Price Linkages Research Articles

Does oil impact gold during COVID-19 and three other recent crises?

The ongoing COVID-19 pandemic has inspired an examination of the oil–gold prices nexus during four recent crises: the COVID-19 pandemic, the gold market crash, the European sovereign debt crisis, and the global financial crisis. Using daily data from May 2007–August 2021, we employ the nonlinear autoregressive distributed lag method to reveal five novel findings. First, this study contrasts with much of the literature, which infers that the relationship between oil and gold prices is strongly positive. Second, we find no oil and gold price relationship in the long term during all the crisis periods. Third, oil prices have substantially lost their power to predict gold prices in recent times and the oil–gold price linkage is not functional across all crisis periods. Fourth, in the short term, only negative Brent and negative West Texas Intermediate price changes cause positive gold price changes during the pandemic and gold market crash, respectively. Fifth, Brent prices have shown no link to gold prices before COVID-19. We argue that gold prices are less sensitive to oil prices than ever, and the uncertainty resulting from the COVID-19 crisis has attracted investors to gold. Our main findings hold under robustness analyses using fractional cointegration/integration models, lag length, and heteroskedasticity-consistent standard errors.

Improvement Measures through Analysis of the Collection System of Recyclable Resources Discharged from Apartment Houses in Korea

To improve recyclable waste collection system, this study conducted a survey of 123 apartments complexes and 229 local governments and suggested the measures to improve the current collection system based on the results of the survey. The survey revealed that respondents preferred public intervention collection. Currently, 80% of apartment complexes input the results of recyclable resources into the circular resource information center, but private collection companies are required to directly input them to simplify the administrative system of local governments. Most apartment complexes choose private collection companies based on down payment, which is likely to result in excessive competition. 97% of apartment complexes that implement the price linkage system set a one-year cycle, indicating that the effectiveness of the price linkage system is poor and measures to improve it are necessary. The public collection ratio of local governments was found to be 100% for detached houses, 48% for obligatory apartment, and 83% for non-obligatory apartment. The mixed collection ratio of detached houses and multi-use facilities is 50%, which is higher than the mixed collection ratio of apartment houses (38%), which is expected to reduce the effectiveness of the recycling system. Hence, it is necessary to establish a collection system based on the source. To establish public responsibility collection, it is necessary to strengthen the standards and the price linkage system for collection companies. To reduce the administrative burden of local governments, it is necessary to manage apartment at a regional level or to entrust local government work.

Graph Convolutional Network-Based Model for Megacity Real Estate Valuation

It is challenging to make precise assessments of real estate prices due to its elevated individual prices, complicated influencing factors, and ambiguous attribute selection. As a result of the high demand for owner-occupied and investment properties, real estate is also a substantial concern for society. How to accurately evaluate its price has been a hot issue for research by major institutions. Real-world applications of real estate valuation impose stringent requirements on the acquisition of datasets and the generalizability of models. On the basis of SRGCNN, a spatial regression model with excellent generalizability, this paper introduces an external attention mechanism to construct the A-SRGCNN model and compares it to the benchmark model utilizing data from Shanghai, Melbourne, and San Diego. For spatial regression, A-SRGCNN employs graph convolutional neural networks, and the external attention mechanism implicitly considers the relationship between property data. Experiments indicate that the A-SRGCNN model outperforms the benchmark model and has improved real estate price estimation accuracy. In the meantime, this paper employs the A-SRGCNN model to conduct zonal experiments and time-division experiments on the secondary real estate market in Shanghai to analyze the real estate price linkages between different zones and the real estate price linkages at different times. It is revealed that Shanghai real estate prices exhibit spatial aggregation and price aggregation, with comparable prices within the same zones, and that the A-SRGCNN model is effective at predicting house prices.

