This paper applies the GARCH‐MIDAS model to examine whether information contained in global economic policy uncertainty (GEPU) can help to predict short‐ and long‐term components of the gold futures ...return variance. Our results show that GEPU positively and significantly forecasts the future monthly volatilities for the aggregate global gold futures market. The forecasting power of GEPU remains strong in an out‐of‐sample setting. Moreover, further out‐of‐sample tests show that the GARCH‐MIDAS model with GEPU and realized volatility outperforms all other specifications, indicating that including low‐frequency GEPU information in the GARCH‐MIDAS model significantly enhances the forecasting ability of the model.
This study presents a comprehensive bibliometric analysis conducted in R Studio of the scientific landscape regarding commodity markets, trading strategies, sustainable production, integration of ...technologies such as machine learning, and their economic impacts, covering publications from 1974 to 2023. Employing a sophisticated query in Scopus, we meticulously compiled and analyzed data, revealing an annual growth rate of 10.46% in related scientific publications, with an average citation rate of 6.60 per document. The results indicate sustained interest in commodity research over time, with a significant increase observed in scientific production, particularly since the early 2008s. International collaboration is prominent, reflecting the global nature of research in commodity markets. Key themes such as “futures markets”, “commodity prices”, and “energy commodities” emerge from the analysis of keywords and bigrams, highlighting areas of interest within the field. Additionally, thematic mapping highlights emerging and niche themes in commodity research, providing insight into evolving trends and areas of specialization. Factorial analysis of keywords reveals the underlying structures of association between key concepts, shedding light on the intricate dynamics of research in the field of commodities. This research delineates the complex interplay between commodity markets and global economic dynamics, offering invaluable insights for academics, policymakers, and market participants aiming to navigate the intricate world of commodities in the digital age.
This study uses bibliometrics to present a retrospective on the Journal of Futures Markets (JFM) on its 40th anniversary. The Journal's annual number of publications and citations grew substantially, ...with US‐affiliated authors being the dominant contributors. Authorship analysis reveals an increase in collaboration and diversity among JFM authors. Bibliographic coupling analysis reveals that the Journal's main themes include commodities, volatility, trading, hedging, arbitrage and pricing, forecasting volatility, and credit default swaps. Its primary citation drivers are article age, article length, number of authors, FT100 affiliation, and references.
This study investigates the effect of index option investors' misreaction to the Taiwan index futures market and examines the channel through which this effect occurs. We find that an increase in ...misreaction during periods of market pessimism leads to greater volatility and illiquidity in the futures market. This negative impact on futures volatility and liquidity can be attributed to market makers' hedging pressure, which is caused by the misreaction occurring within an option market characterized by high volatility and low liquidity during a pessimistic period. Our research presents evidence of a cross-market effect triggered by sentiment-induced misreaction.
•We investigate the effect of option investors' misreaction on the futures market.•Misreaction in periods of market pessimism leads to greater futures volatility and illiquidity.•We examine the channel through which this misreaction effect occurs.•This negative impact on futures volatility and liquidity can be driven by market makers' hedging pressure.•Our study presents evidence of a cross-market effect triggered by sentiment-induced misreaction.
•The spillover relationship among EUA, INE, and Brent is investigated by the TVP-VAR-DY model.•INE as a spillover receiver and EUA and Brent as transmitters.•Spillover peaks during the COVID-19 ...pandemic.•The volatilities of EUA, INE, and Brent are positively influenced by spillovers among them.•EPU, RA, and EMV increase spillovers among EUA, INE, and Brent, while GPR decreases them.
This paper employs the TVP-VAR-DY model to investigate the spillover among the European carbon market (EUA), the Chinese oil futures market (INE), and the Brent oil futures market (Brent) and explores whether their spillovers affect their volatility. Furthermore, we explore the impact of global uncertainties about the economy, finance, and geopolitics on their spillovers. The static analysis shows INE as a spillover receiver and EUA and Brent as transmitters. Dynamic analysis reveals spillover peaks during the COVID-19 pandemic. Furthermore, volatilities are positively influenced by spillovers among them. Finally, economic and financial uncertainties increase spillovers, while geopolitical uncertainties decrease them.
