(PDF) Financial instability, institutional development and economic crisis in Eastern Europe

“Financial instability, institutional development and economic crisis in Eastern Europe” Ola Honningdal Grytten https://orcid.org/0000-0003-1416-0980 https://publons.com/researcher/1534774/ola-grytten/ AUTHORS Viktoriia Koilo https://orcid.org/0000-0001-7953-9970 https://publons.com/researcher/1939207/viktoriia-koilo/ Ola Honningdal Grytten and Viktoriia Koilo (2019). Financial instability, institutional development and economic crisis in Eastern Europe. Investment ARTICLE INFO Management and Financial Innovations, 16(3), 167-181. doi:10.21511/imfi.16(3).2019.16 DOI http://dx.doi.org/10.21511/imfi.16(3).2019.16 RELEASED ON Friday, 06 September 2019 RECEIVED ON Tuesday, 20 August 2019 ACCEPTED ON Tuesday, 03 September 2019 LICENSE This work is licensed under a Creative Commons Attribution 4.0 International License JOURNAL "Investment Management and Financial Innovations" ISSN PRINT 1810-4967 ISSN ONLINE 1812-9358 PUBLISHER LLC “Consulting Publishing Company “Business Perspectives” FOUNDER LLC “Consulting Publishing Company “Business Perspectives” NUMBER OF REFERENCES NUMBER OF FIGURES NUMBER OF TABLES 38 5 3 © The author(s) 2019. This publication is an open access article. businessperspectives.org Investment Management and Financial Innovations, Volume 16, Issue 3, 2019 Ola Honningdal Grytten (Norway), Viktoriia Koilo (Norway) Financial instability, BUSINESS PERSPECTIVES institutional development and economic crisis in Eastern Europe LLC “СPС “Business Perspectives” Hryhorii Skovoroda lane, 10, Abstract Sumy, 40022, Ukraine This paper sheds light on the financial crisis of 2008–2010 in eleven emerging www.businessperspectives.org Eastern European economies (EE11): Armenia, Azerbaijan, Belarus, Bulgaria, Georgia, Kazakhstan, the Kyrgyz Republic, Moldova, Romania, Tajikistan and Ukraine. The aim is twofold. In the first place it seeks to find out if the finan- cial instability hypothesis, as put forward by Minsky and Kindleberger, is a valid explanatory factor for the crisis. Secondly, it tries to map if general institutional frameworks of these countries were developed in order to stand against the factors leading into the financial crisis. To answer these research problems the paper maps cycles of three parameters rep- resenting the real economy, i.e. gross domestic product, manufacturing output and unemployment and four parameters representing the financial markets, i.e. money Received on: 20th of August, 2019 supply, credit volumes, inflation and government debt. The cycle approach is car- Accepted on: 3rd of September, 2019 ried out with the help of a structural time series analysis to isolate cycles in time series. The paper concludes that there were substantial positive financial cycles previous to the financial crisis mirrored by similar cycles in the real economy. Similarly, the results show negative cycles in the same parameters during the years of crisis. It seems that an uncontrolled increase in money and credit caused the © Ola Honningdal Grytten, Viktoriia economy to overheat and thereafter contract into financial and real economy crises. Koilo, 2019 Also, the paper compiles twelve different indices of institutional development. These are standardized and presented in an institutional development matrix, Ola Honningdal Grytten, Professor, showing that the general institutional framework for the eleven economies was Dr. Econ., Department of Economics, weak previous to and under the meltdown of the economies. Norwegian School of Economics, Norway. The construction of an integrated institutional development index on the basis of the same twelve parameters confirms institutional shortcomings, which may have Viktoriia Koilo, Ph.D., Associate made the economies less able to guard themselves from a crisis initiated by both Professor, Hauge School of Management, NLA University domestically and internationally financial instability. College, Norway. Keywords financial crisis, financial instability hypothesis, institutional development, crisis anatomy JEL Classification E32, E44, E51, E52, G15 INTRODUCTION The international financial crises, which started with shrinking house prices during the second half of 2007, also hit Eastern European econ- omies. Conventional wisdom seems to be that the crisis transmitted from Western Europe by international financial markets, causing li- This is an Open Access article, distributed under the