CC BY
37
MSC 60G35, 93E03, 94A12
DOI: 10.14529/ mm p 180213
MODELLING OF "GREEN" INVESTMENTS RISKS
V.G. Mokhov1, G.S. Chebotareva2, P.M. Khomenko2
1South Ural State University, Chelyabinsk, Russian Federation
2
E-mail: mokhov50@mail.ru, g.s.chebotareva@urfu.ru, pavelkhom@gmail.com
The practical lack of methods for quantitative assessment of capital-intensive renewable energy projects taking into account the present uncertainty leads to an increase in the riskiness and reduce the amount of "green" investment in the economy. It resolves the urgency of the developing approaches to the renewable energy projects assessment. The article presents the diagnostics of specific risks and assessment of limiting the investor market share in the renewable energy projects with the optimal risk level. The proposed authors' approach to risks formalization of renewable energy deals is based on the introduction of dummy-variable, evaluation of coefficients' significance, tendency and strength of relation of risks indicators. This approach yields an indicative assessment of the deals' riskiness taking into account sectoral specifics. The developed method to limiting the investor market share is based on the principles of the economic capital theory. It allows for consolidating the current states of investor, project and economy. Research veracity is confirmed by the practical implementation. The results of the study can be used by the management of energy companies, investors and analysts in making financial decisions.
Keywords: renewable energy; "green" investment; modeling; indicator; risks' formalization; limiting investor market share; risk-capital.
Introduction
The share of conventional hydrocarbon energy in Russia is about 65%. The structure of this fuel and energy balance in the Russian power sector will not change significantly till 2020. The concept of sustainable development aimed at the rational use of natural resources without compromising the current and future generations, raises the problem of the transition to renewable energy (RE) in priority. Phased transition to new energy generation technologies, accelerated development of renewable energy, development of systems Smart Grid, tightening climate conditions require the increasing amount of "green" investment [!]•
At the present time the "green" energy projects are characterized as highly risky. In this regard, the problem of modelling and reducing the level of specific risks' in the renewable energy projects is highly actual and practically significant [2-4]. The decision of the problem involves two main stages.
1. Formalization of RE Investment Risks
In the framework of this stage the logit-model (1) as the most popular one for default probability forecasting is used:
PD =-1-, (1)
1 + e-z y '
where PD is probability of RE project default; parameter
z =(bo + bi • Xa + b2 • Xl2 + ... + bn • Xin), (2)
Xy is value of j-th financial indicator for z-th project; bj is evaluation value of j-th coefficient' significance.
bj
(3) for hypothesis bj = 0 [5]:
bbj
bj ■
/
S
(3)
where Xj is the average value of j-th financial indicator for z-th project; S is sampling residual standard deviation.
The empiric base of study for logit-model assessment was formed by experts' opinion. It includes eight specific risks indicators for EE project. Financial stability of EE project is evaluated by: Debt Service Coverage Eatio Xi Working Capital to Current Assets Xi2; Internal Eate of Eeturn Xi3; Discounted Payback Period Xi4; Level of Financial Stress Xi5. Institutional assessment of EE project is based on introduction of the following dummy-variable: Indicator of project type Xi6 is changed by three parameters: new construction Instl, overhaul Inst2 or modernization Inst3 and obey (4); Level of market risk Xi7 is changed from low Inst4 to high Inst6 and obey (5); Level of goodwill Xi8 characterizes the field experience of EE project: from lack of skill Inst9 to more than three projects Instl (conditions 6):
{Instl = 1, Inst2 = 0, Inst3 = 0,
{ Instl = 0, Inst2 = 1, Inst3 = 0,
{Instl = 0, Inst2 = 0, Inst3 = 1,
(4)
{Inst4 = 1, Inst5 = 0, Inst 6 = 0,
(Inst4 = 0, Inst5 = 1, Inst6 = 0,
{Inst4 = 0, Inst5 = 0, Inst6 = 1,
(5)
{Inst7 = 1, Inst8 = 0, Inst9 = 0,
{Inst7 = 0,
Inst8 =1, (6)
Inst9 = 0,
(Inst7 = 0, Inst8 = 0, Inst9 = 1.
As a result the adjusted parameter "z" for formula (1) takes the following form for the EE project:
z = 0, 5578 - 1, 785 • Xn - 0, 237 • Xi2 - 0, 008 • + 0, 018 • Xu - 1, 284 • Xi5+ +0, 459 • Xi6 + 0,167 • Xi7 + 0, 091 • X&.
(7)
The results of coefficient interpretations as well as its tendency and strength of relation are presented in Table 1. The analisis shows Debt Service Coverage Eatio Xii and Level of Financial Stress Xi5 exert the greatest influence on EE project results. Consequently, the corresponding risks are deemed to be most dangerous for the EE project.