Regime shifts are large, abrupt, and persistent critical transitions in the function and structure of ecosystems. Yet, it is unknown how these transitions will interact, whether the occurrence of one ...will increase the likelihood of another or simply correlate at distant places. We explored two types of cascading effects: Domino effects create one-way dependencies, whereas hidden feedbacks produce two-way interactions. We compare them with the control case of driver sharing, which can induce correlations. Using 30 regime shifts described as networks, we show that 45% of regime shift pairwise combinations present at least one plausible structural interdependence. The likelihood of cascading effects depends on cross-scale interactions but differs for each type. Management of regime shifts should account for potential connections.
This paper examines whether structural breaks contain incremental information for forecasting the volatility of copper futures. Considering structural breaks in volatility, we develop four ...heterogeneous autoregressive (HAR) models based on classical or latest HAR‐type models. Subsequently, we apply these models to forecast volatility in the copper futures market. The empirical results reveal that our models exhibit better in‐sample and out‐of‐sample performances than classical or latest HAR‐type models. This suggests that structural breaks contain incremental prediction information for the volatility of copper futures. More importantly, we argue that considering structural breaks can improve the performances of most of existing HAR‐type models.
Highlights
There are many structural break points in return volatility of the copper futures.
We propose 12 new heterogeneous autoregressive models.
Our models outperform the existing heterogeneous autoregressive models.
Structural breaks contain additional ex ante information for volatility forecasting.
The ex ante information is obvious in forecasting mid‐ and long‐term volatilities.
We propose the rolling tail-event driven network technique (RTENET) to measure the dynamic nonlinear tail risk spillover of 20 US commodity futures. In addition, we investigate the effect of economic ...policy uncertainty (EPU) on risk spillover based on quantile-on-quantile regression (QQR). We find that the risk spillover effect increases sharply and that the market is tightly connected when EPU is at a high level. Crude oil, silver and corn, the three greatest risk transmitters in the system, need more attention. More importantly, the effect of EPU on the risk spillover of the commodity futures market is asymmetric and heterogeneous. When the risk spillover falls within extremely high quantiles, a significant positive effect of EPU is observed. In addition, grain and soft crops are more sensitive to EPU. Our findings provide a reference for policy-makers and investors to manage commodity futures markets in different uncertainty periods.
•Rolling tail-event-driven network is used to measure nonlinear risk spillover among commodity futures.•Heterogeneous and asymmetric effects of EPU on risk spillover are observed.•Risk spillover network will be more connected when faced with turmoils.•Silver futures are the greatest risk contributor, followed by corn futures.•Risk spillovers of grain and soft crop are more sensitive to EPU.
To improve the predictability of crude oil futures market returns, this paper proposes a new combination approach based on principal component analysis (PCA). The PCA combination approach combines ...individual forecasts given by all PCA subset regression models that use all potential predictor subsets to construct PCA indexes. The proposed method can not only guard against over-fitting by employing the PCA technique but also reduce forecast variance due to extensive forecast combinations, thus benefiting from both the combination of information and the combination of forecasts. Showing impressive out-of-sample forecasting performance, the PCA combination approach outperforms a benchmark model and many related competing models. Furthermore, a mean–variance investor can realize sizeable utility gains by using the PCA combination forecasts relative to the competing forecasts from an asset allocation perspective.
Esta nota de comentário tem como objetivo explorar quais os limites e os cuidados necessários para analisar a relação entre liquidez e informação assimétrica nos mercados de derivativos ...agropecuários, diante dos métodos empregados no trabalho de Ribeiro et al. (2020, Revista Brasileira de Finanças 18(2)), “O risco de informação assimétrica sobre a liquidez dos contratos futuros de commodities agrícolas”, que investigaram a influência da assimetria de informações sobre a liquidez nos mercados futuros agropecuários (de milho, soja, café e boi gordo) da bolsa brasileira, B3 (Brasil, Bolsa, Balcão).
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