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We document a new empirical phenomenon in which the aggregate positions of money managers, who are sophisticated speculators in the commodity futures market, as disclosed by the Disaggregated Commitments of Traders reports, can predict the cross section of commodity producers' stock returns in the subsequent week. We employ a number of cross-sectional methods, including calendar-time regression analysis, single-sort, double-sort, and Fama–MacBeth regressions, to confirm the predictability results. The results are more pronounced in firms with higher information asymmetry. We thus add more empirical evidence to the literature on costly information processing, which leads to gradual information diffusion across asset markets. [ABSTRACT FROM AUTHOR]

Climate risk has become a hot topic of global concern. This paper aims to explore the impact of investor climate sentiment (ICS) on China' s crude oil futures market, covering the period from March 27, 2018, to December 30, 2022. Firstly, this paper employs the Thermal Optimal Path (TOP) method and discovers that the guiding effect of ICS on the volatility of crude oil futures (RVoil) intensifies over time, progressively becoming a pivotal factor in determining volatility. Secondly, based on the lead-lag relationship between ICS and RVoil, this study divides the sample period into five stages and confirms through the HAR model that ICS has a significant inhibitory effect on crude oil volatility during the guiding phase. In addition, incorporating ICS into the HAR model not only improves the model' s goodness of fit but also significantly reduces the prediction error in out-of-sample forecasts. Finally, by comparing with the full-sample analysis, the volatility prediction results of the segmented samples show that during the guiding phase, the predictive power of ICS for crude oil market volatility is significantly improved. Even in the non-guiding phase, ICS can reduce the prediction error to a certain extent. This result further highlights the advantages of the TOP method in revealing the impact of ICS on the prediction of crude oil volatility. [ABSTRACT FROM AUTHOR]

We estimate the effects of monetary policy on the aggregate economy in Japan during the last three decades when the effective lower bound (ELB) on interest rates was occasionally binding. We use monetary policy surprises from the interest rate futures market as the external instrument to identify monetary policy shocks in the vector autoregressive model. We find that monetary policy has been effective in Japan during the last three decades, and the effect was more persistent in the ELB regime than in the non-ELB regime. In a simulation exercise, we further show that a New Keynesian model with forward guidance can replicate our empirical finding. [ABSTRACT FROM AUTHOR]

The negative correlation between returns and volatility is well known. However, there is no consensus on whether returns cause changes in volatility or vice versa. In this paper, we investigate the contemporaneous relation between the VIX futures and E‐mini S&P 500 futures markets with the aim of shedding new light on the relation between market returns and implied volatility. We use the E‐mini S&P 500 futures (often referred to as SPX futures) as a proxy for stock market returns and VIX futures as a proxy for expectations of implied volatility. We consistently find that stock returns cause changes in expectations of implied volatility. To estimate the coefficients of interest, we use an identification through heteroskedasticity approach which takes advantage of predictable intraday shifts in volatility in the two futures markets. [ABSTRACT FROM AUTHOR]

Based on daily data of China's hog futures and spot prices from January 2021 to October 2023, this paper uses VAR-GARCH-BEKK to test the effectiveness of China's hog futures market. The results of the study indicate that there is a two-way positive leading relationship between the hog futures market and the spot market. There is a significant two-way volatility spillover effect between hog futures market and hog spot market, but the hedging performance of hog futures is poor. Therefore, the effectiveness of China's hog futures market needs to be further improved. [ABSTRACT FROM AUTHOR]

AbstractThis study applies the perfect regression method to empirically examine hedging in the WTI oil futures market. This method eliminates the specification error which allows for the estimation of the true impact of each variable considered on the quantity of oil hedged in this market. The comparative statics results under this method were found to vary in both their magnitude and direction from that of previous empirical hedging models. Moreover, the perfect regression method outperformed every other model considered in terms of its consistency, unbiasedness, and out-of-sample forecasting. This study also presents two novel methods to undertake forecasting with the perfect regression method. It is also the first study to extensively examine the perfect regression method. [ABSTRACT FROM AUTHOR]

US dairy markets have become increasingly volatile, which presents a challenge for dairy farmers and industry participants to manage risk, disturbing the stability of the industry. However, the study of dairy volatility is limited. This article investigates the return comovement and price volatility of four major US dairy commodities: butter, cheese, Class III milk, and dry whey. We also investigate the COVID-19 pandemic's impact on dairy volatility. Our results show that dairy commodities returns and volatilities are positively correlated, but only cheese and Class III milk correlate strongly. The volatilities of butter, cheese, and Class III milk are impacted comparably by return shocks and past volatility. However, dry whey volatility is predominantly driven by past volatility. Using multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) models, we demonstrate that return comovements are time-varying and volatilities are interdependent among dairy commodities. Volatility spillover effects are observed among dairy commodities, especially between dry whey and cheese. The COVID-19 pandemic amplifies dairy volatilities and spillover effects, with a more substantial impact on cheese and milk. Interestingly, dry whey experiences the least impact and performs better during the pandemic. Our results aid market participants in risk management and inform policymakers' decision-making processes. [ABSTRACT FROM AUTHOR]