Table 1
Eesults of risks' indicators formalization
Indicator Tendency and strength of relation Coefficient Indicator Tendency and strength of relation Coefficient
Xi1 + + + -1,785 Xi5 + + + -1,284
Xi2 ++ -0,237 Xi6 -- 0,459
Xi3 + -0,008 Xi7 — 0,167
Xi4 — 0,018 Xi8 — 0,091
2. Limiting the Investor Market Share in Financing Renewable Energy
To ensure sustainability the investor determines the acceptable risk level based on the maximum losses that reduce the operation disturbance and temporary insolvency. It is the first absolute limitation the investor shares in the EE project (formula 8):
NCAP = CCAP — TCAP, (8)
where NCAP is the investor' capital the loss of which would not lead to insolvency; CCAP
TCAP
TCAP
credit rating and counterpart of confidence probability by formula (9) and Table 2:
TCAP = Ps&P • CCAP, (9)
where PS&P is confidence probability value by project duration and rating.
Table 2
Confidence probability value % (Standard & Poor Global Eatings)
Eating 1-Y PD 3-Y PD 5-Y PD Eating 1-Y PD 3-Y PD 5-Y PD
AAA 0,008 0,03 0,1 ВВ 1,15 8,6 15
АА 0,04 0,16 0,28 В 5,8 15,4 32,6
А 0,16 0,4 0,58 ССС or lower 26,57 45,5 60
ввв 0,3 1,4 3 X X X X
The second absolute limitation is the exposure at default value EAD [2] - the potential volume of project obligations at the time of default:
ECAP = EAD . LGD • (N(N-'(PD)+N-'(a)' ^) - PD), (10)
VI - r
where LGD is loss given default; N() is standard normal distribution; N-'() is inverse
r
ECAP
ECAP
in accordance with condition (11). It is an example of three most dangerous existing risks below: _
ECAP = ^ ECAP? + ECAP2 + ECAP? + A + B + C, (11)
where A = 2-ECAP'•ECAP2-pu, B = 2-ECAP'-ECAP3-p'3, C = 2-ECAP?-ECAP3-p23. ECAP', ECAP?, ECAP3 are the capital values for the first, second and third risks; p12, P23, p13 are correlation coefficients between two risks' components. Subject to conditions (8) - (11) the absolute maximum limiting amount of investments in EE project is calculated by (12):
LF = min(NCAP; EAD), (12)
where LF is the limiting volume of investments. The limiting share can be corrected additionally by parameter "X" for the EE projects with high riskiness. It depends on the risks level: PD and LGD. For the purposes of further indication, these indicators were put together in (13):
EL = PD • LGD, (13)
where EL is the level of expected EE project losses. Experts' assessment of dependence between EL and K is presented in Table 3.
Table 3
Correspondence between the K-factor and EL-level
EL 0-0,05 0,06-0,1 0,11-0,15 0,16-0,3 0,31-0,7 0,71-1,2 1,21-2 2,1-3,5
К 1 0,99 0,97 0,95 0,93 0,9 0,85 0,8
EL 3,51-5 5,01-8 8,01-10 10,01-25 25,01-32 32-45 45-75 75-100
К 0,75 0,65 0,5 0,35 0,1 0,005 0,0001 0
The relative value of investor market share is calculated based on (12) and supplemented with total RE project budget amount and parameter "K":
= min(NCAP; K • EAD)
BDG , ( )
where BDG is the total budget of renewable energy project.
Conclusions
1. A topical problem of qualitative assessment of "green" projects is solved.
2. The results are recommended for the use in the development of risk-management programme in energy companies and for private investors.
3. The developed approaches of risks' formalization and limiting the investor market share for renewable energy projects allow improve their attractiveness.
Acknowledgements. The work was supported with a grant of the Russian Science Foundation (project No. 17-78-10039) (chapter 1 and 2) and by Act 211 Government of the Russian Federation, contract No. 02.AOS.21.0011 (chapter 2).
References
1. Porfir'ev B.N. Green Trends in the Global Financial System. World Economy and International Relationships, 2016, vol. 60, no. 9, pp. 5-16. DOI: 10.20542/0131-2227-201660-9-5-16
2. Cleijne H., Ruijgrok W. Modelling Risks of Renewable Energy Investments: Green-X Project. Within the 5th Framework Programme of the European Commission Supported by DG Research, КЕМ A (The Netherlands), July, 2004, 2004, 74 p., available at: http://www.green-x.at/downloads/WP2 - Modelling risks of renewable energy investments (Green-X).pdf
3. Ross В., Lopez-Alcala M., Small A. Modeling the Private Financial Returns from Green Building Investments. Journal of Green Building, 2007, no. 2 (1), pp. 97-105.
