Adaptive Short-Term Forecasting of Selected Financial Processes

A computer based system is proposed for adaptive modeling and forecasting of financial and economic processes, that is constructed with application of system analysis principles. A hierarchical structure of decision making process during forecasts estimation was taken into consideration and the methods were used for describing uncertainties of structural, parametric and statistical nature. To estimate model structure and parameters several mutually supporting estimation techniques were used as well as optimal state estimation procedure for dynamic systems that allowed take into consideration some types of structural and statistical uncertainties. Probabilistic modeling methods make it possible to consider uncertainties of probabilistic type. The problem of short term forecasting for gold price is considered as an example using a set of constructed regression models and Kalman filter for generating optimal estimates of states. The best forecasting results were achieved with optimal filter and autoregression models with trends. Also the models were constructed for conditional variance that provided acceptable quality forecasts for variance (volatility) that could be used for constructing decision making rules in trading operations.

Publication year: 
2014
Issue: 
1
УДК: 
519.766.4
С. 35–41.
References: 

1. R.H. Shumway and D.S. Stoffer, Time Series Analysis and its Applications. New York: Springer Verlag, 2006, 588 p.
2. P.I. Bidyuk et al., Time Series Analysis. Ukraine, Kyiv: Polytechnika, NTUU KPI, 2013, 607 p. (in Ukrainian).
3. R. Harris and R. Sollis, Applied Time Series Modelling and Forecasting. West Sussex: Jоhn Wiley & Sons Ltd., 2005, 313 p.
4. M.Z. Zgurovsky and N.D. Pankratova, The System Analysis: Problems, Methodology, Applications. Ukraine, Kyiv: Naukova Dumka, 2011, 726 p. (in Ukrainian).
5. F.V. Jensen and Th. Nielsen, Bayesian Networks and Decision Graphs. New York: Spinger-Verlag, 2009, 457 p.
6. M.Z. Zgurovsky and Yu.P. Zaichenko, An Introduction to Computing Intelligence. Ukraine, Kyiv: Naukova Dumka, 2013, 406 p. (in Ukrainian).
7. A. Dobson, An Introduction to Generalized Linear Models. New York: CRC Press Company, 2013, 407 p.

References [transliteration]: 

1. R.H. Shumway and D.S. Stoffer, Time Series Analysis and its Applications. New York: Springer Verlag, 2006, 588 p.
2. P.I. Bidyuk et al., Time Series Analysis. Ukraine, Kyiv: Polytechnika, NTUU KPI, 2013, 607 p. (in Ukrainian).
3. R. Harris and R. Sollis, Applied Time Series Modelling and Forecasting. West Sussex: Jihn Wiley & Sons Ltd., 2005, 313 p.
4. M.Z. Zgurovsky and N.D. Pankratova, The System Analysis: Problems, Methodology, Applications. Ukraine, Kyiv: Naukova Dumka, 2011, 726 p. (in Ukrainian).
5. F.V. Jensen and Th. Nielsen, Bayesian Networks and Decision Graphs. New York: Spinger-Verlag, 2009, 457 p.
6. M.Z. Zgurovsky and Yu.P. Zaichenko, An Introduction to Computing Intelligence. Ukraine, Kyiv: Naukova Dumka, 2013, 406 p. (in Ukrainian).
7. A. Dobson, An Introduction to Generalized Linear Models. New York: CRC Press Company, 2013, 407 p.

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