Influence of Chinese National Centralized Drug Procurement on the price of policy-related drugs: an interrupted time series analysis

BackgroundThe Chinese government implemented the first round of National Centralized Drug Procurement (NCDP) pilot (so-called “4 + 7” policy) in mainland China in 2019. This study aims to examine the impact of “4 + 7” policy on the price of policy-related drugs.MethodsThis study used drug purchasing order data from the Centralized Drug Procurement Survey in Shenzhen 2019, covering 24 months from January 2018 to December 2019. “4 + 7” policy-related drugs were selected as study samples, including 25 drugs in the “4 + 7” procurement list and 57 alternative drugs that have an alternative relationship with “4 + 7” List drugs in clinical use. “4 + 7” List drugs were then divided into bid-winning and bid-non-winning products according to the bidding results. Single-group Interruption Time Series (ITS) analysis was adopted to examine the change of Drug Price Index (DPI) for policy-related drugs.ResultsThe ITS analysis showed that the DPI of winning (− 0.183 per month, p < 0.0001) and non-winning (− 0.034 per month, p = 0.046) products significantly decreased after the implementation of “4 + 7” policy. No significant difference was found for the immediate change of DPI for alternative drugs (p = 0.537), while a significant decrease in change trend was detected in the post-“4 + 7” policy period (− 0.003 per month, p = 0.014). The DPI of the overall policy-related drugs significantly decreased (− 0.261 per month, p < 0.0001) after “4 + 7” policy.ConclusionsThese findings indicate that the price behavior of pharmaceutical enterprises changed under NCDP policy, while the price linkage effect is still limited. It is necessary to further expand the scope of centralized purchased drugs and strengthen the monitoring of related drugs regarding price change and consumption structure.

Multi-scenario simulation on the impact of China's electricity bidding policy based on complex networks model

As one of the important measures for China's power system reform, the electricity bidding pricing (EBP) is helpful to realize the effective allocation of power resources. Based on price conduction theory and complex network modeling technology, we construct the price transmission network for the 76 economic sectors in China, identify the critical path of electricity price transmission, and empirically simulate and analyze the economic impact of EBP in different scenarios from the two aspects of whether the CEPL mechanism is implemented or not. The results indicate that the electricity price will be decreased directly by the EBP, and the electricity industry will significantly reduce the impact on other related industries. Particularly, affected by regulatory policies such as electricity price cap, the electricity price caused by EBP cannot be effectively transmitted to the upstream industries. Secondly, with the simultaneous implementation of the coal-electricity price linkage (CEPL) and the EBP, the coal-electricity price transmission will change from one-way conduction style to two-way interaction style, and the impact of electricity price fluctuations on its related industries will be more significant, while the comprehensive impact of the coal industry on its associated industries will be significantly reduced. Thirdly, there are obvious scenario differences in the impact of different intensity of EBP on the macro-economy. The results show that under the mechanism of CEPL, the EBP with 20% of the total power used for bidding pilot has minimal impact on the Chinese economy. Based on the above conclusions, we propose that China should scientifically determine the scale and the pilot regions of electricity bidding under the CEPL, develop more diversified bidding forms and improve more robust supervision system.

Natural resources commodity prices volatility and economic performance: Evidence from China pre and post COVID-19

Volatility in the prices of natural resources particularly in the Covid-19 period is the subject of major concern in recent times. Although many studies have empirically investigated the influence of oil prices on economic growth and Covid-19 on oil prices. However, the current study contributes to the literature by investigating the causal linkage of natural resources commodity prices and economic growth in the pre and post Covid-19 period for China over the period from January 01, 2019, to April 01, 2021. This study employed the wavelet power spectrum, and the wavelet coherence approaches, and the frequency domain causality test, which is known for the causal identification in the long-run, medium-run, and short-run. The empirical findings reveal that the natural resource commodity prices are more vulnerable than the economic performance particularly in the Covid-19 peak period in China. However, the wavelet coherence approach demonstrates that a bidirectional causal association exists between natural resources commodity prices and economic performance at different frequencies and time periods. Additionally, the frequency domain causality test confirms that the natural resource commodity prices volatility significantly causes economic performance only in the medium-run. Based on the empirical findings, this study recommends that innovative technological and precautionary measures must be taken to accommodate or overcome natural disasters in the future, and tackle natural resources commodity prices volatility.

Global Wheat Market Dynamics: What Is the Role of the EU and the Black Sea Wheat Exporters?

