Future Price Volatility Research Articles

Can the sentiment of the official media predict the return volatility of the Chinese crude oil futures?

This study investigates whether the sentiment of Chinese official media towards crude oil influences price volatility of the Chinese crude oil futures (SC). By leveraging textual analysis through Bidirectional Encoder Representations from Transformers (BERT), we quantify the sentiment of oil-related articles published by the primary official media in China. Our main finding, building on both in-sample and out-of-sample analyses, robustly reveals that this sentiment significantly forecasts the one-day-ahead intraday return volatility of SC. Moreover, we extend the analysis to different time horizons (i.e., one-week and one-month-ahead) and find the prominent forecasting power of the official media sentiment as well. We also find that the official media sentiment fails to forecast the price volatility of WTI oil futures, implying that the official media sentiment contains some unique Chinese information. Overall, our study contributes to the existing literature on predicting the return volatility of the Chinese crude oil futures, and offers fresh insights into an essential yet underexplored sentiment, i.e., official media sentiment.

The Fluctuation Characteristics and Risk Investigation of China's Gold Future Market

This paper deeply explores the fluctuation characteristics and risks of China's gold futures market. Through the collection of data on macroeconomic factors, stock index interest rates, and energy futures, the paper employs multiple linear regression models and intermediary variable models to quantitatively analyze the impact of these factors on the volatility of gold futures prices. The research finds that there is a significant positive correlation between the gold futures price and the growth of the U.S. GDP and the silver futures price index, while the impact of other macroeconomic indicators such as China's gold reserves and the inflation rate is not significant. Policy support and market regulation play a key role in the healthy development of the market, and the broad participation of market participants and the innovation of financial products have driven the diversification of the market. The intermediary effect analysis shows that the impact of macroeconomic factors on the gold futures price is partially indirect through the intermediary variable of stock index interest rates.

Price transmission relationships in Taiwan seafood supply chain: Tilapia (Oreochromis niloticus) and milkfish (Chanos chanos)

Tilapia and milkfish are aquaculture species associated with the highest production volume in Taiwan. With both fish species as the research focus, this study explored their price transmission relationships along the seafood supply chain. Our study results revealed that tilapia and milkfish prices had a long-run equilibrium relationship at different stages of the seafood supply chain and presented inconsistent price transmissions in the short run. There are domestic and foreign market channels for tilapia, enabling producers and wholesalers to transmit prices to retailers. By contrast, milkfish are mostly supplied to domestic markets, there is not perfect price transmission in the milkfish supply chain besides between farmers and wholesalers. An accurate price forecasting model can effectively indicate future price volatility and predict seafood prices, especially following government policy changes or in the face of external impacts. Such model can also serve as a crucial reference for future research of tilapia and milkfish industry, producers, and aquaculture farmers. Finally, seafood prices are a key determinant of fisheries and supply chain sustainability. Therefore, the stability of seafood prices, fish market wholesale pricing, and retail pricing are issues meriting continuous attention for a sustainable seafood supply chain.

A new probability forecasting model for cotton yarn futures price volatility with explainable AI and big data

A new probability forecasting model for cotton yarn futures price volatility with explainable AI and big data

Investor Structure and Corn Futures Price Volatility in China: Evidence Based on the Agent-Based Model

Investor Structure and Corn Futures Price Volatility in China: Evidence Based on the Agent-Based Model

Analysis of the potential application of a residential composite energy storage system based on a double-layer optimization model

Along with the further integration of demand management and renewable energy technology, making optimal use of energy storage devices and coordinating operation with other devices are key. The present study takes into account the current situation of power storage equipment. Based on one year of measured data, four cases are designed for a composite energy storage system (ESS). In this paper, a two-tiered optimization model is proposed and is used to optimizing the capacity of power storage devices and the yearly production of the system. Furthermore, this paper performs a comparative analysis of the performance of the four cases from the energy, environmental and economic perspectives. It is concluded that this kind of energy storage equipment can enhance the economics and environment of residential energy systems. The thermal energy storage system (TESS) has the shortest payback period (7.84 years), and the CO2 emissions are the lowest. Coupled with future price volatility and the carbon tax, the electrothermal hybrid energy storage system (HESS) has good development potential. However, the current investment cost is very high, and it will not be possible to recover this cost in 10 years. Finally, it is recommended that the cost of equipment be reduced in combination with subsidies and incentives for further promotion. The research results not only fill a gap in the study area, but also provide some suggestions for further development of industry and research on user-side energy storage.

