This paper studies the nonlinear adjustment between industrial production and carbon prices – coined as ‘the carbon-macroeconomy relationship’ – in the EU 27. We model carbon price returns and industrial production as nonlinear and state-dependent, with dynamics depending on the sign and...

his paper develops a model of carbon pricing by considering two fundamental drivers of European Union Allowances : economic activity and energy prices. On the one hand, economic activity is proxied by aggregated industrial production in the EU 27 (as it provides the best performance in a...

Using the structural unobserved component (UC) modeling, this study will analyze the Senegalese economic growth path after 5 decades of independence by focusing on the potential output, the GDP cycle, and the type of shocks on the GDP. Empirical evidence suggests that an inventory cycle mainly...

The assumption of linearity is implicitly accepted in the process which generates a time series condition submitted to a ARIMA. That is why, in this paper, we shall discuss the research of long memory in the processes: the fractional ARIMA models, denoted as ARFIMA, where d and D, the degree of...

To assess how financial markets and commodities are inter-related, this paper introduces a ‘volatility surprise’ component into the asymmetric DCC with one exogenous variable (ADCCX) framework. We develop an econometric model in which returns and volatility allow to influence pairs of...

This article adopts the asymmetric DCC with one exogenous variable (ADCCX) model developed by Vargas (2008), by updating the concept of ‘volatility surprise’ to capture cross-market relationships. Current methods for measuring spillovers do not focus on volatility interactions, and neglect...

This paper contains the first empirical application of the Dynamic Equicorrelation (DECO) model to a cross-market dataset composed of equities, bonds, foreign exchange rates and commodities during 1983-2013. The originality of our approach consists in examining the volatility equicorrelations,...

This paper proposes a new empirical methodology for computing a cross-market index – coined CMI – based on the Factor DCC-model. This approach solves both problems of treating high-dimensional data and estimating time-varying conditional correlations. We provide an application to a...

This paper proposes a new empirical methodology for computing a cross-market volatility index – coined CMIX – based on the Factor DCC-model, implemented on volatility surprises. This approach solves both problems of treating high-dimensional data and estimating time-varying conditional...

This article proposes a new empirical methodology for computing a cross-market volatility index - coined CMIX - based on the Factor-Dynamic Conditional Correlation (DCC) model, implemented on volatility surprises. This approach solves problems in treating high-dimensional data and estimating...