Skip to main content Accessibility help

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

  • Don Cyr (a1), Lester Kwong (a2) and Ling Sun (a3)


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)


Corresponding author

e-mail: (corresponding author).


Hide All

We thank the attendees at the 2016 American Association of Wine Economists Annual Meeting and an anonymous referee for their valuable comments.



Hide All
Ali, H. H., Lecocq, S., and Visser, M. (2010). The impact of gurus: Parker grades and en primeur wine prices. Journal of Wine Economics, 5(1), 2239.
Ashenfelter, O. (2010). Predicting the quality and prices of Bordeaux wine. Journal of Wine Economics, 5(1), 4052.
Berg, D. (2009). Copula goodness-of-fit testing: An overview and power comparison. European Journal of Finance, 15(7–8), 675701.
Bokusheva, R. (2011). Measuring dependence in joint distributions of yield and weather variables. Agricultural Finance Review, 71(1), 120141.
Bozic, M., Newton, J., Thraen, C. S., and Gould, B. W. (2014). Tails curtailed: Accounting for nonlinear dependence in pricing margin insurance for dairy farmers. American Journal of Agricultural Economics, 96(4), 11171135.
Cardebat, J. M., and Paroissien, E. (2015). Standardizing expert wine scores: An application for Bordeaux en primeur. Journal of Wine Economics, 10(3), 329348.
Cherubini, U., Luciano, E., and Vecchiato, W. (2004). Copula Methods in Finance. West Sussex, U.K.: Wiley.
Cyr, D., Eyler, R., and Visser, M. (2013). The use of copula functions in pricing weather contracts for the California wine industry. Working Paper. Brock University.
Embrechts, P., McNeil, A., and Straumann, D. (1999). Correlation and dependence in risk management: Properties and pitfalls. RISK, May 1999, 6971.
Fermanian, J. D. (2013). An overview of the goodness-of-fit test problem for copulas. In Jaworski, P., Durante, F., and Härdle, W. K. (eds.), Copulae in Mathematical and Quantitative Finance: Proceedings of the Workshop Held in Cracow, 10-11 July (pp. 6189). Berlin: Springer.
Genest, C., Remillard, B., and Beaudoin, D. (2009). Goodness-of-fit tests for copulas: A review and a power study. Insurance: Mathematics and Economics, 44(2), 199213.
Hasebe, T. (2013). Copula-based maximum-likelihood estimation of sample-selection models. Stata Journal, 13(3), 547573.
Jones, G. V., and Storchmann, K. (2001). Wine market prices and investment under uncertainty: An econometric model for Bordeaux crus classes. Agricultural Economics, 26(2), 115133.
Kwong, L. M. K., Cyr, D., Kushner, J., and Ogwang, T. (2011). A semiparametric hedonic pricing model of Ontario wines. Canadian Journal of Agricultural Economics, 59(3), 361381.
Kwong, L. M. K., Ogwang, T., and Sun, L. (2017). Semiparametric versus parametric hedonic wine price models: An empirical investigation. Applied Economics Letters, 24(13), 897901.
Li, D. X. (2000). On default correlation: A copula function approach. Journal of Fixed Income, 9(4), 4354.
Lyons, W. (2015). Robert Parker steps down from Bordeaux futures. Wall Street Journal, February 26, 2015. Accessed at
Nelsen, R. B. (2006). An Introduction to Copulas, 2nd ed. New York: Springer.
Noparumpa, T., Kazaz, B., and Webster, S. (2015). Wine futures and advanced selling under quality uncertainty. Manufacturing and Service Operations Management, 17(3), 116.
Okhrin, O. (2012). Fitting high-dimensional copulae to data. In Handbook of Computational Finance (pp. 469501). Berlin: Springer.
Okhrin, O., Odening, M., and Xu, W. (2013). Systemic weather risk and crop insurance: The case of China. Journal of Risk and Insurance, 80(2), 351372.
Oczkowski, E., and Doucouliagos, H. (2015). Wine prices and quality ratings: A meta-regression analysis. American Journal of Agricultural Economics, 97(1), 103121.
Panchenko, V. (2005). Goodness-of-fit test for copulas. Physica A: Statistical Mechanics and Its Applications, 355(1), 176182.
Salmon, F. (2009). Recipe for disaster: The formula that killed Wall Street. Wired Magazine, February 23, 2009. Accessed at
Schölzel, C., and Friederichs, P. (2008). Multivariate non-normally distributed random variables in climate research – Introduction to the copula approach. Nonlinear Processes in Geophysics, 15(5), 761772.
Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publications de l'Institut de Statistique de l'Université de Paris, 8, 299–231.
Vedenov, D. V. (2008). Application of copulas to estimation of joint crop yield distributions. Contributed paper at the Agricultural and Applied Economics Association 2008 Annual Meeting, Orlando, Florida, USA, July. Accessed at
Woodard, J. D., Paulson, N. D., Vedenov, D., and Power, G. J. (2011). Impact of copula choice on the modeling of crop yield basis risk. Agricultural Economics, 42(s1), 101112.
Xu, W., Filler, G., Odening, M., and Okhrin, O. (2010). On the systemic nature of weather risk. Agricultural Finance Review, 70(2), 267284.
Zimmer, D. M. (2016). Crop price comovements during extreme market downturns. Australian Journal of Agricultural and Resource Economics, 60(2), 265283.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Journal of Wine Economics
  • ISSN: 1931-4361
  • EISSN: 1931-437X
  • URL: /core/journals/journal-of-wine-economics
Please enter your name
Please enter a valid email address
Who would you like to send this to? *



Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed