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Asymmetric price volatility transmission between food and energy markets:

The case of Spain

Fadi Mohamed Teresa Serra

Outlines

• Introduction

• Literature review

• Methodology

• Results

• Conclusions

Introduction: Overview

• The relationship between biofuel and food markets has been receiving growing attention within the economics literature.

• Several research results point towards the conclusion that fuel price shocks govern rising food prices.

• Most of the literature on energy-food price links has focused on price levels. More recently MGARCH models have started to be used to capture the volatility spillover effect.

• In 2010, biodiesel production in the EU reached 9.5 million tons and production capacity was 22 million tons.

•

Introduction: EU production of biodiesel

• Biodiesel in 2010 consumption (National Action Plans): 12,3 million tons

Introduction: EU consumption, trade of biodiesel

• In 2010, Biodiesel production in Spain reached 0.925 million tons, production capacity of 4,100 million tons.

Introduction: Spain's production of biodiesel

• In 2011, consumption of biodiesel has been doubled since 2008 to reach 1,668 million tons.

• In 2010, The contribution of imported biodiesel in local consumption grew to 60%.

• In 2010, Argentina is currently the primary biodiesel supplier to Spain. Its exports throughout 2009 constituted over 30% of total Spain’s imports and this statistic increased to over 50%.

Introduction: Spain’s Consumption, trade of biodiesel

Literature review

• Food and energy price levels relationship literature has provided ample evidence that price transmission mechanisms are usually characterized by nonlinearities [Balcombe and Rapsomanikis 2008; Serra et al 2010; Hassouneh et al, 2012]

• Previous studies within the literature on food and energy price volatility interactions didn’t receive considerable attenttion [Serra et al., 2011; Du et al, 2011; Trujillo-Barrera et al., 2012]

• The literature on food and energy price volatility allowing for exogenous variables in the conditional variance are very scarce [Balcombe, 2011; Serra and Gil, 2012]

• Well-known standard unit root tests are used in our analysis to test for non-stationarity in price series (Dickey and Fuller, 1979; Perron, 1997; KPSS, 1992).

• We used Johansen (1988) ML approach for testing multiple co-integrating vectors.

• Finally, we jointly estimated using ML a standard VECM asymmetric BEKK-MGARCH.

Methodology: Unit roots and Co-integration

• The Co-integration model

• The conditional mean model

• The conditional variance model (BEKK-GARCH)

Methodology: Conditional mean and variance

Results: DATA

We use weekly nominal Spanish biodiesel blend prices ,

international crude oil prices ,

refined Spanish sunflower oil prices ,

Data were observed from 07/11/2006 to 05/10/2010.

• The Co-integration model

• The conditional mean

Results: Co-integration and conditional mean

 

αi

i=1 i=2 i=3  

-0.108***(0.016) -0.027(0.087) -0.073(0.041)

1i0.196***(0.047) 0.161***(0.012) 8.04e-4(0.021)

2i-0.318(0.236) 0.169**(0.062) 0.032(0.090)

3i0.284**(0.116) -3.87e-3(0.032) 0.464***(0.062)

Results: Conditional variance equations

• Our analysis shows that the biodiesel industry in Spain has had an impact on both short-run sunflower oil price levels and price volatility.

• Both the EU and the US advocate for an increase in the role of renewable energies as instruments to reduce dependence on fossil fuels, as well as to reduce greenhouse gas emissions.

• Up to date, marketed biofuels are mainly first generation biofuels that rely on agricultural commodities as feedstock. Hence policies promoting renewable energies have had a relevant effect on the agricultural sector, altering the pattern of the global agricultural land use and production, as well as agricultural price behavior.

Conclusions: Policy implications

• MGARCH models have been found to be characterized by several limitations. Among them it is noteworthy the assumption of normally distributed errors.

• This suggests a possible extension of our analysis to a consideration of conditional volatility models that are robust to misspecifications of the error distribution, like semi-parametric models.

• These models, moreover, allow more flexibility for modeling the possible presence of nonlinearities in the price behavior. Thus, the objective is to use these methods to better capture price behavior when modeling actual price.

Conclusions: Future research

THANKS