On the Volatility Spillover between Agricultural Commodities and Latin American Stock Markets

Vincenzo Candila,Salvatore Farace

doi:10.3390/risks6040116

Abstract

Addressing the volatility spillovers of agricultural commodities is important for at least two reasons. First, for the last several years, the volatility of agricultural commodity prices seems to have increased. Second, according to the Food and Agriculture Organization, there is a strong need for understanding the potential (negative) impacts on food security caused by food commodity volatilities. This paper aims at investigating the presence, the size, and the persistence of volatility spillovers among five agricultural commodities (corn, sugar, wheat, soybean, and bioethanol) and five Latin American (Argentina, Brazil, Chile, Colombia, Peru) stock market indexes. Overall, when a negative shock hits the commodity market, Latin American stock market volatility tends to increase. This happens, for instance, for the relationships from corn to Chile and Colombia and from wheat to Peru and Chile.

Highlights

Since the beginning of the 1990s, the financial literature has displayed remarkable interest in the transmission of volatility, alternatively called volatility spillover, from a source to a recipient.In this regard, how volatility spillover can be estimated and how long it lasts have been largely investigated (Hamao et al 1990; Engle et al 1990; Lin et al 1994, among others)
Once we analyzed which commodity originated a volatility spillover towards a Latin American country, we focus on the size and persistence of such a spillover by using the multivariate generalized autoregressive conditional heteroskedastic (MGARCH)-volatility impulse response function (VIRF) methodology summarized before
This paper focused on investigating the volatility spillovers from selected agricultural commodity markets to five Latin American stock markets (Argentina, Brazil, Chile, Colombia, and Peru)

Summary

Introduction

Since the beginning of the 1990s, the financial literature has displayed remarkable interest in the transmission of volatility, alternatively called volatility spillover, from a source to a recipient In this regard, how volatility spillover can be estimated and how long it lasts have been largely investigated (Hamao et al 1990; Engle et al 1990; Lin et al 1994, among others). Subsequently generalized in a further contribution (Diebold and Yilmaz 2012), lies in the vector autoregressive context and relies on forecast-error variance decomposition. By means of the latter, it is possible to identify a spillover index (and the direction), the main sources, and the recipients of the spillovers. A second method uses the multivariate generalized autoregressive conditional heteroskedastic (MGARCH) class of models (surveyed in Bauwens et al 2006) to firstly estimate the conditional covariance matrices of the variables under investigation, and to apply the volatility impulse response function (VIRF) methodology proposed by Hafner and Herwartz (2006)

Objectives

Methods

Results

Conclusion