=Paper=
{{Paper
|id=Vol-2104/paper_215
|storemode=property
|title=Estimation of Currency Risks in the Process of Enterprise Foreign Economic Activity
|pdfUrl=https://ceur-ws.org/Vol-2104/paper_215.pdf
|volume=Vol-2104
|authors=Valeria Yatsenko
|dblpUrl=https://dblp.org/rec/conf/icteri/Yatsenko18
}}
==Estimation of Currency Risks in the Process of Enterprise Foreign Economic Activity==
https://ceur-ws.org/Vol-2104/paper_215.pdf
Estimation of Currency Risks in the Process of
Enterprise Foreign Economic Activity on the example of
Antonov State Company
Valeria Yatsenko1
1
Taras Shevchenko National University of Kyiv, 90-A, Vasulkivska st., Kiev, 03022
Ukraine
valeriayatsenko5@gmail.com
Abstract. The problem of risk analysis during foreign economic activity is now
becoming ever more relevant especially for Ukrainian economy. In this paper,
we had analyzed the environment of currency risk formation and evaluated
them based on the example of Antonov State Company which is the leader of
the domestic aviation industry as the leading exporter of high-technology
products with a high degree of added value. The currency risk estimation of
Antonov State Company was done through assessment of the foreign exchange
position, matching model, Value at Risk method (VaR) and statistical analysis.
In order to analyze the all range of Ukraine’s economy threats, which is open
economy, we have proposed a methodology for an integrated valuation of
currency risk.
Keywords: risk, currency risk, Value at Risk, currency matching, statistical
analysis.
1 Introduction
In the conditions of rapid changes in the world market situation and the permanent
economic processes transformation, stochasticity and uncertainty are becoming
important factors of perspective growth of business entities. The inherent
consequence of uncertainty is an increase in the risk level, which is significantly
intensified in the conditions of economic activity internationalization and entering
new foreign markets. Particular importance of these trends accrues during the analysis
of the current realities of the Ukrainian economy, which is operating in coordinates of
regulation changeability, volatility of the exchange rate, structural and territorial
imbalances of the economic system, radical changes in export policy, etc [1].
However, in the conditions of active devaluation processes in domestic economy, one
of the key types of risks during foreign economic activity is currency risk. For
instance, I.I. Verbitskaya asserts that currency risk is one of the main barriers
hindering the development of domestic exports [2], since the amplitude of exchange
rate fluctuations is so devastating and unpredictable that one-off situational measures
can not offset negative exchange differences.
The currency risk analysis is on the agenda among domestic and foreign scientists.
For example, the essence of currency risk was investigated by A. Volitskaya [3],
G. Verbitskaya [2], V. Vitlinsky [4], M. Rebruk [5], A. Zorina [6], and others. The
classification of currency risks was studied by A. Gassem [7], J. James [8], J. Daniels
[9], N. Vnukova [10], O. Ukrainiska [11], and others. The role of currency risks in the
economic system and currency risk management were investigated by A. Rumyantsev
[12], S. R. Goldberg et al [13], H. Backlund [14], P. Collier et al [15], and others.
The goal of the paper is to analyze the environment of currency risk formation and
to evaluate it based on the example of foreign economic activity of Antonov State
Company.
The paper has following structure: section 2 is devoted to related works, section 3
demonstrates the environment of currency risk emergence and evaluates the currency
risk of Antonov State Company and section 4 concludes.
2 Related Work
In the conditions of high turbulence of changes in the global market situation and
dynamic economic transformations, "risk as a quantitative measure of uncertainty"
[16], acquires the status of inherent characteristic of economic activity, which covers
all life spheres and serves as "a pervasive phenomenon inherent to all market actors"
[17]. The problem of risk analysis during foreign economic activity is now
becoming ever more relevant whereby according to global management practices,
inadequate analysis or ignoring risk control leads to failure 40% of commercial
transactions [12].
Similar to the risk definition, currency risks are identified with probability of
losses or damage. For instance, experts of the National Bank of Ukraine identify
currency risks as an existing or potential risk for revenues and capital arising from
unfavorable fluctuations in foreign exchange rates and prices for bank metals
[18]. Nevertheless, currency risk is the best example of dualism, meaning the
possibility of getting both negative and positive effects, in the form of positive and
negative exchange differences, which requires a dialectical analysis of the
etymological nature of the “currency risk” category (Table 1).
Table 1. Dialectical analysis of the etymological essence of the "currency risk" category
The laws of dialectics Explanation Economic content
I law only a big open foreign exchange
qualitative changes in the objective
Transformation of Quanti- position and a significant amplitude of
world, are carried out only on the
tative Into Qualitative currency fluctuations lead to the
basis of quantitative changes
Changes emergence of currency risks
Identity and difference are
II law dichotomy of the currency risk effects:
opposites, which interact,
Unity and Struggle of the likelihood of receiving both losses
determine each other; is a source
Opposites (loss of income) and profits (benefits)
and driving force of development
the latest risk management tools are
III law in "new" there is "old", but in abased on 3 main approaches: avoidance,
Negation of the negation transformed form minimization of losses, maximization of
benefits
The economic nature of currency risk is formed by three interrelated parameters: the
volatility of the exchange rate, foreign exchange position, which is defined as the difference in
revenues (incoming cash flow) and payments of the entity (outflow) in foreign currency [11]
and foreign exchange exposure (the sensitivity of the subject to obtaining costs or income). If
we will analyze only one of these three parameters, we can receive distorted management
decisions, since currency risk is possible only if there is an open foreign exchange position,
while its closed form mitigates the sensitivity to any exchange rate changes. In this case, the
unpredictable volatility of the exchange rate can be identified only as an additional catalyst for
currency risk while the real reason is an open foreign exchange position or cash flows in a
foreign currency. That is why, the currency position is the main object of currency risk
management. According to A. Volitska, "specialists of the banking business conduct currency
risk management through the currency position management" [3].
