Co-movement Of Prices Research Articles

A Time-Varying Gerber Statistic: Application of a Novel Correlation Metric to Commodity Price Co-Movements

This study investigates the daily co-movements in commodity prices over the period 2006–2020 using a novel approach based on a time-varying Gerber correlation. The statistic is computed considering a set of probabilities estimated via non-traditional models that give a time-varying structure to the measure. The results indicate that there are several co-movements across commodities, that these co-movements change over time, and that they are tendentially positive. Conditional auto-regressive multithreshold logit models show higher forecasting accuracy for agricultural returns, while dynamic conditional correlation models are more accurate for energy products and metals. The proposed models are shown to be superior in terms of forecasting power to the benchmark method which is based on estimating the Gerber correlation moving a rolling window.

Liquidity effects on price and return co-movements in commodity futures markets

Commodity price comovement is an important research area in finance, and previous studies have investigated the determinants of price comovement using low-frequency (monthly or quarterly) macroeconomic data. In comparison, our paper attempts to scrutinize the liquidity effect on commodity prices and return movements based on daily data. Our findings contribute to the literature in three ways. First, we find significant positively correlated price movements across different commodity markets on a daily basis, and such comovement is driven by the cross-sectional liquidity spillover effect. Second, we observe that a cointegration relationship between individual commodity prices and the global price index can be established only if the liquidity effect is controlled. Finally, instantaneous daily liquidity shocks (i.e., innovation) exert a negative impact on daily commodity returns. However, liquidity shocks do not have a significant impact on monthly returns. Our findings are robust and have significant implications for macroeconomic policymaking, such as managing inflation risk.

Measuring information flow among international stock markets: An approach of entropy-based networks on multi time-scales

Information flow among stock markets leads to the co-movement of stock prices and even the financial risk contagion globally. Measuring information flow on multi time-scales helps to develop optimal portfolio strategy for market participants and establish targeted financial regulation for policy makers, but is a challenge for traditional econometrics. This study aims to construct the information flow networks on multi time-scales among international stock markets. First, we use the data of 31 stock market indices during 2007 to 2018, and decompose each of stock index series into short and mid-long time-scale components by empirical mode decomposition (EMD). Then, we extend a novel concept in information science, called transfer entropy (TE), to measure information flow and characterize causality structure between two stock markets, and find that information flows inside of EU stock markets only cluster on short time-scale but not on mid-long time-scale. Moreover, both out-node and in-node centrality measurements for developed markets in the entropy-based networks are much higher on short time-scale, indicating that these markets are more dominant but more vulnerable to short-term risk contagion. Finally, based on the minimum spanning tree (MST) model, we extract the most probable risk contagion paths from entropy-based networks. The results show that outflow MSTs on short time-scale follow a star-like structure to precipitate the risk contagion in the international stock markets, especially during crisis period (2007∼2010). Overall, we emphasize the financial risk contagion on short time-scale.

From banking integration to housing market integration - Evidence from the comovement of U.S. Metropolitan House Prices

We find that the movement of urban house prices in the U.S. has become more synchronized since the early 2000s. The elevated comovement is substantial, widespread, occurring for the majority of city-pairs, and continued even after the housing market turned to bust in 2007. We investigate whether and to what extent the comovement increase can be explained by banking integration following deregulations in the banking industry. Utilizing novel measures of city-level banking integration based on bank deposit data, we find that an increase in banking integration leads to an increase in the comovement of urban house prices. City-pairs that are more connected through national banking system had experienced a greater increase in comovement, after controlling for a variety of explanatory variables. Our findings can be interpreted as spillover effect, rather than substitution effect, of banking integration at work.

Stock Price Movement Prediction Based on a Deep Factorization Machine and the Attention Mechanism

The prediction of stock price movement is a popular area of research in academic and industrial fields due to the dynamic, highly sensitive, nonlinear and chaotic nature of stock prices. In this paper, we constructed a convolutional neural network model based on a deep factorization machine and attention mechanism (FA-CNN) to improve the prediction accuracy of stock price movement via enhanced feature learning. Unlike most previous studies, which focus only on the temporal features of financial time series data, our model also extracts intraday interactions among input features. Further, in data representation, we used the sub-industry index as supplementary information for the current state of the stock, since there exists stock price co-movement between individual stocks and their industry index. The experiments were carried on the individual stocks in three industries. The results showed that the additional inputs of (a) the intraday interactions among input features and (b) the sub-industry index information effectively improved the prediction accuracy. The highest prediction accuracy of the proposed FA-CNN model is 64.81%. It is 7.38% higher than that of traditional LSTM, and 3.71% higher than that of the model without sub-industry index as additional input features.

Commodity prices co-movements and financial stability: A multidimensional visibility nexus with climate conditions

This paper investigates the nexus between climate-related variables, commodity price co-movements and financial stability. First, we project the commodity price time series onto a multilayer network. Centrality measures computed on the network are used to detect the existence of common trends between the series and to characterize the role of different nodes during phases of market downturns and upturns, unveiling the onset of financial instability. Then, an econometric analysis is introduced to show how climate-related variables affect financial stability by influencing co-movements of commodity prices. Overall, the paper reveals how synthetic indicators of commodity price co-movements generate valuable signals to study the nexus between climate-related conditions and the dynamics of financial systems.

