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R2 = 1--^-vMr = 0,8102
Ш - Ycv)
R2 indicates that in 81,02 % cases the changes in the cost of oil (BRi) lead to changes in the USD/CAD exchange rate.
Thus, using the parabolic model based on the USD/CAD exchange rate dependence from the cost of oil, it is possible to determine USD/CAD exchange rate taking into account changes in the prices of oil. The fall in oil prices leads to the growth of USD/CAD exchange rate. Accordingly, the rising cost of oil leads to an increase of U.S. dollar exchange rate to Canadian dollar.
List of sources
1.Bank of Canada official website Bank of Canada http://www.bankofcanada.ca/
2.The Federal reserve system of the United States official website http://www.federalreserve.gov/
3.Chen, S-S. Oil prices and real exchange rates / S-S. Chen, H-C. Chen // Energy. Economics. - 2007. - No 29(3).
4.Dooley, M.P. Exchange Rates, Country Preferences, and Gold / M.P. Dooley, P. Isard, M. Taylor // NBER. Working Paper. Cambridge, MA - 1992. - October. -No4183. - 47 p.
5. Трегуб И.В. Математические модели динамики экономических систем -монография, М.: 2009.
6.Трегуб И.В. Investment project risk analysis in the modern Russian economy // research in empirical international trade. - Slovenia: working papers. June. 2012.
УДК 330.43
Fedotova E.
the master student of International Finance Faculty Financial University under the Government of the Russian Federation
Professor: Tregub I. V. Russia, Moscow THE INFLUENCE OF ECONOMIC GROWTH ON THE FINANCIAL SECTOR BY EXAMPLE OF GERMANY Abstract. The paper analyses the influence of macroeconomic factors on financial sector development in Germany. Data Envelopment Analysis is used to determine the extent to which these factors affect the financial sector and to understand which indicators play significant role in Germany in 20 years' perspective (from1995 - 2015). The results confirm that the relationship between economic variables and financial ones take place.
Key words: Germany, financial variables, economic variables, import, export, inflation, GDP. Country overview
The German economy is the fifth largest economy in the world in PPP terms and the Europe's largest as a leading exporter of machinery, vehicles, chemicals, and household equipment and benefits from a highly skilled labor force. Germany
faces significant demographic challenges to sustained long-term growth. Low fertility rates and a large increase in net immigration are increasing pressure on the country's social welfare system and necessitate structural reforms.
Reforms launched by the government of Chancellor Gerhard Schroeder, deemed necessary to address chronically high unemployment and low average growth, contributed to strong growth and falling unemployment. These advances, as well as a government subsidized, reduced working hour scheme, help explain the relatively modest increase in unemployment during the 2008-09 recession - the deepest since World War II. The new German Government introduced a minimum wage of about $11.60 (8.50 euros) per hour that took effect in 2015.
In 2008 and 2009 in Merkel's second term stimulus and stabilization efforts and tax cuts increased Germany's total budget deficit to 4.1% in 2010, but slower spending and higher tax revenues reduced the deficit to 0.8% in 2011 and in 2015 Germany reached a budget surplus of 0.9%. A constitutional amendment approved in 2009 limits the federal government to structural deficits of no more than 0.35% of GDP per annum as of 2016, though the target was already reached in 2012.
However, the German economy suffers from low levels of investment, and a government plan to invest 15 billion euros during 2017-18, largely in infrastructure, is intended to spur necessary private investment. Following the March 2011 Fukushima nuclear disaster, Chancellor Angela Merkel announced in May 2011 that eight of the country's 17 nuclear reactors would be shut down immediately and the remaining plants would close by 2022. Germany plans to replace nuclear power largely with renewable energy, which accounted for 27.8% of gross electricity consumption in 2014, up from 9% in 2000. Before the shutdown of the eight reactors, Germany relied on nuclear power for 23% of its electricity generating capacity and 46% of its base-load electricity production. Domestic consumption, bolstered by low energy prices and a weak euro, are likely to drive German GDP growth again in 2017.
The empirical part
This work investigates the relationship between a number of endogenous (dependent) factors and exogenous (independent) ones for the time period between 1995 and 2015 in Germany.
As for endogenous factors, they represent financial sector and include market capitalization of listed domestic companies, total value of stocks traded, total reserves including gold (current US$) and interest rates (annual, %). Exogenous factors relate to economic growth, including gross domestic product, foreign direct investment, import and export of goods and services (current US$) as well as consumer price index, annual inflation and unemployment rate (%).
