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19Расчет интегрального индекса жизненного цикла монопрофильного муниципального образования позволит определить не только тенденции, происходящие в экономике, социальной сфере и на уровне инфраструктуры моногородов, но и определить этап его жизненного цикла. Выявление этапа жизненного цикла моногорода способствует выработке наиболее эффективных управленческих решений по вопросам дальнейшего развития монопрофильной территории, что является актуальным для многих регионов России, особенно сибирских регионов, на территории которых находится значительная доля монопрофильных поселений.
СПИСОК ЛИТЕРАТУРЫ: 1. Развитие моногородов России: монография /И.Н. Ильина и др.- М.: Финансовый университет, 2013. -168 с.
2. Антонова, И. С. Моделирование инфраструктуры диверсификации экономики моногорода / И. С. Антонова // Вестник СибГАУ.- 2016.- Том 17, № 4. - С. 1104-1112.
3. Минаева, И.В. Методика оценки социально-экономического развития моногородов/ И.В. Минаева.// Российское предпринимательство. - 2013. - № 19 (241). - С. 46-52.
4. Коновалова, Т. А. Факторы и условия, обеспечивающие функционирование и развитие моногородов Российской Федерации // Молодой ученый. - 2013. - №4. - С. 233-237.
5. Моногород: управление развитием / Т.В. Ус-кова, и др.. - Вологда: ИСЭРТ РАН, 2012. - 220 с.
COMPARATIVE STUDY OF THE CURRENCY RISK MANAGEMENT EFFICIENCY IN RUSSIAN HIGH-TECH COMPANIES IN THE TELECOMMUNICATION SECTOR OF THE ECONOMY
Shvets S.
DSc. in Economics,
National Research University Higher School of Economics, Russia
Sobolev A.
PhD candidate in Innovation management National Research University Higher School of Economics, Russia
Abstract
Telecommunication enterprises in Russia have to constantly update their own material and technical base in order to maintain their competitive positions in the market. Uninterrupted performance at the brink of the capacity of modern technologies causes a steady demand for equipment and hardware components that include a great share of innovative parts, and the vast majority of them are not produced in the domestic market. Advanced currency risk management as an integral part of the enterprise risk management system can deliver the best options for purchases policy and free capital allocation in the money market, thus it can significantly improve the overall corporate efficiency in high-tech enterprises.
Keywords:
telecommunication enterprises, high-tech, innovative components, asset management, currency risk, VaR methodology, capital allocation.
1. Problem background
The last decades of development of management as a science are characterized by deepening and expanding role of corporate risk management for the day-to-day operations of the companies acting in the market. In many ways, this is due to the increased instability of the macroeconomic environment in which businesses have to operate, regardless of their industry affiliation. However, this process is especially notable in the segment of high-tech companies that combine and use in the production process different advanced achievements of scientific developments and their implementations based on the cutting-edge technologies. In Russia, these trends are exacerbated by imposed foreign economic sanctions, and that increases even more the need for an efficient risk management system. Dependence of the majority of the companies on imported components is able to put a successfully functioning business on the brink of financial instability in the shortest time possible. But even with a relatively constant supply of the required components, their price can impact significantly the balance sheet indicators due to
the influence of currency risks factors. As the majority of international purchases settlements are made in currency (usually - in US dollars), and the exchange rates can be highly volatile, many organizations face challenges that can lead to their collapse, as it was observed in 2014-2015.
For the moment, three categories of Russian companies in terms of the development of risk management systems can be identified:
1)companies with a formal risk management system designed to meet the common ISO 31000 [1] requirements only;
2)companies with a satisfactory risk management system that use advanced risk measurement and control techniques (COSO, FERMA);
3)companies that are introducing the integrated risk management system (ERM), as well as the use of the advanced methods for assessing risk exposures, and information disclosure to the Board of directors.
The majority of small and medium-sized companies in the Russian economy are characterized by an
extremely low level of development of the risk management systems. Nevertheless, the objective susceptibility of the business to external risks remains unchanged regardless of the level of its readiness to perceive this exposure, and the main risk for companies engaged in the foreign economic activity is the currency risk. Its underestimation strengthens the threat of significant losses, deterioration of corporate liquidity, loss of the position in the financial market as well as it stimulates the corporate market value to decline, and in some cases, it may result in a bankruptcy [2]. On the other hand, in case of the extremely conservative risk assessment, there is an excessive demand in economic capital provisions - the funds that could be used more efficiently. That is why the study of the existing approaches for currency risk assessment in high-tech companies is deeply connected with the improvement in their efficiency, and it can be used for the working out the better strategies for the currency risk management.
