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Economic cybernetics [Text]: int. sci. journal / Donetsk national university; [head of ed.board Yu.G.Lysenko]. - Donetsk: DonNU, 2013. - №4-6(82-84) - 95 p.
The distribution of the characteristics of the maximum expected utility portfolio based on VaR: the impact of investor's risk aversion coefficient
Zabolotskyy T.N., Vitlinskyy V.V.
Purpose and subject of research
This study investigates the problem of rational choice
of portfolio structure using the expected utility based on Value-at-Risk.
Research methodology
The study used formal mathematical methods, the method
of economic and mathematical modeling, methods of portfolio construction, analytical methods of research.
Value results
We discuss the correctness of sample estimator of
rational structure weights. Specifically, we define densities of the sample
estimators for main portfolio characteristics and elaborate how the densities
depend on investors' risk aversion coefficient. Our empirical tests allow us to
give some recommendations about the rational choice of the coefficient based on
the data from PFTS Ukrainian stock exchange market. It is shown that the high
values (larger than 4) of this coefficient should be avoided as well as the low
values (in the neighborhood of one).
Conclusions
The paper examines properties of portfolio
characteristics with maximum expected utility based on Value-at-Risk. The use
of this risk measure in portfolio theory is fully consistent with
recommendations of the main banking documents. From the theoretical point of
view application of expected utility function for portfolio constructing is a
generalization of the portfolio constructing problem with minimum risk and given
level of portfolio return.
Key words: portfolio construction, Value-at-Risk, rational structure
weights, portfolio characteristics.
References:
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portfolio weights / Y. Okhrin, W. Schmid // Journal of econometrics. – 2006. –
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Operational risk consultative document, supporting document to the New Basel
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6. Jorion P. Value at Risk: the new benchmark for
managing financial risk / P. Jorion. – New York: McGraw-Hill Professional,
2002. – 544 p.
7. Kaminskyy A. B. Modelling of risk aversion using the
Value at Risk methodology / A. B. Kaminskyy // Theoretical and practical
problems of economics. – 2005. – № 6. – P. 145-154 (in ukrainian).
8. Vitlinskyy V. V. An integrated approach to applying
the Value at Risk methodology / V. V. Vitlinskyy, A.B. Kaminskyy // Economic
Cybernetics. – 2004. – № 5-6. – P. 4-14 (in ukrainian).
9. Alexander G. J. Economic implication of using a
mean-VaR model for portfolio selection: a comparison with mean-variance
analysis / G. J. Alexander, M. A. Baptista // Journal of economic dynamics
& control. – 2002. – №26. – P. 1159 – 1193.
10. Bodnar T. Minimum VaR and Minimum CVaR optimal
portfolios: estimators, confidence regions, and tests / T. Bodnar, W. Schmid,
T. Zabolotskyy // Statistics & Risk Modeling. – 2012. – №29. – P. 281-314.
11. Bliss R. R. Option-implied risk aversion estimates
/ R. R. Bliss , N. Panigirtzoglou // The journal of finance. – 2004. – №59(1).
– P. 407-446.
12. Bollerslev T. Dynamic estimation of volatility risk
premia and investor risk aversion from option-implied and realized volatilities
/ T. Bollerslev, M. Gibons, H. Zhou// Journal of econometrics. – 2011. -
№160(1). – P. 235-245.
13. Mansini R. Conditional value at risk and related
linear programming models for portfolio optimization / R. Mansini, W. Ogryczak,
M. G. Speranza // Annals of operations research. – 2007. – №152. – P. 227-256.
14. Fama E.F. Foundations of finance / E.F. Fama. – New
York: Basic Books. 1976. – 391 p.
15. Bodnar T. Econometrical analysis of the sample
efficient frontier / T. Bodnar, W. Schmid // The European journal of finance. –
2009. – №15. – P. 317-335.
Modeling of production and logistics systems with non-identical hardware setup
Medvedieva M.I.
Purpose and subject of research
We consider
the problems of function modeling production and logistics systems with
unreliable equipment and non-identical readjustment before servicing the next
batch of orders, based on queuing theory models. To solve this problem we
present the flexible manufacturing system in the form of a single-channel
queuing system, where the input Poisson flow intensity of orders per unit time.
Research methodology
The study used formal mathematical methods, the method
of economic and mathematical modeling, queuing theory, analytical methods of research.