Overall, currency risk, as a scientific category, is characterized by a complex dichotomous
nature, combining negative and positive results. In this case, currency risk can be identified as
a situational set of probability events with subjective-objective nature triggered off open
foreign exchange position and unpredictable changes in the exchange rate as a result of
information asymmetries, the effect of which is to obtain both negative and positive exchange
rate differences.
3. Evaluation of Currency Risk in the Process of Foreign Economic
Activity
3.1 Analysis of the Environment of Currency Risk Formation
Despite the significant pressure on the national currency at the beginning of 2017, the volatility of
the hryvnia during this year was moderate, which is primarily due to positive trends in the
international economic development (fig. 1).
30 80
25 average - 19,01
median - 21,89 60
20 deviation - 15,11 ↑ the world economy+↑
exchange rate
range of variability - 20,06 prices of minerals 40
15 dispersion - 58,75
↑ fiscal expen-ditures 20
10 at the end of the year
5 0
irrational behavior of
households → activation
0 of the "black market" -20
1-Jan-13
1-Mar-13
1-May-13
1-Jul-13
1-Sep-13
1-Nov-13
1-Jan-14
1-Mar-14
1-May-14
1-Jul-14
1-Sep-14
1-Nov-14
1-Jan-15
1-Mar-15
1-May-15
1-Jul-15
1-Sep-15
1-Nov-15
1-Jan-16
1-Mar-16
1-May-16
1-Jul-16
1-Sep-16
1-Nov-16
1-Jan-17
1-Mar-17
1-May-17
1-Jul-17
1-Sep-17
1-Nov-17
USD/UAH The pace of monthly growth
Fig. 1. Dynamics of UAH / USD exchange rate in 2013-2017 [19]
First of all, there was slight increase in the international economics; secondly, there was the
restoration of world financial stability as a result of the rising dynamics of financial markets, the
introduction of new macro-prudential requirements and the restoration of profitability of systemic
financial institutions; and thirdly, there was a favorable market situation on the world mineral
markets (steel, iron ore, energy commodities) [20].
The forecast, built on monthly average weighted exchange rate data of the inter-bank
market during 01.2014-01.2018, has shown the average hryvnia exchange rate for 2018 will
not exceed the rate of 3071.02 UAH / USD, and by the beginning of 2019 it is likely to be
3299.09 UAH / USD. (fig. 2). The reliability of this forecast is evidenced by the symmetric
mean absolute percentage error (SMAPE), which indicates the existence of 1% error
prediction. However, Mean Absolute Error (MAE) and Root mean squared error (RMSE)
(39 and 45 respectively) confirm, that the volatility of the hryvnia will be maintained which
can be explained by the dominant influence of non-economic factors (political, informational,
behavioral) on the exchange rate dynamics.
Значення
Values 5000 Statistics Values
NBU rate of $ 100
4000 Alpha 1,00
3000 Beta 0,00
Forecast
Прогноз 2000 Gamma 0,00
1000 MASE 0,46
Low 0 SMAPE 0,01
Прив'язка до
01.01.2014
01.05.2014
01.09.2014
01.01.2015
01.05.2015
01.09.2015
01.01.2016
01.05.2016
01.09.2016
01.01.2017
01.05.2017
01.09.2017
01.01.2018
01.05.2018
01.09.2018
01.01.2019
probability
низької MAE 38,90
ймовірності RMSE 45,29
Прив'язка
High до
високої
probability
ймовірності
Fig. 2. Forecast dynamics of USD / UAH exchange rate change in 2018 [19]
The transformational nature of the domestic economy determines the priority of the export-
oriented development strategy, which provides for macroeconomic equilibrium in the condition
of the immature of financial markets. However, the current trends in commodity and mineral
markets necessitate a qualitative modernization of the national production system, which should
be driven by high-tech industries with a high degree of added value, the best example of which is
aircraft construction. That is why the object of this study is the leader of the domestic aviation
industry - Antonov State Company as the leading exporter of high-technology products with a
high degree of added value.
Antonov's particular expertise is in the fields of very large airplanes and airplanes using
unprepared runways. Antonov has built a total of approximately 22,000 aircraft, and thousands
of its planes are currently operating in the former Soviet Union and in developing countries. In
addition to aircraft construction divisions, Antonov founded Antonov Airlines which operates
international charter services in the world of oversized cargo market and accounted for about
70% of Antonov's total revenue. On 12 May 2015 the Ministry of Economic Development and
Trade transferred it to the Ukroboronprom (Ukrainian Defense Industry) [21].