Table 1. Benchmark da
ta
Financial Sector
Year GDP (current bn US$) Fo rei gn direct investm ent (BoP, current bn US$) Imports of goods and services (current bn US$) Exports of goods and services (current bn US$) CPI (2010 YoY= 100) Inflation (annual %) Unemploy ment, total (% of total labor force) (national estimate) Market cap of listed domestic companies (current bn us$) Total reserves (includes gold, current bn US$) Interest Rates (annual ,%)
XI X2 X3 X4 X5 X6 X7 Y1 Y3 Y4
1995 2 592 12 558 570 80,50 1,72 8,20 577 122 6,86
1996 2 504 6 553 574 81,60 1,45 8,80 665 118 6,23
1997 2 219 13 537 563 83,20 1,88 9,90 825 105 5,66
1998 2 243 24 564 593 84,00 0,94 9,80 1 094 108 4,58
1999 2 200 56 579 595 84,50 0,57 8,90 1 432 93 4,49
2000 1 950 248 596 601 85,70 1,47 7,90 1 270 87 5,26
2001 1 951 57 587 622 87,40 1,98 7,80 1 072 82 4,80
2002 2 079 51 586 677 88,60 1,42 8,50 686 89 4,78
2003 2 506 65 725 817 89,60 1,03 9,80 1 079 97 4,07
2004 2 819 -20 857 999 91,00 1,67 10,70 1 195 97 4,04
2005 2 861 60 935 1 080 92,50 1,55 11,20 1 202 102 3,35
2006 3 002 87 1 078 1 237 93,90 1,58 10,30 1 638 112 3,76
2007 3 440 51 1 251 1 480 96,10 2,30 8,70 2 105 136 4,22
2008 3 752 31 1 407 1 631 98,60 2,63 7,50 1 111 139 3,98
2009 3 418 57 1 123 1 292 98,90 0,31 7,70 1 292 179 3,22
2010 3 417 86 1 266 1 444 100,00 1,10 7,10 1 430 216 2,74
2011 3 757 97 1 500 1 684 102,10 2,08 5,80 1 185 234 2,61
2012 3 544 65 1 414 1 630 104,10 2,01 5,40 1 486 249 1,50
2013 3 753 63 1 482 1 706 105,70 1,50 5,20 1 936 199 1,57
2014 3 879 9 1 518 1 771 106,60 0,91 5,00 1 739 193 1,16
2015 3 363 46 1 319 1 573 106,90 0,23 4,50 1 716 174 0,50
In order to assess internal relationships between factors, the correlation analysis has been conducted. Correlation analysis typically gives us a number result that lies between +1 and -1, the positive sign means direct correlation whereas the negative sign denotes inverse correlation. And the closer the number moves towards 1, the stronger the correlation is. Usually for the correlation to be considered significant, the correlation must be 0.5 or above in either direction.
The most significant correlation of Exp can be observed with the inflation factor, stated for 0,98. Simultaneously, the most significant correlation of Total reserves can be found with FDI factor, for -0,95.
Table 2. Correlation analysis
GDP' FDI' Imp' Exp' CPI' Infi' Unempl' MarCap' TotRes' IntR'
GDP" l
FDI' -0.78 l
Imp' 0,90 -0,93 l
Exp' 0,91 -0,94 0,87 l
CPI' 0,97 -0,82 0,89 0,91 1
Infi' 0.87 -0,93 0,96 0,98 0,93 1
Unempl' -0,84 0,64 -0,76 -0,71 -0,73 -0,64 l
MarCap' 0,10 0,04 -0,15 -0,04 0,16 -0,04 0,02 l
TotRes' 0,88 -0,95 0,89 0,96 0,91 0,92 -0,72 0,04 l
IntR' 0,62 -0,67 0,53 0,68 0,65 0,60 -0,33 0,15 0,77 l
The system describing the impact of economic growth on the financial sector is following:
Y1(MarCap) = a1+bn*ln(GDP)+b12*ln(CPI)+b13*ln(Exp)+b14*FDI + e1
Y3(TotRes)=a2+b21 * ln(GDP)+b22 * ln(FDI)+b23 * ln(Imp)+b24 * ln(Exp)+ b25 * ln(CPI)+b25 * ln(Infl)+b26 * ln(Unempl)+e2
Y4(IntR)=a3+b31*ln(MarCap)+b32*ln(Imp)+b33*ln(Exp)+b34*ln(TotRes)+ _ b35*Infl+e3
The assumption of the suggested equations system is that the analyzed economic factors can be approximated by a log-normal distribution. Further on, the testing of the hypothesis is provided. Taking the logarithm of a factor and constructing a linear regression model minimizes the relative deviation from the regression line.
