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{{Paper
|id=Vol-2713/paper02
|storemode=property
|title=Casual analysis of financial and operational risks of oil and gas companies in condition of emergent economy
|pdfUrl=https://ceur-ws.org/Vol-2713/paper02.pdf
|volume=Vol-2713
|authors=Inesa Khvostina,Serhiy Semerikov,Oleh Yatsiuk,Nadiia Daliak,Olha Romanko,Ekaterina Shmeltser
|dblpUrl=https://dblp.org/rec/conf/m3e2/KhvostinaSYDRS20
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==Casual analysis of financial and operational risks of oil and gas companies in condition of emergent economy==
https://ceur-ws.org/Vol-2713/paper02.pdf
41
Casual analysis of financial and operational risks of oil
and gas companies in condition of emergent economy
Inesa Khvostina1[0000-0001-5915-749X], Serhiy Semerikov2,3,4[0000-0003-0789-0272],
Oleh Yatsiuk1[0000-0002-3943-7352], Nadiia Daliak1[0000-0002-1599-842X],
Olha Romanko1[0000-0003-1587-1370] and Ekaterina Shmeltser5[0000-0001-6830-8747]
1 Ivano-Frankivsk National Technical University of Oil and Gas,
15 Karpatska Str., Ivano-Frankivsk, 76019, Ukraine
2 Kryvyi Rih State Pedagogical University, 54 Gagarin Ave., Kryvyi Rih, 50086, Ukraine
3 Kryvyi Rih National University, 11 Vitalii Matusevych Str., Kryvyi Rih, 50027, Ukraine
4 Institute of Information Technologies and Learning Tools of NAES of Ukraine,
9 M. Berlynskoho Str., Kyiv, 04060, Ukraine
5 State University of Economics and Technology,
5 Stepana Tilhy Str., Kryvyi Rih, 50006, Ukraine
inesa.hvostina@gmail.com, semerikov@gmail.com,
olegstya@gmail.com, nadiya_d82@ukr.net, olgaromanko11@gmail.com,
shmelka0402@gmail.com
Abstract. The need to control the risk that accompanies businesses in their day-
to-day operations, and at the same time changing economic conditions make risk
management an almost indispensable element of economic life. Selection of the
main aspects of the selected phases of the risk management process: risk
identification and risk assessment are related to their direct relationship with the
subject matter (risk identification to be managed; risk analysis leading to the
establishment of a risk hierarchy, and, consequently, the definition of risk
control’ methods) and its purpose (bringing the risk to acceptable level). It is
impossible to identify the basic patterns of development of the oil and gas
industry without exploring the relationship between economic processes and
enterprise risks. The latter are subject to simulation, and based on models it is
possible to determine with certain probability whether there have been qualitative
and quantitative changes in the processes, in their mutual influence on each other,
etc. The work is devoted to exploring the possibilities of applying the Granger
test to examine the causal relationship between the risks and obligations of oil
and gas companies. The analysis is based on statistical tests and the use of linear
regression models.
Keywords: risk, risk identification, casual analysis, causality.
1 Introduction
One of the most important factors that accompany any business activity, including
production and commercial activity, is risk. This is because every business operates in
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Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
42
a volatile and uncertain environment, in which unforeseen events can occur. Currently,
many businesses manage risk by using an early warning system that allows early
detection of the threat and initiation of appropriate corrective processes. For businesses,
this is a signal to take action to improve the situation [9]. Today, risk is associated with
every decision, whether operational, investment or financial. The planning’s nature
shows that the predicted future values of the variables taken into account are not certain
but only probable. Different quantitative parameters assume values only under certain
assumptions. The need to monitor risk performance is an indispensable element in
achieving the objective of business entities, as the entity, as a market-oriented,
earnings-oriented entity, is subject to any management and casual events. Risks that
threaten the capital used by the owner or owners mean that the costs associated with
realizing that risk, unless they can be transferred to someone else, are borne by the
enterprise. Therefore, for the continuity of the company, it is crucial to determine the
impact of the adverse event of a casual event on the value of assets and to take
appropriate measures to transfer this burden to other entities.