In order to overcome the limited capacity of the domestic market due to low demand and to
increase the efficiency of activity based on economy of scale , Antonov State
Company promotes for active participation in the international division of labor, reorientates
centers of strategic interests, activates expansion into foreign markets of Azerbaijan (Silk Way
Airlines), Saudi Arabia (Taqnia Aeronautics), Turkey (Turkish Aerospace Industries), India
(Reliance Defense) and China (Beijing A-Star Space and Technology) (fig. 3). Nevertheless, this
strategy is progressive, it is characterized by high risk since these countries are characterized by
political instability, high costs of opportunism, volatility of exchange rates, etc.
Azerbaijan
Turkey
China
Saudi
Arabia India
Fig. 3. Area activities of Antonov State Company [22]
What is interesting, the current foreign-economic contracts of Antonov State Company are
not limited by export supplies, but provide for joint research, personnel exchange and joint
production of airplanes. As a result, the production process in the territory of customers will
be accompanied by costs in the national currency of the partner country. That is why we will
analyze the environment of currency risk formation in the economic systems of these
countries (fig. 4).
USD/CNY
average - 6,4 7,0 Historical Stock Exchange ↑ import 3
median - 6,25 minimum collapsed + export 2
deviation - 6,5 6,05097 1
2,34
exchange rate
0
range of
6,0 -1
variability -
0,87 Capital understated -2
5,5 outflow inflation -3
dispersion -
0,07
USD/CNY The pace of monthly growth
USD/ INR
80 8
average - The collapse of the US tax reform
70 Chinese stock 6
63,19 60
median - 50
exchange 4
2
63,8
exchange rate
40
deviation - 0
30
0,97 20 -2
range of 10 ↑ oil prices -4
variability - 0 -6
1-Jan-13
1-Mar-13
1-May-13
1-Jul-13
1-Sep-13
1-Nov-13
1-Jan-14
1-Mar-14
1-May-14
1-Jul-14
1-Sep-14
1-Nov-14
1-Jan-15
1-Mar-15
1-May-15
1-Jul-15
1-Sep-15
1-Nov-15
1-Jan-16
1-Mar-16
1-May-16
1-Jul-16
1-Sep-16
1-Nov-16
1-Jan-17
1-Mar-17
1-May-17
1-Jul-17
1-Sep-17
1-Nov-17
14,45
dispersion -
13,52
USD/INR The pace of monthly growth
USD/AZN
average-1,19 2,5
floating regime ↓ investments + oil 60
median- prices + capital
2,0 outflows 40
1,05 exchange rate
1,5 official rate
deviation- decrease 20
12,31 1,0
range of 0,5 0
variability- 0,0 -20
1,17
dispersion-
0,17 USD/AZN The pace of monthly growth
USD/TRY
average- 5,0 attempt at a Trump’s 15
2,71 terrorist attacks + political coup Victory
4,0 political instability 10
median- 2,73
exchange rate
deviation- 3,0 5
0,66 2,0 0
range of
1,0 -5
variability - mutually exclusive of the visa
2,16 0,0 regime with the US -10
dispersion -
0,40
USD/TRY The pace of monthly growth
USD/SAR
average - 3,760 ↓ oil prices 0,4
3,75 0,2
exchange rate
median - 3,755
3,75 0
deviation - 3,750
-0,2
0,01
3,745 fixed rate (soft pegs) -0,4
dispersion -
1-Jan-13
1-Apr-13
1-Jan-14
1-Apr-14
1-Jan-15
1-Apr-15
1-Jan-16
1-Apr-16
1-Jan-17
1-Apr-17
1-Jul-13
1-Oct-13
1-Jul-14
1-Oct-14
1-Jul-15
1-Oct-15
1-Jul-16
1-Oct-16
1-Jul-17
1-Oct-17
0,00
range of
variability -
0,01 USD/SAR The pace of monthly growth
Fig. 4. Dynamics of exchange rate changes in 2013-2017 [19]
In order to study the nature of currency risks, we have done a correlation analysis of
macroeconomic indicators (foreign direct investments, gross national income, exports, added
value of industry, inflation and interest rates) and the exchange rate of these countries during
2001-2016 (Table 2). Based on the experience of emerging markets, it is logical to assume that
one of the key determinants of the successful export of these countries is the price
competitiveness of goods and services, which is directly dependent on the exchange rate.
However, this hypothesis has been empirically confirmed only for the Chinese economy: the
correlation between exports and the Chinese Yuan Renminbi rate is characterized by an ideal
negative correlation at 0.969. The results of the correlation analysis for Turkey and India have
shown a high level of positive correlation, indicating unidirectional changes in exports and the
national currency rates, which can be explained by its closed economies and receptive internal
markets. The low correlation coefficients of Azerbaijan has shown the dominant influence of
structural, non-economic factors on the exchange rate formation and dynamics. What is more,
Saudi Arabia has demonstrated the absence of a correlation between macroeconomic
indicators and exchange rate due to soft peg of Saudi Arabia Riyal to United States Dollar.