Time–frequency correlation and risk spillovers between Euramerican mature and Asian emerging crude oil futures markets

PurposeThis paper aims to empirically investigate time–frequency linkages between Euramerican mature and Asian emerging crude oil futures markets in terms of correlation and risk spillovers.Design/methodology/approachWith daily data, the authors first undertake the MODWT method to decompose yield series into four different timescales, and then use the R-Vine Copula-CoVaR to analyze correlation and risk spillovers between Euramerican mature and Asian emerging crude oil futures markets.FindingsThe empirical results are as follows: (a) short-term trading is the primary driver of price volatility in crude oil futures markets. (b) The crude oil futures markets exhibit certain regional aggregation characteristics, with the Indian crude oil futures market playing an important role in connecting Euramerican mature and Asian emerging crude oil futures markets. What’s more, Oman crude oil serves as a bridge to link Asian emerging crude oil futures markets. (c) There are significant tail correlations among different futures markets, making them susceptible to “same fall but different rise” scenarios. The volatility behavior of the Indian and Euramerican markets is highly correlated in extreme incidents. (d) Those markets exhibit asymmetric bidirectional risk spillovers. Specifically, the Euramerican mature crude oil futures markets demonstrate significant risk spillovers in the extreme short term, with a relatively larger spillover effect observed on the Indian crude oil futures market. Compared with India and Japan in Asian emerging crude oil futures markets, China's crude oil futures market places more emphasis on changes in market fundamentals and prefers to hold long-term positions rather than short-term technical factors.Originality/valueThe MODWT model is utilized to capture the multiscale coordinated motion characteristics of the data in the time–frequency perspective. What’s more, compared to traditional methods, the R-Vine Copula model exhibits greater flexibility and higher measurement accuracy, enabling it to more accurately capture correlation structures among multiple markets. The proposed methodology can provide evidence for whether crude oil futures markets exhibit integration characteristics and can deepen our understanding of connections among crude oil futures prices.

Forecasting realized volatility of crude oil futures prices based on machine learning

AbstractExtending the popular HAR model with additional information channels to forecast realized volatility of WTI futures prices, we show that machine learning‐generated forecasts provide better forecasting quality and that portfolios that are constructed with these forecasts outperform their competing models resulting in economic gains. Analyzing the selection process, we show that information channels vary across forecasting horizon. Variable selection produces clusters and provides evidence that there are structural changes with regard to the significance of information channels.

Unveiling the critical nexus: Volatility of crude oil future prices and trade partner’s cash holding behavior in the face of the Russia–Ukraine conflict

This study investigates the spillover effects of the Russia–Ukraine conflict on corporate finance, particularly focusing on cash-holding decisions of Chinese-listed firms from Q1 2020 to Q1 2023. Utilizing a Difference-in-Difference approach, we analyze how firms with trade links to Russia and Ukraine adjust their cash holdings in response to the conflict. A significant aspect of our analysis is the emphasis on Shanghai crude oil futures prices, alongside West Texas Intermediate (WTI) prices, to understand the impact of oil market volatility on these decisions. Our findings reveal that such volatility, especially in Shanghai crude oil futures, significantly influences firms’ cash holdings, with an increased tendency to hoard cash in times of rising uncertainties. This behavior is particularly pronounced in non-state-owned, general trade, and export firms. The study highlights how geopolitical tensions and fluctuating oil prices affect corporate financial strategies, offering insights for policymakers and corporate managers in navigating economic uncertainties.

Forecasting EUA futures volatility with geopolitical risk: evidence from GARCH-MIDAS models

This paper examines whether the information contained in geopolitical risk (GPR) can improve the forecasting power of price volatility for carbon futures traded in the EU Emission Trading System. We employ the GARCH-MIDAS model and its extended forms to estimate and forecast the price volatility of carbon futures using the most informative GPR indicators. The models are examined for both statistical and economic significance. According to the results of the Model Confidence Set tests for the full-sample and sub-sample data, we find that the extended model, which accounts for the threat of geopolitical risk, exhibits superior forecasting ability for the full-sample data, while the model that includes drastic changes in geopolitical risk in Phase II and the model that considers serious geopolitical risk in Phase III have the best predictive power. Moreover, all GPR-related variables we use contribute to increasing economic gains. In particular, the threat of geopolitical risk contains valuable information for future EUA futures volatility and can provide the highest economic gains. Therefore, carbon market investors and policymakers should pay great attention to geopolitical risk, especially its threat, in risk and portfolio management.

