Further evidence on the explanatory power of spot food and energy commodities market prices for futures prices

被引：2
作者：
Cartwright P.A. [1 ]
Riabko N. [2 ]
机构：
[1] ESG Management School, 59 rue Nationale, Paris
[2] Market Studies Department, FranceAgriMer, 12 rue Henri Roi-Tanguy, Montreuil-sous-Bois Cedex
来源：
Review of Quantitative Finance and Accounting | 2016年 / 47卷 / 3期
关键词：
Causality; Commodities prices; Non-normality; Oil; Temporal aggregation;
D O I：
10.1007/s11156-015-0513-5
中图分类号：
学科分类号：
摘要：
This research is focused on analyzing spillover effects from crude oil to agricultural commodities futures markets. Moreover, emphasis is placed on the “reverse” relationships between spot and futures markets with particular attention given to the interrelationships. The study is interesting for reasons of economics and finance as well as for taking into account geo-political considerations. This study lends insight into the empirical validity of reverse regressions hypothesizing that spot prices today contain information useful for predicting forward rates in the future. This paper considers the importance of the effects of temporal aggregation as well as alternative time series model specifications and assumptions on the distributions of residuals. In addition to the assumption of normality, the paper considers use of a fat-tailed distribution (multivariate t-distribution) to examine the robustness of results that are based on the normality assumption. Finally, models are compared in terms of ex post predictive validity. © 2015, Springer Science+Business Media New York.
引用
收藏
页码：579 / 605
页数：26
相关论文
共 69 条
  • [1] Affleck-Graves J., McDonald B., Nonnormalities and tests of asset pricing theories, J Finance, 44, pp. 889-908, (1989)
  • [2] Altay-Salih A., Pinar M., Leyffer S., Constrained nonlinear programming for volatility estimation with GARCH models, SIAM Rev, 45, pp. 485-503, (2003)
  • [3] Andersen T.G., Bollerslev T., Answering the skeptics: yes, standard volatility models do provide accurate forecasts, symposium on forecasting and empirical methods in macroeconomics and finance, Int Econ Rev, 39, pp. 885-905, (1998)
  • [4] Baffes J., Oil spills on other commodities, Resour Policy, 32, pp. 126-134, (2007)
  • [5] Balcombe K., The nature and determinants of volatility in agricultural prices: an empirical study from 1962–2008, 2–24, in Commodity Market Rev, 2009–2010. Food and Agriculture Organization of the United Nations. FAO, Rome, (2010)
  • [6] Bauwens L., Laurent S., Rombouts J.V.K., Multivariate GARCH models: a survey, J Appl Econom, 21, pp. 79-109, (2006)
  • [7] Beak J., Seo J.Y., A study on unoberserved innovations of oil price: evidence from global stock, bond, foreign exchange, and energy markets, Rev Pac Basin Finance Mark Policies, 18, (2015)
  • [8] Black S., Rational response to shocks in a dynamic model of capital asset pricing, Am Econ Rev, 66, pp. 767-779, (1976)
  • [9] Bollerslev T., Generalized autoregressive conditional heteroskedasticity, J Econom, pp. 307-327, (1986)
  • [10] Bollerslev T., Engle R., Nelson D.B., ARCH models, Handbook of econometrics, (1994)
1234567