Table 2. Correlation matrix of macrofactors and exchange rate of partners of Antonov State Company
[24]
Partner Added FDI, Inflation, Loan interest Export,
GNI, USD
countries value, % USD % rate,% USD
1 2 3 4 5 6 7
China -0,435 0,969 -0,049 0,004 -0,966 * 0,984
India -0,821 0,516 -0,486 -0,505 0,638** 0,834
Azerbaijan -0,517 0,091 0,087 -0,538 -0,360 0,005
Turkey 0,728 0,191 -0,323 - 0,580 0,831
Saudi Arabia -0,385 -0,157 -0,363 - 0,058 0,240
* correlation coefficients more than 0.75 indicate high correlation level [23]
** for 15 observations with 95% significance level the valid correlation is 0.51 [23]
It is logical to assume, that for the export-oriented economy of Ukraine, the level and
dynamics of the exchange rate should be the key factor of economic development. However,
the correlation analysis based on statistics of 2001-2016 has shown a low level of correlation
between export and exchange rate - 0.078 (Table 3).
Table 3. Correlation matrix of macrofactors and exchange rate of Ukraine [24]
2001-2016
Added Loan Exchange
Macro indicators FDI Inflation GNI Exports
value interest rate rate
Value Added 1
FDI 0,085 1
Inflation -0,087 0,349 1
Loan interest rate 0,060 -0,582 -0,173 1
Exchange rate -0,674 -0,222 0,395 0,080 1
GNI -0,533 0,647 0,293 -0,692 0,270 1
Export -0,515 0,685 0,123 -0,588 0,078 0,939 1
qualitative restart of the domestic economy;
activation of market regulation mechanisms;
structural modernization of market relations.
2013-2016
Added Loan Exchange
Macro indicators FDI Inflation GNI Exports
value interest rate rate
Value Added 1
FDI -0,089 1
Inflation -0,213 -0,229 1
Loan interest rate -0,067 -0,054 0,970 1
Exchange rate 0,556 0,037 0,641 0,781 1
GNI -0,186 0,143 -0,912 -0,965 -0,898 1
Export -0,422 0,210 -0,794 -0,868 -0,955 0,968 1
This can be explained by the underdevelopment of market regulation instruments, the
dominance of political factors on hryvnia exchange rate, raw material specialization and
structural imbalances, the inertia of changes in the world mineral trade, and, as a result, the
insensitivity of domestic exporters to changes in exchange rates and inflation. To the
contrary, the economic reforms carried out in 2013-2017 led to a qualitative restart of the
domestic economy by intensifying market-based regulatory mechanisms. Consequently, this
leads to the ideal negative correlation between hryvnia and Ukrainian exports (-0.955),
confirming the structural modernization of market relations, in particular currency ones.
Based on the obtained correlation matrices, we have performed regression modeling of the
exchange rate in conditions of economic instability of Ukraine in the EViews 7 where
regressand is hryvnia exchange rate (ER) while independent variables include gross domestic
product (GDP), inflation rate (IFL), current and financial account (CA and CFA), and official
reserves (OR01) (fig. 5). Using least square method we have obtained the following model
specification (1).
ER=-6170.97+0.08252CA-0.0516CFA+0.00416GDP+60.3724IFL-0.03853OR01 (1)
According to the results, current account, GDP and inflation directly effect on the hryvnia
exchange rate while the financial account and official reserves have a reverse effect on the
regressand.
Estimation Command: Hypothesis:
========================= The formation and dynamics of the
LS ER C GDP CA CFA IFL OR01 exchange rate (ER) are influenced by
such determinants as the balance of
Estimation Equation:
========================= the current account (CA), the balance
of operations
ER = C(1) + C(2)*GDP + C(3)*CA + C(4)*CFA + C(5)*IFL + with capital and
C(6)*OR01 financial operations (CFA), inflation
(ifl), GDP (GDP) and the official
Substituted Coefficients:
========================= reserves (OR) .
ER = -6170.97158681 + 0.00416486435665*GDP +
0.0825222552424*CA - 0.0516160739704*CFA + Model specification
60.3724034098*IFL - 0.0385302200352*OR01
Fig. 5. Representations -of the model in the EViews 7 [24]
The most significant factor of the hryvnia exchange rate is GDP (probability <0.05
i <0.01); among others essential determinants we can note the balance of current and
financial accounts while official reserves and inflation are secondary factors (table 4).
Since the model is adequate for Fisher's criterion and has a high explanatory
capability with an adjusted determination coefficient of 81.7%, the model
comprehensibly represents the link between exchange rate and the analyzed
determinants. In order to analyze and control the exchange rate differences of national
currencies, based on constructed regression models, lets consider the effect of macro
determinants changes on the exchange rates of countries in value terms using elasticity
coefficients (Table 5).
Thus, with the help of econometric models, a hierarchy of the most significant
determinants of the probability criterion is determined which will allow to assess the effect of
the macrofactors changes on the level and dynamics of exchange rates in value terms based on
the elasticity coefficients. This is an effective tool for financial management in forecasting and
managing exchange rate differences and costs from joint ventures on the territory of partner
countries of Antonov State Company.
