Information Diffusion, Cluster formation and Entropy-based Network Dynamics in Equity and Commodity Markets

This paper investigates the dynamic causal linkages among U.S. equity and commodity futures markets via the utilization of complex network theory. We make use of rolling estimations of extended matrices and time-varying network topologies to reveal the temporal dimension of correlation and entropy relationships. A simulation analysis using randomized time series is also implemented to assess the impact of de-noising on the data dependence structure. We mainly show evidence of emphasized disparity of correlation and entropy-based centrality measurements for all markets between pre- and post-crisis periods. Our results enable the robust mapping of network influences and contagion effects whilst incorporating agent expectations

Information Diffusion, Cluster formation and Entropy-based Network Dynamics in Equity and Commodity Markets

This paper investigates the dynamic causal linkages among U.S. equity and commodity futures markets via the utilization of complex network theory. We make use of rolling estimations of extended matrices and time-varying network topologies to reveal the temporal dimension of correlation and entropy relationships. A simulation analysis using randomized time series is also implemented to assess the impact of de-noising on the data dependence structure. We mainly show evidence of emphasized disparity of correlation and entropy-based centrality measurements for all markets between pre- and post-crisis periods. Our results enable the robust mapping of network influences and contagion effects whilst incorporating agent expectations

Information measure for financial time series: quantifying short-term market heterogeneity

A well-interpretable measure of information has been recently proposed based on a partition obtained by intersecting a random sequence with its moving average. The partition yields disjoint sets of the sequence, which are then ranked according to their size to form a probability distribution function and finally fed in the expression of the Shannon entropy. In this work, such entropy measure is implemented on the time series of prices and volatilities of six financial markets. The analysis has been performed, on tick-by-tick data sampled every minute for six years of data from 1999 to 2004, for a broad range of moving average windows and volatility horizons. The study shows that the entropy of the volatility series depends on the individual market, while the entropy of the price series is practically a market-invariant for the six markets. Finally, a cumulative information measure - the `Market Heterogeneity Index'- is derived from the integral of the proposed entropy measure. The values of the Market Heterogeneity Index are discussed as possible tools for optimal portfolio construction and compared with those obtained by using the Sharpe ratio a traditional risk diversity measure

Dependence structure between the international crude oil market and the European markets of biodiesel and rapeseed oil

Being an environmentally friendly fuel obtained from rapeseed oil, biodiesel is used extensively in Europe. However, the dependence structure between global crude oil prices and the European prices of biodiesel and rapeseed oil is understudied and unclear. In this paper, we address this gap by utilizing asymmetric copulas and cross-quantilogram approaches on daily data. The results of the DCC-Student-t copula indicate that during bearish periods the conditional connectedness between crude oil prices and biodiesel (rapeseed oil) prices are stronger than during bullish periods, indicating increased co-movement with a decline in crude oil prices. The application of cross-quantilogram indicates that an increase in crude oil price positively influences biodiesel prices reflecting an asymmetric dependence structure among the assets. There is evidence of shifts in the dynamics of quantile dependency during periods of financial and economic turmoil. Overall, the results show a significant dependence between the global crude oil market and the European markets of biodiesel and rapeseed oil in specific periods and under specific market conditions, which have important implications for policymakers and investors.© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

CORRELATION OF ECONOMIC INDICATORS OF PROCUREMENT OF AGRICULTURAL PRODUCTS FOR THE NEEDS OF THE DEFENCE SYSTEM

In the current situation of war conflicts, but also as a consequence of the COVID-19 pandemics, the economic crisis caused by the lack of goods, primarily food, energy sources, weapons and military equipment and multiple other products and services, has induced price increases and inflation. In this regard, there are substantial challenges present in the field of procurement of agricultural products. In that sense, we methodologically included analysis and synthesis, compilations, inductions and deductions of “military budgets”, GDP and some other indicators in several currently most significant countries and in the Republic of Serbia, in the context of compromised global security. The aim is to attain relevant indicators and conclusions which will provide certain guidelines for improvement of procurement of agricultural products for the needs of the defense system in the future

To what extent can new web-based technology improve forecasts? Assessing the economic value of information derived from Virtual Globes and its rate of diffusion in a financial market

As the rate of information availability increases, the ability to use web-based technology to improve forecasting becomes increasingly important. We examine Virtual Globe technology and show how the arrival of unprecedented types of web-based information enhances the ability to forecast and can lead to significant, measurable economic benefits. Specifically, we use market prices in a betting market over an eighteen-year period to examine how new elevation data from Virtual Globes (VG) enabled improved forecasting decisions and we explore how this information diffused through the betting market. The results demonstrate how short-lived, profitable opportunities arise from the arrival of novel information, and the speed at which markets adapt over time to account fully for new data

