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This study investigates the impacts of the Russia–Ukraine conflict on the cross-correlation between agricultural commodity prices and crude oil prices.

The authors used MultiFractal Detrended Fluctuation Cross-Correlation Analysis (MF-X-DFA) to explore the correlation behavior before and during conflict. The authors analyzed the price connections between future prices for crude oil and agricultural commodities. Data consists of daily futures price returns for agricultural commodities (Corn, Soybean and Wheat) and Crude Oil (Brent) traded on the Chicago Mercantile Exchange from Aug 3, 2020, to July 29, 2022.

The results suggest that cross-correlation behavior changed after the conflict. The multifractal behavior was observed in the cross correlations. The Russia–Ukraine conflict caused an increase in the series' fractal strength. The study findings showed that the correlations involving the wheat market were higher and anti-persistent behavior was observed.

The study was limited by the number of observations after the Russia–Ukraine conflict.

This study contributes to the literature that investigates the impact of the Russia–Ukraine conflict on the financial market. As this is a recent event, as far as we know, we did not find another study that investigated cross-correlation in agricultural commodities using multifractal analysis.

Market links (and price discovery) between financial assets and lead–lag relationships are topics of interest for financial economists, financial managers and analysts. The lead–lag relationship analysis should consider both short and long-term investors. From a portfolio diversification perspective, the first type of investor is generally more interested in determining the co-movement of financial assets at higher frequencies, which are short-run fluctuations, while the latter concentrates on the relationship at lower frequencies, or long-run fluctuations. The paper aims to discuss these issues.

For this study, a technique was employed known as the wavelet approach, which has recently been imported to finance from engineering sciences to study the co-movement dynamics between global sukuk and bond markets. Data cover the period from January 2010 to December 2015.

The results indicate that: there is no unidirectional causality from developed market bond indices to Malaysia and Dow Jones indices, which is promising for fixed-income investors of a developed market; and in relation to emerging markets, the Malaysian sukuk market has a bidirectional causality with Indonesia, Malaysia, India and South Korea bond indices but not China bond indices, while in terms of the Dow Jones sukuk index, there is no unidirectional causality between the listed emerging markets and the sukuk index except Indonesia’s market during the sample period.

This analysis provides evidence regarding the timely and appropriate measure of correlation changes and the behaviour of sukuk and bond indices globally, which is beneficial to the management of sukuk and bond portfolios.

The evidence hitherto unexplored, which was produced by the application of a wavelet cross-correlation amongst the selected sukuk and bond indices, provides robust and useful information for international financial analysts as well as long and short-term investors.

This study aims to systematically reveal the complex interaction between uncertainty and the international commodity market (CRB).

A composite uncertainty index and five categorical uncertainty indices, together with wavelet analysis and detrended cross-correlation analysis, were used. First, in the time-frequency domain, the coherency and lead-lag relationship between uncertainty and the commodity markets were investigated. Furthermore, the transmission direction of the cross-correlation over different lag periods and asymmetry in this cross-correlation under different trends were identified.

First, there is significant coherency between uncertainties and CRB mainly in the short and medium terms, with natural disaster and public health uncertainties tending to lead CRB. Second, uncertainty impacts CRB more markedly over shorter lag periods, whereas the impact of CRB on uncertainty gradually increases with longer lag periods. Third, the cross-correlation is asymmetric and multifractal under different trends. Finally, from the perspective of lag periods and trends, the interaction of uncertainty with the Chinese commodity market is significantly different from its interaction with CRB.

First, this study comprehensively constructs a composite uncertainty index based on five types of uncertainty. Second, this study provides a scientific perspective on examining the core and diverse interactions between uncertainty and CRB, as achieved by investigating the interactions of CRB with five categorical and composite uncertainties. Third, this study provides a new research framework to enable multiscale analysis of the complex interaction between uncertainty and the commodity markets.