The most common way to assess the currency risk is associated with a group of so-called quantile-based risk measures (QBRM), or stochastic risk metrics. These metrics involve the use of the apparatus of the probability theory and mathematical statistics to build a probabilistic model of the evolution of the risk factors. Various estimation metrics for the currency risk (VaR-analysis, ETL (ES) -analysis, SRM-analysis and others) are characterized by their inherent advantages and disadvantages revealed by comparative analysis and mathematical checks in a number of academic researches [3, 4].
In Russian practice, the main method of the currency risk assessment is represented by the VaR-ap-proach. The most advanced high-tech companies in the telecommunication market try to apply scenario analysis methods and monitor the dynamics of return on risk-adjusted capital (RORAC). Thus, all the companies can be divided into several groups based on the criterion of the risk perception:
1)young risk-averse companies - if there are two strategies with the same expected profit possible but different risk levels, they will choose a strategy with a higher degree of risk in order to quickly establish a client base and / or penetrate a new market;
2)risk-neutral businesses - from two strategies with the same expected profit but with the relevant risk levels, none of them will be chosen with a clear preference;
3)risk-adverse organizations (usually represented by large corporations) - if possible, they will always choose a more conservative policy options. Nevertheless, it does not mean that they are not ready to carry out actions that lead to risk exposure. For them, a higher level of risk should always assume a much greater compensation for its adoption.
There is also a tendency to reduce risk tolerance as a business evolves: a much bigger risk exposure is typical for the small companies with shareholders inclined to perceive the companies' development prospects with a great deal of optimism. As the operations increase, the inevitable limitations related to the size of the working capital appear, and that rises requirements to operations
specifications, internal control systems, personnel qualification as well as technical and information infrastructures. Effectiveness and depth of the analysis of all the risk factors as well as the proper formalization of the risk management procedures define the prospect of growth of a company. With respect to the currency risk, it means the necessity to choose the most efficient model for assessing the risk exposure of a company, but the balance of the calculations accuracy and the expenses associated with collecting of information should be maintained in every case.
2. Models of the currency risk assessment applied by the Russian telecommunications companies
The basis element in the currency risk management is the choice of the assessment metric, as the adequate exposure calculation increases the ability of the management and the Board of directors to make informed decisions about further business operations.
According to the recommendations of a number of expert communities, several principles were developed to simplify the choice of the metrics:
- intuitive clarity for interpretation;
- stability over the time;
- computational simplicity;
- clarity for the top management considerations;
- subadditivity (the risk of the aggregate portfolio is smaller than the sum of individual risks);
- possibility of easy aggregation / decomposition in the process of assessment of the overall risk and distribution of the risk capital.
Among the quantitative risk analysis methods, we can distinguish between:
- analytical (or expert) estimations;
- stochastic model evaluations.
Analytical methods allow to determine the probability of loss based on different calculation approaches of the experts involved. This group includes scenario method, sensitivity analysis, equivalent method. The main drawback of the analytical methods is associated with the high level of influence of every expert, so the human factor can directly impact the results of the risk assessment.
Stochastic methods of the risk assessment assume the determination of the probability distribution of losses based on the statistical data from the previous periods and the risk area description. Usually, for the practical implementation, the following stochastic methods are applied: decision trees, clustering, neural nets, value at risk methodology (VaR). The advantages of these methods are defined by their ability to analyze and assess different scenarios within the given set of events, as well as to aggregate composite risk factors within a single approach based on historical data.
Russian telecommunication companies in order to measure the aggregate risks of a trading portfolio have adopted approaches to currency risk management that originated in banking during the late 1980s and early 1990s. At present, these methods are still widely used as a mean of capital adequacy calculation by several transnational banks and international companies around the world as well as by international organizations such as the Bank for International Settlements, the European Banking Authority, and others.
