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1 – 10 of 245Wenwen Xi, Dermot Hayes and Sergio Horacio Lence
The purpose of this paper is to study the variance risk premium in corn and soybean markets, where the variance risk premium is defined as the difference between the historical…
Abstract
Purpose
The purpose of this paper is to study the variance risk premium in corn and soybean markets, where the variance risk premium is defined as the difference between the historical realized variance and the corresponding risk-neutral expected variance.
Design/methodology/approach
The authors compute variance risk premiums using historical derivatives data. The authors use regression analysis and time series econometrics methods, including EGARCH and the Kalman filter, to analyze variance risk premiums.
Findings
There are moderate commonalities in variance within the agricultural sector, but fairly weak commonalities between the agricultural and the equity sectors. Corn and soybean variance risk premia in dollar terms are time-varying and correlated with the risk-neutral expected variance. In contrast, agricultural commodity variance risk premia in log return terms are more likely to be constant and less correlated with the log risk-neutral expected variance. Variance and price (return) risk premia in agricultural markets are weakly correlated, and the correlation depends on the sign of the returns in the underlying commodity.
Practical implications
Commodity variance (i.e. volatility) risk cannot be hedged using futures markets. The results have practical implications for US crop insurance programs because the implied volatilities from the relevant options markets are used to estimate the price volatility factors used to generate premia for revenue insurance products such as “Revenue Protection” and “Revenue Protection with Harvest Price Exclusion.” The variance risk premia found implies that revenue insurance premia are overpriced.
Originality/value
The empirical results suggest that the implied volatilities in corn and soybean futures market overestimate true expected volatility by approximately 15 percent. This has implications for derivative products, such as revenue insurance, that use these implied volatilities to calculate fair premia.
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Qianqian Mao, Yanjun Ren and Jens-Peter Loy
The purpose of this paper is to detect the existence of price bubbles and examine the possible contributing factors that associate with price bubble occurrences in China…
Abstract
Purpose
The purpose of this paper is to detect the existence of price bubbles and examine the possible contributing factors that associate with price bubble occurrences in China agricultural commodity markets.
Design/methodology/approach
Using recently developed rolling window right-side augmented Dickey–Fuller test, we first detect the dates of price bubbles in China's two important agricultural commodity markets, namely corn and soybeans. Then, we use a penalized maximum likelihood estimation of a multinomial logistic model to estimate the contributing factors of price bubbles in both markets, respectively.
Findings
Results from the bubble detection indicate that price bubbles account for 5.48% (3.91%) of the studied periods for corn (soybeans). More importantly, we find that market liquidity and speculation have opposite effects on the occurrences of bubbles in the corn and soybeans market. World stocks-to-use and exchange rates affect the occurrences of bubbles in a different way for each commodity, as well. Price bubbles are more likely associated with strong economic activity, high interest rates and low inflation levels.
Originality/value
This is the first study considering commodity-specific features into the formation of price bubbles. Through accurately identifying the bubble dates and fixing the estimation bias of rare events models, this study enables us to obtain robust results for each commodity. The results imply that China's corn and soybeans market respond differently to the speculative activity and external shocks from international markets. Therefore, future policy regulations on commodity markets should focus on more commodity-specific factors when aiming at avoiding bubble occurrences.
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Ran Lu and Hongjun Zeng
The purpose of this paper is to examine the volatility spillover and lead-lag relationship between the Chicago Board Options Exchange volatility index (VIX) and the major…
Abstract
Purpose
The purpose of this paper is to examine the volatility spillover and lead-lag relationship between the Chicago Board Options Exchange volatility index (VIX) and the major agricultural future markets before and during the Coronavirus disease 2019 (COVID-19) outbreak.
Design/methodology/approach
The methods used were the vector autoregression-Baba, Engle, Kraft and Kroner-generalized autoregressive conditional heteroskedasticity method, the Wald test and wavelet transform method.
Findings
The findings indicate that prior to the COVID-19 outbreak, there was a two-way volatility spillover impact between the majority of the sample markets. In comparison, volatility transmission between the VIX index and the agricultural future market was significantly lower following the COVID-19 outbreak, the authors observed greater coherence at higher frequencies than at lower frequencies, implying that the interdependence between the two VIX indices and the agricultural future market was stronger over a longer time-frequency domain and the VIX’s signalling effect on various agricultural future prices after the COVID-19 outbreak was significantly lower.
