Adaptive modelling for forecasting economic and financial risks under uncertainty in terms of the economic crisis and social threats
DOI:
https://doi.org/10.15587/2312-8372.2018.135483Keywords:
adaptive modelling, uncertainty identification, risk estimation, decision support systemAbstract
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.
References
- Zgurovsky, M. Z. (2016). Technology foresight of ukrainian economy in the medium (up to 2020) and long term (until 2030) time horizons. Visnik Nacional’noi’ Akademii’ Nauk Ukrai’ni, 1, 57–68. doi: http://doi.org/10.15407/visn2016.01.057
- Szeles, M. R., Munoz, R. M. (2016). Analyzing the regional economic convergence in Ecuador. Insights from parametric and nonparametric models. Romanian Journal of Economic Forecasting, XIX (2), 43–65.
- Zivkov, D., Njegic, J., Papic-Blagojevic, N., Petronijevic, J. (2016). Monetary effectiveness in small transition economy – the case of the republic of Serbia. Romanian Journal of Economic Forecasting, XIX (3), 5–18.
- Garcia, M. G. P., Medeiros, M. C., Vasconcelos, G. F. R. (2017). Real-time inflation forecasting with high-dimensional models: The case of Brazil. International Journal of Forecasting, 33 (3), 679–693. doi: http://doi.org/10.1016/j.ijforecast.2017.02.002
- Stovicek, K. (2007). Forecasting with ARMA models. The case of Slovenian inflation. Prikazi in analize, XIV/1, 23–55.
- Dobrescu, E. (2017). Modelling an Emergent Economy and Parameter Instability Problem. Journal for Economic Forecasting of Institute for Economic Forecasting, 2, 5–28.
- Arora, S., Little, M. A., McSharry, P. E. (2013). Nonlinear and nonparametric modeling approaches for probabilistic forecasting of the US gross national product. Studies in Nonlinear Dynamics and Econometrics, 17 (4), 395–420. doi: http://doi.org/10.1515/snde-2012-0029
- Glass, K. (2014) Real-time Macroeconomic Data and Uncertainty. DEP (Socioeconomics) Discussion Papers. Macroeconomics and Finance Series, 6, 1–29
- Eeckhoudt, L., Gollier, C., Schlesinger, H. (2004). Economic and financial decisions under uncertainty. Princeton: Princeton University Press, 248.
- Penman, S. H. (2010). Financial Forecasting, Risk and Valuation: Accounting for the Future. Abacus, 46 (2), 211–228. doi: http://doi.org/10.1111/j.1467-6281.2010.00316.x
- Clements, M. P., Franses, P. H., Swanson, N. R. (2004). Forecasting economic and financial time-series with non-linear models. International Journal of Forecasting, 20 (2), 169–183. doi: http://doi.org/10.1016/j.ijforecast.2003.10.004
- Sekerke, M. (2015). Bayesian Risk Management: A Guide To Model Risk And Sequential Learning In Financial Markets. Hoboken: John Wiley & Sons, Inc., 240. doi: http://doi.org/10.1002/9781118864784
- Bidyuk, P., Gozhyj, O., Korshevnyuk, L. (2012). Development of Decision Support Systems. Mykolaiv: Petro Mogyla Black Sea National University, 379.
- Petersen, I. R., Savkin, A. V. (1999). Robust Kalman filtering for signals and systems with large uncertainties. Boston: Birkhouser, 202. doi: http://doi.org/10.1007/978-1-4612-1594-3
- Statistical publication. (2016). The official website of the State Statistics Service of Ukraine. Available at: http://www.ukrstat.gov.ua/
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Copyright (c) 2018 Petro Bidyuk, Tatyana Prosyankina-Zharova, Oleksandr Terentiev, Mariya Medvedieva
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