The VaR methodology is used to assess risks arising from different bank operations with currencies, and it provides the robust loss estimates for a certain period of time calculated with a predefined certainty level under the assumption of normal market conditions. The practical implementation of such a methodology for companies outside the banking area can benefit with the possibility of taking into account different risk parameters at the same time (time horizon, probability descriptors and monetary outcome representation), and that clearly distinguishes this approach from the traditional risk metrics as standard deviation of portfolio yield and coefficient of variation of currency positions. The VaR estimations can be carried out be by various methods, all of which have a similar structure but differ in calculations of probable changes in the value of the portfolio.
From a practical point of view, a number of approaches can be distinguished in accordance with the difference in computational complexity of the operations performed, and that determines the prevalence of the methods in the telecommunication companies.
The most basic and simple to implement approach includes calculation of the logarithmic increments in currencies prices (Fig. 1) with the subsequent calculating of the standard deviation (Fig. 2). As a result, the well-known formula (Fig. 3) is usually the end point of
the calculations for the vast majority of the companies.
ri
r, = lnC-iH (Fig. 1)
rt-1
where rt' and r^-1 are exchange rates of the i-th currency in two periods.
a2 =-^ Yf=1(ri - ¡i)2, n = i£j=in (Fig. 2)
n—1 n
VaR = kKxaxV, (Fig. 3)
where ka is a quantile of normal distribution given the certainty level of a, and V is the currency exposure for the i-th currency.
Some telecommunication companies try to take into account the greater impact of the latest prices on VaR assessment, so they use the weighted deviation from average instead of the standard deviation. However, even such a modification does not exclude the latent risk of this approach associated with the assumption of the normal distribution of portfolio return. Usually, a set of standard diagnostic test (QQ charts, box plots etc.) is applied to the inputs of the models to check for the normality of the data. Thus, the parametric approach to VaR calculation can be used successfully only in case of such a normality is reliably proved prior to the calculations.
As an alternative to the parametric risk assessment, a non-parametric approach can be taken into consideration. It includes various historical modeling methods, estimates and distribution diagrams, bootstrap models, and others.
In case of presence of the personnel capable of performing advanced analytics, and availability of computing infrastructure, advanced types of analysis based on Monte-Carlo simulations can be applied to the collected datasets. In contrast to parametric methods, the simulation approach takes into account the influence of non-linearities in the market prices dynamics. Unlike historical modeling, it can generate an infinite number of scenarios, and therefore to test myriads of possible future outcomes.
Despite the high popularity of the VaR approach for currency risk assessment, it has not only certain advantages but also a number of significant drawbacks (Table 1).
Table 1
Main advantages and disadvantages of the VaR approach
Advantages Disadvantages
1. It provides the unification of the assessment of various types of risk. 2. It can be applied to all types of currency positions and portfolios, and that allows to compare the exposures. 3. It allows to aggregate risks of individual currency positions with respect to correlations within the portfolio. 4. It allows considering simultaneous changes in all of the risk factors. 5. It gives the estimates of possible income under the assumption of accepting the losses due to the risk events. 6. It has the most intuitive and simple monetary representation in national currency or any desired currency a company might be interested in. 7. It allows to carry out efficient business unit separation based on the accepted risk model. 8. It helps to increase the effectiveness of currency risk management based on the introducing the VaR-limits for specific opened positions. 1. It gives information about the possible losses only at the end of the time horizon and does not encounter possible loses in intra-horizon timeframes. 2. It does not take into account the variability effects and clustering of volatility, positive autocorrelation of yields, and deviation correlations. 3. In the classic form, it does not take into account the risk of endogenous and exogenous liquidity of the market (or specific financial instrument). 4. It does not include information about the nature of the distribution and the range of large-scale but rare losses ('fat tails' events). 5. VaR is not a coherent metric of risk as it does not correspond to the subadditivity criterion: under certain conditions, aggregate risk can be higher than the sum of individual VaR estimates of specific positions. 6. It significantly depends on the choice of model parameters (choice of confidence level, time horizon, etc.). 7. It is based on the assumption that the position remains unchanged during the time horizon within which the calculations are performed. 8. Under certain conditions, it does not encourage to diversify risks and stipulates the creation of undervalued and overvalued positions.
Thus, the main advantage of using the VaR approach is the possibility of obtaining a scientifically based metric for the adequate risk assessment as well as for evaluating the aggregate portfolio. However, recognizing the drawbacks of the approach is expected to encourage management of the companies to develop the new, more sophisticated, stochastic risk assessment models that allow to level out some of the disadvantages of the VaR metric while sustaining its advantages.
