monthly housing prices of 99 major cities in China for the years 2010–2019 by using correlation-based hierarchical analysis and synchronisation analysis, through which one could determine interactions and interdependence among the prices, heterogeneous patterns in price synchronisations and their changing paths over time. Empirical results show that the degree of comovements is slightly lower after March 2017 but no persistent drop is found. Several groups of cities are identified, each of which has its members showing relatively strong but volatile price synchronisations. Certain cities show potential of serving as price leaders within a group. Results here could be useful to policy analysis regarding housing price comovements.

Inductive Representation Learning on Dynamic Stock Co-Movement Graphs for Stock Predictions

Co-movement among individual firms’ stock prices can reflect complex interfirm relationships. This paper proposes a novel method to leverage such relationships for stock price predictions by adopting inductive graph representation learning on dynamic stock graphs constructed based on historical stock price co-movement. To learn node representations from such dynamic graphs for better stock predictions, we propose the hybrid-attention dynamic graph neural network, an inductive graph representation learning method. We also extended mini-batch gradient descent to inductive representation learning on dynamic stock graphs so that the model can update parameters over mini-batch stock graphs with higher training efficiency. Extensive experiments on stocks from different markets and trading simulations demonstrate that the proposed method significantly improves stock predictions. The proposed method can have important implications for the management of financial portfolios and investment risk. Summary of Contribution: Accurate predictions of stock prices have important implications for financial decisions. In today’s economy, individual firms are increasingly connected via different types of relationships. As a result, firms’ stock prices often feature synchronous co-movement patterns. This paper represents the first effort to leverage such phenomena to construct dynamic stock graphs for stock predictions. We develop hybrid-attention dynamic graph neural network (HAD-GNN), an inductive graph representation learning framework for dynamic stock graphs to incorporate temporal and graph attention mechanisms. To improve the learning efficiency of HAD-GNN, we also extend the mini-batch gradient descent to inductive representation learning on such dynamic graphs and adopt a t-batch training mechanism (t-BTM). We demonstrate the effectiveness of our new approach via experiments based on real-world data and simulations.

THE INFLUENCE OF SOCIAL AND MAINSTREAM MEDIA ON STOCK PRICE SYNCHRONICITY IN CHINA

ABSTRACT In this study, we aim to explore the influence of social and mainstream media on stock return comovement in China. The impact of mainstream media on stock price comovement is influenced by social media and vice versa. We generally hypothesize that mainstream and social media interact and then impact stock price synchronicity. We choose 13,745 listed Chinese companies to run our tests. The results suggest that our hypothesis is supported. Our finding supports information-based theory. Keywords Mainstream Media, Social Media; Stock Return Comovement/Stock Price Synchronicity; Information-based theory; Sentiment-based theory

Extreme price co-movement of commodity futures and industrial production growth: An empirical evaluation

This paper studies how the extreme price co-movement of commodity futures indicates industrial production (IP) growth. In this regard, we model synchronized movements and large price changes into one measure by characterizing upside and downside price extremes. We find that the derived price extremes are positively associated with IP growth over the next quarter. We further conclude that such impact is not symmetric, as the impact led by downside extremes is robust whereas that of upside extremes is not. Our results reinforce the informational friction theory as well as those financial studies that emphasize downside risk.