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“…Chevallier et al (2008) [6] found that the industrial production impact positively (negatively) the carbon market during periods of economic expansion (recession), confirming the relationship between macroeconomics and the price of carbon. (Burniaux (2000) [7], Ciorba et al (2001) [8], Sjim (2005) [9] and van der Mensbrugghe (1998)) in the same way showed that energy prices influence CO 2 prices. Redmond and Convery (2007) [10], Battaler et al (2013) [11], Alberola et al (2008) [6] and all studies including energy variables, assumed geometrical brownian motion process for modeling energy prices.…”
ADInternational audienceWe examine the dependence between the volatility of the prices of the carbon dioxide “CO2” emissions with the volatility of one of their fundamental components, the energy prices. The dependence between the returns will be approached by a particular class of copula, the SCAR (Stochastic Autoregressive) Copulas, which is a time varying copula that was first introduced by Hafner and Manner (2012) [1] in which the parameter driving the dynamic of the copula follows a stochastic autoregressive process. The standard likelihood method will be used together with EIS (Efficient Importance Sampling) method, to evaluate the integral with a large dimension in the expression of the likelihood function. The main result suggests that the dynamics of the dependence between the volatility of the CO2 emission prices and the volatility of energy returns, coal, natural gas and Brent oil prices, do vary over time, although not much in stable periods but rise noticeably during the period of crisis and turmoils
“…Chevallier et al (2008) [6] found that the industrial production impact positively (negatively) the carbon market during periods of economic expansion (recession), confirming the relationship between macroeconomics and the price of carbon. (Burniaux (2000) [7], Ciorba et al (2001) [8], Sjim (2005) [9] and van der Mensbrugghe (1998)) in the same way showed that energy prices influence CO 2 prices. Redmond and Convery (2007) [10], Battaler et al (2013) [11], Alberola et al (2008) [6] and all studies including energy variables, assumed geometrical brownian motion process for modeling energy prices.…”
ADInternational audienceWe examine the dependence between the volatility of the prices of the carbon dioxide “CO2” emissions with the volatility of one of their fundamental components, the energy prices. The dependence between the returns will be approached by a particular class of copula, the SCAR (Stochastic Autoregressive) Copulas, which is a time varying copula that was first introduced by Hafner and Manner (2012) [1] in which the parameter driving the dynamic of the copula follows a stochastic autoregressive process. The standard likelihood method will be used together with EIS (Efficient Importance Sampling) method, to evaluate the integral with a large dimension in the expression of the likelihood function. The main result suggests that the dynamics of the dependence between the volatility of the CO2 emission prices and the volatility of energy returns, coal, natural gas and Brent oil prices, do vary over time, although not much in stable periods but rise noticeably during the period of crisis and turmoils
The picture on disparities in productivity growth and in unemployment across European regions reveals the existence of a slow and not very systematic convergence of labor productivity toward a common level, and of an even more uncertain convergence of unemployment rates. This paper uses a unified framework to study both phenomena. We adopt a three-sector perspective (agriculture, industry and services) to assess whether sectoral dynamics helps explaining the observed heterogeneity in the growth and employment regional performances. The main theoretical hypotheses upon which our empirical investigation is based are obtained by models on the dual-economy (e.g. Mas Colell and Razin 1973), where predictions on how out-migration from agriculture can generate convergence are formulated; and by Baumol (1967), where the role of an expansion of services on aggregate growth is studied. Part of our evidence is based on the use of cluster analysis to identify subsets of regions homogeneous in terms of variables such as sectoral dynamics, labor market dynamics, and overall productivity growth. The results are largely consistent with the adopted theoretical framework. Regions that start from a low agricultural share are the richest and grow relatively slowly; regions that start from very high agricultural shares are characterized by a fast decline of that share and by higher than average growth rates; they also show a limited decline in their employment rates. Regions specialized in service activities show a particularly slow rate of productivity growth and a rising employment rate. More generally, we find a large body of evidence suggesting that convergence in aggregate productivity is strongly associated with out-migration from agriculture, and that the magnitude of the impact of the latter on aggregate regional growth depends significantly on which sector absorbs the migrating workers.
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