The most popular alternative to the classic VaR metric is the 'tail loss' evaluation (ETL - expected tail loss). The approach is represented by a group of metrics with various names: expected shortfall (ES), tail conditional expectation (TCE), tail VaR (TVaR), conditional VaR (conditional VaR - CVaR), etc. Despite the terminology differences, the only concept covered by the approach is to assess the average scale of losses exceeding a given level, and that provides additional information on the nature of the profits and losses in the 'tails' of the distribution. Some of the metrics (for example, ES) use the threshold value of the VaR estimates, while others (for example, TCE) rely on the quantile breakdown of losses distribution. Both groups provide identical
risk assessments under conditions of continuous distribution, but the last group loses coherence in case of discrete losses distribution.
Mathematically, ETL metric can be represented in a fairly simple form (Fig. 4).
ETL = E (L | L > VaRa), a e (0;1), where(Fig. 4) L is a random losses distribution function F(L); a is the confidence level (close to 1); VaRa the a-quantile of F(L). In case of a continuous distribution, the ETL metric can be calculated using an integral form (Fig. 5).
ETL =
-L- j
1 -a J
VaRu (L)du
(Fig. 5)
The simplest way to implement this approach is to 'cut the tail' into a large number of sectors, each with the same probability distribution, and to calculate the VaR metrics associated with each 'slice'. In this case, the ETL is equal to the mean of the metrics corresponding to levels from a to 1.
ETL is a coherent risk metric, although it has a number of drawbacks (Table 2).
Table 2
Main advantages and disadvantages of the ETL approach_
Advantages Disadvantages
1. It has all the advantages of the VaR approach. 2. It is a subadditive and coherent risk metric. 3. It is quite intuitive and easy to calculate. 4. It has convexity, so optimization theory tools can be applied. 5. It focuses on assessing the extent of losses, and not only their possibility. 6. It allows us to estimate the average size of losses that exceed the specified level of the VaR figures. 7. It mitigates the impact of the choice of the model parameters on the results of the risk assessment. 1. In the classic form, it does not take into account market liquidity risk. 2. It does not vary in accordance with intra-day portfolio volatility as the metric is usually calculated at the end of the day. 3. It evaluates only the average level of losses that exceed the VaR metric, and that can provide risk neutral assessment in the boundary levels.
Thus, the ETL approach retains all the advantages of VaR approach, but provides with the estimates of the average size of losses that exceed the specified levels used in the VaR metrics. However, this metric is far more difficult to interpret, and it can miss desired clarity of connections between the calculated amounts of possible losses and the target business limitations set by the investors under the certain risk tolerance level.
Yet another metric gaining popularity among the Russian telecommunication companies in the recent years is represented by the spectral risk metrics (SRM) which is based on the willingness to tolerate risk. In contrast to the metrics described above, it allows to assign different weights to the risk quantiles, and not to use equal weights during the assessment, as is the case of the ETL calculation. In general, the SRM is the weighted average value of the quantile distribution (Fig. 6).
SRMv= ]cp(p)qpdp,
(Fig. 6)
where (flip) is the weights of quantiles function (or the risk spectrum).
The function SRM^ assumes that several conditions are met:
- flip) > 0 V p £ [0,1];
- J01fl(P)dP =1;
- fl (Pi) < fl (P2) V O<P1<P2<I.
The first condition requires that all values of the weights are non-negative, the second one states that the weighted probability sum is equal to 1, and the third one shows the connection between the losses and the willingness to tolerate risks: the greater losses are associated with the bigger weights. It is important to note that every investor has specific requirements for risk tolerance levels, and that can be formalized with a set of functions. For example, an exponential function of risk tolerance with an explicit coefficient of tolerance k can be selected for the calculations (Fig. 7).
flip) =
ke-k(1-V)
(Fig. 7)
The connection between the weighting function and risk tolerance also reveals some aspects of currency risk metrics discussed above. So, the ETL metric is characterized by assigning the equal weights to all
-k
1-e
0
losses in the 'tail area', so in terms of risk tolerance such a calculation can be considered risk neutral at least within that area. Thus, risk-averse investors need an increasing weight function, and that goes beyond the ETL approach. As for the VAR approach, greater weights
are associated with p-levels defined by the corresponding a-values. Thus, the investors are pushed to embrace risks within the 'tail area', and that is not always acceptable. The spectral risk metrics has advantages that are more significant that the corresponding drawbacks (Table 3).
