The aim of this paper is to examine the context of the decentralization in Morocco, its evolution, the objectives and the finalities of this process, as well as the modalities and rules of administrative division. In addition, it analyzes the impact of decentralization on public and fiscal policies, and on economic growth. A detailed analysis of local finances and the role of each administrative level is presented. This analysis shows that Morocco has to ignore the regional identity during the administrative division, which will provoke conflicts and social risks. For local finances, transfers of resources to local authorities remain low. In terms of sharing tax resources, there is a lack of harmony, communication and information sharing between the different levels and the state. Our contribution will focus on the issues of administrative division, spatial optimum, regional balance, good governance, autonomy and regional taxation, as well as the division of powers. This paper also attempts to analyze the constraints and assess the potential of the regions. Finally, it seems that the speed of application of the process is slow.

In this paper we decompose the realized volatility of the GARCH-RV model into continuous sample path variation and discontinuous jump variation to provide a practical and robust framework for non- parametrically measuring the jump component in asset return volatility. By using 5-minute high-frequency data of MASI Index in Morocco for the period (January 15, 2010 - January 29, 2016), we estimate parameters of the constructed GARCH and EGARCH-type models (namely, GARCH, GARCH-RV, GARCH-CJ, EGARCH, EGARCH-RV, and EGARCH-CJ) and evaluate their predictive power to forecast future volatility. The results show that the realized volatility and the continuous sample path variation have certain predictive power for future volatility while the discontinuous jump variation contains relatively less information for forecasting volatility. More interestingly, the findings show that the GARCH-CJ-type models have stronger predictive power for future volatility than the other two types of models. These results have a major contribution in financial practices such as financial derivatives pricing, capital asset pricing, and risk measures.