This study examines the impact of futures trading on market efficiency and price discovery in the U.S. real estate investment trusts (REITs) market. First, we present inconclusive evidence regarding efficiency improvement in the U.S. REIT spot market following the introduction of futures trading. To investigate the interplay between spot and futures markets, we analyze their respective roles in price discovery and find that, unlike in stock and bond markets, the spot market predominantly exhibits price leadership in the U.S. REITs market, despite the growing market size of futures. We find evidence that the limited role of futures in price discovery is associated with an increase in speculative demand, which outweighs hedging pressure. These findings suggest that policymakers should carefully monitor investor trading motives in the U.S. REITs market and consider revising market regulations to enhance liquidity, ensuring that increased liquidity does not primarily result from heightened speculative demand. [ABSTRACT FROM AUTHOR]

This paper adopts the visibility graph (VG) methodology to analyze the dynamic behavior of West Texas Intermediate (WTI), Brent and Shanghai (SC) crude oil futures during the COVID-19 pandemic and Russia–Ukraine conflict. Utilizing daily and high-frequency data, our study reveals a clear power-law decay in VG degree distributions and highlights pronounced clustering tendencies within crude oil futures VGs. We also uncover an inverse correlation between clustering coefficients and node degrees, further identifying that all VGs adhere not only to the small-world property but also exhibit intricate assortative mixing. Through the time-varying characteristics of VGs, we observe that WTI and Brent demonstrate aligned behaviors, while the SC market, with its unique trading mechanisms, deviates. Notably, the five-minute assortativity coefficient provides deep insights into the markets reactions to these global challenges, underscoring the distinct sensitivity of each market. [ABSTRACT FROM AUTHOR]

This document presents a portfolio optimization framework that employs a hybrid quantum computing algorithm and a futures market sentiment indicator—The Market Sentiment Meter (MSM) variable, developed jointly by CME Group and 1QBit. The methodology used was the Variational Quantum Eigensolver (VQE). The work presented here is divided into four portfolio optimization problem formulations, of binary and continuous variable formulations, determining which assets to pick their weights. This work demonstrates that adding the MSM variable can improve the performance of hybrid quantum solutions, by informing the asset selection problem with market environment information through the four MSM states. [ABSTRACT FROM AUTHOR]

We study the impact of two changes in the minimum tick size, a reduction and a subsequent increase, on the trading behavior of fast and slow traders in the spot foreign exchange market. We find that the most notable impact of the tick size reduction is a substantial increase in the liquidity demand of high-frequency traders (HFTs) and not the decrease in their liquidity provision discussed by prior literature. We show that this change in behavior is linked to the higher frequency of price signals that arises with the smaller tick size and to the ability of fast traders to profit from it, often at the detriment of slower traders. Following the tick size decrease and the increase in liquidity demand by HFTs in the spot market, the role of the spot market in price discovery drops relative to that of the futures market. We discuss these findings in the context of the impact of HFTs on the information content of financial markets. This paper was accepted by Agostino Capponi, finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.02935. [ABSTRACT FROM AUTHOR]

Farmers make production decisions despite future output price uncertainty. As a result, farmers' expectation of future output price is an important determinant of investment and the supply of commodities. However, our understanding of the process by which farmers form their expectations is still limited. This study uses direct measures of farmers' financial condition expectations collected through the Purdue University–CME Group Ag Economy Barometer to measure the effect of surprise information on farmers' short‐ and long‐term expectations. The effect is identified using an event study framework previously used to examine the impact of market information on commodity futures markets. Using ordered logistic regressions and variation between professional and United States Department of Agriculture forecasts of corn ending stocks, we demonstrate that farmers' short‐term expectations of the financial condition of the broader agricultural economy is altered by surprise information. This study provides a novel step toward understanding the process by which farmers incorporate new information in their price expectations. For example, our findings suggest that farmers perceive short‐term corn market information surprises will affect the U.S. agricultural sector to a greater degree than their farm. Additionally, farmers do not perceive that short‐term corn market information surprises will carry long‐term implications. [ABSTRACT FROM AUTHOR]

Electronic commodity trading witnesses a massive volume of order messages every trading day, but little is known about their informativeness. We examine limit order dynamics and their role in price discovery in the Chicago Mercantile Exchange (CME) corn, soybean, and wheat futures markets from January 2019 to June 2020, using order‐level data. Between 75% and 79% of the large number of limit orders submitted are then deleted, which contrasts with the much smaller proportion getting executed or revised. Aggressive trades and limit orders substantially contribute to price discovery, whereas nonaggressive trades and limit orders, representing most market events, play a minor role. Following public information releases, there is a shift in trading strategies, with trades contributing more to price discovery and aggressive limit orders contributing less, compared to nonrelease days. Our findings suggest that most limit orders in agricultural futures markets continue to play the traditional role of uninformed liquidity provision. [ABSTRACT FROM AUTHOR]