This paper aims to investigate the relationship between oil price shocks and world food prices between 1974 and 2018.

The authors use the SVAR model to disentangle the oil price into supply, aggregate demand and oil-specific demand shocks and apply the detrended cross-correlations analysis to measure the association between oil price shocks and food returns/volatility and analyze contagion effects between oil and food markets.

The results show that the correlations between oil and food prices depend on whether oil prices changes are driven by supply or demand shocks. Particularly, food returns (volatility) are positively (negatively) more dependent on the oil price changes driven by aggregate demand (oil specific demand) shocks. Further analysis dealing with contagion analysis between oil and food markets shows a contagion effect during the food crisis of 2006–2008. Oil-specific demand shocks are the main source of this phenomenon.

This study differentiates itself from the previous literature by simultaneously disentangling oil price into supply, aggregate demand and oil-specific demand-driven shocks and evaluating the cross-correlations between each shock type and food returns/volatility. Specifically, this study has the originality of detecting the main source of contagion effects between oil and food markets over the food crisis of 2006–2008.

The results of this study are important for policymakers and investors. They should account for the oil price fluctuations differently depending on whether the oil price shocks are driven by the demand or supply side. Moreover, they should anticipate an increase (decrease) in food prices due to a positive (negative) oil shock. In addition, special attention should be accorded to the world oil demand. Finally, when a food crisis occurs, markets operators should focus more on the specific oil-demand shocks, as it is the most contributor to possible contagion effects between oil and food markets.

This study differentiates itself from the previous literature by simultaneously disentangling oil price into supply, aggregate demand and oil-specific demand-driven shocks and evaluating the cross-correlations between each shock type and food returns/volatility. Specifically, this study has the originality of detecting the main source of contagion effects between oil and food markets over the food crisis of 2006–2008.

In this paper, we study the effect of global energy and food inflation on household inflation expectations during the period 1988M01–2020M03 for a set of European economies.

We use multifractal de-trended cross-correlation analysis to estimate the non-linear and time-varying cross-correlation. We provide additional robustness tests using the Autoregressive-Distributed Lag method.

We find that household inflation expectations, global energy inflation and global food inflation are all multifractal. We also find that the household inflation expectations, global energy inflation and global food inflation are positively correlated (i.e., they are persistent). However, household inflation expectations respond more when the volatility of the global energy inflation is lower than when the volatility is higher. The correlation between household inflation expectations and global food inflation does not depend on the level of volatility.

First, paying attention to the global commodity inflation might help anchor inflation expectations better. It is so because Central Bank's efficacy in achieving price stability may be weakened if there is a relationship between commodity inflation and inflation expectation. This task would become even more difficult in the average inflation targeting regime than inflation targeting regime if actual inflation is persistently different from the target inflation. Second, our results also emphasize the importance of effective strategy for communicating to households about actual inflation, inflation target and keep them updated about how monetary policy functions.

We contribute to the literature by estimating the cross-correlation between household inflation expectations with the global commodity inflation, conditional to the volatility of the commodity inflation under consideration.

Recently, many scholars have been paying more attention to studying the existence and application of multifractality. However, most researches concentrate on studying multifractal features of returns or volume separately, and ignore the correlation between them. The purpose of this paper, therefore, is to give an empirical test on multifractal features of price‐volume correlation in China metal futures market and then to conduct a comparative analysis from time and space dimensions, in order to better understand metals futures market behavior.

This paper gives an empirical test by means of multifractal detrended cross‐correlation analysis (MF‐DCCA) approach, which is a technique employed in statistical physics to detect multifractal features of two cross‐correlated nonstationary time series.

Empirical results show that the price‐volume correlation in China metal futures market is multifractal and that long range correlation and non‐Gaussian probability distribution are the main reasons for the existence of multifractality. Also, a comparative analysis is conducted and it is found that although China metal futures market is becoming more and more effective, the effectiveness is lower than that in mature LME metal futures markets. The futures market still needs further development.

