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(Showing results 1 to 4 of 4)



Linear and Non-linear Causality between CO2 Emissions and Economic Growth

Marco R. Barassi and Nicola Spagnolo

Year: 2012
Volume: Volume 33
Number: Number 3
DOI: 10.5547/01956574.33.3.2
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Abstract:
In this paper we investigate the casual effects between per capita economic growth and carbon dioxide emissions. The focus on the causal analysis in both mean and variance differentiate this study from other contributions to the literature. The analysis is conducted for six countries. We find substantial evidence of feedback in the causality in mean and volatility spillovers between emissions and output growth in the six countries under examination Keywords: CO2 Emissions, Growth, Multivariate GARCH, Volatility http://dx.doi.org/10.5547/01956574.33.3.2



Financial Speculation in Energy and Agriculture Futures Markets: A Multivariate GARCH Approach

Matteo Manera, Marcella Nicolini, and Ilaria Vignati

Year: 2013
Volume: Volume 34
Number: Number 3
DOI: 10.5547/01956574.34.3.4
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Abstract:
This paper analyses futures prices of four energy commodities (crude oil, heating oil, gasoline and natural gas) and five agricultural commodities (corn, oats, soybean oil, soybeans and wheat), over the period 1986�2010. Using DCC multivariate GARCH models, it provides new evidence on four research questions: 1) Are macroeconomic factors relevant in explaining returns of energy and nonenergy commodities? 2) Is financial speculation significantly related to returns in futures markets? 3) Are there significant relationships among returns, either in their mean or variance, across different markets? 4) Is speculation in one market affecting returns in other markets? Results suggest that the S&P 500 index and the exchange rate significantly affect returns. Financial speculation, proxied by Working�s T index, is poorly significant in modelling returns of commodities. Moreover, spillovers between commodities are present and the conditional correlations among energy and agricultural commodities display a spike around 2008.



Analysis of mean and volatility price transmissions in the MIBEL and EPEX electricity spot markets

A Ciarreta and A Zarraga

Year: 2015
Volume: Volume 36
Number: Number 4
DOI: 10.5547/01956574.36.4.acia
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Abstract:
We use multivariate Generalized Autoregressive Conditional Heteroscedastic models to assess evidence of electricity market integration between Spain, Portugal, Austria, Germany, Switzerland and France from 7-1-2007 to 2-29-2012. Spillovers and price convergence are used as indicators of integration. Evidence of dynamic conditional correlation is found for the pairs Spain-Portugal, Germany-Austria and Switzerland-Austria. Weak evidence of integration is found between Spain-France and Germany-France since no cross volatility transmissions are estimated. There are increasing price convergence and significant mean and volatility spillovers in the rest of the country pairs. We conclude that the European Union target of achieving a single electricity market depends largely on increasing interconnections and efficient rules of market operation.



Understanding Dynamic Conditional Correlations between Oil, Natural Gas and Non-Energy Commodity Futures Markets

Niaz Bashiri Behmiri, Matteo Manera, and Marcella Nicolini

Year: 2019
Volume: Volume 40
Number: Number 2
DOI: 10.5547/01956574.40.2.nbeh
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Abstract:
We look at the dynamic conditional correlations (DCCs) between oil, natural gas and other non-energy commodity futures markets, obtained from a DCC-GARCH model over the period 1998-2014. They are positive and display a sharp increase around year 2008 and a subsequent decrease. The DCCs between energy and metals are larger than the energy-agriculture ones. To understand how macroeconomic and financial factors, as well as speculative activity, influence them, we estimate an ARDL(1,1) model, adopting a pooled mean group (PMG) estimator. We observe that macroeconomic and financial variables are significantly correlated with the energy-agriculture and energy-metals DCCs. Speculative activity contributes to explain the energy-agriculture DCCs but not those of the energy-metals.





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