Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-26T21:51:45.907Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  23 October 2017

Don Cyr ,
Lester Kwong  and
Ling Sun
Don Cyr*
Affiliation:
Goodman School of Business, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, Ontario, L2S 3A1, Canada
Lester Kwong
Affiliation:
Department of Economics, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, Ontario, L2S 3A1, Canada; e-mail: [email protected].
Ling Sun
Affiliation:
Department of Economics, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, Ontario, L2S 3A1, Canada; e-mail: [email protected].
*
e-mail: [email protected] (corresponding author).
Get access Rights & Permissions [Opens in a new window]

Abstract

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)

Keywords

Bordeaux futurescopula functionswine ratings
Type
Articles
Information
Journal of Wine Economics , Volume 12 , Issue 3 , August 2017 , pp. 252 - 266
Copyright
Copyright © American Association of Wine Economists 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

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

References

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), 22–39.Google Scholar
Ashenfelter, O. (2010). Predicting the quality and prices of Bordeaux wine. Journal of Wine Economics, 5(1), 40–52.Google Scholar
Berg, D. (2009). Copula goodness-of-fit testing: An overview and power comparison. European Journal of Finance, 15(7–8), 675–701.CrossRefGoogle Scholar
Bokusheva, R. (2011). Measuring dependence in joint distributions of yield and weather variables. Agricultural Finance Review, 71(1), 120–141.Google Scholar
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), 1117–1135.Google Scholar
Cardebat, J. M., and Paroissien, E. (2015). Standardizing expert wine scores: An application for Bordeaux en primeur. Journal of Wine Economics, 10(3), 329–348.Google Scholar
Cherubini, U., Luciano, E., and Vecchiato, W. (2004). Copula Methods in Finance. West Sussex, U.K.: Wiley.Google Scholar
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.Google Scholar
Embrechts, P., McNeil, A., and Straumann, D. (1999). Correlation and dependence in risk management: Properties and pitfalls. RISK, May 1999, 69–71.Google Scholar
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. 61–89). Berlin: Springer.Google Scholar
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), 199–213.Google Scholar
Hasebe, T. (2013). Copula-based maximum-likelihood estimation of sample-selection models. Stata Journal, 13(3), 547–573.Google Scholar
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), 115–133.Google Scholar
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), 361–381.Google Scholar
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), 897–901.Google Scholar
Li, D. X. (2000). On default correlation: A copula function approach. Journal of Fixed Income, 9(4), 43–54.Google Scholar
Lyons, W. (2015). Robert Parker steps down from Bordeaux futures. Wall Street Journal, February 26, 2015. Accessed at http://www.wsj.com/articles/robert-parker-steps-down-from-bordeaux-futures-1424966697.Google Scholar
Nelsen, R. B. (2006). An Introduction to Copulas, 2nd ed. New York: Springer.Google Scholar
Noparumpa, T., Kazaz, B., and Webster, S. (2015). Wine futures and advanced selling under quality uncertainty. Manufacturing and Service Operations Management, 17(3), 1–16.Google Scholar
Okhrin, O. (2012). Fitting high-dimensional copulae to data. In Handbook of Computational Finance (pp. 469–501). Berlin: Springer.CrossRefGoogle Scholar
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), 351–372.CrossRefGoogle Scholar
Oczkowski, E., and Doucouliagos, H. (2015). Wine prices and quality ratings: A meta-regression analysis. American Journal of Agricultural Economics, 97(1), 103–121.Google Scholar
Panchenko, V. (2005). Goodness-of-fit test for copulas. Physica A: Statistical Mechanics and Its Applications, 355(1), 176–182.Google Scholar
Salmon, F. (2009). Recipe for disaster: The formula that killed Wall Street. Wired Magazine, February 23, 2009. Accessed at http://archive.wired.com/techbiz/it/magazine/17-03/wp_quant?currentPage=all.Google Scholar
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), 761–772.Google Scholar
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.Google Scholar
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 http://ageconsearch.tind.io/bitstream/6264/2/464004.pdf.Google Scholar
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), 101–112.Google Scholar
Xu, W., Filler, G., Odening, M., and Okhrin, O. (2010). On the systemic nature of weather risk. Agricultural Finance Review, 70(2), 267–284.CrossRefGoogle Scholar
Zimmer, D. M. (2016). Crop price comovements during extreme market downturns. Australian Journal of Agricultural and Resource Economics, 60(2), 265–283.CrossRefGoogle Scholar