The processes occurring in the operations of oil and gas companies are related to the
various forms, frequency and nature of the risks involved and necessitate the study of
their cause and effect relationships. Given that risk is an integral part of business entities
and a necessary element of economic decision making, the risk of which increases
significantly in the conditions of dynamism and instability of the business environment,
there is a need to consider risk as an object of managerial influence and its complex
analysis.
The purpose of the article is to present the role of identification and measurement of
financial risk in the process of managing enterprises of the oil and gas industry by
establishing interdependencies between its elements.
2 Background
Risk research has recently received considerable attention. A lot of work is devoted to
examining the role and importance of risk for practice. The main issues of the theory
and practice of economic risk assessment are outlined in the works of Mohamed Abdel-
Basset [1], Bamikole Amigun [2], Vanessa E. Daniel [5], Luca Salvati [32], Qianqian
Zhou [35] and others. A wide range of issues related to risk assessment and forecasting
are investigated by Olga O. Degtiareva [6], Oleh G. Dzoba [3], Iryna Yu. Ivchenko
[12], Oleh Ye. Kuzmin [17; 18], Valentyna V. Lukianova [19], Nazar Yu. Podolchak
[31], Liliia I. Rishchuk [25], O. V. Shcherbak [33], Halyna I. Velikoivanenko [21],
Valdemar V. Vitlinskyi [4] and others. It is worth noting that most scientists in their
scientific work focus on the study of classical methods of risk assessment, while the
latest methods, which are the most promising for the functioning of enterprises in a
dynamic business environment, remain underutilized.
One of the methods to reduce the risk of an enterprise is its insurance [16]. This
method of risk management must be considered in conjunction with other methods of
risk diversification. For optimal risk management at an enterprise it is necessary to use
portfolio theory [24]. Economic, financial and other parameters of the enterprise’s
43
operation act as constraints, and minimization of the enterprise's risk can be used as an
optimization criterion.
For the analysis of financial and economic sustainability of the enterprises are used
as classical statistical modeling techniques and advanced mathematical tools such as
fractal analysis [20] as well as the methods of artificial intelligence [14; 22].
To predict financial time series, artificial intelligence tools are often used, which
include as machine learning methods [7; 15].
The works [8; 34] are devoted to a comparative analysis of the complexity of
traditional stock indices and social responsibility indices using the example of Dow
Jones Sustainability Indices and Dow Jones Industrial Average and opens up new
opportunities for investor risk management. A scientific approach to risk assessment
taking into account the manifestation of emergent properties and using the method of
taxonomy and factor analysis for oil and gas companies is proposed in [13].
In [11], an approach is proposed for assessing the financial efficiency of a business
model of an industrial enterprise, where the integral indicator of the financial
components of a business model is modeled using the method of fuzzy sets and
taxonomic analysis, which will help to more objectively assess the level of financial
standing of an industrial enterprise.
3 Methodology
The oil and gas industry of Ukraine is one of the most important components of the fuel
and energy complex, but the needs of the domestic economy in oil and gas are only
partially met at the expense of its own production. This issue is becoming increasingly
relevant today. In order to increase the economic potential of oil and gas companies, it
is necessary to infuse investment resources. However, the instability of tax legislation
is one of the most significant shortcomings of Ukraine's current tax system, which deter
investors. Inconsistency in the application, interpretation and implementation of tax law
can lead to litigation, which can ultimately lead to additional taxes, penalties and
penalties, and these amounts can be significant. All the above clearly indicates that the
oil and gas companies of Ukraine operate in an emergent economy and this fact largely
determines the high relevance of our study.
In the ordinary course of business, certain claims are raised against oil and gas
companies. If the risk of an outflow of financial resources related to such claims is
considered probable, a liability is recognized in the provisions for litigation. If
management estimates that the risk of an outflow of financial resources related to such
claims is probable or the amount of expenses cannot be estimated reliably, the provision
is not recognized and the corresponding amount disclosed in the consolidated financial
statements.