This paper presents a novel 5-factor model for agricultural commodity risk premiums, an approach not explored in previous research. The model is applied to the specific cases of corn, soybeans, and wheat. Calibration is achieved using a Kalman filter and maximum likelihood, with data from futures markets and analysts' forecasts. Risk premiums are computed by comparing expected and futures prices. The model considers that risk premiums are not solely determined by contract maturity but also by the marketing crop years. These crop years, in turn, are influenced by the respective harvest periods, a crucial factor in the agricultural commodity market. Results show that risk premiums vary across commodities, with some exhibiting positive and others negative values. While maturity affects risk premiums' size, sign, and shape, the crop year plays a critical role, especially in the case of wheat. As speculators in the financial markets demand a positive risk premium, its sign provides insights into whether they are buyers or sellers of futures for each crop year, maturity, and commodity. This research offers valuable insights into grain price behavior, highlighting their similarities and differences. These findings have significant practical implications for market participants seeking to refine their trading and risk management strategies and for future research on the industry structure for each crop. Moreover, this enhanced understanding of risk premiums can be directly applied in the finance and agricultural industries, improving decision-making processes. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to examine the long-run relationship between stock market development and Nigerian economic growth from 1980 to 2020. Design/methodology/approach: Market capitalization, number of listed companies, total value traded ratio and turnover ratio are used. An autoregressive distributed lag model is used for the analysis. Findings: The market capitalization ratio and turnover ratio have positively significant links with economic growth. The number of listed companies has a negative and non-significant impact on economic growth. Total value traded ratio has a negatively significant link with economic growth in the short run. The positive but insignificant relationship between traded value ratio and turnover ratio in the long run growth means that the Nigerian stock market is growth inducing and on the right track as stock market liquidity drives growth. Research limitations/implications: The government and Security Exchange Commission should increase the market liquidity level by improving the trading infrastructure. The government and regulatory authorities should improve and effectively implement the existing policies that would ensure stock market growth. This facilitates the investors' speed to purchase and sell shares. The Securities and Exchange Commission should reduce transaction costs to encourage active trading activities. The market should be diversified with investment instruments such as derivatives, futures and swap options which would limit the adverse effect of listed companies in the market. To increase the stock market liquidity, the Security and Exchange Commission should apply moral suasion to bring private companies that have met certain financial thresholds to convert to public companies. Government should improve on the legislation to encourage more private companies to list on the stock exchange. Originality/value: The study findings add value in that stock market development has a positive impact on economic growth in Nigeria. [ABSTRACT FROM AUTHOR]

Motivated by the ongoing debate on whether optimal or naive diversification should be preferred when distributing wealth across investment alternatives, this article investigates how the choice of covariance estimator affects mean‐variance portfolio selection. In an environment tailored to ideal tradability, we construct optimal commodity futures portfolios based on 12 promising covariance matrix estimators and compare their out‐of‐sample investment performance to a simple, equally weighted investment strategy by means of bootstrap testing. We find that neither the naive allocation approach nor the advanced covariance estimators can outperform the traditional sample covariance matrix. Because this empirical result is robust to modifications of the research design (including alternative investigation periods, data frequencies, estimation window sizes, holding period lengths, weight constraint specifications, and transaction cost levels), it opposes the recurrent suggestion of the equity‐oriented literature that the sample covariance matrix should not be used for the purpose of portfolio optimization. [ABSTRACT FROM AUTHOR]

Low volatility has become a mainstream investment style over the past two decades, recognized for delivering high risk-adjusted returns. Many investors fail to fully capitalize on this strategy, however, due to benchmark constraints. Low-volatility stocks tend to lag during prolonged bull markets, a challenge that can be addressed using leverage. This article outlines five use cases to leverage upon the low-volatility effect, including an enhanced strategy, an alternative to the 60/40 asset allocation, and the use of long and short extensions with stocks and market futures. These approaches help investors aiming to meet objectives ranging from stable performance, consistent outperformance, market-neutral returns, or as an alternative for put options, unlocking the full potential of this underutilized factor. [ABSTRACT FROM AUTHOR]

This article shows that the return performance of long-short inter-commodity trades is higher than that of intra-commodity trades in a statistical arbitrage pairs trading strategy. Two futures contracts form an intra-commodity pair if they hold the same underlying commodity but have different expiration dates; they form an inter-commodity pair if the two underlying contracts are for different commodities. The outperformance result holds true in most major futures markets, and also when we consider cross-sector aggregations of these markets. They find the number of mispricing opportunities is the highest in the Energy futures market and mispricing is lowest in the Grains & Softs futures market. Overall, and using the parlance in the oil and gas industry, our results suggest statistical arbitrage mispricing opportunities are more likely found in "crack-spread" trades than in "calendar-spread" trades. [ABSTRACT FROM AUTHOR]

Purpose: This study investigates the impacts of the Russia–Ukraine conflict on the cross-correlation between agricultural commodity prices and crude oil prices. Design/methodology/approach: 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. Findings: 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. Research limitations/implications: The study was limited by the number of observations after the Russia–Ukraine conflict. Originality/value: 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. [ABSTRACT FROM AUTHOR]