Over the last two decades, three countries in the Black Sea Region—Russia, Ukraine, and Kazakhstan—became global leaders in grain production and trade, and replaced the USA and France as the most previous largest wheat exporting countries. In this study we investigate world wheat price linkages and identify the current “price leaders” of the global wheat market. This empirical analysis is focused on the price relationships between eight of the largest wheat exporting countries and uses a cointegration framework and a vector error-correction model. The results show that, regarding price formation on the world wheat market, the French price is more important for transmitting price signals to other wheat export markets compared to the USA. Furthermore, our results indicate that, despite being leaders in wheat export volumes, the Black Sea wheat prices in Russia and Ukraine adjust to price changes in France, the USA, and Canada. Albeit unrealistic in the short run, the creation of the futures market in the Black Sea region might significantly improve the participation of Black Sea markets in price formation of the global wheat market.

Non-linear cointegration between oil and stock prices: The role of interest rates

The Zero Lower Bound (ZLB) has been suggested as an explanation as to why oil and stock prices have become highly correlated post 2008. Our paper contributes to the literature on this topic by testing (i) whether there is a long-run relationship between oil prices and stock prices (measured by the MSCI World Index) that is non-linear depending on the interest rate levels and, if this is the case, (ii) whether the co-movement between them is stronger when interest rates are very low. To do so, we apply a cointegrating smooth transition regression approach using a global shadow rate as the transition variable to take into account the possible effects of unconventional monetary policy measures on the oil-stock price linkage. We find evidence in favor of the two hypotheses tested. These results have important implications for portfolio managers and investors, since the benefits of portfolio diversification by investing in oil would be lower in a ZLB context. In addition, from a policymakers’ perspective, the results could be revealing that, in this context, central banks could exert a greater influence than in “normal times” not only on equity prices, but also on global oil prices.

Causal Nexus Between Crude, Gold, Dollar and Stock Markets in India: Empirical Explorations Before, During and After the Global Recession

This research study empirically examines the price linkages among oil, dollar, gold and stock markets in India over period from 1999:1 to 2019:12. We employ cointegrated vector error correction model (VECM) and Granger causality test to study the long-run and short-run relationships between commodity and financial markets before, during and after the global financial crisis. Our analysis finds the dependency on price movements in asset markets is time-varying and countercyclical in India. Findings suggest the asymmetric structure of price correlations among asset markets across three temporal periods on either side of the crisis. Our study offers useful insights into the strategic asset allocations to investors in response to economic cycles, to help optimise potential portfolio returns and provide protection towards some downside risks. JEL Codes: C58, D53, F51

Booms in Agricultural &amp; Non-Agricultural Prices: Who Is Responsible?

This paper investigates the cross-market price linkages in actively traded, major agricultural and energy commodities, impacted by three global episodes over the 1971-2018 time frame. As episodes, we identify the rapid increase of prices during the 1970’s, the commodity depression of the 1990’s, the credit crunch and sovereign debt crisis in the 2000’s. We find that during the rapid price increase in 1970’s, all the major agricultural commodity are influenced by a series of events, including the Soviet Union’s unexpected purchase of a large amount of grain in the global markets. During the commodity depression, a negative relationship is shown by all the agricultural commodities. The credit crunch and sovereign debt shows a positive impact for only wheat whereas oats show a negative relationship. We also investigate these three episodes on non-agricultural commodity sectors (wood and animal, precious metal and energy sector). The paper demonstrates the increase in the energy prices supporting the cross-market linkages between the stock and crude oil markets as a result of the financialization of commodities.

Is There Any Asymmetry Between Wholesale and Consumer Food Prices in Selected European Countries and Turkey

Vertical price linkages (transmission mechanism) constitute an important pillar of food and agricultural economics research. These linkages contain information on the relationships between the economic units of the supply chain at different levels. This study aims to investigate the existence of an imperfect price transmission mechanism in the food markets of the selected countries between January 2000 and January 2018. For this purpose, the transmission mechanism between producer and consumer prices in the food supply chain is analyzed via a nonlinear autoregressive distributed lag model (NARDL). The results show that asymmetric price transmission is not a market anomaly, but it is a common situation in the market. The results imply that the presence of asymmetric price transmission in the food market might trigger inequalities by mainly decreasing the income of the lower-income groups.