The first type of analysis is considering how Market Capitalization (Y1) is influenced by all the exogenous factors.
The model is considered quite inapplicable because the dependence of endogenous factors from exogenous is proved by R-square = 0,7162, so there is a rather low dependence between the exogenous variables and the endogenous variable. F-calculated (4,6872) is higher than F-critical (2,8321), so there is a linear effect between factors.
As for significance of each factor, the most important are X4, X6, X7, cause their t-statistics is more than t-critical (2,16). If we will predict the amount of Y1 for 2015 it deviates from the real amount by 6,34%, that is out of the 95% confidential interval. All in all, the model is non-adequate and further analysis is not needed.
Secondly, we can consider model of dependence of Interest Rate (Y4) from the exogenous factors. R-square= 0,9731 is showing a high dependence between the exogenous variables and the endogenous variable. By F-test (F-calculated= 67,2961 more than F-critical= 2,8321), so there is a significant linear effect and our model is correctly specified. From T-test we can conclude that factors X1,X2, X4 are significant (t-critical= 2,1604). If we will predict the amount of Y1 for 2015, using this model, it deviates from the real amount by 32,9%%, that is significantly out of
the 95% confidential interval, the model is non-adequate and we cannot make conclusions.
The last equation is showing the influence of factors on Total Reserves (Y3). The model will be the following:
TotRes=-806045422926,24+0,1038*GDP-0,0145*FDI+0,6758*Imp-0,7757*Exp+9107357313,42*CPI+10859475377,82*Infl-3194237229,82*Unempl R-square is 0,8763, that shows a dependence between the exogenous variables and the endogenous variable, close to be enough for analysis. F-calculated= 13,1602 more than F-critical= 2,8321, so there is a significant linear effect and our model is correctly specified. From T-test we can understand that factors X1,X2, X4 are significant, because their t-statistics is more than t-critical= 2,1604.
Table 3. T-test
# t-statistics Influence
X 1 0,10 non-significant
X 2 -0,01 non-significant
X 3 0,68 non-significant
X 4 -0,78 non-significant
X 5 9107357313 significant
X 6 10859475378 significant
X 7 -3194237230 significant
Moreover, by calculating forecast value of 2015 using this model we can understand the accuracy of prediction. We can see from the table 4 that predicted value is in the 95% confidential interval (the mistake of prediction is 0,72%), hence the model is very adequate.
Table 4. Forecast value estimation
Prediction1 "or 2015
R2015 -theoretical 174 989 530733
Lower boundaries (Y 2cnL. 125 442 290472
Upper boundaries (Y2015 224 536 770 994
R2015 -real 173 730 921330
Mistake of prediction
5 0,7192%
Goldfeld-Quandt test has showed, that residuals of the model are homoscedastic and the second Gaus-Markov theorem is confirmed.
Table 5. GQ-test
From the Durbin-Watson test we can conclude that DW=2,06 lays between du and (4-du) that indicates there is no autocorrelation in residuals, the third Gaus-Markov theorem is confirmed.
Table 5. GQ-test
DW 2,06355691
dU 2,33937 ==> 4-dU 1,66063
dL 0,59454 ==> 4-dL 3,40546
Conclusion
Analyzing economic factors that influence financial sector in a country (Germany taken as an example), we have shown that inflation and unemployment have the most significant impact on monetary reserves formation. This paper is consistent with other authors when providing empirical evidence. Overall, financial development is related to economic growth even in industrial countries. Finance is important for growth at early stages of economic developments, besides economic growth make influence on financial sector.
References
1.Трегуб И.В. Математические модели динамики экономических систем -монография, М.: 2009.
2.Трегуб И.В. Прогнозирование экономических показателей - монография, М.: 2009.
3.Tregub I.V. Econometrics. Model of real system - монография, М.: 2016. 160 р. 4.Suslov M.Yu.E., Tregub I.V. Ordinary least squares and currency exchange rate // International Scientific Review. 2015. № 2 (3). С. 35.
5.World Bank Indicators. Website:
http://databank.worldbank.org/ data/reports. aspx?source=world-development-indicators# (date: 16.06.2016).