Risk network of global energy markets

This study evaluates extreme uncertainty connectedness among top global energy firms. The sample comprises of 68 firms from four energy-related subsectors (oil & gas, oil & gas related equipment and services, multiline utilities, and renewable energy). To provide an overview of tail connectedness, we construct a high-dimensional network between firms by utilizing a generalized error decomposition and a sparse vector autoregression framework with a latent common factor. Our empirical results indicate that between the four subsectors, the renewable energy subsector exhibits the highest uncertainty transmission to other underlying subsectors, primarily credited to an increased within-subsector idiosyncratic uncertainty before the COVID-19 crisis. After the burst of the COVID-19 pandemic, due to the higher connectedness, the role of the renewable energy companies in the spillover network is further intensified. The uncertainty connectedness demonstrates a time-varying trait. While the oil and gas subsector exhibits greater long-term linkages with the oil and gas related equipment and services subsector, the long-run dynamics exhibit a lower interconnectedness as compared to the short-run. Finally, there is an increased connectedness among companies operating in the same subsector with similar size, attributing to similarity and competition

The role of economic and financial uncertainties in predicting commodity futures returns and volatility : evidence from a nonparametric causality-in-quantiles test

We analyze the ability of economic and financial uncertainties in predicting movements in commodity futures markets. Using daily data over the period of 8th May 1992 to 31st August 2016 on 21 commodity futures covering agriculture, energy, metals and livestock, we find that: (a) Linear predictive tests provide virtually no evidence of predictability; (b) Linear models are misspecified due to nonlinearity and hence, results from the framework cannot be relied upon, and; (c) Using a k-th order nonparametric causality-in-quantiles test, which is robust to misspecification in the presence of nonlinearities, we find evidence that measures of uncertainty can predict returns and/or volatility of as many as 20 of the commodities considered at least at one point of their respective conditional distributions for returns and variance. In general, we highlight the importance of modeling nonlinearity, higher order moments, and quantiles of returns and volatility when carrying out predictability analysis involving commodity futures and uncertainty.Juncal Cunado gratefully acknowledges financial support from Ministerio de Economia, Industria y Competitividad (ECO2017-83183-R).http://www.elsevier.com/locate/econbase2020-06-01hj2018Economic

Systemic risk contribution of banks and non-bank financial institutions across frequencies: The Australian experience

The Australian financial sector (AFS) is highly concentrated and interconnected. Besides, Australian banks' lending portfolios are dominated by residential mortgage loans, and 70% of insurance companies' revenues arise from non-policyholder sources. The AFS also performed relatively well during the global financial crisis (GFC). Given these distinctive features, in this paper, we examine the systemic risk contribution of Australian banks, insurance companies, and other financial services providers. We use a flexible copula-based delta conditional value-at-risk (ΔCoVaR) method across different frequencies. Further, we study the systemic risk determinants in a panel setting. We find that the major Australian banks are systemically more important than all other financial institutions. Systemic risk is typically higher after the GFC than in the pre-crisis period, despite the introduction of more stringent capital requirements. In addition, the short-term ΔCoVaR is significantly higher than the medium- and long-term ΔCoVaRs. Finally, institution-specific characteristics and market-wide variables explain the cross-sectional and time-series variation in systemic risk, and their explanatory power varies across frequencies.publishedVersio

Markets and Supply Chains: An Investigation of the Institutions Influencing the Farm-Supply Chain Interface

Farm-level operations have lasting and amplified impacts that promulgate the entire supply chain, and the farm is increasingly in the forefront of today’s headlines on topics such as social responsibility, environmental sustainability, traceability, and food safety. Despite its significance, however, the farm remains a ‘black box’ and has traditionally operated independently with little information-sharing, trust, or collaboration with buyers downstream. This dissertation begins to unpack this ‘black box’ by employing different methodologies to identify the factors influencing exchange in the farm-supply chain interface. In Essay 1, I examine why the farm continues to be a challenge for ‘traditional’ collaborative approaches to buyer-supplier exchange. I use an interpretive approach to identify the individual and institutional factors influencing farmers’ operations decision-making. Field interviews reveal that farmers approach buyer-supplier exchange differently and tend to rely more heavily on market mechanisms to coordinate activities with buyers and inform their decision-making. In Essay 2, I build on this finding to examine the institutional factors influencing exchange in the spot market, which accounts for a majority of the total value of agricultural commodity production. I use a proprietary data set and time series econometrics to investigate how spot market exchanges between farmers and buyers are influenced by the futures market—an institution serving critical informational and risk management functions in the industry. In line with the predictions of Austrian economics, the findings indicate that farmers and buyers use the information conveyed by the futures market as they negotiate prices in the spot market. In Essay 3, I build on this finding and further explore how the futures market influences spot market exchanges by examining how information asymmetry affects the price adjustment process. I draw on economic theory to develop hypotheses that are tested using a proprietary data set and nonlinear time series econometrics. The findings suggest that buyers exploit their informational advantage by adjusting spot market prices asymmetrically. Taken together, the three essays demonstrate how institutions influence decision-making and exchange in the agricultural supply chain and offer important insights for theory, practice, and public policy