Measuring volatility spillovers and asymmetric responses of Agri commodity prices: evidence from spices and rubber futures in India

PurposeThis paper aims to investigate price responses and volatility spillovers between commodity spot and futures markets. The study ultimately seeks the evidence-based claims on the efficiency of the long run and short run horizontal price transmissions from futures markets to spot markets.Design/methodology/approachThis study used the most recent daily price series of pepper, cardamom and rubber, during the period 2004–2019, use “cointegration-ECM-GARCH framework” and verify the persisting validity of the “expectancy theory” of commodity futures pricing.FindingsThe results offer overwhelming evidence of futures market dominance in the price discoveries and volatility spillovers in spot markets. However, this paper finds asymmetric responses between cash and futures prices across markets. The hedging efficiency of futures contracts is commodities specific’ where spices futures are more efficient than the rubber futures.Practical implicationsThe study passes on vital information to the producers and traders of spices and rubber who have a potential interest in the use of futures contracts to make profits from arbitrage between futures and cash markets.Originality/valueThe paper is unique in terms of understanding asymmetric price linkages in markets for plantation crops.

Analysis on the Housing Price Relationship Network of Large and Medium-Sized Cities in China Based on Gravity Model

The relationship among cities is getting closer, so are housing prices. Based on the sale price of stocking houses in thirty-five large and medium-sized cities in China from 2010 to 2021, this study established the modified gravity model and used the method of social network analysis to explore the spatial linkage of urban housing prices. The results show that: (1) from the overall network structure, the integration degree of housing price network in China is still at a low stage, and the influence of housing price is polarized; (2) from the individual network structure, Beijing, Shanghai, Shenzhen, Nanjing, Hangzhou, and Hefei have a higher degree of centrality. Chengdu, Xining, Kunming, Urumqi, and Lanzhou stay in an isolation position every year; (3) from the results of cohesive subgroup analysis, different cities play different roles in the block each year and have different influences on other cities. (4) Emergencies, such as outbreaks of COVID-19, also have an impact on the housing price network. Structural divergence among urban housing prices has become more pronounced, and the diversity of house price network has been somewhat reduced. Based on the above findings, this paper puts forward some recommendations for the healthy development of housing market from the perspective of housing price network.

How has the minimum support price policy of India affected cross-commodity price linkages?

For several decades, the government of India has implemented a minimum support price (MSP) policy for agricultural commodities to reduce price risk levels for farmers. Concerns have been raised about whether this policy could affect market integration of related agricultural products, modify price incentives, and ultimately alter resource allocation and production between commodities. This study uses a panel vector auto-regression model across six states for the period 2002-2017 to analyse the effects of the MSP on the transmission of price shocks between cereals and oilseeds. The results demonstrate that the MSP partially and completely offsets price linkages between agricultural commodities, potentially introducing distortions in price incentives that affect land allocation and production between commodities. Beyond the effects of the MSP across commodities, Indian authorities can expect that price shocks on maize be transmitted to soybean over the next production period. Finally, this study demonstrates that the use of alternative data frequencies can identify differences in market reactions over time that can be related to production cycles and delays in price transmission.

Fungicide Cost Reduction with Soybean Rust-Resistant Cultivars in Paraguay: A Supply and Demand Approach

Soybean rust (SBR) is one of the most serious diseases for Paraguay’s economy. To avoid excessive financial losses due to SBR, farmers utilize fungicides. Increasing fungicide costs are, therefore, becoming a threat to farmers’ incomes. Developing SBR-resistant cultivars is a possible solution to this problem. To investigate the effects of SBR-resistant cultivars on soybean farmers in Paraguay, we constructed a model for the supply and demand of soybeans considering yields, cultivated area, changes in the stock quantity of soybeans, exports of soybeans and soybean products, feed demand for soybean cake and price linkage functions. We established three scenarios: an SBR pandemic in which fungicides become ineffective (Scenario 1) and the adoption of SBR-resistant cultivars in 33% (Scenarios 2) and 75% (Scenarios 3) of cultivated areas. The estimation of these three scenarios demonstrates that SBR-resistant cultivar adoption will significantly reduce current fungicide costs for farmers by 112–253 million United States dollars (USD). The potential benefits of the widespread dissemination of SBR-resistant cultivars are also considered in terms of economic disparities and environmental risks. To establish a more sustainable agricultural industry, earlier dissemination of such cultivars is required.