Stock prices and the risk-free rate: An internal rationality approach

The co-movement of stock prices and the risk-free rate in the United States is weak in terms of the correlation and variance decomposition. It is essential for investors and policymakers to understand such co-movement, especially when several well-known asset pricing models imply a much stronger relationship than the one empirically observed. To explain this inconsistency, this paper presents a model with “internally rational” agents who optimally update their subjective beliefs about stock prices. Compared with the risk-free rate, agents’ subjective beliefs are essential for generating stock market volatility. Quantitatively, our model can jointly produce basic asset market facts and the weak co-movement.

Implicit government guarantees and the externality of portfolio diversification: A complex network approach

Policy changes have a significant impact on financial markets. In this study, we build and calibrate a heterogeneous-agent model of a complex network using real data, then empirically examine the effects of policy changes on the risk contagion mechanism in the Chinese bond market. We find that the externality of diversification is positive, that is, a diversified portfolio can reduce the adverse effects of a negative shock, however, the positive externality weakened after the break of implicit government guarantee. Our findings show that policy changes influence the market through price co-movement and diversified portfolios. Moreover, the externality of diversification and the effect of policy changes vary between government and corporate bonds. Finally, investment strategies are proposed based on the findings.

Fuel price co-movements among France, Germany and Italy: A time-frequency investigation

We investigate the co-movements of fuel prices among France, Germany and Italy, using weekly data from January 3, 2005 to November 9, 2020 and making use of a time-frequency framework. Our wavelet coherence analysis indicates strong co-movements at medium and small frequencies, which are largely driven by the international oil prices. Further, when we implement a partial wavelet coherence method and control for the effect of crude oil prices, we notice that the co-movements diminish. They manifest themselves more strongly between France and Italy, and Germany and France whereas this is true only for a smaller extent between Italy and Germany. The fuel taxes negatively affect the price co-movements. At the same time, the co-movements of gasoline prices are stronger compared with those recorded by diesel prices. Our findings highlight the important role of international oil prices in driving fuel prices in the medium and long run, the heterogeneity of the European fuel tax systems, and the lack of a real cost competition relying on fuel price dynamics.

Co-movement of energy prices and stock market return: environmental wavelet nexus of COVID-19 pandemic from the USA, Europe, and China.

This work aims to study the time-frequency relationship between the recent COVID-19 pandemic and instabilities in oil price and the stock market, geopolitical risks, and uncertainty in the economic policy in the USA, Europe, and China. The coherence wavelet method and the wavelet-based Granger causality tests are applied to the data (31st December 2019 to 1st August 2020) based on daily COVID-19 observations, oil prices, US-EPU, the US geopolitical risk index, and the US stock price index. The short- and long-term COVID-19 consequences are depicted differently and may initially be viewed as an economic crisis. The results illustrate the reduced industrial productivity, which intensifies with the increase in the pandemic’s severeness (i.e., a 10.57% decrease in the productivity index with a 1% increase in the pandemic severeness). Similarly, indices for oil demand, stock market, GDP growth, and electricity demand decrease significantly with an increase in the pandemic severeness index (i.e., a 1% increase in the pandemic severeness results in a 0.9%, 0.67%, 1.12%, and 0.65% decrease, respectively). However, the oil market shows low co-movement with the stock exchange, exchange rate, and gold markets. Therefore, investors and the government are recommended to invest in the oil market to generate revenue during the sanctions period.

Precautionary Liquidity Shocks, Excess Reserves and Business Cycles

This paper identifies a precautionary banking liquidity shock via a set of sign, zero and forecast variance restrictions imposed. The shock proxies the reluctance of the banking sector to lend to the real economy induced by an exogenous change in financial intermediaries' preference for high liquid assets. The identified shock has sizeable and state (volatility) dependent effects on the real economy. To understand the transmission of the shock, we develop a DSGE model of financial intermediation with credit and liquidity frictions. The precautionary liquidity shock is shown to work through two channels: it increases the level of reserves and the deposit rate. The former is a balance sheet effect, which reduces the loan-to-deposit ratio. The higher deposit rate affects the intertemporal decisions of households and the cost of borrowing to firms. The overall effect is a downward co-movement in output, consumption, investment and prices, which is amplified the higher are the long-run risk in the economy and the responsiveness of banks to potential risk.

