Future Volatility Research Articles

Food Financialization: Impact of Derivatives and Index Funds on Agri-Food Market Volatility

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.

Integrating sentiment information for risk prediction: the case of crude oil futures market in China

AbstractThis paper incorporates investor sentiment indexes into the traditional standard heterogeneous autoregressive (HAR) model to improve its power on predicting crude oil futures risk. Using the 5-min high-frequency trading data to construct the daily realized volatility, the original and revised HAR models are used for in-sample regression and out-of-sample forecasting on a daily, weekly, and monthly basis. The results show that the sentiment indexes and the search trend contain incremental information for forecasting the realized volatility of INE crude oil futures in the short and medium term. The search volume is the best indicator for weekly risk forecasting of INE crude oil futures. No robust index can improve the performance of HAR-type model on long-term risk prediction. This paper thus highlights that market participants should select appropriate strategies to minimize risk when volatility is at stake for their decisions.

Have the causal effects between equities, oil prices, and monetary policy changed over time?

We reexamine the contemporaneous causal effects between the U.S. stock prices, crude oil prices, and monetary policy from 2005 to 2022. Our study offers two main contributions. First, we generalize a novel identification approach based on exogenous intraday shifts in the volatility in futures markets from two markets to multiple markets. Second, we examine contemporaneous causal effects between the U.S. stock prices, crude oil prices, and monetary policy. We show that the coefficients measuring contemporaneous causality have substantially changed over time. Specifically, we find that since 2008 stock returns affect crude oil returns. This time variation is also evident in the effect of monetary policy on the crude oil returns. We show that this time variation is consistent with two explanations: the zero lower bound (ZLB) and increased synchronization of crude oil prices with the business cycle.

Forecasting Chinese Stock Market Volatility With Volatilities in Bond Markets

ABSTRACTIn this paper, we investigate whether the bond markets contain important information that can improve the accuracy of stock market volatility forecasts in China. We use realized volatility (RV) implemented by different maturity treasury bond futures contracts to predict the Chinese stock market volatility. Our work is based on the heterogeneous autoregressive (HAR) framework. Empirical results show that the volatility of treasury bond contracts with longer maturities (especially 10 years) has the best effect on predicting the Chinese stock market volatility, both in sample and out of sample. Two machine learning methods, the scaled principal component analysis (SPCA) and the least absolute shrinkage and selection operator (lasso), are also more effective than the HAR benchmark model's prediction. Finally, mean–variance investors can achieve substantial economic gains by allocating their investment portfolios based on volatility forecasts after introducing treasury bond futures volatility.

Forecasting the volatility of crude oil futures: New evidence from jump-induced volatility

This paper proposes an augmented heterogenous autoregressive (HAR) model with time-varying jumps to forecast the realized volatility (RV) of crude oil futures. Jump-induced volatility of crude oil futures is obtained from a GARCH-jump process, then used to augment the HAR model. The results based on both the in-sample and out-of-sample analyses suggest that jumps offer added information for forecasting the RV of crude oil futures, surpassing the incremental information contained in the crude oil implied volatility index (OVX). Various robustness tests confirm these findings. Our findings have key implications for energy market investors, risk managers, and policymakers.

Option listing and underlying commodity futures volatility in China

This study examines the impact of option listing on the volatility of underlying commodity futures markets in China, filling a gap in research on the trading effects of options. We construct the counterfactual volatility for these optioned commodity futures and estimate the average treatment effect of option listing. Our findings reveal a reduction in the volatility of underlying commodity futures after the option listing, a result that withstands various robustness checks. Furthermore, we find the decreased volatility is associated with a lower trading volume in these markets. The declined trading volume may stem from informed traders diverting to option markets and from noise traders being more cautious about trading. Our paper highlights distinct outcomes of option listing, particularly pertinent to emerging derivative markets.

“Volatility in a Mug Cup”: Spillovers among cocoa, coffee, sugar futures and the role of climate policy risk

Hot beverages have surged in popularity due to the increasing emphasis on healthy living among consumers. This trend extends upstream to prosper futures markets for essential raw materials within the supply chain. However, there is a significant gap in the literature concerning volatility spillovers within these futures markets, specifically cocoa, coffee, and sugar futures. Given the susceptibility of these raw material agricultural productions to weather conditions, an intriguing question emerges regarding the potential impact of climate policy uncertainty on the volatility spillover dynamics of these commodity prices. This study employs the TVP-VAR-DY methodology to quantify volatility spillovers among global mainstream cocoa, coffee, and sugar futures, then the Time-Varying Granger Causality test is deployed to uncover the influence of climate policy uncertainty. The findings underscore a pronounced and statistically significant spillover effect among the volatilities of coffee-related commodity futures, with the U.S. sugar futures market emerging as a pivotal conduit for risk transmission. In exploring the impact of climate policy uncertainty, it is discerned that global climatic patterns exert some unique and substantive influence on systemic risk spillovers. These insights provide a basis for stakeholders in the hot drink industrial chain to think about effective risk management mechanisms.