The goal of this paper is to study the behavior of non-ferrous metal futures' volatilities in Chinese future market by applying a multifractal perspective. In particular, in order to obtain key indicators that describe the characterization of non-ferrous metal futures' volatility behavior, we uses noise-removed EMD-MF-DFA and original MF-DFA methods to conduct a comparative analysis on the return time series of four non-ferrous metal futures, which are Aluminum future, Copper future, Zinc future and Lead future traded on the Shanghai Futures Exchange. This numerical study shows that the indicator established in characterizing the volatility of four non-ferrous metal futures is robust. In addition, we have the following four conclusions: First, there are obvious multifractal phenomena of non-ferrous metal futures in Chinese market, and it shows that Aluminum future has the largest degree of multifractality, and Copper future has the smallest degree of multifractality, which indicates that Aluminum future has the highest volatility complexity, and Copper future has the smallest volatility complexity. Second, it is found that the volatility complexity of these four non-ferrous metal futures is caused by long-range correlation. Third, this study also supports the current judgment that "Copper future has the greatest investment opportunity". Finally, combined with analysis results, we also give suggestions to investors, producers, and regulators body for non-ferrous metal futures market in China. [ABSTRACT FROM AUTHOR]

This study aims to improve the prediction ability of realized volatility in the Chinese crude oil futures market by characterizing the volatility of volatility (VOV) and its jump components, as well as the Markov regime-switching feature. We extend the HAR-DJI-GARCH model to include the continuous and jump volatility of volatility while incorporating the Markov regime-switching feature through the MS-GARCH framework, thus offering a novel approach for capturing the intricate, nonlinear behaviour of crude oil futures volatility. Model parameters are estimated by improving the maximum likelihood approach, and the performance of the proposed model is compared to that of other models via out-of-sample R2, the CW test, the MCS test and various robustness checks. The empirical findings suggest that the incorporation of VOV, particularly jump information, alongside Markov regime switching significantly enhances the predictive power for the volatility of the Chinese crude oil futures market. [ABSTRACT FROM AUTHOR]

This paper investigates the asymmetric cointegration between possible domestic determinants of crude oil futures prices during the COVID-19 pandemic period. We perform comparative analysis of West Texas Intermediate (WTI) and newly-launched Shanghai crude oil futures (SC) via the Quantile Autoregressive Distributed Lag (QARDL) model. The empirical results show the long- and short-run impacts of stock markets, interest rate, coronavirus panic and corn futures on WTI futures prices, while economic policy uncertainty is a driver for the long-run WTI price dynamics. However, the influence of stock market, interest rate and COVID-19 panic on SC is significant in the short term. There also exists short- and long-run positive responses of China's crude oil futures to corn prices. Overall, the impacts of domestic price drivers are heterogeneous across market circumstances (bullish, bearish and normal) and countries. These empirical evidences provide practical implications for investors and policymakers. [ABSTRACT FROM AUTHOR]

The efficient use of text data is very important in investor sentiment research and other fields. Through the sentiment classification of text data containing investor sentiment, we can effectively and accurately identify the sentiment contained in the text. This paper takes the futures market forecast text published by 21 futures companies as the data source and constructs a sentiment classification model of the market forecast text based on BERT (Bidirectional Encoder Representations from Transformers) according to the characteristics of the market forecast text. The sentiment classification of the market forecast text is carried out by using the sentiment classification model of the market forecast text based on BERT and a classification model based on the classical classification algorithm. The classification effects of different models are compared. The results show that the optimized BERT model has the best classification effect. This enriches the research methods of investor sentiment measurement in the financial field and improves the accuracy of this kind of sentiment measurement result. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this article is to verify whether the Swiss franc (CHF), the US dollar (USD), and the Japanese yen (JPY) continue to function as safe haven currencies in the financial markets of the 21st century. Design/methodology/approach: Analysis of correlations of logarithmic returns of major currency indices with the S&P 500 index during periods of financial instability and additional verification of net positions of selected market participants. Findings: Based on the analysis of the return correlations of CHF, JPY, and USD with the S&P 500 during periods of heightened uncertainty in the 21st century, these currencies still serve as safe havens. However, the net positions of large speculators in the futures markets do not confirm that this is being utilized. Practical implications: The conclusions may help both businesses and individuals stabilize portfolio volatility during periods of heightened uncertainty in financial markets. Social implications: Conclusions may help mitigate social inequalities arising during financial crises by appropriate currency diversification of held assets. Originality/value: The research comprehensively addresses the current situation during periods of heightened volatility in the 21st century. Additionally, the analysis of return correlations is supplemented by verification of net positions in the futures market using commitment of traders reports. This work is directed towards businesses, households investing surplus finances, and financial institutions. [ABSTRACT FROM AUTHOR]

This study examines the impact of the global pandemic on the returns and volatility of China's commodity futures market from December 2019 to April 2021. Our analysis reveals that the regimes of futures returns in the general commodity, industrial, and metal markets are positively correlated with the regimes of pandemic cases, while the regimes of pandemic cases are negatively correlated with the returns of energy and precious metal futures. In contrast, futures volatilities exhibit inverse relationships with pandemic cases. With the exception of precious metals, which are widely considered safe-haven assets, the risk level of the commodity futures market, as measured by return volatility, is heightened by the level of pandemic cases. Bivariate SVAR results suggest that the pandemic has a greater but short-run impact on futures returns, while its effects on futures volatilities are relatively lesser but long-lasting. [ABSTRACT FROM AUTHOR]