The paper's conclusions would help to understand the nonlinear dependency relationship and potential dynamics mechanism in price‐volume correlation.

The usefulness of probability density and cross‐correlation of the vibration acceleration signal of rolling element bearings has been investigated. These measurements have been performed on ball bearings with and without simulated defects in their races after mounting them on a test rig. The measurement results show that both probability density and cross‐correlation of the bearings′ vibration signal can be used to detect defects in them.

This study aims to examine the asymmetric S-curve between the trade balances of Pakistan and China at the commodity level using disaggregated data.

This study focuses on Pakistan and China bilateral trade based on commodity-level data. This study delves into the S-curve phenomena by examining time series data from 1980 to 2023 across 32 three-digit industries/commodities.

The findings show significant evidence in favor of the “asymmetric S-curve” in 27 out of the 32 industries studied. This study confirms that the devaluation of home currency is not a viable solution always to improve trade balance.

This study considers 32 three-digit industries limiting the generalizability of findings. Due to data unavailability, the authors fail to consider other industries. In the absence of quarterly data on industry-level trade between Pakistan and China, annual data from 1980 to 2023 were used in generating the cross-correlation functions. Previous literature frequently resorted to the general consumer price index with its inherent aggregation issues, whereas this study has opted for commodity price indices to overcome the shortcomings in the estimation of S-curves at the commodity level.

The findings have practical relevance in guiding policy decisions regarding commodity trade, whereas the industry-wise analysis enriches the understanding of the short-term effects of currency depreciation on trade balance dynamics.

The S-curve hypothesis predicts a negative cross-correlation between a country's current exchange rate and its past trade balance and a positive cross-correlation between the current exchange rate and its future trade balance. Previous empirical S-curve studies had the limitation of assuming symmetry in cross-correlation with both current and future trade balance values.

The authors propose a semiparametric approach for testing independence between two infinite-order cointegrated vector autoregressive series (IVAR(∞)). The procedures considered can be viewed as extensions of classical methods proposed by Haugh (1976, JASA) and Hong (1996b, Biometrika) for testing independence between stationary univariate time series. The tests are based on the residuals of long autoregressions, hence allowing for computational simplicity, weak assumptions on the form of the underlying process, and a direct interpretation of the results in terms of innovations (or shocks). The test statistics are standardized versions of the sum of weighted squares of residual cross-correlation matrices. The weights depend on a kernel function and a truncation parameter. Multivariate portmanteau statistics can be viewed as a special case of our procedure based on the truncated uniform kernel. The asymptotic distributions of the test statistics under the null hypothesis are derived, and consistency is established against fixed alternatives of serial cross-correlation of unknown form. A simulation study is presented which indicates that the proposed tests have good size and power properties in finite samples.

When investors experience extreme uncertainty, they seek “safe havens” to reduce their risk, to limit their losses and to protect the value of their portfolios. The purpose of this paper is to examine the safe-haven properties of Bitcoin compared to the stock market.

Based on intraday data, this study compares the price efficiencies of Bitcoin and Morgan Stanley Capital Index (MSCI) using Multifractal Detrended Fluctuation Analysis for the second half of 2020. This study then evaluates Bitcoin’s safe-haven property using Detrended Partial-Cross-Correlation Analysis (DPCCA).

This study finds that the price efficiency of Bitcoin is lower than that of MSCI. Further, Bitcoin was not a safe haven at any time for the MSCI index. The net cross-correlations between Bitcoin and MSCI are weak and they vary at different time scales.

The behavior of market prices varies over time. Therefore, it is important to replicate this study for other time periods.

The paper sheds light on the price behavior of Bitcoin during a period of instability. The results suggest that the construction of portfolios should differ based on the time horizons of the investors.

The authors compare Bitcoin against a global equity index instead of a specific country index or commodity. They also demonstrate the applicability of DPCCA in finance research.