There is a claim between businesses and some natural gas suppliers about the volume
or prices of the natural gas being supplied and other claims. Management estimates its
potential liabilities for such claims at UAH 5890 million (2016: UAH 1380 million;
2017: UAH 3928 million; 2018: UAH 4246 million) [29; 28; 30; 27; 26]. Management
cannot reliably estimate the amount of potential losses on these liabilities, if any.
44
The activities of the oil and gas industry are characterized by a number of financial
risks: market risk (including foreign exchange and interest rate risk), concentration risk,
credit risk and liquidity risk. Management reviews and aligns its risk management
policies to minimize the adverse impact of these risks on the Group's financial
performance.
The main categories of financial instruments are presented by structure of financial
assets and financial liabilities.
Market risk. Market risks arise from open positions in (a) foreign currencies, (b)
interest-bearing assets and liabilities, and (c) investments, all of which are affected by
general and specific market changes in condition of emergent economy.
Currency risk. Oil and gas companies operate in Ukraine, and their dependence on
foreign exchange risk is mainly determined by the need to purchase natural gas from
foreign suppliers, which is denominated in US dollars. The Group also receives foreign
currency loans and does not hedge its foreign currency positions.
Dependence on currency risk is presented on the basis of the carrying amount of the
respective currency assets and liabilities.
Table 1 provides information on the sensitivity of profit or loss to reasonably
possible changes in the exchange rates applied at the reporting date, provided that all
other variables remain stable. The risk was calculated only for monetary denominations
denominated in currencies other than the functional currency.
Table 1. Profit or loss sensitivity to reasonably possible changes in exchange rates [29; 28; 30;
27; 26]
In millions of December December December 31, December 31,
Ukrainian Hryvnias 31, 2019 31, 2018 2017 2016
US dollar Strengthening
(2540) (2865) (3 400) (4 957)
by 10%
US dollar weakening by
2540 2865 3 400 4 957
10%
Euro Strengthening by
225 239 251 299
10%
Euro weakening by 10% (225) (239) (251) (299)
Granger causality is applied to components of a stationary vector random process. At
the heart of the definition is a well-known postulate that the future cannot affect the
past.
The essence of the Granger test is that the variable x is causal for the variable y, that
is, under the influence of x → y changes of x must precede changes of y, not vice versa.
Therefore, under the above conditions, it is necessary that the following actions be
performed at the same time: the variable x makes a significant contribution to the
forecast of y, while the variable does not significantly contribute to the forecast of the
variable x [10]. To determine whether x is the cause of y, determine what proportion of
the variance of the current value of variable y can be explained by past values of the
variable y itself, and whether adding past values of variable x can increase the
45
proportion of explanatory variance. The variable x is the cause of y if x contributes to
the prediction of y. In the regression analysis, the variable x will be the cause of y when
the coefficients at logs x are statistically significant, but the most commonly
investigated cause and effect relationships are two-sided. In other words, the variable
xt is not a Granger cause for the variable ut if excluding from the model information
about the past values of the variable xt does not impair the predicted value of ut when
used to construct models of both time series. The quality of the forecast in this case is
estimated by the standard error. The scheme of model analysis for the presence and
direction of causality is shown in figure 1.
1. Do the test of series stationarity with the help
of Augmented Dickey-Fuller or KPSS
Yes Is a series No
stationary?
3a. Do a causality test 2. Do a test of cointegration
(Graiger’s or Xciao’s test) (Yohansen’s test)
GDP TEND, TEND GDP
Yes
No
Is there a
cointegration?
3b. Cointegration verification
on models of error correction
Fig. 1. The scheme of analysis of the model for the presence and direction of causality
To perform this test, three indicators were selected for six NGSUs and Ukrnafa Public
Joint-Stock Company (PJSC) for the six months 2016-2019. These include financial
risk, operational risk, and contingent and contractual commitments under
Chernihivnaftogaz, Poltavanaftogaz, Okhtyrkanaftogaz, Dolynaftogaz,
Borislavnaftogaz, Nadvirnaftogaz and Ukrnafa PJSC. Causality testing involves the use
of stationary time series. Stationarity is verified in Eviews software, which
automatically calculates the required metrics. The functionality of the program
proposes to use the Dickey-Fuller and Phillips-Perron test to check the stationarity of a
number of selected indicators.