Table 3
Main advantages and disadvantages of the SRM approach_
Advantages Disadvantages
1. It has all the advantages of both, the VaR and the ETL approaches. 2. It is insensitive to the choice of confidence level. 3. It makes possible to adapt the risk metrics to a certain level of risk tolerance. 1. In the classic form, it does not take into account liquidity risks in the market. 2. It does not provide with the intra-day risk assessment as the calculations are normally done at the end of the day. 3. It is based on the weighting function that is even less intuitive to the investors than the ETL approach.
3. Comparative study of the results of currency risk assessment using different approaches
In order to evaluate the feasibility of the transition from the simplest to more advanced metrics for currency risk assessment, a comparative study of their performance has been conducted based on the data from companies of the Russian telecommunications sector of the economy. The basis size of the analyzed opened position (risk exposure) was set to $1 million. Recalculation of prices of assets nominated in Russian rubles to US dollar equivalents was performed based on the information of the Russian interbank market for the period of 2007 to 2016. The time horizon of one trading day with a 250 trading days retrospective was chosen for calculations. The assessment of uncovered losses
was performed for the same datasets within each of the approaches described above.
Currency risk VaR assessments were performed at 99% confidence level, and the ETL estimates were calculated stepwise for the interval from 99% to 100% in increments of 0.01%. The SRM metrics were evaluated stepwise for the interval from 95% to 100% in increments of 0.5%. As the spectral metrics assume the use of risk tolerance levels, three of them were chosen to simulate balanced, aggressive and conservative risk exposure preferences. Accordingly, the spectra were constructed using linear and exponentially increasing functions with different weight coefficients and confidence levels corresponding to the respective quantiles of loss distributions (Table 4).
Table 4
SRM models comparison
Year Model 1 Model 2 Model 3
(Balanced) (Aggressive) (Conservative)
2007 0.0184 0.0801 0.0146
2008 0.0364 0.0945 0.0179
2009 0.0552 0.0997 0.0237
2010 0.0733 0.1032 0.0312
2011 0.0918 0.1033 0.0437
2012 0.1096 0.1039 0.0613
2013 0.1279 0.1042 0.0851
2014 0.1454 0.1053 0.1317
2015 0.1647 0.1052 0.2152
2016 0.1829 0.1055 0.3807
As the USD/RUB exchange rate experienced significant fluctuations in the recent years, the decision to divide trading history into three periods was made. The relatively stable periods were observed in 2007 and 2013, crisis period refers to 2008-2009 and 2014-2016, and a period of mixed trends covers 2010-2012. The aggregated indicators within the periods are calculated as the arithmetic mean of the corresponding indicators for the selected years.
It can be seen (Fig. 8) that almost all currency risk metrics (except the spectral analysis for the 'aggressive' model during the period of 2007-2016) demonstrate uncovered losses occurrence not higher than 15%. The 'aggressive' model provides the maximum values of the risk exposure for the entire period analyzed, due to the high level of risk tolerance assumed. The smallest share of uncovered losses for the whole period analyzed was observed in the ETL risk assessment and the SRM assessment within the conservative approach.
Fig. 8. Share of observations with uncovered losses occurred, %
It should be noted that all the models demonstrate peaks in shares of uncovered losses during the crisis years of 2008 and 2014 and also in 2011, after a six-month USD/RUB downtrend was observed (the Russian ruble strengthened against the US dollar before the sharp devaluation). After the crisis periods, the analyzed indicators show the significant decrease in values due to the increase in the risk capital allocated by the companies to absorb potential future losses.
12
The study of the usage of the allocated risk capital to cover arising losses shows that in most cases the calculated metrics fluctuate at levels below 10% (Fig. 9). The highest values are associated with the spectral risk metric under the 'aggressive' model, and that appears to be expected. In periods of relative stability, all the approaches for the currency risk assessment demonstrate similar results.