terms of the quidity crises and thereafter capital crisis, ending up in busts in the Creative Commons Attribution 4.0 real economy (Bracke & Martin, 2012; Jungmann & Sagemann, 2011; International license, which permits unrestricted re-use, distribution, Åslund, 2018). In addition, fragile political and economic institutions and reproduction in any medium, seem to have been unable to set up a stronghold against the evolvement provided the original work is properly cited. of the crisis. http://dx.doi.org/10.21511/imfi.16(3).2019.16 167 Investment Management and Financial Innovations, Volume 16, Issue 3, 2019 1. RESEARCH PROBLEM al development indicators in order to elaborate on their modernization, integration into the global This paper investigates the financial crisis of 2008– economy, and their ability to serve as defence for 2010 using two approaches. The first departures in financial crisis. the financial instability hypothesis as set up by Minsky (1982, pp. 13-39) and Kindleberger (1996). Thirdly, if macroeconomic financial instability The hypothesis is also in line with the argument follows the pattern of the crisis, it reveals booms of the two Nobel Prize winners Finn Kydland and and busts in financial indicators in line with the Edward Prescott. Drawing on empirical research Minsky-Kindleberger approach. In addition, the they argue expansion and contraction in money level of institutional development contributes to and credit be decisive for business cycles (Kydland, understanding to what extent these economies 1990, pp. 3-18). The second approach is to investi- were able to handle the situation. gate institutional stability: was there a framework within the economies capable of both preventing and reducing the scale of financial crisis? 3. DEFINITIONS The research problem is to find out if a Minsky- Before presenting a theoretical framework the Kindleberger approach can shed light on domes- understanding of financial crises is clarified. The tic financial instability as a major force for the de- paper defines financial crises as situations where velopment of the Eastern European branch of the financial institutions or assets lose significant val- international financial crisis. This is seen in light ues and the markets are not able to provide nec- of important institutional development indicators essary means of payment. Goldsmith (1982) de- for these economies. fines financial crises as: “sharp, brief, ultra-cycli- cal deterioration of almost all financial indicators, The paper studies the financial crisis in eleven short-term interest rates, asset prices, commercial emerging Eastern European economies (EE11), i.e. insolvencies and failure of financial institutions”. Armenia, Azerbaijan, Belarus, Bulgaria, Georgia, Kazakhstan, the Kyrgyz Republic, Moldova, Financial crises were considered almost equivalent Romania, Tajikistan and Ukraine. It maps their to credit crunches and bank panics until the mid trend and cycle components of production by us- 1900s. A modern understanding would also include ing a Hodrick-Prescott filter. Significant positive asset crashes and currency and sovereign defaults. cycle values indicate financial overheating, which thereafter caused significant downturns. Claessens and Kose (2013) highlight that finan- cial crises are multidimensional, often associated If booms and busts follow the pattern of financial with four phenomena: significant fall in credit vol- key indicators as money and credit volumes, we umes and prices of assets, disruption of external conclude that huge swings in the economies to a financing and financial intermediation, negative large extent can be explained by a financial insta- asset balance and need of huge support from gov- bility approach. ernments. We define financial crises as negative shocks in financial markets, causing lack of credit to the economy. 