Adaptive modelling for forecasting economic and financial risks under uncertainty in terms of the economic crisis and social threats

Анотація

The objects of the research are socio-economic processes in the context of structural transformations that take place as a result of socio-political crisis in the country. One of the most problematic points is absence of comprehensive study and lack of justification for prediction of anticipating potential threats in the humanitarian and social spheres and ways to overcome them aiming to stable and positive development of the national economy.In the course of the study the system analysis and system theory methods, mathematic and econometric modelling methods were used. System analysis and system theory are used to study the state and behaviour of national economy and its subsystems in conditions of current uncertainties and risks characteristic of the social treatments and structure changes. Mathematical and statistical modelling methods and decision making theory were used for forecasting development of non-stationary nonlinear processes which identify modern state of Ukrainian economy.The paper considers the problem of developing the methods for solving tasks of modelling and estimating selected types of risks with the possibility for application of alternative data processing techniques, modelling and estimation of parameters and states for the national economy and its components within the current condition of socio-political transformations and structural reforms. To find «the best» model structure it is recommended to apply adaptive estimation schemes that provide for an automatic search in a definite selected range of model structure parameters (type of distribution, model order, time lags, and nonlinearities). Also the adaptive estimation schemes proposed help us to cope with the model structure and parameters uncertainties. The general methodology was proposed for solving selected problem of dynamic process forecasting and estimation of several kinds of socio-economic and financial risks using appropriate statistical data in computer based decision support systems.Results of the research would be useful to other countries where approximately the same kind processes take place.

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