10
U
■ 1 al
Mil 1.1 III 1.1 M
_LJiUi
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 ■ VaR BETL ■ Model 1 ■ Model 2 Model 3
Fig. 9. Share of cases offull use of the created risk provisions, %
4. Discussion
Efficient currency risk management in the real sector of the economy is a comprehensive task involving the use of different tools and strategies for hedging operations, insurance coverage against possible losses or evasion of projects with a high degree of uncertainty. However, the choice of the currency risk assessment metrics can significantly impact the spectrum of possible managerial decisions, especially during turbulent periods in the financial markets. The obtained results of the comparative analysis of performance of the various currency risk assessment metrics within the Russian
telecommunication companies allow to provide some practical recommendations. Thus, during the periods of relative stability in the market the ETL approach can be used as a sufficient mean for the currency risk evaluation, while during the crisis periods as well as during the periods with the mixed trends, the application of the SRM approach can be more relevant.
The presented study covers the examples of the Russian telecommunication companies, but the proposed outcomes might be relevant to other companies acting in the Russian economy due to the solid statisti-
cal basis in the described approaches. To prove the universality of the obtained conclusions additional studies are needed to be carried out.
REFERENCES:
1. International Organization of Standardization. 2009. Risk management—principles and guidelines. Geneva, Switzerland: International Organization of Standardization.
2. Morgan, M. Granger, and Max Henrion. 1990. Uncertainty: A guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge, U.K. : Cambridge University Press.
3. Peterson, Martin. 2002. What is a de minimis risk? Risk Management 4 (2): p. 47-55.
4. Cox, Louis Anthony, Jr. 2002. Risk analysis foundations, models, and methods. Boston: Kluwer Academic.
ASSESSMENT OF EFFICIENCY OF REGIONAL CLUSTER AND NETWORK STRUCTURE
Shibayeva T.
Assistant to the chairman of arbitration court, Arbitration court of the Republic of Khakassia, Abakan.
Belyakova G.
Dr.Econ.Sci., professor, professor of department "Economy and management of business processes", Institute of management of business processes and FGAOOU WAUGH'S economies Siberian Federal University, Krasnoyarsk
Plotnikova T.
PhD in Technological Sciences, associate professor, associate professor "Economy and management", The Khakass technical institute - Siberian Federal University VO FGAOU branch, Abakan
ОЦЕНКА ЭФФЕКТИВНОСТИ РЕГИОНАЛЬНОЙ КЛАСТЕРНО-СЕТЕВОЙ СТРУКТУРЫ
Шибаева Т.А.
Помощник председателя арбитражного суда, Арбитражный суд Республики Хакасия, г. Абакан.
Белякова Г.Я. Д.э.н., профессор, профессор кафедры «Экономика и управление бизнес-процессами», Институт управления бизнес-процессами и экономики ФГАОУ ВО Сибирский федеральный университет, г. Красноярск
Плотникова Т.Н.
К.т.н., доцент, доцент кафедры "Экономика и менеджмент", Хакасский технический институт - филиал ФГАОУ ВО Сибирский федеральный университет, г.
Абакан
Abstract
Feature of the regional economic systems having mainly cluster structure is their cluster and network character. Development of cluster and network systems has the features and regularities. The estimated mechanism constructed on influence factors is used to effective management of cluster and network economy.
The system of assessment of efficiency of activity of cluster and network regional economy given in article is a basic element for the choice of the operating influence.
Аннотация
Особенностью региональных экономических систем, имеющих преимущественно кластерную структуру является их кластерно-сетевой характер. Развитие кластерно-сетевых систем имеет свои особенности и закономерности. Для эффективного управления кластерно-сетевой экономикой применяется оценочный механизм, построенный на факторах влияния.
Система оценки эффективности деятельности кластерно-сетевой региональной экономики, приведённая в статье, является основным элементом для выбора управляющего воздействия.
Keywords: cluster and network economy, estimated indicators, the operating influences.
Ключевые слова: кластерно-сетевая экономика, оценочные показатели, управляющие воздействия.
Современные тенденции развития экономики связаны с таким явлением как кластерно-сетевые структуры. Кластерно-сетевые системы являются порождением функционирования кластеров в пространственной привязке с другими экономическими субъектами через сетевые взаимодействия [1]. Таким образом, новые системы - "кластерно-сетевые", представляют со-
бой гибрид кластерных структур и сетей в виде сетевого пространства взаимосвязей [2]. Функционирование кластеров связано с процессом сетевизации, характеризующегося не только видами сетевых взаимодействий, но и появлением новых структурных образований в виде кластерно-сетевых систем с набором только им присущих свойств. Следовательно,