Modelling price volatility in energy futures during Covid-19

Modelling price volatility in energy futures during Covid-19

Modelling Price Volatility in Energy Futures during Covid-19

Modelling Price Volatility in Energy Futures during Covid-19

What do we know about the value and market impact of the US Department of Agriculture reports?

AbstractThe purpose of this paper is to comprehensively review the literature on the value and impact of the US Department of Agriculture (USDA) reports and discuss theoretical issues and empirical evidence. Only a handful of studies provide direct estimates of the welfare benefits of USDA information using a rational expectations framework in storable commodity markets. Most studies examine partial evidence of welfare benefits associated with market response to USDA information. These studies include examinations of the impacts of USDA report releases on cash and futures price volatility and other market dynamics, impacts of report releases on options implied volatility, impacts of unanticipated information in USDA reports, as well as informational value, returns, and survey‐based studies. We discuss the methodological and empirical contributions of these studies as well as their shortcomings and potential opportunities for future work.

Study on the Influence of Cotton Option Launch on The Price Fluctuation of Underlying Futures

This paper takes cotton options as the research object, and empirically tests the impact of cotton options on the price fluctuation of the underlying futures in the short and medium term by establishing the ARMA-GARCH model with the introduction of two dummy variables. The study found that the launch of cotton options increased the volatility of the underlying futures price in the short term, and increased the volatility in the medium and long term. After the launch of cotton options, the supply of national policies against the epidemic played a certain stabilizing role in the volatility of futures prices, but the policy effect has a time lag. Based on this, institutional investors should be encouraged to use cotton options and futures risk management, build a risk prevention and control mechanism for sudden public crisis events, popularize the knowledge of options and futures, and improve the transparency of cotton trading.

Volatility forecasting of crude oil futures based on Bi-LSTM-Attention model: The dynamic role of the COVID-19 pandemic and the Russian-Ukrainian conflict

The COVID-19 epidemic and the Russian-Ukrainian conflict have created significant uncertainty in the crude oil market, greatly increasing the difficulty of crude oil futures price volatility forecasting. The objective of this study is to investigate the time-varying nature of the impact of major events on the crude oil market and to examine how to accurately predict crude oil futures price volatility during major shocks. To this end, we propose a novel crude oil futures price volatility forecasting framework based on the Bidirectional Long and Short-Term Memory Neural Network-Attention Mechanism Model (Bi-LSTM-Attention), which contains period division, variable screening, model prediction, and assessment indicators, to further analyze in-depth the impacts of COVID-19 and the Russia-Ukraine conflict on the volatility of crude oil futures price. In addition, we also investigate the impact of external factors on the crude oil market, including economic status, geopolitical events, and the COVID-19 pandemic. Empirical evidence demonstrates that the impact of major events on crude oil futures price volatility is significantly time-varying and differentiated. Particularly during high shock periods, the Fake News Index plays an extremely influential role in driving crude oil futures price volatility. Moreover, our proposed model reliably captures the trend of crude oil futures price volatility during these violent shocks. The contributions of this study are to provide valuable insights into the impact of significant events on crude oil price volatility and contribute to a deeper understanding of crude oil market dynamics.

The impact of financial speculation on futures contracts price movements: A study of the US markets for dairy commodities

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.

The Influence of Investor Sentiment on Futures Price Volatility: Evidence from the Chinese Crude Oil Market

With the progress of crude oil financialization, crude oil futures has become an important derivative product, and its price discovery function has been widely concerned by investors. The relationship between sentiment and price can be studied by constructing oil market sentiment index. Considering that the investor sentiment of single-market energy futures may not reflect the cross-market impact well, this paper selected OI, PSY, RSI and other commonly used futures trading indicators, and used principal component analysis to construct the investor sentiment indexes of single-market (crude oil) and cross-market (crude oil, fuel oil, coking coal, coke) respectively. VAR, Granger test and linear regression are used to explore the influence of investor sentiment on the volatility of crude oil futures prices. The results show that: (i) There is a Granger causality relationship between single market (crude oil) investor sentiment and crude oil price; (ii) There is no Granger causality relationship between cross-market investor sentiment and crude oil price; (iii) Single market and cross-market investor sentiment have an asymmetric effect on the price fluctuation of crude oil futures market.

