MANAGING MACROECONOMIC RISKS BY USING STATISTICAL SIMULATION

Sa？etak The paper analyzes the possibilities of using statistical simulation in the macroeconomic risks measurement. At the level of the whole world, macroeconomic risks are, due to the excessive imbalance, significantly increased. Using analytical statistical methods and Monte Carlo simulation, the authors interpret the collected data sets, compare and analyze them in order to mitigate potential risks. The empirical part of the study is a qualitative case study that uses statistical methods and Monte Carlo simulation for managing macroeconomic risks, which is the central theme of this work. Application of statistical simulation is necessary because the system, for which it is necessary to specify the model, is too complex for an analytical approach. The objective of the paper is to point out the previous need for consideration of significant macroeconomic risks, particularly in terms of the number of the unemployed in the society, the movement of gross domestic product and the country’s credit rating, and the use of data previously processed by statistical methods, through statistical simulation, to analyze the existing model of managing the macroeconomic risks and suggest elements for a management model development that will allow, with the lowest possible probability and consequences, the emergence of the recent macroeconomic risks. The stochastic characteristics of the system, defined by random variables as input values defined by probability distributions, require the performance of a large number of iterations on which to record the output of the model and calculate the mathematical expectations. The paper expounds the basic procedures and techniques of discrete statistical simulation applied to systems that can be characterized by a number of events which represent a set of circumstances that have caused a change in the system’s state and the possibility of its application in the field of assessment of macroeconomic risks. The method has no limitations. It can be used to study very complex systems by using special computer programs. The method uses reasonable estimates for important economic inputs to determine a set of results, not only one outcome at one point in time, yet there is a multiple-possibilities estimation of a certain risk performance, regarding the range of economic variables used in the model. Attempt to influence and influence itself on certain macroeconomic risks, in today’s economy, occupies one of the primary imperatives of the world, therefore, this paper deals with the mutual correlation and application of statistical