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.

Trade can buffer climate-induced risks and volatilities in crop supply

Abstract Climate change is intensifying the frequency and severity of extreme events, posing challenges to food security. Corn, a staple crop for billions, is particularly vulnerable to heat stress, a primary driver of yield variability. While many studies have examined climate impact on average corn yields, little attention has been given to the climate impact on production volatility. This study investigates the future volatility and risks associated with global corn supply under climate change, evaluating the potential benefits of two key adaptation strategies: irrigation and market integration. A statistical model is employed to estimate corn yield response to heat stress and utilize NEX-GDDP-CMIP6 climate data to project future production volatility and risks of substantial yield losses. Three metrics are introduced to quantify these risks: Sigma (σ), the standard deviation of year-on-year yield change, which reflects overall yield volatility; Rho (ρ), the risk of substantial loss, defined as the probability of yield falling below a critical threshold; and Beta (β), a relative risk coefficient that captures the volatility of a region's corn production compared to the globally integrated market. The analysis reveals a concerning trend of increasing year-on-year yield volatility (σ) across most regions and climate models. This volatility increase is significant for key corn-producing regions like Brazil and the United States. While irrigated corn production exhibits a smaller rise in volatility, suggesting irrigation as a potential buffer against climate change impacts, it is not a sustainable option as it can cause groundwater depletion. On the other hand, global market integration reduces overall volatility and market risks significantly with less sustainability concerns. These findings highlight the importance of a multidimensional approach to adaptation in the food sector. While irrigation can benefit individual farmers, promoting global market integration offers a broader solution for fostering resilience and sustainability across the entire food system.

Does speculation increase volatility in grain futures markets? Evidence from the Interwar Chicago Board of Trade

Abstract A key justification for futures regulation in the interwar period was the idea that speculators were making grain prices volatile, and therefore speculative activity needed to be restricted. This paper uses new data on grain futures contracts traded at the Chicago Board of Trade to empirically assess whether speculators Granger caused volatility in futures markets during the interwar period. We find that speculators did not Granger cause volatility, but volatile markets Granger caused speculative activity. These results suggest that speculators entered volatile markets but did not increase volatility.

Volatility dynamics in energy and agriculture markets: An analysis of domestic and global uncertainty factors

PurposeThis study aims to explore the intricate relationship between uncertainty indicators and volatility of commodity futures, with a specific focus on agriculture and energy sectors.Design/methodology/approachThe authors analyse the volatility of Indian agriculture and energy futures using the GARCH-MIDAS model, taking into account different types of uncertainty factors. The evaluation of out-sample predictive capability involves the application of out-sample R-squared test and computation of various loss functions.FindingsThe research outcomes underscore the significant impact of diverse uncertainty factors such as domestic economic policy uncertainty (EPU), global EPU (GEPU), US EPU and geopolitical risk (GPR) on long-run volatility of Indian energy and agriculture (agri) futures. Additionally, the study demonstrates that GPR exhibits superior predictive capability for crude oil futures volatility, while domestic EPU stands out as an effective predictor for agri futures, particularly castor seed and guar gum.Practical implicationsThe study offers practical implications for market participants and policymakers to adopt a comprehensive perspective, incorporating diverse uncertainty factors, for informed decision-making and effective risk management in commodity markets.Originality/valueThe research makes an inaugural attempt to examine the impact of domestic and global uncertainty indicators on modelling and predicting volatility in energy and agri futures. The distinctive feature of considering an emerging market also adds a novel dimension to the research landscape.

High frequency volatility of oil futures in China: Components, modeling, and prediction

AbstractThis 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 ( ) and realized bi‐power variations ( ) 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.

Climate risk and energy futures high frequency volatility prediction

This study explores the impact of climate risk on the volatility of energy futures, including heating oil, crude oil, and natural gas. In particularly, employing the heterogeneous autoregressive model and its extensions, we find that incorporating climate risk information significantly improves predictive accuracy for high-frequency volatility of energy futures both in- and out-of-sample. These results are robust across different forecasting mechanisms and periods. Additionally, we demonstrate that climate risk provides incremental information compared with existing predictors of energy futures volatility, leading to improved prediction accuracy when combining various predictors. Finally, we demonstrate that considering climate risk can lead to increased economic gains for investors in heating oil and crude oil markets. Our findings provide valuable insights for market participants in the realms of energy portfolio selection and risk management.

Functional Volatility Relationship Analysis and Prediction in International Crude Oil Futures Markets

ABSTRACTTo measure intraday volatility in international crude oil futures markets, we use the functional conditional variance to measure volatility and focus on volatility relationship analysis and prediction. This paper analyzes the simultaneous and predictive volatility relationships in crude oil futures markets. For covariate markets with significantly positive predictive volatility relationships, this paper empirically extends the fGARCH‐X model so that it can introduce the volatility characteristics of covariate markets. The empirical application shows that using the fGARCH‐X model can generally improve the predictive effects of functional volatility in crude oil futures markets. The robustness results indicate that the improvement in volatility prediction is significant. This study is beneficial for the stable development of international crude oil futures markets and is valuable for investors' investment decision‐making.