Machine Learning Predictions of Housing Market Synchronization across US States: The Role of Uncertainty

We analyze the role of macroeconomic uncertainty in predicting synchronization in housing price movements across all the United States (US) states plus District of Columbia (DC). We first use a Bayesian dynamic factor model to decompose the house price movements into a national, four regional (Northeast, South, Midwest, and West), and state-specific factors. We then study the ability of macroeconomic uncertainty in forecasting the comovements in housing prices, by controlling for a wide-array of predictors, such as factors derived from a large macroeconomic dataset, oil shocks, and financial market-related uncertainties. To accommodate for multiple predictors and nonlinearities, we take a machine learning approach of random forests. Our results provide strong evidence of forecastability of the national house price factor based on the information content of macroeconomic uncertainties over and above the other predictors. This result also carries over, albeit by a varying degree, to the factors associated with the four census regions, and the overall house price growth of the US economy. Moreover, macroeconomic uncertainty is found to have predictive content for (stochastic) volatility of the national factor and aggregate US house price. Our results have important implications for policymakers and investors.

Price Leadership and Asynchronous Movements of Multi-Market Listed Stocks

We examine the asynchronous price movements of the same assets traded on multiple markets. Our sample of stocks simultaneously traded in three different financial markets (Shanghai Stock Exchange, New York Stock Exchange, and Stock Exchange of Hong Kong) represents an ideal setting to investigate the stock price co-movement, directions, and leadership. We find strong evidence that foreign prices lead home prices, but not the reverse effect. Contrary to theory predictions, investors in the firms’ home market respond to idiosyncratic fluctuations in the stock returns in the host markets. It suggests investors pay attention to fluctuations in the stocks listed on the more institutionally developed markets.

The Cross-Shareholding Network and Risk Contagion from Stochastic Shocks: An Investigation Based on China’s Market

This investigation performs a detailed analysis of how the cross-shareholding network enhances stock market comovement and leads to the rise of the risk contagion effect. We construct a model with random shocks to describe the interdependence of stock prices in the cross-shareholding network and how the risk contagion effect occurs. The simulation results show that market volatility is not sensitive to changes in the shareholding ratio. Furthermore, both the simulation results and their empirical verification show that volatility is particularly sensitive to changes in the reciprocity and density of the network. Firms with high centrality contribute more to strengthening stock price comovement than firms with low centrality. Unlike the contagion of risks such as bank failures among interbank credit networks, we find that cross-shareholding networks magnify and spread small but continuous external shocks. The results of this paper highlight the importance of the network structure, which helps to provide a quantitative and accurate policy basis for the prevention and control of risk contagion caused by cross-shareholdings.

Application of Vector Error Correction Model (VECM) and Impulse Response Function for Daily Stock Prices

Vector Error Correction Model is a cointegrated VAR model. This idea of Vector Error Correction Model (VECM), which consists of a VAR model of the order p - 1 on the differences of the variables, and an error-correction term derived from the known (estimated) cointegrating relationship. Intuitively, and using the stock market example, a VECM model establishes a short-term relationship between the stock prices, while correcting with the deviation from the long-term comovement of prices. An Impulse Response Function traces the incremental effect of a 1 unit (or one standard deviation) shock in one of the variables on the future values of the other endogenous variables. Impulse Response Functions trace the incremental effect of the marketing action reflected in the shock. The data used in this analysis are 4 (four) daily plantation stocks prices in Indonesia with time period of January to July in three years which are 2018, 2019, and 2020. The objective of this study is to determine the relationship among 4 (four) stocks prices with VECM and to know the behaviour of each stocks prices with Impulse Response.

Regional House Price Co-Movement in the Us: The Medium Cycle is Not the Business Cycle

Regional House Price Co-Movement in the Us: The Medium Cycle is Not the Business Cycle

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.

Modeling stock price comovement from the respective of social media information diffusion

Modeling stock price comovement from the respective of social media information diffusion

Can Global Economic Policy Uncertainty Drive the Interdependence of Agricultural Commodity Prices? Evidence from Partial Wavelet Coherence Analysis

This paper employed wavelet coherence and partial wavelet coherence to investigate the time‐frequency effect of global economic policy uncertainty on the comovement of five agricultural commodities such as maize, oat, rice, soybean, and wheat using monthly data from January 1997 to December 2019. In general, we observed heterogeneity in comovement structures of the agricultural commodities market at different time‐frequency scales which are profound at high frequencies from the bivariate wavelet coherence. The partial wavelet coherence analysis shows that global economic policy uncertainty is a driver of agricultural commodity market connectedness. This implies that extreme changes in economic policy uncertainty have the tendency to influence commodity price comovement. This poses risk to the stability of the agricultural commodities market, which requires the policymaker’s intervention to protect against the spillover risk contagion effect in uncertain times.

Investigating dynamic price co-movements in the international milk market using copulas: The role of trade agreements

The present paper investigates price dependence in the international skim milk powder markets. Time-varying and dynamic stochastic copulas are applied on bi-weekly prices from Europe, Oceania, and the United States, covering the period from September 1998 to May 2017. The copulas’ empirical results support that price correlations increase over time in the three regions, while simple correlation coefficients do not endorse any increase. This is because dynamic stochastic copulas are flexible structures introducing time variation into the densities. The increase in price dependence is mainly due to free trade agreements, most of them enacted after 2007, leading to greater integration in the international milk market. The highest degree of price dependence is found between the EU and Oceania, the leading regions in the global skim milk powder market.