Перегляд за Автор "Levenchuk, L. B."

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  • Ескіз
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    Bayesian modelling of risks of various origin
    (КПІ ім. Ігоря Сікорського, 2021) Kuznietsova, N. V.; Trofymchuk, O. M.; Bidyuk, P. I.; Terentiev, O. M.; Levenchuk, L. B.
    Background. Financial as well as many other types of risks are inherent to all types of human activities. The problem is to construct adequate mathematical description for the formal representation of risks selected and to use it for possible loss estimation and forecasting. The loss estimation can be based upon processing available data and relevant expert estimates characterizing history and current state of the processes considered. An appropriate instrumentation for modelling and estimating risks of possible losses provides probabilistic approach including Bayesian techniques known today as Bayesian programming methodology. Objective. The purpose of the paper is to perform overview of some Bayesian data processing methods providing a possibility for constructing models of financial risks selected. To use statistical data to develop a new model of Bayesian type so that to describe formally operational risk that can occur in the information processing procedures. Methods. The methods used for data processing and model constructing refer to Bayesian programming methodology. Also Bayes theorem was directly applied to operational risk assessment in its formulation for discrete events and discrete parameters. Results. The proposed approach to modelling was applied to building a model of operational risk associated with incorrect information processing. To construct and apply the model to risk estimation the risk problem was analysed, appropriate variables were selected, and prior conditional probabilities were estimated. Functioning of the models con structed was demonstrated with illustrative examples. Conclusions. Modelling and estimating financial and other type of risks is important practical problem that can be solved using the methodology of Bayesian programming providing the possibility for identification and taking into consideration uncertainties of data and expert estimates. The risk model constructed with the methodology proposed illustrates the possibilities of applying the Bayesian methods to solving the risk estimation problems.
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    Operational risk estimation using system analysis methodology
    (КПІ ім. Ігоря Сікорського, 2024) Bidyuk, P. I.; Tymoshchuk, O. L.; Levenchuk, L. B.
    Financial risks are considered today as popular research topics due to the existing practical necessity for the use of their mathematical models, estimates of possible loss in many areas of human activities, forecasting, and respective managerial decisions in financial and other spheres where capital, obligations, stocks, bonds, and other activities are circulating successfully. Financial processes today exhibit sophisticated forms of evolution in time that require the application of sophisticated modeling, risk estimating, forecasting, and decision-making/support methods, techniques, and procedures. The system analysis approach is applied to solving such problems as a unique and universal research methodology. The financial risks, specifically the operational ones in the study considered, are classified as nonlinear and nonstationary processes that require appropriate methods for analysis and a rather sophisticated analytical description to estimate and forecast possible loss. The results of operational risk analysis are achieved in the form of systemic methodology, models constructed with statistical data, regression analysis, and Bayesian techniques, and estimated loss with the models. The models and system analysis approach proposed for analyzing financial processes are suitable for practical applications, provided the users have appropriate statistical data and expert estimates.
  • Ескіз
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    Uncertainties in data processing, forecasting and decision making
    (КПІ ім. Ігоря Сікорського, 2023) Levenchuk, L. B.; Tymoshchuk, O. L.; Guskova, V. H.; Bidyuk, P. I.
    Abstract. Forecasting, dynamic planning, and current statistical data processing are defined as the process of estimating an enterprise’s current state on the market compared to other competing enterprises and determining further goals as well as sequences of actions and resources necessary for reaching the goals stated. In order to perform high-quality forecasting, it is proposed to identify and consider possible uncertainties associated with data and expert estimates. This is one of the system analysis principles to be hired for achieving high-quality final results. A review of some uncertainties is given, and an illustrative example showing improvement of the final result after considering possible stochastic uncertainty is provided.