Table 4. Estimation output [19; 24]
Variable Coefficient Std. Error t-Statistic Prob.
r-squared C -6170.972 4794.790 -1.287016 0.2055
=81,8 ≥ 0.75 GDP 0.004165 0.000318 13.08586 0.0000
→ high
explanatory CA 0.082522 0.038222 2.159015 0.0369
ability CFA -0.051616 0.025230 -2.045788 0.0474 Prob < 0.01
and 0.05 →
IFL 60.37240 47.37216 1.274428 0.2099 significant
OR01 -0.038530 0.027850 -1.383515 0.1742 determinants
Prob> 01
0.838014 Mean dependent var 944.8000 and 0.05→
Prob (F- R-squared
insignificant
statistic)<0. Adjusted R-squared 0.817765 S.D. dependent var 596.2080 determinants
01 I 0.05 254.5150 Akaike info criterion 14.03770
→ model is
S.E. of regression
adequate Sum squared resid 2591115. Schwarz criterion 14.27622
Log likelihood -316.8672 Hannan-Quinn criter. 14.12705
F-statisti c 41.38685 Durbin-Watson stat 1.359757
Prob(F-statistic) 0.000000
Table 5. Analysis of the macrofactors of the exchange rates of the partner countries of
Antonov State Company [19; 24]
Country Specification Significance Impact Elasticity
ER = -6170,972+ CA* - Significant: CA, Direct: СА, 1% changes in factors leading to
0,083CFA* 0,052+ GDP* CFA, GDP GDP, IFL changes in exchange rate CA -0,13;
Ukraine
0,004+ IFL * 60,372- Insignificant: Indirect: CFA, CFA -0,069%; GDP 1,28%; IFL
OR*0,039 IFL, OR OR 6,446%; OR 0,003 %.
The models constructed for the national currencies of Saudi Arabia and China inadequately reflect the
interconnection of macro-factors and the dynamics of exchange rate changes
ER = -0,829 CA* 1,32E- 1% changes in factors leading to
Significant: CA,
11+CFA* 2,01E-11+ Direct: CA, changes in exchange rate CA 0,072%;
Azer- CFA
GDP*4,20E-12 GDP, IFL, OR CFA 0,066%; GDP 0,139%; IFL
baijan Insignificant:
+IFL*8,23E-05 -OR*1,82E- Indirect: CFA 0,002 %; OR -0,092%.
GDP, IFL, OR
11
ER = 36,281+CA* 1,05E- Significant: 1% changes in factors leading to
Direct: CA
10-CFA* 8,46E-11+GDP* GDP, IFL changes in exchange rate CA - 0,04%;
India Indirect: CFA
2,33E-11- IFL* 1,134- Insignificant: CFA -0,028%; GDP 0,491%; IFL -
GDP IFL, OR
OR*2,08E-11 CA, CFA, OR 0,211%; OR -0,068%.
ER = 1,632 +CA* 9,08E- Significant: IFL, Direct: CA, 1% changes in factors leading to
12+CFA* 3,73E-12-GDP* OR OR changes in exchange rate CA - 0,17%;
Turkey
2,15E-12-IFL* Insignificant: CA, Indirect: CFA, CFA 0,026 %; GDP - 0,9%; IFL -
0,017+OR*2,25E-11 CFA, GDP GDP, IFL, 0,697%; OR 1,111 %.
3.2 An Estimation of Currency Risks of Antonov State Company
The classical and the most simple method for analyzing the sensitivity of currency risk ( as
follows, operational currency risk) is the assessment of the value of foreign exchange position
which is defined as the difference between the amount of revenue (incoming cash flow) and
enterprise payments (outflow) in foreign currency [11]. In order to ensure exchange market
stability and limit the risk associated with conducting currency transactions which can lead to
significant losses the National Bank of Ukraine sets limits on the open foreign exchange
position [18].
Empirical calculations have shown that the foreign exchange position of Antonov State
Company is mostly short open, as 2010-2012 and 2014 were characterized by a negative net
cash flow in foreign currency, while a long open foreign exchange position was inherent in
2009 and 2013 (table 6). Analysis of the foreign exchange position of Antonov State
Company also allows us to identify "bottlenecks" of its activity. For instance, investment
activity is characterized by stable negative result. The key feature of Antonov State Company
is the innovative activity and the focus on the high technology development. This
characteristic can explain obtaining unexpected financial gains due to foreign exchange rate
differences in 2009 and 2013, since, according to Marshall-Lerner theory, a significant
fluctuation of the national currency leads to positive financial and economic effects for
representatives of industries with a high added value, the best example of which is aviation.
Table 6. Dynamics of changes in net cash flow in foreign currency, 2009-2014 [11, 25]
Indicator 2009 2010 2011 2012 2013 2014
from operating, ths US dollars 23669,1 10814,65 2211,597 2210,819 24949,82 -9233,81
from the
investment, ths US dollars -1742,24 -11284,1 -4962,48 -2246,94 -5403,8 -2706,61
from financial, ths US dollars 11898,94 -6,21727 1265,285 -298,166 -15082,3 -1807,13
Net cash flow, ths US dollars 33825,8 -475,715 -1485,6 -334,29 4463,743 -13747,5
40 000,00 Чистий грошовий
Net cash flow, thsпотік,
US dollars
тис. дол..
indicators
Level of
20 000,00 from
від operating, тис.