Purpose: This study investigates the mispricing dynamics of NIFTY 50 Index futures, drawing upon daily data spanning from January 2008 to July 2023. Design/methodology/approach: The study employs both a single regime analysis and a tri-regime model to understand the fluctuations in NIFTY 50 Index futures mispricing. Findings: The study reveals a complex interplay between various market factors and mispricing, including forward-looking volatility (measured by the NIFVIX index), changes in open interest, underlying index return, futures volume, index volume and time to maturity. Additionally, the relationships are regime-dependent, specifically identifying the regime-dependent nature of the relationship between forward-looking volatility and mispricing, the impact of futures volume on mispricing, the effect of open interest on mispricing, the varying influence of index volume and the influence of time to maturity across the three distinct regimes. Practical implications: These findings offer valuable insights for policymakers and investors by providing a detailed understanding of futures market efficiency and potential arbitrage opportunities. The study emphasizes the importance of understanding market dynamics, transaction costs and timing, offering guidance to enhance market efficiency and capitalize on trading opportunities in the evolving Indian derivatives market. Originality/value: The Vector Autoregression (VAR) and Threshold Vector Autoregression Regression (TVAR) models are deployed to disentangle the interrelationships between NIFTY 50 Index futures mispricing and related endogenous determinants. Research highlights: This study investigates the Nifty 50 Index futures mispricing across three distinct market regimes. We highlight how factors like volatility, futures volume, and open interest vary in their impact. The study employs vector auto-regressive and threshold vector auto-regressive models to explore the complex relationships influencing mispricing. We provide valuable insights for investors and policymakers on improving market efficiency and identifying potential arbitrage opportunities. [ABSTRACT FROM AUTHOR]

This study explores whether frequent trading is profitable to investors in an emerging stock index futures market. Our analyses, based on long‐term data from 2010 to 2023, indicate that the effect of trading frequency differs across investor types and market conditions. Only some domestic institutions gain additional profits from more frequent trading, and such a tendency is apparent when the futures price falls and when the futures market volatility is low. Foreign investors experience losses as they trade more when the market is bearish and are frequently net long. The performance of domestic individuals does not depend on their trading frequency in general; however, they lose more from trading when the market is bearish and when the market is less volatile. [ABSTRACT FROM AUTHOR]

With economic globalization and the financialization of agricultural products continuing to advance, the interconnections between different agricultural futures have become closer. We utilize a TVP‐VAR‐DY model combined with the quantile method to measure the risk spillover between 11 agricultural futures in the United States and China from July 9, 2014, to December 31, 2022. We obtain several findings. First, CBOT corn, soybean, and wheat are identified as the primary risk transmitters, with DCE corn and soybean as the main risk receivers. Second, sudden events or increased economic uncertainty can enlarge the overall risk spillovers. Third, there is an aggregation of risk spillovers amongst agricultural futures based on the dynamic directional spillovers. Lastly, the central agricultural futures under the conditional mean are CBOT corn and soybean, while CZCE hard wheat and long‐grained rice are the two risk‐spillover centers in extreme cases, as per the results of the spillover network and minimum spanning tree. [ABSTRACT FROM AUTHOR]

This study explores the financialization of agricultural commodities, focusing on how financial derivatives and index funds impact the volatility of agro-food markets. Using a Dynamic Conditional Correlation (DCC) GARCH model, we analyze volatility spillovers among key agricultural commodities, particularly maize, and related financial assets over a sample period from 2007 to 2020. Our analysis includes major financial assets like Exchange-Traded Funds (ETFs), the S&P 500 index, and agribusiness corporations such as ADM and Bunge and the largest corn flour producer, GRUMA. The results indicate that financial speculation, especially via passive investments such as ETFs, has intensified price volatility in commodity futures, leading to a systemic risk increase within the sector. This study provides empirical evidence of increased market integration between the agro-food sector and financial markets, underscoring risks to food security and economic stability. We conclude with recommendations for regulatory actions to mitigate systemic risks posed by the growing financial influence in agricultural markets. [ABSTRACT FROM AUTHOR]

This paper investigates the high‐frequency volatility modeling and prediction for crude oil futures in China, a new asset class emerging in recent years. Two volatility measures, the realized variance (RV) and realized bi‐power variations (RBV) are constructed at various frequencies by virtue of 1‐minute crude oil futures prices. The distinctive components of these volatility estimators are further identified to exploit the information contents in the in‐sample explanatory power of the realized variance dynamics and the out‐of‐sample prediction of realized variance across different horizons, leading to four new HAR‐RV‐type models. First, the empirical results show that the continuous component of the weekly realized variance, representing investors' trading behavior in the medium‐term, is the dominant factor driving up volatility trends in China's crude oil futures market over a range of market conditions. Second, the monthly jump component in realized variance presents the significant in‐sample explanatory power, and yet marginally improves prediction performance in realized variance during the two out‐of‐sample periods. Finally, these results are robust toward various market/model setups, over day‐ and night‐trading hours, and across a range of prediction horizons and relative to prediction benchmarks. [ABSTRACT FROM AUTHOR]