Originality/value
The authors conducted the first comprehensive investigation of the VIX’s correlation with major agricultural futures, especially during COVID-19. The findings contribute to a better understanding of the risk transmission mechanism between the VIX and major agricultural commodities futures contracts. And our findings have significant implications for investors and portfolio managers, as well as for policymakers who are concerned about the price of agricultural futures.
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Hung-Gay Fung, Yiuman Tse, Jot Yau and Lin Zhao
This study explores the price linkage between the Chinese commodity futures market and other dominant futures markets, and examines the forces behind the price linkages. The…
Abstract
This study explores the price linkage between the Chinese commodity futures market and other dominant futures markets, and examines the forces behind the price linkages. The contribution by the trading hour innovations in the United States (or United Kingdom) market to the overnight price changes in the Chinese market is larger in scale than the contribution by the daytime information from the Chinese market to the overnight returns of the corresponding US (or UK) market. Several futures have significant interactions of the domestic and foreign factors in the price linkages while the Chinese domestic factors explain better the global market price linkage in some futures (aluminum, gold, and corn), demonstrating the leading role of the Chinese futures markets in these world markets.
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Dimitrios Panagiotou and Konstantinos Karamanis
The aim of this study is to investigate for monotonicity, linearity and symmetry for the price volatility–trading volume relationship in the futures markets of agricultural…
Abstract
Purpose
The aim of this study is to investigate for monotonicity, linearity and symmetry for the price volatility–trading volume relationship in the futures markets of agricultural commodities.
Design/methodology/approach
Empirical findings are produced with the use of a highly flexible, nonparametric approach. Data are daily prices and volumes from the commodities of corn, hard red wheat, oats, rice and soybeans.
Findings
Results reveal violations of monotonicity locally but not globally. Volume and price volatility have, in all markets, a nonlinear relationship to each other, indicating that the strength of the relationship does not remain constant over the entire joint distribution. Global symmetry is rejected for the markets of oats and hard red wheat but cannot be rejected for the remaining three markets. The latter suggests that large values of good volatility are likely to occur together with high trading volumes, as do large values of bad volatility in these markets.
Originality/value
To the best of the authors’ knowledge, this is the first empirical work to test simultaneously for monotonicity, linearity and symmetry between price volatility and trading volume in the futures markets of agricultural commodities.
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The purpose of this paper is twofold. The first is to estimate the correlation between market activity and volatility on an exchange that does not use continuous auctions to find…
Abstract
Purpose
The purpose of this paper is twofold. The first is to estimate the correlation between market activity and volatility on an exchange that does not use continuous auctions to find prices. The second is to estimate the sensitivity of that relationship to differences in opinions across traders regarding asset value.
Design/methodology/approach
Both objectives are accomplished by using seven years of trader‐level data from the Tokyo Grain Exchange, which uses rapid sequences of Walrasian tâtonnement auctions to discover prices. On the TGE, only one futures contract trades at any given time and all of a commodity's futures contracts are auctioned in a rapid sequence, with only seconds between a sequence's auctions. The results are interpreted under the hypothesis that this design causes traders' beliefs to become more accurate and more uniform as a sequence progresses.
Findings
Intraday volume is u‐shaped while intraday volatility is downward sloping. The volume–volatility link is positive and stays constant or strengthens as traders' beliefs about value become more precise. The link is driven by trades originating from small futures commission merchants, especially those trades entered on behalf of customers.
Research limitations/implications
Evidence that accounting for cross‐correlations when estimating volatility can have an important effect on estimates is presented. Researchers are encouraged to further explore the implications of cross‐correlations.
Practical implications
The paper includes implications for existing theory, the measurement of volatility, and the design of central exchanges.
Originality/value
This paper uses the TGE as a natural laboratory to test theory. It is the first such study to use data from an exchange that does not use continuous auctions, and the first to document the simultaneous existence of u‐shape volume and downward‐sloping volatility.