4. Mokhov V.G., Chebot areva G.S., Demyanenko T.S. Complex Approach to Assessment of Investment Attractiveness of Power Generating Company. Bulletin of the South Ural State University. Series: Mathematical Modelling, Programming and Computer Software, 2017, vol. 10, no. 2, pp. 150-154. DOI: 10.14529/mmpl70213
5. Simchera V.M. Metody mnogomernogo analiza statisticheskih dannyh [Methods of Multivariate Analysis of Statistical DataJ. Moscow, Finansy i statistika, 2008. 400 p. (in Russain)
Received March Ц, 2018
УДК 330.322.013+001.895 Б01: 10.14529/ттр180213
МОДЕЛИРОВАНИЕ РИСКОВ <ЗЕЛЕНЫХ> ИНВЕСТИЦИЙ
В.Г. Мохов1, Г. С. Чеботарева2, П.М. Хоменко2
1 Южно-Уральский государственный университет, г. Челябинск,
Российская Федерация
2
Практическое отсутствие методов количественной оценки капиталоемких проектов в сфере возобновляемой энергетики с учетом существующей неопределенности
ведет к повышению рискованности сделок в данной сфере и способствует снижению объема «зеленых» инвестиций в экономике. Это предопределило актуальность разработки подходов к оценке проектов по возобновляемой энергетике при их реализации. Статья посвящена диагностике состояния специфических рисков и оценке предельной доли участия инвесторов в проектах возобновляемой энергетики, с учетом обеспечения оптимального уровня риска. Предложен авторский поход к формализации рисков финансирования возобновляемой энергетики на основе введения dummy-переменных, оценки значимости коэффициентов, а также направления и силы связи риск-индикаторов. Он позволяет получить индикативную оценку степени рискованности сделки с учетом отраслевых особенностей. Разработанный метод ограничения доли участия инвестора в проекте, основанный на базовых принципах теории экономического капитала, позволяет консолидировано учитывать текущее состояние инвестора, проекта и экономики. Достоверность результатов исследования подтверждена практической реализацией. Результаты исследования рекомендуется использовать менеджментом энергокомпаний, инвесторам и аналитикам в процессе принятия финансовых решений.
Ключевые слова: возобновляемая энергетика; «зеленые» инвестиции; моделирование; индикатор; формализация рисков; ограничение доли инвестора; риск-капитал.
Литература
1. Порфирьев, Б. «Зеленые» тенденции в мировой финансовой системе / Б. Порфирьев // Мировая экономика и международные отношения. - 2016. - Т. 60, № 9. - С. 5-16.
2. Cleijne, H. Modelling Risks of Renewable Energy Investments: Green-X Project / H. Cleijne, W. Ruijgrok // Within the 5th Framework Programme of the European Commission Supported by DG Research, KEMA (The Netherlands), July, 2004 [Электронный ресурс]. -2004. - 74 p. - URL: http://www.green-x.at/downloads/WP2 - Modelling risks of renewable energy investments (Green-X) .pdf
3. Ross, B. Modeling the Private Financial Returns from Green Building Investments / B. Ross, M. Lopez-Alcala, A. Small // Journal of Green Building. - 2007. - № 2 (1). - P. 97-105.
4. Mokhov, V.G. Complex Approach to Assessment of Investment Attractiveness of Power Generating Company / V.G. Mokhov, G.S. Chebotareva, T.S. Demyanenko // Вестник ЮУрГУ. Серия: Математическое моделирование и программирование. - 2017. - Т. 10, № 2. - С. 150-154.
5. Симчера, В.М. Методы многомерного анализа статистических данных / В.М. Симчера. -М.: Финансы и статистика, 2008. - 400 с.
Вениамин Геннадьевич Мохов, доктор экономических наук, профессор, кафедра «Математическое и компьютерное моделирование», Южно-Уральский государствен-ныи университет (г. Челябинск, Российская Федерация), mokhov50@mail.ru.
Галина Сергеевна Чеботарева, кандидат экономических наук, доцент, старший научный сотрудник, кафедра «Системы управления энергетикой и промышленными предприятиями:», Уральский федеральный университет (г. Екатеринбург, Российская Федерация), g.s.chebotareva@urfu.ru.
Павел Михайлович Хоменко, старший преподаватель, кафедра «Банковский и ин-
:
Российская Федерация), pavelkhom@gmail.com.
Поступила в редакцию Ц марта 2018 г.