In conducting an extensive examination, we scrutinize the efficacy of algorithmic trading strategies applied to Futures CopperMainContinuous in the Shanghai Futures Exchange, utilizing a comprehensive data set spanning from January 2020 to December 2022. To mitigate the potential risk of data‐snooping bias—the probability that any favorable results may inadvertently arise from random events rather than the inherent value of the strategies employed to generate these results—our study prudently conducts a reality check and advanced assessments. Throughout the evaluated period, the benchmark demarcation between the in‐sample and out‐of‐sample stages is established in February 2022. Regrettably, our meticulous exploration fails to identify any successful or advantageous algorithmic trading strategies within these categories, particularly following the systematic elimination of data snooping bias. These results underscore the intrinsic challenges in accurately identifying and implementing profit‐generating algorithmic trading strategies within the volatile and intricate futures market. [ABSTRACT FROM AUTHOR]

Abstract In conducting an extensive examination, we scrutinize the efficacy of algorithmic trading strategies applied to Futures CopperMainContinuous in the Shanghai Futures Exchange, utilizing a comprehensive data set spanning from January 2020 to December 2022. To mitigate the potential risk of data‐snooping bias—the probability that any favorable results may inadvertently arise from random events rather than the inherent value of the strategies employed to generate these results—our study prudently conducts a reality check and advanced assessments. Throughout the evaluated period, the benchmark demarcation between the in‐sample and out‐of‐sample stages is established in February 2022. Regrettably, our meticulous exploration fails to identify any successful or advantageous algorithmic trading strategies within these categories, particularly following the systematic elimination of data snooping bias. These results underscore the intrinsic challenges in accurately identifying and implementing profit‐generating algorithmic trading strategies within the volatile and intricate futures market.

With the escalation of global trade, the Chinese commodity futures market has ascended to a pivotal role within the international shipping landscape. The Shanghai Containerized Freight Index (SCFI), a leading indicator of the shipping industry’s health, is particularly sensitive to the vicissitudes of the Chinese commodity futures sector. Nevertheless, a significant research gap exists regarding the application of Chinese commodity futures prices as predictive tools for the SCFI. To address this gap, the present study employs a comprehensive dataset spanning daily observations from March 24, 2017, to May 27, 2022, encompassing a total of 29,308 data points. We have crafted an innovative deep learning model that synergistically combines Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures. The outcomes show that the CNN-LSTM model does a great job of finding the nonlinear dynamics in the SCFI dataset and accurately capturing its long-term temporal dependencies. The model can handle changes in random sample selection, data frequency, and structural shifts within the dataset. It achieved an impressive R² of 96.6% and did better than the LSTM and CNN models that were used alone. This research underscores the predictive prowess of the Chinese futures market in influencing the Shipping Cost Index, deepening our understanding of the intricate relationship between the shipping industry and the financial sphere. Furthermore, it broadens the scope of machine learning applications in maritime transportation management, paving the way for SCFI forecasting research. The study’s findings offer potent decision-support tools and risk management solutions for logistics enterprises, shipping corporations, and governmental entities.

ABSTRACT To the national economy, increasing the forecasting accuracy of realised volatility (RV) on crude oil futures markets is of critical strategic importance. However, the RV of crude oil futures cannot be accurately predicted with a single model. For this study, we adopt a hybrid model which combines gated recurrent unit (GRU) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to forecast the RV of crude oil futures. Moreover, back propagation neural networks (BP), Elman neural networks (Elman), support vector regression machine (SVR), autoregressive model (AR), heterogeneous autoregressive model (HAR), and their hybrid models with CEEMDAN are adopted as comparisons. In general, this article demonstrates the superiority of the CEEMDAN‐GRU model in RV forecasting from several aspects: for both evaluation criteria, CEEMDAN‐GRU achieves the highest RV forecasting accuracy in emerging and developed crude oil futures markets; furthermore, the empirical results are robust to alternative realised measures and training sets of different lengths. [ABSTRACT FROM AUTHOR]

Algorithmic trading is becoming the dominant approach in many electricity spot and futures markets. This paper focuses on the emerging interest in the less documented real-time imbalance markets, by developing reinforcement learning agents to find profit-making opportunities algorithmically. We develop a repeatable experimental setting to compare different market participants and explore the applications of Q-learning with neural networks for three types of market participants: a non-physical trader, a gas generator, and a battery electricity storage system. We backtest all three agents using British data across summer and winter months to compare their profits, risks and various experimental design considerations. [ABSTRACT FROM AUTHOR]

The global development and enhancement of electricity financial markets aim to mitigate price risk in the electricity spot market. Power producers utilize financial derivatives for both hedging and speculation, necessitating careful selection of portfolio strategies. Current research on investment strategies for power financial derivatives primarily emphasizes risk management, resulting in a lack of a comprehensive investment framework. This study analyzes six short-term electricity futures contracts: base day, base week, base weekend, peak day, peak week, and peak weekend. A multi-agent deep reinforcement learning algorithm, Dual-Q MADDPG, is employed to learn from interactions with both the spot and futures market environments, considering the hedging and speculative behaviors of power producers. Upon completion of model training, the algorithm enables power producers to derive optimal portfolio strategies. Numerical experiments conducted in the Nordic electricity spot and futures markets indicate that the proposed Dual-Q MADDPG algorithm effectively reduces price risk in the spot market while generating substantial speculative returns. This study contributes to lowering barriers for power generators in the power finance market, thereby facilitating the widespread adoption of financial instruments, which enhances market liquidity and stability. [ABSTRACT FROM AUTHOR]

