Journal of Futures Markets

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• Air pollution, weather factors, and realized volatility forecasts of agricultural commodity futures

  This study investigates the potential effects of environmental factors on fluctuations in agricultural commodity futures markets, by constructing a new category of daily exogenous predictors related to air pollution, weather, climate change, and investor attention. The empirical results from out‐of‐sample analyses suggest that the heterogeneous autoregressive (HAR) model incorporating all these exogenous predictors is more likely to outperform other HAR‐type models. Additionally, economic evaluations demonstrate the superior performance of models incorporating investors' attention to climate change or extreme weather as predictors. While not all exogenous predictors are equally important for volatility forecasts, adopting appropriate variable selection methods to handle different sets of exogenous predictors can lead to better performance than the HAR benchmark. With the inclusion of air pollution or weather factors in the HAR model, a portfolio with an annualized average excess return of 16.2068% or a Sharpe ratio of 10.0431 can be achieved for the wheat futures, respectively.

• Revisiting the puzzle of jumps in volatility forecasting: The new insights of high‐frequency jump intensity

  Motivated by the puzzling null impact of high‐frequency‐based jumps on future volatility, this paper exploits the rich information content in high‐frequency jump intensity with a mark structure under the heterogeneous autoregressive framework. Our proposed model shows that harnessing jump intensity information from the marked Hawkes process leads to significantly superior in‐sample fit and out‐of‐sample forecasting accuracy. In addition to statistical significance evidence, we also illustrate the economic significance in terms of trading efficiency. Our findings hold for a variety of competing models and under different market conditions, underlying the robustness of our results.

• A tale of two contracts: Was the SHFE copper futures market disrupted by the listing of INE bonded copper futures?

  Leveraging tick‐by‐tick data, this study provides the first analysis of Shanghai International Energy Exchange (INE) bonded copper futures' performance in market quality and price discovery. In particular, we investigate the effects of the market opening (listing of INE bonded copper futures) on Shanghai Futures Exchange (SHFE) copper futures' market quality and price discovery. Our results show that the market quality and price discovery of INE bonded copper futures in the first year of the listing is not promising. Our synthetic control method results suggest that market openness does not significantly reduce SHFE copper futures' market quality in terms of activity, liquidity, and volatility. Moreover, market openness does not significantly reduce the SHFE copper futures' price discovery effectiveness. Overall, the performance of new INE bonded copper futures needs improvement, while its listing did not disrupt SHFE copper futures. Our results suggest that a dual‐contract mode is an alternative option for internationalization in China's commodity futures markets.

• Uncertainty and investment: Evidence from domestic oil rigs

  We provide new evidence on the response of investment to uncertainty, using granular and high‐frequency (weekly) data on domestic oil drilling and oil prices since 2012, corresponding to the period of widespread horizontal drilling and hydraulic fracturing in the United States. Weekly data permits much weaker identifying restrictions than is required with monthly data that is common in the literature. We measure domestic drilling activity by the number of rigs drilling for oil, and we measure oil uncertainty by implied volatility from options on oil futures and the return on delta‐neutral straddles from options on oil futures. We show that the number of oil drilling rigs are tightly linked to both oil prices and oil uncertainty, and we find that oil uncertainty significantly decreases the number of drilling rigs, with a one standard deviation increase in uncertainty reducing the number of drilling rigs by up to 5%.

• Hedging pressure and oil volatility: Insurance versus liquidity demands

  This study evaluates the dual role of hedging pressure (HP) in oil futures markets and analyses its effects on weekly oil volatility. We find that HP driven by hedgers' insurance demands is negatively related to volatility, while HP driven by speculators' short‐term liquidity demands is positively related to volatility. Oil volatility tends to be more responsive to speculators' short‐term liquidity demands than variations induced by hedgers' insurance demands. These channels are also significant determinants of volatility in inverted and normal markets, with the effects being more pronounced in inverted markets. Under low financial and business‐cycle risk environments, the two HP channels typically have a measurable impact on volatility. These opposing effects of HP on weekly volatility provide empirical support on the significance of the dual role of hedgers in oil markets, as price insurance seekers and as short‐term liquidity providers.

• Journal of Futures Markets: Volume 44, Number 2, February 2024
• Predictability of commodity futures returns with machine learning models

  We use prevailing machine learning models to investigate the predictability of futures returns in 22 commodities with commodity‐specific and macroeconomic factors as predictors. Out‐of‐sample prediction errors for the majority of futures contracts are lowered compared with those obtained by the baseline models of AR(1) and forecast combinations. Using Shapley values to explain feature importance, we identify dominant predictors for each commodity. A long–short portfolio strategy based on monthly light gradient‐boosting machine predictions outperforms the benchmark linear models in terms of annual return, Sharpe ratio, and max drawdown.

• Market‐wide overconfidence and stock returns

  In this paper, a novel measurement of overconfidence over the market is developed based on the size of ambiguity (the confidence of investors in information). The proposed measure of market‐wide overconfidence is consistent with the predictions motivated by prior literature. It has a significant negative association with the next‐month market excess return. Associations between the overconfidence measure and riskier portfolio returns behave stronger and last longer, implying a risk‐taking proclivity of overconfident investors.

• Dynamic connectedness between energy markets and the Brazilian cash market: An empirical analysis pre‐ and post‐COVID‐19

  Brazil's significant commodity production is internationally recognized, yet the absence of a mature futures market exposes it to price risks and international shocks. This study explores the dynamic connectedness between commodity futures and the Brazilian cash markets, using a time‐varying parameter vector autoregressive model. We also assess COVID‐19's impact on this connectedness. We find a significant influence of oil prices on Brazilian ethanol prices, and particularly emphasize the Heating Oil spillover effect on ethanol in the post‐COVID‐19 era. We also note the ascension of Brazilian soybean spot markets' international significance since 2017, amplifying their role in global grain price discovery. Finally, by computing hedge ratios and effectiveness between commodity futures contracts and Brazilian spot prices, our study reveals soybean cash price as the most effective hedge. These insights deepen comprehension of connectedness within Brazilian commodity markets, thereby guiding investors and policymakers in strategic energy policy decisions.

• Can technical indicators based on underlying assets help to predict implied volatility index

  Given the widespread use of technical analysis and the tight relationship between derivatives and the underlying assets, we employ the copula approach to investigate whether the technical indicators based on underlying assets convey extra information about the future movements of implied volatility (IV) indexes. The empirical results, based on long samples of five well‐known IV indexes, suggest that although the technical indicators are not informative for forecasting the future prices of IV indexes, they can provide extra information about the size of forecasting errors of the IV indexes. The findings are also robust to the impact of COVID‐19. The technical indicators are then used to extend Threshold ARCH and Exponencial GARCH models for improving the estimation of Value at Risks (VaRs). The out‐of‐sample forecast results show that the proposed model outperforms the benchmark in estimating the VaRs. These findings have implications for pricing options of IV indexes and managing the risks of IV‐related portfolios.

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• Commodity momentum and reversal: Do they exist, and if so, why?

  Questions as to why differences in momentum and reversal patterns seem to emerge in commodity futures compared with spot markets, and how these patterns can be explained, remain unanswered. To investigate these questions, I examine 23 commodities over a period of 60 years. I first show that...

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• A new option for mortality–interest rates

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