Value results
Order
processing takes place in the order of their receipt, the order processing time
has exponential distribution law with parameter . The device has a feature
consisting in the fact that after the processing of all orders that are in the
system, it goes into an idle state and to move it into working condition
necessary readjustment, which begins after the date of the request to a free system.
Duration changeover has an exponential distribution with parameter . In addition, it is assumed
that the equipment in the process of its operation may fail and recover. In the
event of equipment failure, it is repaired (recovery) work. Maintenance of
equipment (recovery, prevention and readjustment) carries a team of workers.
Conclusions
We assume that the device can fail at any time, as during
maintenance applications (running), the application is under maintenance at the
time of failure of the device is lost. Orders that are in the system at the
time of equipment failure (if any), are served after the repair. After the
restoration is necessary to readjust the equipment, the duration of which is
different from the duration, which began after the receipt of the request to a
free system (from the initial changeover). For the described characteristics of
the system are found, which are used to find the basic operating parameters of
the system used to construct economical criteria.
Key words: queuing system, the stationary probability, reliability,
prevention, rehabilitation and readjustment of equipment.

1. Radio-Electronics-Television
Manufacturers Association, 1955, Electronic. Applications Reliability Review,
v. 3, № 1, May, p.18.
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Б.В., Беляев Ю.К., Соловьев А.Д. Математические методы в теории надежности. –
М.: Наука, 1965. – 524 с.
3. Румянцев
Н.В. Моделирование гибких производственно-логистических систем. – Донецк:
ДонНУ, 2004. – 235 с.
4. Новиков О.А.
Петухов С.И. Прикладные вопросы теории массового обслуживания. – М.: «Советское
радио», 1969. – 400 с.
Approximate analytical solution of the inflasion kinetic model
Vitlinskyi V.V., Koliada Y.V., Perten S.I.
Purpose and subject of research
The aim of the article is to highlight the mathematical formalization
basic principles of the approximate inflation kinetic models solutions, the
exact solutions of which are obtained only by numerical integration.
Research methodology
The basis in achieving this goal is the detailed analysis of the
numerical solutions integral curves, which allowed to identify and formulate
changes in the behavior of numerical solution, and caused the changes in the
model parameters values.
Value results
An approach described in the article expands the range of possibilities
in the modeling of inflation processes, namely: the prediction of their quantitative
level for particular conditions; the creation of alternative development
scenarios; the definition of anti-inflation measures necessary to achieve the
optimal rate of inflation for the economy. Approximate solutions in the
analytical form based on exponential functions are offered. The estimation
error of the abovementioned solutions is implemented. The formulas of the peak
significance computation of the inflationary surge, which took place
in the result of money supply increase, are obtained.
Conclusions
The approximate analytical solutions of the model are made, that
facilitates the transition to the next quality level of the inflation processes
development within the inflation kinetic model.
Key words: kinetic
models, modeling of inflation processes, money supply increase, approximate
analytical solution.
References
1. Vasylieva, A.T., Hostynets,
V.S. (2009), “Pro znakhodzhennia koefitsiientiv kinetychnoi modeli infliatsii”
[On the coefficients finding of the inflation kinetic model], Economic bulletin
of National technical university of Ukraine «Kyiv polytechnical institute»,
no.6, pp.417-421.
2. Vitlinskyi, V.V., Koliada,
Yu.V., Kravchenko, T.V. and Trokhanovskyi, V.I. (2013), Adaptyvni modeli v
ekonomitsi [Adaptive models in economics], pain [electronic resource], Kyiv
National Economic University named after Vadym Getman, Ukraine.
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“Investigation of nonlinear models of economic dynamics”, Zovnishnia torhivlia:
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S.I. (2011), “Mathematical modeling of inflation in Ukraine”, Economic
cybernetics, no.1-3, pp.16-25.
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S.I. (2007), “The synergetic effect of the inflationary process”, Proc. Int. Sch.
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6. Koliada, Yu.V. (2010),
“Fazovi ta parametrychni portrety typovykh matematychnykh modelei neliniinoi
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mathematical models of economic dynamics], Modeliuvannia ta informatsiini
systemy v ekonomitsi: Zb. nauk. Prats [Modelling and information systems in
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7. Nakoryakov, V.E., Gasenko,
V.G. (2004), “A kinetic model of inflation”, Economics and Mathematical
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8. Novozhylova, M.V., Koiuda,
P.N. and Chub, I.A. (2005) Modeliuvannia ekonomichnoi dynamiky [Modeling of
economic dynamics], Kharkiv National University of Construction and
Architecture, Ukraine.