операційної, ths US dollars
дол..
0,00 from
від the
інвестиційної, тис.
2009 2010 2011 2012 2013 2014 investment, ths US dollars
дол..
-20 000,00 від фінансової,
from financial,тис.
ths дол..
US dollars
Період
Time
The next stage of this analysis is the currency matching model (Table 7), which is the
method of accounting for and estimation of currency risk through mutual calculation of risks
on liabilities and assets. It describes the relationship between profits (losses), generated by
open foreign exchange position, and foreign exchange differences and helps to choose the an
appropriate strategy for managing currency risks [26] (2).
∆ = ( − ) (2)
where ∆P - profit (loss) of foreign currency revaluation due to changes in the exchange rate;
VP - foreign exchange position ;
s та s - predictable and current exchange rates.
If we choose, between two alternatives: the strategy of currency matching which aims to
eliminating currency risk by keeping foreign exchange position closed, and a strategy of
maximizing profits, which involves receiving speculative profits by open foreign exchange
position, for Antonov State Company it is expedient to choose a strategy for maximizing
profits. Since in the conditions of unpredictable currency volatility, currency matching
strategy is unsuitable, because a wide range of hedging instruments opens the possibility of
realizing currency speculations in order to generate financial benefits.
Table 6. Estimation of currency risks of Antonov State Company by currency matching [10,
25, 27]
Indicator 2009 2010 2011 2012 2013 2014
Net cash flow, ths US dollars 33 825,8 -475,715 -1 485,6 -334,29 4 463,743 -13 747,5
Actual exchange rate, UAH/USD 7,985 7,9617 7,9897 8,097 8,295 15,875
Expected exchange rate, UAH/USD 7,9 9,023 8,82 9,3 8,2 11,7
Expected exchange rate, ths US dollars -2 875,19 -504,876 -1 233,49 -402,151 -424,056 57 395,81
Profit / loss from revaluation,
1 897,433 -1 119,36 -732,693 -385,328 -224,834 11 150,36
ths US dollars
The currency risk estimation based on an open foreign exchange position only can lead to
incorrect strategic decisions, since the level of currency risk is determined by the exchange
rates volatility, which is nothing more than a standard deviation. Therefore, during risks
estimation we should take into account not only parameter of value, but also parameter of
stability [10; 26], which is determined by Value at Risk method (VaR) proposed by J.P.
Morgan. The reason of its popularity is analyzing of three elements: a relatively high level of
confidence (typically either 95% or 99%), a time period (a day, a month or a year) and an
estimate of loss (expressed either in currency or percentage terms). VaR method characterizes
the maximum possible size of losses on an open foreign exchange position of a company for a
certain time with a certain degree of probability (usually 95% or 99%) [10; 26].
Let us analyze risks for the US dollar and the national currencies of the key partner
countries of Antonov State Company as alternative currencies for conducting internal
settlements using the VAR method from 01.01.15 to 31.03.15. [19] (Table 8).
Table 8. Currency risk estimation of Antonov State Company by VaR [25, 28-32]
Indicators USD IDR TRY CNY AZN SAR Sum
Influence of exchange rate changes
177 012 177 012 177 012 177 012 177 012 177 012 177 012
on the balance of funds, ths UAH
Probability of tolerated risk,% 99 -
Quantum of normal distribution for
2,33 2,33 2,33 2,33 2,33 2,33 -
99%probability
The exchange rate at the date of
15,7689 0,2419 6,793 2,5333 20,1027 3,9891 -
balance sheet, 31.12.14*
Average exchange rate increase
0,134633 0,001416 0,020896 0,02098 0,13491 0,036378 -
(mathematical expectation)
The standard deviation of the daily
0,046374 0,07825 0,059973 0,04527 0,119216 0,076932 -
rate of growth
Time horizon, days 10
VaR for 10 days 4 705,25 -32 022,6 -21 036,3 -15 103,5 -25 288,5 -25 290,3 - 114 036
The share of losses from the foreign
2,7 -18,1 -11,9 -8,5 -14,3 -14,3 -64,4
exchange position for 10 days,%
The obtained results indicate that with 99% probability of tolerated risk during the first 10
days of the Q2 2015 (01.04.15-10.04.15), losses from the exchange rate fluctuations of
alternatives to the US dollar currencies would not exceed 114 036 ths UAH. In the result,
there will be 67,1% change in the balance of cash due to changes in foreign exchange rates.
The biggest part of expected losses, 32,022.6 ths UAH, Antonov State Company would
receive due to the volatility of the Indian Rupee. While fluctuations of others exchange rates,
would also lead to negative effects totaling 118,741 ths UAH where the biggest shares would
be created by Indian Rupee, the Azerbaijani Manat and Saudi Arabia Riyal fluctuations. At
the same time, the volatility of the US dollar would generate positive effects in the amount of
4,705, 259 ths UAH, which is a 2.7% change in the cash balance.
In order to make insightful analysis of currency risks of partner countries of Antonov State
Company followed by joint production of aircraft in the territory of customers, we will
perform a statistical analysis of the exchange rate variability (Table 9).
