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The Scenario Analysis allows the study of possible future events on the basis of the different performances of some economic or financial variables (scenarios).

The method of the Scenario Analysis supplements and integrates simulation techniques since it leads to choose, among the various future outcomes of interest and in a flexible way, the trajectory which comes from the scenario that best corresponds to the set of events which are happening.

Correct use of this technique suggests that:

- the scenarios correspond to several degrees of trends of the economic state of interest;
- the scenarios are defined by a small number of variables with respect to the number of simulated outputs;
- the selected independent variables, through their possible combinations, must be representative in sufficient detail of the various economic situations that may happen during the chosen forecast period.

The estimation of future realisations of variables of interest on the basis of different scenarios leads to a better understanding of the implications between the simulated outputs and the scenario variables.

In general, every scenario has its own probability to occur which reflects the degree of likelihood that the related hypotheses may happen. The user can choose to utilise the forecasts related to the scenario more probable and/or more coherent with his perception of the behaviour of the economy.

GRETA uses this approach in making its monthly reports MEFIM, MEFIM Plusand GAM. In this case, the levels of bank and inter-bank rates, interest rate term structure and banks' volumes are forecasted on the basis of three scenarios, defined from the refinancing rate of the central banks, market indexes, industrial production indexes and consumer price indexes. These variables are a useful guideline to define various possible trends of the world-wide economy.

GRETA applies the scenario analysis to the GREM (GRETA Regional Macroeconometric Model) that is a quarterly product dedicated to the Italian regions. GREM is a macroeconometric model based on annual data and provides forecasts of some variables judged relevant in the regional economic programming process and in the evaluation of the European Community programs. G.R.E.M. is developed for all the Italian regions.

GRETA has also taken advantage of this approach with reference to Asset Allocation models. In particular, approaches to find an efficient way to optimise the portfolio composition, on the basis of operator's views on the future behaviour of the economy, have been developed. The main advantage is to obtain a link between the business cycle and the expected portfolio performance, thanks to the specification of several scenarios of asset price realisations.

 

 

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