Crop price comovements during extreme market downturns

Zimmer, David M.

doi:10.1111/1467-8489.12119

Cited by 6 publications

(3 citation statements)

References 34 publications

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“…The use of copula-function modeling has since expanded quickly in several areas of research, but particularly in civil and mechanical engineering as well as climatology (e.g., Xu, Filler, Odening, and Okhrin, 2010; Schölzel and Friederichs, 2008). In the area of agricultural economics, given the tail dependence frequently observed and the implications for crop insurance, copulas have seen increasing attention and use in modeling the bivariate relationships between weather variables, prices, and crop yields (e.g., Zimmer, 2016; Bozic, Newton, Thraen, and Gould, 2014; Okhrin, Odening, and Xu, 2013; Bokusheva, 2011; Woodard, Paulson, Vedenov, and Power, 2011; Vedenov, 2008). The only known application related to viticulture or wine is that of Cyr, Eyler, and Visser (2013).…”

Section: Copula-function Modelingmentioning

confidence: 99%

An Examination of Tail Dependence in Bordeaux Futures Prices and Parker Ratings

2017

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This paper explores the nonlinearities of the bivariate distribution of Bordeaux en primeur, or wine futures, prices and Parker “barrel ratings” for the period of 2004 through 2010. In particular, copula-function methodology is introduced and employed to examine the nature of the bivariate distribution. Our results show a significant nonlinear relationship between Parker ratings and wine prices, characterized by significant positive tail dependence and higher correlation between high ratings and high prices. Marginal distributions for Parker ratings and wine prices are then identified and Monte Carlo simulation is employed to operationalize the relationship for risk-management purposes. (JEL Classifications: C19, G13, L66)

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Section: Copula-function Modelingmentioning

confidence: 99%

An Examination of Tail Dependence in Bordeaux Futures Prices and Parker Ratings

2017

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“…However, transmission mechanisms between monthly agricultural prices are usually focused on the zero frequency, whereas seasonality is ignored (Verissimo 2001;Susanto et al 2008;Alves et al 2013;Caldarelli 2013;Lee and G omez 2013;Abidoye and Labuschagne 2014;Baquedano and Liefert 2014;Adammer and Bohl 2015;Lajdov a and Bielik 2015;Akhter 2016;Aruga and Li 2016;Fousekis et al 2016a;Garc ıa-Germ an et al 2016;Hatzenbuehler et al 2016). Fousekis et al (2016b) and Zimmer (2016) estimate models for monthly price comovements using copula functions, but seasonal effects are not analysed. Other papers about the stationarity of monthly agricultural price series only pay attention to the zero frequency, and seasonal variations are either ignored (Zeng et al 2011;Ott 2014a,b) or removed by seasonal adjustment procedures (Garc ıa-Enr ıquez et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Evolving splines and seasonal unit roots in weekly agricultural prices

Hernández

Martin-Rodriguez

2017

Aust J Agric Resour Econ

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The univariate statistical properties of agricultural price series need to be examined as a first step in the analysis of price transmission mechanisms. However, in the case of weekly price series, increasingly available, the testing procedures usually applied in this step are not suitable to deal with evolving seasonal effects. In this study, a method of testing for seasonal unit roots in weekly series of agricultural prices is described. When the deterministic seasonal component does not remain constant over time, the restricted evolving spline model (RESM) is shown to be a useful parametric formulation to capture the deterministic seasonal pattern. Therefore, the RESM model should be included as a deterministic component in auxiliary regression for unit root tests at seasonal frequencies. This proposal is applied to three weekly series of Canary Islands banana prices. From the standard seasonal unit root tests, the null hypothesis is failed to be rejected at the 5% or 10% significance level at some seasonal frequencies for each one of the series. Once critical values are obtained by simulation exercises when the RESM model is included, the hypotheses of unit root are rejected at each one of the seasonal frequencies for all of the three series.

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“…Similarly when modeling changes in housing prices in the interval surrounding the financial crisis of 2008, the Gaussian copula was found to be inappropriate as it imposes constant dependence interactions even for extreme events (Zimmer 2012). Zimmer (2014) has shown that commodity price-price correlations strengthen when prices decline.…”

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confidence: 99%

Land Resilience and Tail Dependence among Crop Yield Distributions

Hennessy

Feng

et al. 2017

American J Agri Economics

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Rate setting procedures for United States crop yield and revenue insurance contracts employ methods that presume correlations to be state invariant. Whether this is true matters. If yield-yield correlations strengthen when crops are subject to widespread stress, then diversification opportunities for private insurers weaken when most needed, and an insurer's portfolio of retained business may not be as diversified as standard statistics would suggest. For government outlays, such tail dependence will increase the transactions and political costs of reallocations from the general fund. In this paper we propose a simple model of yield correlations according to interactions between a weather outcome and a land unit's yield resilience to adverse shocks, as might be measured by the United States Soil Conservation Service's land capability classification. Our model shows that yield-yield tail dependence is to be expected and, furthermore, should take a particular form. In better growing regions, yield correlations across units should be stronger in right tails than in left tails, whereas in marginal growing regions the reverse should apply. Using USDA Risk Management Agency unit level data and a variety of statistics, we find strong evidence in favor of this land yield resilience hypothesis. Our findings call into question the appropriateness of current USDA rate-setting methodologies, which posit constant state-conditional ordinal correlations by implicitly assuming that yields can be represented by a Gaussian copula. A goodness-of-fit test rejects the standard Gaussian copula model, implying that existing RMA rate-setting methods are deficient.

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Crop price comovements during extreme market downturns

Cited by 6 publications

References 34 publications

An Examination of Tail Dependence in Bordeaux Futures Prices and Parker Ratings

An Examination of Tail Dependence in Bordeaux Futures Prices and Parker Ratings

Evolving splines and seasonal unit roots in weekly agricultural prices

Land Resilience and Tail Dependence among Crop Yield Distributions

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