Table 9. Statistical estimations of the exchange rates volatility of the partner countries of
Antonov State Company from 01.01.2017 to 31.12.2017 [19, 28-32]
Grou
Coefficients USD CNY TRY AZN IDR SAR
p
Mathematical expectation 26,59 3,9 7,29 14,63 0,4 7,19
Center metrics
Median 26,58 3,92 7,32 14,81 0,4 7,18
Mode 25,9 0,00 7,37 14,00 0,38 7,18
Deviation between expectation
0,06 0,49 0,48 1,23 0,37 0,15
from the median%
Average reliability * reliable
Range of deviation 2,6267 4,294 0,9473 2,515 0,1483 0,815
bigger quartile, 26,7997 4,006 7,4687 14,073 0,4067 7,183
Variation
smaller quartile, 27,0217 3,923 7,4276 15,225 0,3967 7,25
Interquartile range ( − ) -0,222 0,083 0,0411 -1,153 0,01 -0,067
Mean deviation 0,478 0,114 0,146 0,45 0,013 0,05
Dispersion 0,327 0,135 0,036 0,283 0,0 0,006
Standard dispersion 0,571 0,367 0,191 0,532 0,02 0,079
Linear 1,797 2,920 2,004 3,077 3,264 0,699
Others
Coefficient of
Quadratic 2,149 9,408 2,617 3,638 4,953 1,099
variation
oscillations 9,877 109,973 12,996 17,193 37,245 11,329
Asymmetry 0,108 -9,762 -0,974 0,251 -2,7 -0,252
Asymmetr
Type of asymmetry (+) right (-) left (+) right (-) left right
excess
y and
Excess -0,492 102,909 -0,24119 -0,201 11,404 9,655
flat- lepto-
Distribution type flat-topped leptokurtic
topped kurtic
Dynamics - + - + + +
Approximation coefficient 2,6 27,9 12 59,9 1,4 3,2
Reliability trend unreliable credible unreliable
Trend
extremely
Homo- Relatively Hetero- relatively
Qualitative homogeneity hetero-
geneous homogeneous geneoushomogeneous
geneous
Insig- Signi- little relatively
Fluctuation relatively
nificant ficant significantinsignificant
An average is considered to be the most important statistical indicator that displays a
typical or "central" but not always adequate meaning, in the range of the variative dynamics.
That is why the simplest indicator of the statistical series reliability is the deviation between
median and average, which ensures the adequacy of the forecast data and should be less than
5%. The US dollar is characterized by the smallest variation among the alternatives (0.06%),
which indicates a high degree of reliability of the center as an indicator in order make
management decisions.
The standard error of the mean (standard dispersion) and interquartile range reflect the
degree of drilling property. The whole range of the currencies demonstrates a relatively low
level of volatility that indicates that the fluctuations of these currencies are around mathematical
expectations. Similar results reflect the interquartile range, which is as a refining indicator for
exchange rate fluctuations.
The level of statistical homogeneity will be analyzed using the variation indices. The biggest
quadratic variation has Chinese Yuan Renminbi, which reflects the qualitative heterogeneity of
the set of statistical data. Using oscillation coefficient, we can note that the statistical mass of the
Chinese Yuan Renminbi is extremely heterogeneous, which substantially deforms the forecast
of the probable exchange rate. Significant heterogeneity is also typical for Indian Rupee, whereas
United States Dollar has insignificant fluctuations, which indicates a high level of forecasts
objectivity based on the average and its qualitative homogeneity. The dynamics of the dollar, as
well as the Azerbaijani Manat and the Saudi Arabia Riyal are closely related to the symmetric
distribution, reflecting the regular trends in currency fluctuations facilitating the process of
perspective forecasting.
Thus, the statistical analysis of currency risks has shown the priority of cooperation between
Antonov State Company and Saudi Arabia due to the qualitative homogeneity of the time series
because of the result Saudi Arabia Riyal had been pegged to the United States Dollar. The low
level of currency risk is also typical for the Indian Rupee and Turkish Lira, but the political
component still remains as the key determinant of its fluctuation. In contrast, the relatively high
level of currency risk is inherent to the United States Dollar and Azerbaijani Manat as a result of
floating exchange rate regime, which essentially depends on the oil prices volatility.
However, in the domestic economic system, currency risk is complex and complicated due
to the export-oriented strategy. In this case, the effectiveness of economy depends on stable flow
of foreign exchange earnings generated by exporters as a key source of foreign currency and the
factor of hryvnia stabilization. As the result, Ukrainian economy significantly depends on the
level of domestic demand, indicators of development of foreign states. In order to analyse the all
range of Ukrainian economic threats, which is an open economy, we will propose a
methodology for an integrated valuation of currency risk (3).
= ∗ − ∗ ! +#∗ + $ ∗ %&' (3)
де − ()*+,-./ ()0(1.*2- 23 14--+)15 -( 6 23 . *-.0(), 7.-*)+-,
та − 14--+)15 -( 6 23 ;6-.()+ .)0 7.-*)+- 124)*-(+ ,
! − *ℎ+ -( 6 23 +12)2=(1 /2>02>) () 7.-*)+- 124)*-(+ ;
? @ − ()3/.*(2) -( 6 23 *ℎ+ 7.-*)+- 124)*-(+ ;
, , # та $ − >+(,ℎ* 12+33(1(+)* .