9. Osechkina, T.A.,
Postanogova, E.E. (2012) “Mathematical model of an assessment of inflation”,
Prikladnaja matematika i mehanika [Applied mathematics and mechanics], Perm
National Research Politechnic University, pp. 148-158
10. Tabachnikov, Ya.A. (2008)
“Kineticheskaja model’ infljacii, uchityvajuwaja infljacionnye ozhidanija”
[Kinetic model of inflation, taking into account the inflation expectations], Applied
Statistics. Actuarial and Financial Mathematics, no.1-2, pp. 92–100.
11. Vytlynskyi, V.V., Kolyada,
Yu.V., Perten, S.Y. (2009), “Dynamics of the risk by means of watching economic
indexes rates”, Modeling and Analysis of Safety and Risk in Complex Systems:
Proceeding of the Ninth International Scientific School, Saint-Petersburg,
SUAI, pp. 99-104p
Modeling of a flexible manufacturing system with changeover between two production cycle
Rumyantsev N.V.
Purpose and subject of research
In this
paper we consider a model of a flexible manufacturing system based on the
theory of queues, which itself is flexible manufacturing system is represented
as a single-channel queuing system with changeover and loss requirements. It is
assumed that the system input Poisson flow intensity of orders per unit time.
Research methodology
The proposed approach to the modeling of time series is based on the
methodology of multivariate analysis and continuity equation, which relates the
probability density function of the state variables of the system with their
speeds.
Value results
Order processing takes place in the order of their
receipt, the order processing time has exponential distribution law with
parameter . The device has a feature consisting in the fact that after the processing
of all orders that are in the system, it goes into an idle state and to move it
into working condition necessary readjustment, which begins in a random time
having an exponential distribution with parameter , and the duration of its also has exponential distribution law, but with a
parameter . Requirements entering the
system, as the waiting time for the changeover and the changeover time for the
lost. Note that the delay in the onset of readjustment due to the fact that the
company needs to put new equipment or tooling for the continuation of the
process. In this case, you can save money because the repair team, performs
work on installing new equipment, can be outsourced.
Conclusions
For the described characteristics of the system are
found, which are used to find the basic operating parameters of the system,
namely, the probability of finding equipment changeovers, likelihood of a denial
of service and the average queue length.
Key words: queuing system, the stationary probabilities of the
states of the system, the average queue length, readjustment, loss of orders, delays in the impatient
customers.
1. Румянцев
Н.В. Гибкие логистические системы с переналадкой в начале периода занятости и
потерей требований / Н.В.Румянцев // - Науковий журнал «Бізнес Інформ», № 4,
2012. - Харків: ФОП Александрова К.М.; ВД «ИНЖЕК», 2012. – С. 25-27.
2. Румянцев
Н.В. Гибкие логистические системы с переналадкой в конце периода занятости и
потерей требований / Н.В.Румянцев// - Науковий журнал «Бізнес Інформ», № 5,
2012. - Харків: ФОП Александрова К.М.; ВД «ИНЖЕК», 2012. – С. 51-54.
3. Гнеденко
Б.В. Введение в теорию массового обслуживания / Б.В. Гнеденко, И.Н. Коваленко.–
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Modeling multivariate nonstationary time series of economic dynamics based on fokker-planck equation
Isaienko A.A., Glushchevsky V.V., Isaienko A.N.
Purpose and subject of research
The actual problem of modeling of the multivariate nonstationary time
series of economic dynamics is being researched for the purpose of analysis,
forecasting and decision-making in financial markets.
Research methodology
The proposed approach to the modeling of time series is based on the
methodology of multivariate analysis and continuity equation, which relates the
probability density function of the state variables of the system with their
speeds.
