Research background: The study analyzes whether financial speculation destabilizes commodity prices in light of recent price volatility and spikes in agricultural commodities. The study delves deeper into the US dairy futures markets, which are less studied by other authors in their research and relatively new in comparison to other agricultural commodity markets. These dairy commodity futures contracts provide dairy businesses and farmers the chance to hedge against price risks, which are particularly crucial in uncertain economic times such as the post-2020 COVID-19 pandemic timeframe. The analysis makes use of the weekly returns on futures contracts for nonfat milk powder, butter, milk class III, and cheese that are obtained from the Chicago Mercantile Exchange (CME). Purpose of the article: Conduct an empirical study to evaluate the effect of financial speculation on dairy product prices on US commodity markets, including the post-2020 timeframe. Methods: Time series analysis is used in the investigation: the generalized auto-regressive conditional heteroskedasticity (GARCH) method, the Granger causality test, and the Augmented Dickey-Fuller (ADF) test. Findings & value added: Our analysis's findings show that, even though most commodities experienced an increase in return volatility during the post-2020 period, there is no evidence for financial speculation being the cause of increased returns from dairy futures contracts. The research also suggests that financial speculation, in some cases, even lowers the volatility of dairy futures prices. Therefore, non-commercial market participants may help to distribute price risks, making these markets more liquid.
This paper deals with an analysis of the information flow on and between three European stock markets operating in Frankfurt, Vienna, and Warsaw. We examine causal links between returns, volatility, and trading volume as well as the time of reaction to a news release and changes in the duration of causal interference. To model the conditional variance, we use the ARMA(1,1)-EGARCH-M(1,1) model. We investigate linear and nonlinear Granger causalities on the three stock exchanges using Bayesian large sample correction of the critical values in significance tests. The results of our study confirm the dominant role of the Frankfurt Stock Exchange, since the most significant linear relationship is the causality running from DAX30 returns to the returns of the ATX20 and WIG20 (which exists irrespective of the time of the day, presence of important public news, and lag length of the underlying VAR models). Moreover, the empirical results of this paper confirm the strong impact of announcements of macroeconomic news from the U.S. economy on the structure of both linear and nonlinear causal links on the three markets under study.
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