Many financial markets use designated market makers (DMMs), but the impacts of DMMs on agricultural futures markets – and in particular, how to arrange DMMs among contracts expiring in different months – are largely neglected. In 2017, Chinese exchanges started recruiting DMMs for inactive contracts when they become nearby contracts to address the discontinuous trading activity of nearest‐to‐maturity contracts, which enables us to study the benefit and cost of recruiting DMMs for inactive contracts using a quasi‐experimental framework. Leveraging tick‐by‐tick data on corn and soybean meal futures, we find that DMMs improve the market quality of inactive contracts without disrupting the market quality of dominant contracts. Heterogeneity analysis in policy settings suggests that more DMMs are conducive to improving market quality for corn and soybean meal futures. We demonstrate that DMM policy is a feasible measure to facilitate continuous activeness in Chinese agricultural futures markets. Our results are important for exchanges and regulators seeking to better design and implement designated market‐making programs in agricultural futures markets. [ABSTRACT FROM AUTHOR]

We empirically examine whether feedback traders are active in the Bitcoin and the extent to which their presence is affected by a series of noise-related factors (sentiment; volume; liquidity) at three different frequencies (hourly; daily; weekly) for the April 2013–July 2019 period based on Bitstamp data. Our findings suggest that positive feedback trading grows stronger for higher (hourly; daily) frequencies, with its presence manifesting itself mainly during periods of high/improving sentiment and high/rising volume/liquidity. Additional tests reveal that the significance of hourly feedback trading is identified during hours corresponding to the trading hours of major European/North American markets. Overall, our results confirm extant literature evidence on the prevalence of noise trading in cryptocurrencies, while further showcasing that the factors motivating feedback trading in other asset classes (equities; ETFs; futures) exhibit similar effects over the presence of feedback traders in the cryptocurrency market. [ABSTRACT FROM AUTHOR]

Soybeans, a vital source of protein for animal feed and an essential industrial raw material, are the most traded agricultural commodity worldwide. Accurate price forecasting is crucial for maintaining a resilient global food supply chain and has significant implications for agricultural economics and policymaking. This review examines over 100 soybean price forecast models published in the last decade, evaluating them based on the specific markets they target—futures or spot—while highlighting how differences between these markets influence critical model design decisions. The models are also classified into AI-powered and traditional categories, with an initial aim to conduct a statistical analysis comparing the performance of these two groups. This process unveiled a fundamental gap in best practices, particularly regarding the use of common benchmarks and standardised performance metrics, which limits the ability to make meaningful cross-study comparisons. Finally, this study underscores another important research gap: the lack of models forecasting soybean futures prices in Brazil, the world's largest producer and exporter. These insights provide valuable guidance for researchers, market participants, and policymakers in agricultural economics. [ABSTRACT FROM AUTHOR]

Enhancing marine carbon sequestration through nearshore aquaculture is a novel scientific approach to addressing global climate change and facilitating low-carbon development. Scientifically estimating the quantity and price of China's marine fisheries carbon sinks provides a crucial foundation for promoting marine carbon trading. In this article, firstly, the long-term carbon storage capacity of China's marine carbon sequestration fishery available from 1979 to 2022 for carbon trading is calculated. And then a transcendental logarithmic production function model incorporating ridge regression analysis, and an accounting equation for estimating the shadow price of China's marine fisheries carbon sequestration are established. Simultaneously, the distortion level of China's marine fisheries carbon sequestration prices from 2015 to 2022 is measured, and the reasons and economic effects of the distortion in prices are analyzed. The research results show that: 1) The capacity of a net carbon sequestration in China's marine carbon sequestration fishery for carbon trading, ranged from 78,869.01 tons in 1979 to 1,232,762.27 tons in 2022, with an average annual capacity of 592,472.07 tons and an average annual growth rate of 7.48%; 2) The price of China's marine fisheries carbon sinks increased from 39.46 CNY in 1979 to 375.96 CNY in 2022, with an average annual growth rate of 6.00%. The average annual price was 167.87 CNY; 3) There were varying degrees of distortion in China's marine fisheries carbon sequestration prices from 2015 to 2022, which decreased annually with the construction of China's own carbon trading market and the practice of trading. To realize the value of marine fisheries carbon sequestration, it is necessary to actively promote the development of voluntary emission reduction markets, develop carbon trading futures markets, and strengthen the dynamic monitoring system for resources. [ABSTRACT FROM AUTHOR]

This study investigates the cross‐border risk spillovers between the US soybean futures market and Chinese soybean‐related futures markets. We first confirm the existence of strong tail dependence between US soybean futures and four Chinese soybean‐related futures by conducting a novel quantile‐Granger causality test. Second, tests under MVMQ‐CAViaR further provide evidence of risk spillovers from CBOT soybean futures to the DCE No.1 soybean, No.2 soybean, soybean meal, and soybean oil futures in value‐at‐risk at different quantiles. Lastly, results from the quantile impulse‐response function reveal the time‐varying and asymmetric property of risk spillover effects. In addition, we compare the results from two subsample periods and identify different risk spillover effects across markets at different quantiles that may contribute to the investors' decision‐making under extreme market conditions. [ABSTRACT FROM AUTHOR]

