Hedging price risk in the presence of crop yield and revenue insurance

Mahul, Olivier

doi:10.1093/erae/30.2.217

Cited by 57 publications

(41 citation statements)

References 23 publications

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“…The results are in line with those of e.g. Mahul (2003) and might be explained by the fact that boarder protections measures make Switzerland to a rather closed economy for agricultural goods and that most of the crops produced in Switzerland are concentrated on a small area at the Swiss Plateau (e.g. Finger, 2012b).…”

Section: Discussionsupporting

confidence: 84%

Where is the risk? Price, yield and cost risk in Swiss crop production

Benni¹,

Finger²

2014

RAEStud

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Risk management strategies are of increasing importance in agriculture. An important question is, what type of risk management strategies are required to reduce farmers' income risks. Applying a variance decomposition approach using data of more than 3000 Swiss farms over a five year time period, this paper quantifies the direct and indirect effects of yields, prices and costs on net revenue variability at the farm-level. We find that costs play only a minor role in determining income variability but price and yield risks are of outmost importance and very crop-specific. For instance, price risks dominate for intensive wheat and sugar beet producer; while corn and barley producer tend to suffer more form production risks. Group comparisons and logistic regressions results show that more intensively producing farms tend to suffer more from price risk, while yield risks are dominant for less intensive producers.

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Section: Discussionsupporting

confidence: 84%

Where is the risk? Price, yield and cost risk in Swiss crop production

Benni¹,

Finger²

2014

RAEStud

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“…The result is consistent with earlier literature. The total demand for food changes only moderately from year to year, while supply can fluctuate considerably due to weather conditions (Rolfo 1980, Newbery et al 1981, Losq 1982, Lapan and Moshini 1994, Tirupattur and Hauser 1996, Mahul 2003. The negative covariance between price and yield create a partial "natural hedge" 5 , which weakens the role of income risk reducing from forward contracts (ceteris paribus).…”

Section: Pietola K and Liu X Forward Hedging Under Price And Producmentioning

confidence: 99%

“…Mahul (2003) showed futures and crop yield insurance are complements. When either option or crop yield insurance is available, forward contracts become more attractive to the producers.…”

Section: Pietola K and Liu X Forward Hedging Under Price And Producmentioning

confidence: 99%

Forward hedging under price and production risk of wheat

Xing¹,

Pietola²

2008

AFSci

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This paper estimates optimal hedging ratios for a Finnish spring wheat producer under price and yield uncertainty. The contract available for hedging fixes the price and quantity at the time of sowing for a delivery at harvest. Autoregressive models are used to obtain point forecasts for the conditional mean price and price volatility at harvest. Expected yield and yield volatility are estimated from the field experiment data. A range of coefficients of absolute risk aversion are used in the computations. The results suggest that yield volatility is large and it dominates the price volatility in the optimal hedging decisions of the Finnish wheat producers. The point estimate for the price and yield correlation is negative and has a large magnitude. Thus, a negative correlation between the price and the yield, as signalled by the point estimate, will decrease the optimal hedging ratio since the Finnish farmers do not have access to selling put options when they enter in a forward contract.

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“…Some applications of the power method have included such topics as ANOVA (e.g., Berkovits, Hancock, & Nevitt, 2000;Lix & Fouladi, 2007), asset pricing theories (Affleck-Graves & MacDonald, 1989), business-cycle features (Hess & Iwata, 1997), cluster analysis (Steinley & Henson, 2005), item parameter estimation (Kirisci, Hsu, & Yu, 2001), item response theory (Harwell, Stone, Hsu, & Kirisci, 1996;Stone, 2003), factor analysis (Benson & Fleishman, 1994;Flora & Curran, 2004), price risk (Mahul, 2003), structural equation models (Hau & Marsh, 2004;Henson, Reise, & Kim, 2007), and toxicology (Hothorn & Lehmacher, 2007).…”

Section: Introductionmentioning

confidence: 99%

A characterization of power method transformations through the method of percentiles

Kuo

Headrick

2017

Communications in Statistics - Simulation and Computation

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This paper derives closed-form solutions for the fifth-ordered power method polynomial transformation based on the method of percentiles (MOP). A proposed MOP univariate procedure is described and compared with the method of moments (MOM) in the context of distribution fitting and estimating skew, kurtosis, fifth-and sixthordered functions. The MOP methodology is also extended from univariate to multivariate data generation. The MOP procedure has an advantage over the MOM because it does not require numerical integration to compute intermediate correlations. In addition, the MOP procedure can be applied to distributions where mean and(or) variance do(does) not exist. Simulation results demonstrate that the proposed MOP procedure is superior to the MOM in terms of estimation, relative bias, and relative error.

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Hedging price risk in the presence of crop yield and revenue insurance

Abstract: Abstract

Cited by 57 publications

References 23 publications

Where is the risk? Price, yield and cost risk in Swiss crop production

Where is the risk? Price, yield and cost risk in Swiss crop production

Forward hedging under price and production risk of wheat

A characterization of power method transformations through the method of percentiles

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