The three components of the integrated level reflect the external threats generated by the
economic systems of partner countries, which include statistical estimates of currency and
inflation risks, as well as the economic slowdown. The third parameter characterizes the
volatility of the hryvnia exchange rate. Overall, the integral assessment of currency risk is based
on real effective exchange rate, the economic content of which is to reflect the exchange rate
change, adjusted for inflation in Ukraine and in countries - major trading partners of Ukraine
[18]. However, the proposed methodology includes the risk of economic slowdown of partner
countries, which is the main threat of Ukrainian economy in the medium term [20].
According to the integral level of the currency risk, Turkish Lira and Saudi Arabia Riyal,
despite the stability of the exchange rate based on soft peg, are characterized by the highest risks
due to inflation risks (Table 10).
Table 10. Statistical analysis of currency risks of Antonov State Company partners [24, 28-32]
Partner Currency Inflation Risk of economic Integral
A B C
currency risk risk slowdown indicator
TRY 0,32 0,5 254,07 0,3 20,43 0,2 842,96
CNY 0,78 0,5 8,74 0,3 3,86 0,2 28,74
IDR 61,12 0,5 5,07 0,3 4,37 0,2 46,59
SAR 0,00 0,5 123,87 0,3 8,75 0,2 411,15
AZN 139,21 0,5 69,00 0,3 20,56 0,2 295,49
Based on the assessment of the exchange rates fluctuations of the trading partners of
Antonov State Company we summarize the optimal market strategies and traded positions in
the foreign exchange market during February 2018 (Table 11).
Table 11. Analysis of exchange rates volatility of the key partner countries of Antonov State
Company [19]
Parameters Forecast
Annual Annual Trader
Pair Rate for 1 5 year old dynamics
change bid ask range position
month
Bulls
USD/ 24,415-
27,7719 2,1% 27,521 28,0219 (77%) / Buy
UAH 28,9125
Bears (23%)
Bulls
USD/ 63,245- (45%)/ Actively
64,23 -4,4% 64,22 64,24
INR 67,465 Bears sell
(55%)
Bulls
USD/ 3,3845- Actively
3,7691 1,93% 3,7652 3,773 (64%)/
TRY 3,9825 buy
Bears (36%)
USD/ 3,7487- Bulls
3,7508 0,01% 3,7468 3,7548 Sell
SAR 3,7525 (100%)
Bulls
USD/ 6,2707- Actively
3,3034 -8,24% 6,3024 6,3044 (30%)/
CNY 6,9226 sell
Bears (70%)
USD/ 1,665- Bulls
1,689 -12,79% 1,675 1,7030 Neutral
AZN 1,9397 (100%)
The final choice of country, nature and depth of cooperation depends on the
purpose of the activity, the risk appetite level and the financial capacity of the aircraft
producer to accept a certain level of risk [1].
4 Conclusions
The paper presents a theoretical general conclusion and a new solution to the
scientific problem, which is to systematize theoretical basis and develop practical
recommendations for optimizing the currency risk management of the company by the
example of Antonov State Company. The obtained results indicate the achievement of the
goal and give an opportunity to make the following conclusions and make suggestions:
the nature of the independent macroeconomic factors influence on the exchange rate
of the partner countries of Antonov State Company was determined and degree of its impact
were studied using the elasticity coefficients based on external environment modeling of the
exchange rates formation and volatility in EViews 7;
the statistical analysis of currency risks has shown the priority of cooperation of
Antonov State Company with Saudi Arabia due to the quality homogeneity of the time series
because of the result Saudi Arabia Riyal had been pegged to the United States Dollar. The low
level of currency risk is also typical for the Indian Rupee and the Turkish Lira, but the political
component still remains as the key determinant of its fluctuation. In contrast, the relatively
high level of currency risk is inherent to the United States Dollar and Azerbaijani Manat as a
result of floating exchange rate regime, which essentially depends on the oil prices volatility.
According to an alternative method of currency risk analysis - VaR, the positive foreign
exchange differences of Antonov State Comoany are received due to US dollar volatility in
the amount of 4,705,259 ths USD in Q1 2015; analysis of the company currency position
indicates the necessity of choosing a maximizing profits strategy, in contrast to the strategy
of currency matching;
In order to analyze the all range of Ukraine’s economy threats, which is open
economy, we have proposed a methodology for an integrated valuation of currency risk. As
the result, Turkish Lira and Saudi Arabia Riyal, despite the stability of the exchange rate based
on soft peg, are characterized by the highest risks due to inflation risk;
Analysis of currency risks of Antonov State Company proves that the choice of
management methods depends on the scale and nature of the risks impact. In this
case, in our opinion, it should be used scenario approach to manage cureency risk: to take a
certain level of risk within the risk appetite, if it does not exceed acceptable level of losses,
hedge risks - without exceeding the critical level of looses by using such tools as forwards and
swap agreements, leads & lags strategy; avoid and eliminate risks in case of a catastrophic
losses level.
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