Chapter 3. Has agricultural price volatility increased since 2007?

In this chapter, we assess the development of price volatility in major agricultural markets over the past years. We base this analysis on univariate GARCH models for the commodities under consideration, and then analyse these estimated volatilities further with a specific focus on spillovers between closely related agricultural markets. These volatility dynamics are captured in vector autoregression (VAR) models for five different groups of key agricultural markets. Since volatility is inherently unobservable (and hence must be estimated), we first introduce the major conceptual choices which the researcher faces when conducting analyses of agricultural price volatility. In particular, we discuss the issues of time horizons, ex-ante versus ex-post perspectives, and estimation methods. We then take a closer look at the development of price volatility on key agricultural markets, and put those into context with the recent literature. Finally, we provide some additional results on the distinction between short- and long-term volatility.

BRÜMMER Bernhard;  DONMEZ Ayca;  JAGDHANI Tinoush Jamali;  KORN Olaf;  MAGRINI Emiliano;  SCHLÜSSLER Kristina; 

2016-02-29

Earthscan from Routledge

JRC97038

978-1-138-93741-3,   

https://books.google.es/books?id=sK5YCwAAQBAJ&pg=PP1&lpg=PP1&dq=agricultural,    markets,    instability,    earthscan&source=bl&ots=Ond-kmQUo4&sig=_OaQliuTW_ueDvl7RT4ahCL3si8&hl=en&sa=X&ved=0ahUKEwjFjOHotJLLAhUExxQKHfv5AZIQ6AEINzAD#v=onepage&q=agricultural%20markets,    https://publications.jrc.ec.europa.eu/repository/handle/JRC97038,