Semi-parametric models of spatial market integration

We develop fully flexible, semi-parametric models of price linkages in spatially distinct regional US markets for plywood and lumber products. The models are developed within the framework of generalized additive models (GAM) which are estimated using penalized maximum likelihood estimation methods and generalized cross-validation techniques. Such nonlinear models are a natural extension to an extensive literature that has developed increasingly flexible models of the Law of One Price and spatial market integration. The GAM estimates and marginal responses to price changes exhibit substantial nonlinearities. Dynamic impulse responses demonstrate significant asymmetries in price responses to exogenous shocks. The results are largely consistent with efficiently linked regional markets for lumber and plywood products in that price transmission elasticities are generally close to unity and localized market shocks evoke equilibrating adjustments in regional markets.

Exploring Spatial Price Relationships: The Case of African Swine Fever in China

We use a temporary ban on inter-province shipping of live hogs induced by the 2018 outbreak of African Swine Fever (ASF) in China as a natural experiment to study spatial mechanisms behind the dynamics of market integration. With a unique dataset of weekly provincial hog prices, we employ a novel spatial network model to estimate the strength of price co-movement across provinces pre and post the ban. Results indicate that, in the highly integrated national market prior to the ban, longer geographical distances between two provinces did not weaken the strength of their price linkage. The ban broken down spatial integration. Longer distances became a significant obstacle to spatial price linkage in the post-ban periods, implying faster re-integration of hog prices between proximate provinces than remote ones. The negative effect of distance can be rationalized by the interplay between arbitrage opportunities and imperfect information. Our findings highlight information transparency as a key to market integration post shipping bans used to curb animal pandemics like ASF. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Analysis of the diffusion effect of urban housing prices in China based on the spatial-temporal model

This article contributes to the recent literature in the spatial diffusion pattern of house prices by constructing spatial-temporal model to test the spatial relationships of 70 housing markets in China. Drawing on the spatial-temporal method proposed by Holly, a multiregional spatial-temporal model suitable for China is formulated which are applied to study the spatial-temporal diffusion effect of urban housing prices in China. Firstly, the diffusion mechanism of urban housing prices is analyzed from theoretical perspective, and four cities are assumed as dominant cities, namely, Beijing, Shanghai, Shenzhen and Sanya. Secondly, the spatial linkage of the housing prices of 70 large and medium-sized cities from 2007 to 2017 is examined by calculating the global Moran's I index. Thirdly, a spatial-temporal model is constructed to empirically analyze the diffusion effects of the housing prices of 70 sample cites. The study concludes that 1) significant global spatial autocorrelation is observed among the housing prices in the 70 cites; 2) empirical analysis shows that Beijing, Shanghai, Shenzhen and Sanya are the dominant cities of the 70 cities; and 3) the spatial effects of the four dominant cities on other cities differ in areas, degrees and duration.

The Linkages between Crude Oil and Food Prices

This paper aims to indicate the linkages between crude oil prices and selected food price indexes (dairy, meat, oils, cereals, and sugar) and provide an empirical specification of the direction of the impact. This paper reviews the fuel–food price linkage models with consideration to the time series literature. This study adopts several methods, namely the Augmented Dickey–Fuller test, Granger causality test, the cointegration test, the vector autoregression model, and the vector error correction model, for studying the price transmission among the crude oil and five selected food groups. The data series covers the period between January 1990 and September 2020. The empirical results from the paper indicate that there are long-term relationships between crude oil and meat prices. The linkage of crude oil prices occurred with food, cereal, and oil prices in the short term. Furthermore, the linkages between the analyzed variables increased in 2006–2020.

Integration of Domestic and International Markets of Indian Coffee

Coffee is an important commercial crop of the tropics and it is the largest and widely traded tropical agricultural commodity. The present study was conducted to examine integration of domestic and international markets of Indian coffee prices during 2005-06 to 2016-17. The domestic markets selected for coffee prices were Bangalore, Chennai and Hyderabad markets. Similarly, international markets were International coffee organization (ICO), New York and London. The results revealed that, Johansen’s Co-integration of Arabica and Robusta group prices had long run equilibrium and also implied strength and stability of price linkages between selected markets. Granger causality test showed that ICO and New York markets uni-directionally influenced the price of Bangalore, Chennai and Hyderabad markets, respectively but there was bi-directional causality among the selected domestic wholesale Robusta group markets. Bangalore and Chennai markets are considered to be dependent on the other markets, the speed of adjustment in Robusta group coffee was registered 54 per cent and 33 per cent, respectively. There is need to spend much more money on demand creation, like cafe coffee day, which help to promote coffee consumption within the country. So our producers fetch the better prices for their produce.