Value results
Equation of
motion of a point in a multidimensional phase space of state variables derived
under the assumption that the evolution of the economic system based on the
interaction of two factors - the growth and dissipation. It is assumed that the
growth rate has a deterministic function, which means that there is a causal
link between variables, and the diffusion component of the velocity is
proportional to the gradient of the state probabilities in a local point of
phase space. In this case the state of the system is determined by multivariate
Fokker-Planck equation. On the basis of two-dimensional Fokker-Planck equations
is constructed model of the real economic process - trading on the stock
exchange. The structure of the model equations of nonlinear responsible
paradigm of financial markets and agreed with the results of empirical
research. We derive differential equations for the evolution of one-dimensional
distributions of prices, trading volume, and spread their moments that are
needed to complete the system for the unknown probability density functions.
Conclusions
The evolution equations are based on sample data and agreed with the
two-dimensional Fokker- Planck equation. Modeling the dynamics and forecasting
of trades carried out by numerical integration of the equations of evolution in
a sliding window of the sample. The proposed approach to modeling allows the
best use of the information contained in the multivariate time series and to
obtain high prediction accuracy. Verification of the model performed on the
rows indices trading on the Ukrainian stock market.
Key words: multivariate
time series, Fokker-Planck equation, forecasting,
stock exchange.

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volumes, V.1, 864p.
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A.I., 2005. Models and methods of social and economic forecasting, 396p.
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The method of constructing functions rationing term-sets for displaying function of belonging
Bakurova A.V., Ivanov V.N.
Purpose and subject of research
Development of the construction method of function rationing variables
therm-sets, representing their reflection on the interval [0;1] for modeling of
operational risk based on fuzzy sets. This will eliminate the impact of
different dimensions of the input variables corresponding to various factors,
operational risk, and display the membership functions of the input variables
in common co-ordinates.
Research methodology
The theoretical basis are the approaches of the theory of fuzzy sets and
fuzzy logic. The proposed method allows to establish the dependence of the
values of operational risk, regardless of the values of the input variable
metrics.
Value results
An analysis
of the work of scientists associated with the development of methods and models
of risk assessment in the process of bank activity has been given. The method
of constructing the functions of regulation to eliminate the impact of
different dimensions of input variables has been suggested, which has allowed
to display the membership functions of the input variables in common co-ordinates.
Prospects for further research consist in building an operational risk warning
system based on the algorithms of fuzzy inference and descriptions of possible
situations of its occurrence.
Conclusions
The scientific novelty of this work consists in the development of the
method of the construction of the functions of regulation to eliminate the
impact of different dimensions of input variables, which allows to display the
membership functions of the input variables in common co-ordinates.
Key words: operational risk, fuzzy sets, function of belonging.
Vitlinsky V.V., 2003. Conceptual aspects of risks in the economic sphere. Proceedings of
International Scientific School MA BR 2003 SPb, pp. 200-206.
Kaminsky
A.B., 2006. Modeling financial risks. Publishing-polygraphic centre "Kyiv
University", 304 p.
Pervozvansky
A.A. and Pervozvanskay T.N.,1994. Financial market: the calculation and risk.
Infra-Moscow, 192 p.
Sazykin
B.V., 2008. Operational risk management in commercial Bank. Moscow – Summit, 282
p.
Solozhentsev
E. D., 2011. I^3 technology for the economy. Science, 387 p.
Chernyak O.
I. and Peshko A.V., 1997. Determination of the optimal portfolio of securities
and accounting methods retrospective data. Banking, pp. 58-61.
Yastremsky
O.I., 1992. Modeling of economic risk. Lybid, 176 p.
Cruz M.G.,
2002. Modeling, Measuring and Hedging
Operational Risk. - John Wiley & Sons Ltd, - XV, 330 p.
Bakurova
A.V., 2010. Self-organization of socio - economic systems: models and methods.
Classic private University, 238 p.
Ramazanov S.
K. and Burbelo O.A, Vitlinsky V.V. and others. 2012. Risks, security, crisis
and sustainable development of economy: methodology, methods, models of
management and decision-making. Noulij, 946 p.
Problem of Identification of Parameters of the Model of Management of Innovation Processes in Reprocessing Agricultural Enterprises
Babenko V.A.
Purpose and subject of research
The main approaches in the identification of parameters of economic and
mathematical models are considered, the problem of a posteriori identification
of the dynamical model parameters of innovative process management of
reprocessing agricultural enterprises is researched.
Research methodology
Solutions for a posteriori identification of parameters of the dynamic
model of innovative process management of reprocessing agricultural enterprises
propose an algorithm that reduces to the implementation of solutions of systems
of linear algebraic equations, the formation of the linear regression equation
and use the method of least squares.