The Dickey-Fuller test is based on the estimation of the parameter λ = α1 – 1 of the
equation ΔYt = λYt–1 + εt,, equivalent to the autoregression equation. If the value of the
Student's t-statistic for the parameter λ is less than the lower threshold of the DF-
statistic, then the null hypothesis λ = 0 (about the presence of a single root α1 = 1) should
be rejected and the alternative about the stationarity of the process Yt should be
accepted [23]. As a result of the Dickey-Fuller test, it was found that even at a
significance level of 10%, the hypothesis of stationarity of series should be rejected. To
46
bring the original variables to the stationary form, the transition to the analysis of the
second differences of these series was performed. The calculations revealed that the
hypothesis of stationarity of a series should be accepted (figure 2).
Null Hypothesis: D(X1,2) has a unit root
Exogenous: None
Lag Length: 0 (Automatic - based on SIC, maxlag=1)
t-Statistic Prob.
Augmented Dickey-Fuller test statistic -3.3939 0.0067
Test critical values: 1% level -3.1095
5% level -2.0439
10% level -1.5973
Null Hypothesis: D(X2,2) has a unit root
Exogenous: None
Lag Length: 1 (Automatic - based on SIC, maxlag=1)
t-Statistic Prob.
Augmented Dickey-Fuller test statistic -2.402 0.0319
Test critical values: 1% level -3.271
5% level -2.082
10% level -1.599
Null Hypothesis: D(X3,2) has a unit root
Exogenous: None
Lag Length: 1 (Automatic - based on SIC, maxlag=1)
t-Statistic Prob.
Augmented Dickey-Fuller test statistic -4.072 0.0042
Test critical values: 1% level -3.271
5% level -2.082
10% level -1.599
Fig. 2. Investigation into the stationarity of the second series differences using the Dickey-
Fuller test (series X1 – Financial risk, series X2 – Operational risk, series X3 – Contingent and
contractual obligations (Ukrnafta PJSC))
However, there are other tests to check the series for stationarity. Given that the random
components of the ADF test can be autocorrelated, have different variances (i.e.,
heteroskedasticity may be present) and not necessarily normal distributions, compared
to the ADF test, the Phillips-Perron test can be used to consider wider classes time
series.
Conducting the Phillips-Perron test, which is also present in the Eviews software,
shows the same results: the investigated series are non-stationary and the other series
differences are stationary (figure 3).
47
Null Hypothesis: D(X1,2) has a unit root
Exogenous: Constant
Bandwidth: 4 (Newey-West automatic) using Bartlett kernel
Adj. t-Stat Prob
Phillips-Perron test statistic -6.614176 0.0049
Test critical values: 1% level -5.604618
5% level -3.694851
10% level -2.982813
Null Hypothesis: D(X2,2) has a unit root
Exogenous: Constant
Bandwidth: 4 (Newey-West automatic) using Bartlett kernel
Adj. t-Stat Prob
Phillips-Perron test statistic -4.209688 0.0315
Test critical values: 1% level -5.604618
5% level -3.694851
10% level -2.982813
Null Hypothesis: D(X3,2) has a unit root
Exogenous: Constant
Bandwidth: 4 (Newey-West automatic) using Bartlett kernel
Adj. t-Stat Prob
Phillips-Perron test statistic -4.103371 0.0346
Test critical values: 1% level -5.604618
5% level -3.694851
10% level -2.982813
Fig. 3. Investigation into the stationarity of the second series differences using the Phillips-
Perron test (series X1 – Financial risk, series X2 – Operational risk, series X3 – Contingent and
contractual obligations (Ukrnafta PJSC))
The results obtained by the ADF test can also be verified by visual analysis of the
autocorrelogram and partial autocorrelogram (figure 4).