Exploring calendar effects: the impact of WASDE releases on grain futures market volatility

ABSTRACT The monthly issuance of the World Agriculture Supply and Demand Estimates (WASDE) by the United States Department of Agriculture (USDA) plays a pivotal role in shaping market dynamics through its comprehensive projections and assessments of agricultural commodities. This paper examines the influence of WASDE reports on the intraday volatility of grain futures. We analyse the calendar effect on volatility, focusing on the diurnal patterns on WASDE release days compared to other trading days. This study leverages high-frequency intraday data to explore the immediate and sustained market reactions to the reports, aiming to identify distinct volatility patterns and their implications for traders and market analysts. Our findings confirm the presence of unique diurnal volatility patterns on WASDE release days, significantly diverging from non-release days. This research contributes to the literature by providing refined insights into how public information releases affect market volatility, offering valuable perspectives for understanding and anticipating market movements in response to agricultural reports.

Volatility dynamics of agricultural futures markets under uncertainties

The objective of this study is to examine the effect of various uncertainty measures on the realized volatility of agricultural futures markets. In doing so, we use a range of uncertainty indicators in our analysis to investigate whether news-based uncertainty measures (e.g., geopolitical risk and economic policy uncertainty) have better predictive contents than the market-based uncertainty measures (e.g., crude oil volatility index, the US equity market VIX and exchange rate VIX). This comparison is important given that employing both measures has some specific benefits. Methodologically, we consider the application of the LASSO (least absolute shrinkage and selection operator) method as well as the heterogenous autoregressive (HAR) process. The in-sample estimates indicate that among the various news-based and market-based risk measures the latter provide better forecasts for the realized volatility of agricultural futures markets. The out-of-sample forecasts also confirm the same with the LASSO method outperforming the HAR process.

A comparison of cryptocurrency volatility-benchmarking new and mature asset classes

The paper analyzes the cryptocurrency ecosystem at both the aggregate and individual levels to understand the factors that impact future volatility. The study uses high-frequency panel data from 2020 to 2022 to examine the relationship between several market volatility drivers, such as daily leverage, signed volatility and jumps. Several known autoregressive model specifications are estimated over different market regimes, and results are compared to equity data as a reference benchmark of a more mature asset class. The panel estimations show that the positive market returns at the high-frequency level increase price volatility, contrary to what is expected from the classical financial literature. We attributed this effect to the price dynamics over the last year of the dataset (2022) by repeating the estimation on different time spans. Moreover, the positive signed volatility and negative daily leverage positively impact the cryptocurrencies’ future volatility, unlike what emerges from the same study on a cross-section of stocks. This result signals a structural difference in a nascent cryptocurrency market that has to mature yet. Further individual-level analysis confirms the findings of the panel analysis and highlights that these effects are statistically significant and commonly shared among many components in the selected universe.

Misreaction, hedging pressure, and its effect on the futures market

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.

How Do Futures Contracts on Agricultural Commodities Respond to El Nino Weather Events?

This study examines how futures contracts on wheat, soybeans, corn and sugarcane respond to El Nino weather events over the period March 2010 to April 2024. We use the monthly ENSO forecast data from the Climate Prediction Center of the National Oceanic and Atmospheric Administration to benchmark the start of an El Nino forecast, which we define as the first reporting month in which a current or forecasted El Nino probability exceeds 50%. We examine two periods around each event: the six months leading up to the El Nino forecast, and the six months following the event date. We find that El Nino weather events have significant effects on commodity futures prices. Wheat, soybean and corn futures prices (expressed in dollars) generally decline after an El Nino forecast, consistent with improved harvests due to an increase in rain. We also find that El Nino events are associated with increased volatility in commodity futures markets. Our study can inform hedgers and speculators who participate in the agricultural derivatives markets, as well as policymakers who may be interested in initiating or adjusting any subsidies granted to farmers or food consumers, as compensation for price pressures.

Forecasting Crude Oil Volatility Using the Deep Learning‐Based Hybrid Models With Common Factors

ABSTRACTBased on empirical evidence of the Chinese commodity futures volatility dynamics, we propose a novel and flexible hybrid model, denoted as SAE‐HAR‐DL, which combines a supervised autoencoder (AE) with the deep learning‐based HAR model framework to capture essential common factor information and uses the reconstruction error of the AE component as a regularizer to enhance the generalization ability of the testing subsample. The empirical findings strongly support the effectiveness of this model in accurately forecasting crude oil futures volatility in the post‐COVID‐19 era, compared to the HAR, HAR‐PCA, and HAR‐DL models. Moreover, a robustness check also demonstrates the positive contribution of common factors to the volatility prediction of other commodity futures. Notably, we establish that these common factors act as effective regularizers, mitigating prediction losses within the HAR model in extreme risk events such as the COVID‐19 pandemic and the Russia–Ukraine conflict.