The impact of the Russia–Ukraine war on the United States natural gas futures prices

PurposeThis paper attempts to investigate the impact of the Russia–Ukraine war on the returns and volatility of the United States (US) natural gas futures market.Design/methodology/approachThe study uses secondary data of 996 trading day provided by the US Department of Energy and investing.com websites and applies the event study methodology in addition to the generalized autoregressive conditional heteroscedastic (GARCH) family models.FindingsThe findings from the exponential EGARCH (1,1) estimate are the best indication of a significant positive effects of the Ukraine–Russia war on the returns and volatility of the US natural gas futures prices. The cumulative abnormal returns (CARs) of the event study show that the natural gas futures prices reacted negatively but not significantly to the Russian–Ukraine war at the event date window [−1,1] and the [−15, −4] event window. CARs for the longer pre and post-event window display significant positive values and coincides with the standard finance theory for the case of the US natural gas futures over the Russia–Ukraine conflict.Originality/valueThis is the first study to examine the impact of the Russia–Ukraine war on natural gas futures prices in the United States. Thus, it provides indications on the behavior of investors in this market and proposes new empirical evidence that help in investment analyses and decisions.

Impact of geopolitical risk on the volatility of natural resource commodity futures prices in China

Volatile natural resource prices greatly affect economic growth, economic volatility, and financial market. Analyzing the factors affecting price volatility is of great significance for policymakers to formulate regulations, for financial institutions to control financial risks and for investors to improve their investment returns. We study the impact of geopolitical risk on the price volatility of coal, copper, crude oil, gold, and iron ore in the Chinese futures market. The paper uses the Geopolitical Risk (GPR) index, the Geopolitical Threat (GPT) sub-index and the Geopolitical Action (GPA) sub-index developed by Caldara and Iacoviello to measure geopolitical risk in broader sense. We utilize the closing prices of the most recently expired futures contracts from their starting trading date to 1 August 2022 to determine volatility represented by conditional variance. The GARCH and CGARCH models are built with the help of the Kalman filtering method and a TVP-VAR-SV model. The results of GARCH model show that geopolitical risk significantly increases the price volatility of coal, iron ore, and crude oil futures; significantly decreases the price volatility of gold; and has no significant effect on the price volatility of copper futures. By CGARCH model, we illustrate that geopolitical risk significantly increases the overall volatility, persistent volatility, and temporary volatility of crude oil; significantly increases the overall volatility and temporary volatility of coal and iron ore futures; significantly decreases the overall volatility and persistent volatility of gold futures; and has no significant impact on all three types of volatility of copper futures prices. The results of the Kalman filter analysis uncover that the response of each commodity volatility to changes in geopolitical risk increases when geopolitical risk is high. Additionally, geopolitical risk has the most effect on the price volatility of copper and crude oil with high external dependence. Estimates from the TVP-VAR-SV model reveal that the short-term impact of geopolitical risk shocks on the volatility of each the futures prices of each commodity is large and of long duration, and the response of commodity futures volatility to the same level of geopolitical risk shocks is higher and longer when major geopolitical events occur such as Paris Terrorist Attack, USA/Iran Tension Escalation, Syria Missile Attack, and Russian-Ukraine War. Moreover, geopolitical shocks have the most effect on the energy commodities of crude oil and coal.

Predicting natural gas futures’ volatility using climate risks

In this paper, we examine the tracking and predictive power of two kinds of climate risks—namely, climate policy uncertainty (CPU) and climate-related disasters—on the price volatility of natural gas futures. The GARCH-MIDAS model was adopted to incorporate daily natural gas futures prices with monthly CPU indices and disaster frequencies. The empirical results showed a robust predictive relationship between disaster frequency and natural gas price volatility under both in-sample and out-of-sample scenarios, while combining the CPU index with other predictors could not improve the out-of-sample forecasting performance. We believe these findings could provide insights for traders and market regulators.