Let’s do a Granger causality test. The length of lag p should be chosen from the
longest lag, which can still help in predicting. Analysis of the cross correlograms
48
indicates the choice of p = 2. In addition, it is confirmed by the well-known rule that
the number of lags should not exceed the number of observations divided by 4. Consider
the Granger causality for two variables. The model form below is:
E =∑ e (1)
E =∑ e (2)
а)
b)
c)
Fig. 4. Self-correlates of the second time series differences: a) Financial risk, b) Operational
risk, c) Contingent and contractual obligations (Ukrnafta PJSC)
The absence of a causal relationship from x to y means that when cj = 0 at j = 1, ..., p
that is, past values of x do not affect y. The absence of causality from y to x means that
bj = 0 at j = 1, ..., p.
When the process is stationary, then hypotheses about causality can be tested using
F-statistics. The null hypothesis is that one variable is not a Granger cause for another
variable.
The results of the test are presented in table 2. Recall that the hypothesis about the
causality of this factor is accepted (and the null hypothesis, respectively, is rejected) at
a probability of less than 0.05, with a probability greater than 0.05 is accepted null
hypothesis.
49
Table 2. Granger test results
Indicator
Company Contingent and
Financial risk Operational risk
contractual
(Х1) (Х2)
obligations (Х3)
Chernihivnaftogaz The hypothesis is The hypothesis is The hypothesis is
accepted accepted accepted
Nadvirnaftogaz The hypothesis is The hypothesis is The hypothesis is
accepted rejected (Х2→X3) accepted
Borislavnaftogaz The hypothesis is The hypothesis is The hypothesis is
accepted accepted accepted
Poltavanaftogaz The hypothesis is The hypothesis is The hypothesis is
accepted accepted accepted
Dolinanaftogaz The hypothesis is The hypothesis is The hypothesis is
accepted accepted rejected (Х3→X1)
Okhtyrkanaftogaz The hypothesis is The hypothesis is The hypothesis is
rejected (Х1→X2) accepted accepted
Ukrnafta The hypothesis is The hypothesis is The hypothesis is
accepted accepted accepted
As a result of the causal analysis, it was found that the change in operational risk at
Nadvirnanaftogaz is the cause of contingent and contractual obligations, but not vice
versa; at Dolynaftogaz a number of contingent and contractual dynamics are the cause
of a number of financial risks for Granger; at Okhtyrkanaftogaz financial risk is the
cause of operational risk, and the connection is also one-sided. Other hypotheses
regarding causality are not accepted.
4 Results
Effective and integrated risk management requires the integration of risk management
into the enterprise management process. The market economy creates both
opportunities to achieve planned profits, as well as the risk of losses due to adverse
changes in the environment of the company and mistakes within the organization.
Many methods and approaches to the risk management process indicate that an
important aspect of the company is to look for optimal solutions to existing threats.
Decisions on this issue should be taken as a result of the risk management process, in
which the placement and awareness of the importance of the risk that threatens the
company become a very important starting point.
Risk identification and measurement are important here because they determine the
choice of risk control method, which means specific decisions and financial costs. In
assessing this, particular attention should be paid to the importance of contingent and
contractual obligations, which often go unnoticed and neglected, and omissions of
which can be significant.
The obtained results allow us to adjust the policy of activity of oil and gas enterprises
for 2 years in advance (lag length) depending on the risks involved and the peculiarities
of the socio-economic status of the territories.
50
The scientific novelty of the study is to formalize the areas of relationships between
the risk of oil and gas companies and its elements on the basis of testing by the Granger
method. Using the results of the proposed testing allows to determine the direction of
causal links between financial risk, which depends on currency risk, that is determined
by the need to purchase natural gas from foreign suppliers, operational risk of
concentration on revenues from gas transportation and trade payables, as well as
contingent and contractual obligations that pose the risk that one party to a financial
instrument will cause a financial loss to the other party as a result of a default. This
contributes to the definition and coordination of risk management policies to minimize
their negative impact on the financial performance of oil and gas companies.
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