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Dimitrios Panagiotou and Alkistis Tseriki
The cross-quantilogram analysis is employed. The latter can assess the temporal association between two stationary time series at different parts of their joint distribution. Data…
Abstract
Purpose
The cross-quantilogram analysis is employed. The latter can assess the temporal association between two stationary time series at different parts of their joint distribution. Data are daily prices and trading volumes from the futures markets of five agricultural commodities, namely, corn, hard red wheat, oats, rice and soybeans.
Design/methodology/approach
The objective to the present work is to investigate for directional predictability between returns and volume (and vice versa) in the futures markets of agricultural commodities.
Findings
The empirical results reveal evidence, weak as well as strong, that extreme low values of returns are likely to lead high levels of volume. There is also weak evidence that extreme low values of volume are likely to precede high values of returns, except for the futures markets of oats where there is very strong evidence that low values of volume are likely to lead high values of returns. For the commodity of soybeans, there is very strong evidence that extreme high levels of volume are likely to lead high values of returns, but they are very short lived.
Research limitations/implications
Agricultural futures have been recently characterized by increased volatility leading hedgers to be looking for diversification. The present findings suggest that when price crashes occur, investors who suffer losses wish to sell, increasing this way the trading activity. Concurrently, the results reveal that extreme low levels of trading volume might signal a possible price turn around for traders.
Originality/value
This is the first study that employs the quantilogram approach in order to investigate for potential predictability from returns to volume and from volume to returns, in the futures markets of agricultural commodities.
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Steffen Volkenand, Guenther Filler and Martin Odening
The purpose of this paper is to investigate and compare the impact of order imbalance on returns, liquidity and price volatility in agricultural futures markets on an intraday…
Abstract
Purpose
The purpose of this paper is to investigate and compare the impact of order imbalance on returns, liquidity and price volatility in agricultural futures markets on an intraday basis. The authors examine whether order imbalance is more powerful to explain variations in asset prices compared to other indicators of trading activity, particularly trading volume.
Design/methodology/approach
Using Chicago Mercantile Exchange best bid best offer data, the impact of order imbalance is analyzed via regression analyses. The analyses are carried out for corn, wheat, soy, live cattle and lean hogs in March 2008 and March 2016.
Findings
Results confirm the positive relation between order imbalance and returns as well as between order imbalance and price volatility as suggested by market microstructure models. Order imbalance, however, does not generally outperform trading volume as an explanatory variable.
Practical implications
For some contracts, returns can be predicted using lagged order imbalance. This offers the opportunity to derive profitable trading strategies.
Originality/value
This paper is one of the first attempts to explore the relationship between order imbalance and returns, liquidity and volatility for agricultural commodity futures on an intraday basis, accounting for the increased trading volume and for the high speed at which new information enters the market in an electronic trading environment.
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Luiz Eduardo Gaio and Daniel Henrique Dario Capitani
This study investigates the impacts of the Russia–Ukraine conflict on the cross-correlation between agricultural commodity prices and crude oil prices.
Abstract
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.
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Yan Sun and Ken Seng Tan
The purpose of this paper is to propose a new margin protection (MP) scheme for the producers of hog, cattle and dairy in the developing countries.
Abstract
Purpose
The purpose of this paper is to propose a new margin protection (MP) scheme for the producers of hog, cattle and dairy in the developing countries.
Design/methodology/approach
The proposed MP scheme is inspired by the Livestock Gross Margin (LGM) program that has been successfully implemented in US directly implementing the LGM program in developing countries can be difficult due to the rudimentary of the futures market with limited futures listing. To address this issue, the authors proxy the futures prices by relating to some relevant spot prices via an econometric model. The proxied futures prices, in turn, enable the implementation of a generalized LGM, which the authors denote as the MP scheme.
Findings
As China is the world’s largest consumption and production of pork, the authors describe the proposed MP scheme by demonstrating how a generalized LGM can be constructed for the Chinese hog producers. By empirically comparing to the pilot hog price index insurance for the Beijing’s hog producers, the authors find that the proposed MP scheme is more effective in providing MP for the producers.
Research limitations/implications
The proposed MP scheme still requires the availability of some relevant spot prices in order to use an econometric model to proxy the missing futures prices.
Originality/value
The value of this research stems from demonstrating how an MP scheme can be constructed for developing countries that have rudimentary futures markets.
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