High‐frequency trading (HFT) and algorithmic trading (AT) have attracted considerable attention from the academic and regulatory communities, often highlighted for their contributions to enhancing market liquidity. However, the distinctive market framework in China may alter the operational dynamics of intraday trading, indicating that traditional HFT/AT paradigms might not fully apply. This study investigates the evolution of market quality in China from an HFT/AT perspective, using publicly available high‐frequency data for six futures products traded on the Shanghai Futures Exchange and the Dalian Commodity Exchange. Our findings reveals improvements in contract continuity and liquidity diversification from a daily perspective. Furthermore, the intraday analysis—especially following the increased availability of more granular data to market participants—suggests the emergence of more sophisticated algorithmic traders who enhance liquidity provision and contribute to reduced slippage costs for investors and hedgers. [ABSTRACT FROM AUTHOR]

This paper aims to study the volatility forecasting of Chinese crude oil futures from the large‐scale variables perspective by considering both the information on international futures markets volatility and technical indicators of Chinese crude oil futures. We employ the dual feature processing method (LASSO‐PCA) by integrating least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA) to extract important factors of the large‐scale exogenous variables. Besides the traditional ordinary least squares (OLS) estimation, the nonlinear support vector regression (SVR) approach is employed to integrate with the LASSO‐PCA method. The empirical results show that both the OLS and SVR combined with LASSO‐PCA can improve the forecasting accuracy, especially SVR‐LASSO‐PCA owns the best forecasting performance. The analysis of the selected variables finds international futures volatility is chosen more frequently. These results are further validated through a series of robust experiments. Finally, the time difference between China and the United States is also considered in order to obtain more reasonable out‐of‐sample forecasting. [ABSTRACT FROM AUTHOR]

This study is the first to conduct a comprehensive analysis of the price discovery and market liquidity aspects of China's crude oil futures market compared to WTI and Brent. With intraday-day data consolidated into 1-second intervals and three measures of price discovery, we find that China's crude oil futures market reports encouraging signs in terms of price discovery and efficiency, also showing great resilience during the COVID-19 pandemic. The market has obtained a dominant role in price discovery relative to WTI and Brent during its day trading hours, and has almost caught up with Brent in terms of market liquidity. [ABSTRACT FROM AUTHOR]

As an essential material foundation, the development of the mineral resources capitalization market is crucial for promoting high-quality growth in the mining industry. By analyzing the current state of China's mineral resource capitalization market, including its futures market, risk exploration capital market, and credit system construction, this paper proposes strategies for establishing a multi-level mineral capital market system. Drawing from international experience and aligning with the realities of China's mining industry development, the paper provides a systematic analysis. China's mining capital market is still in its early stages, with a lagging mining sector and challenges in financing exploration enterprises. Establishing a futures market, a risk exploration capital market, and a sound mining credit system can effectively promote the development of the mining capital market and strengthen the security of industrial and supply chains. By improving the multi-tiered mining capital market and market regulation system, China can enhance its mining industry's international competitiveness and provide a solid foundation for the nation' s sustainable economic development. This research offers reflections and references for establishing and improving China' s mineral resource capitalization market. [ABSTRACT FROM AUTHOR]

Given thermal coal's significance as a tactical energy source, price projections for the commodity are crucial for investors and decision-makers alike. The goal of the current work is to determine whether Gaussian process regressions are useful for this forecast problem using a dataset of closing prices of thermal coal traded on the China Zhengzhou Commodity Exchange from January 4, 2016, to December 31, 2020. This is a significant financial index that has not received enough attention in the literature in terms of price forecasting. Our forecasting exercises make use of Bayesian optimizations and cross-validation. The price from January 02, 2020, to December 31, 2020 is successfully predicted by the generated models, with the out-of-sample relative root mean square error of 0.4210%. Gaussian process regressions are shown to be useful for the thermal coal price forecast problem. The outcomes of this projection might be used as independent technical forecasts or in conjunction with other forecasts for policy research that entails developing viewpoints on price patterns. [ABSTRACT FROM AUTHOR]

This study investigates the impact of climate policy uncertainty (CPU) on energy and metal commodity futures markets by employing quantile regression, which accounts for various (bearish, normal, and bullish) markets. Our results reveal that the impact of CPU shocks is heterogeneous and market condition‐specific. Particularly, CPU exerts a significantly negative effect on all commodities, except natural gas, in a bearish market. Under a normal market, the impact of CPU on energy returns varies across commodities whereas for a bullish market, the CPU effect is mixed. The results also reveal natural gas to be a good hedge instrument for climate policy risk. We further conducted channel analysis using the theory of storage and hedging pressure hypothesis. The key finding reveals inventory level as the transmission channel of climate policy risk. Our findings have implications for the inventory management strategies of producers and suggest that regulators should consider market‐based policies in their decarbonization efforts. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative genetically optimized dynamic composite strategy for achieving profitability in futures markets. Utilizing daily data from 35 actively traded futures contracts (1984–2022), we highlight the potential advantages of integrating the momentum effect into dynamic moving average strategies. This enhancement can boost the strategy's capability to capture and capitalize on market trends, ensuring consistent and stable returns. The developed dynamically composite technical trading strategy aspires to be a valuable reference for investors and the finance academic community, contributing to advancements in the field. [ABSTRACT FROM AUTHOR]