Value results
An algorithm
for solving the problem of a posteriori identification of parameters of the
dynamic model of innovative process management of reprocessing agricultural
enterprises proposed algorithm makes it possible to develop efficient numerical
procedures to implement a computer simulation of the dynamics of the system of
innovative process management of agricultural enterprises.
Conclusions
Results presented in this paper can be used for
economic-mathematical modeling and solving optimization processes of
forecasting and data management in a lack of information and the availability
of risk, as well as for the development of appropriate software and hardware
systems to support effective management decisions in practice.
Key words: posteriori identification, innovation process in
agriculture, economic and mathematical modeling, discrete dynamical system, the risks of agricultural production.

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The Modern Approaches to Managing Development of the Enterprise in the Field of Non-bank Financial Services
Zherlitsyn D. M. and Berlin V.M.
Purpose and subject of research
Synthesis of the modern approaches to the management of development and
also justification of tool set implementation strategy to increase the
effectiveness of the management of the enterprise in the non-banking financial
services.
Research methodology
The theoretical framework is based on the methods of system analysis to
the business management of the financial sector, innovative models of financial
management, modern methods of economic cybernetics.
Value results
The proposed
theoretical approach is used to improve the system of development of the
enterprise in the non-banking financial services through the introduction of
complex administrative innovations.
Conclusions
The result of the study is the author's model of
implementation of managerial innovations to stimulate the development of the
enterprise sphere of non-bank financial services. The proposed integrated
approach combines the tools of investment management, economy and mathematical
methods, modern market theory and microeconomic analysis to a single
organizational and economic model. The proposed approach enhance the
competitiveness and sustainability of the enterprise in the field of
non-banking financial services.
Key words: development management, management of innovation, the
filed of non-bank financial services, enterprise management, financial
management.

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Mechanism of prediction strategies indicators financial resources industrial enterprises
Serduk V.N., Zorina M.S.
Purpose and subject of research
This article describes the features of strategy management of an
industrial enterprise financial resources, the conditions for increasing its
efficiency and proposed economic- mathematical model of the mechanism of
forecasting indicators strategic financial management of an industrial
enterprise, which improves the efficiency of operation of the business through
improved management of financial resources , to improve the accuracy and
timeliness of management decisions based on a scenario assess the economic
impact of the use of different measures for financial management of an
industrial enterprise.
Research methodology
The theoretical framework is based on the methods of system analysis to
the business management of the financial sector, innovative models of financial
management, modern methods of economic cybernetics.
Value results
The main of
this model are five scenarios for allocating financial resources of the
industrial enterprises in the respective activity.
Conclusions
According to the results of simulation experiments, we can conclude that
the application of the economic-mathematical model to minimize the cost of
financial resources and the value of their involvement. Addressing financial
management is the foundation of efficiency, competitiveness and financial
sustainability of the Ukrainian industrial enterprises.
Key words: industrial
enterprises, financial resources, strategies indicators, mechanism of
prediction.

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Model in managing the implementation of ERP systems
Poluektova N.R., Alekseevskiy D.G.
Purpose and subject of research
The aim of the study is to find methods for effective
management of ERP- systems.
Research methodology
The theoretical framework is based on the methods of system analysis to
the business management of the financial sector, innovative models of financial
management, modern methods of economic cybernetics.
Value results
The complication and the increasing cost of complex
systems of operational business management (ERP-systems) cause deepening of
problems of evaluating their effectiveness. We need mechanisms that allow
manage the selection, implementation and use of such systems. As a result, the
model in managing the implementation of integrated enterprise management
systems was built.
Conclusions
The complication and the increasing cost of complex
systems of operational business management (ERP-systems) cause deepening of
problems of evaluating their effectiveness. We need mechanisms that allow
manage the selection, implementation and use of such systems. As a result, the
model in managing the implementation of integrated enterprise management
systems was built. The model allows to evaluate the impact of management
factors such as top management support , quality of communication , the degree
of awareness among staff and others on the speed of implementation of automated
functions. A system dynamic approach allows account of the positive feedbacks
that occur in the system in the implementation of information management
systems. Simulation experiments with the model help to formalize and present in
quantitative form some qualitative performance of ERP- systems.
Key words: ERP-system
implementation management, system dynamics.

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