2. OUTLINE The outline of the paper is as follows: 4. THEORETICAL It first discusses the theoretical framework of the FRAMEWORK AND DATA financial instability hypothesis to explain the According to Minsky and Kindleberger, financial evolvement of financial crises. crises commonly start with financial instability, where financial markets are exposed to distur- Secondly, it investigates the general institutional bances ending in lost sustainable equilibriums framework of the EE11 by looking at institution- (Minsky, 1982, 1986). This approach is often char- 168 http://dx.doi.org/10.21511/imfi.16(3).2019.16 Investment Management and Financial Innovations, Volume 16, Issue 3, 2019 acterized as the instability hypothesis. According Expansion, due to increase in demand for credits to Kindleberger (1996), this might typically hap- and willingness to grant such. pen through significant exogenous macroeco- nomic shocks, causing the economy to run faster Minsky emphasizes a three-step financial taxono- by drawing on extended credits. my. This implies the most common way to finance investments during periods of stability is hedge fi- Minsky pays attention to endogenous factors, i.e. nancing, basically drawing on business surpluses shortcomings within the system in dealing with and normal borrowing. In times of rapid expan- disturbances in financial markets. System errors sion and credit growth, speculative finance, draw- make financial instability evolve in times of mis- ing on future increase in asset prices, is more com- match between short- and long-term sustainable mon. Finally, Ponzi finance becomes important equilibriums. when capital emissions are necessary for further growth. 4.1. Theory Monetary Expansion brings a market to a maxi- Both agree that positive expectations and lack of mum with an over heated economy and asset bub- stability may cause demand for credit to over ex- bles. When market expectations turn, markets and pand and positive credit bubbles arise. Markets asset prices fall. Hence, one has reached the new become overheated due too money surplus and phase, Revulsion. Negative expectations will domi- asset bubbles arise. Speculation in continuous nate and a period of crisis, Discredit, will follow. growth in asset prices cause bubbles to increase further. This will go on until markets turn due to Kindleberger gives an exogenous neo-classical negative shifts in expectations, often called the model, but still substantially inspired by Minsky. “Minsky Moment”. Expected losses make markets He starts with an exogenous shock, leading to fall deeper facing credit crunches, crashes and re- monetary expansion, which the financial markets cessions (Kindleberger & Aliber, 2015, pp. 33-76). are not able to deal with in a controlled manner. This leads to the first phase, Manias, implying the Minsky’s model can be described in five phases. creation of bubbles. This is followed by Panic. Both Displacement is when a market looses its natural evolve due to loss of financial stability. The mar- growth pattern due to a positive shift in demand. kets then turn into a third phase, Crashes, when If one expects this to be permanent, the market asset prices fall steeply. This leads to credit crunch moves into its next phase, Overtrading, with sur- and Crises, which is the fourth phase. If the crisis plus activity compared to sustainable equilibriums. lasts it will infect other markets, which implies the Overtrading promotes the third phase, Monetary fifth phase, Diffusion. Monetary expansion Speculation Over Bubble Turning Disturbance Nervousness Crisis Diffusion heating economy point Financial instability creation Figure 1. Seven-step dynamic model for financial crisis http://dx.doi.org/10.21511/imfi.16(3).2019.16 169 Investment Management and Financial Innovations, Volume 16, Issue 3, 2019 Kindleberger also puts attention to the impact of tify the components. The HP filter minimize the hegemonial powers, which due to their size, stand- variance of ct subject to a penalty for variation in ing and role are able to influence markets signifi- the second difference of gt : cantly. Thus, they are decisive for financial stabili- T ty and the development of financial crises. ∑ min ( xt − gt ) + 2 gt t =1 (5) Empirical studies by Tornell and Westermann T −1 ∑ +λ ( gt +1 − gt ) − ( gt − gt −1 )  , 2 (2005) conclude that a financial instability ap- proach can be applied for the vast majority of fi- t =2 nancial crises. They argue that financial liberaliza- tion tends to cause boom-bust cycles. Eichengreen where ( xt − gt ) denotes the cycle component and (1990) argues that financial instability may cause ( gt +1 − gt ) − ( gt − gt −1 )  is the difference in the illusive stability, i.e. temporary stability mismatch- trend growth rate from period t until t + 1, where ing long-term sustainable stability. A similar argu- as λ controls the smoothness of the growth ment is found with Reinhart and Roghoff (2009). component. Combining Minsky’s and Kindleberger’s theories This implies that a smoothing parameter equal to with empirical research ended up with a formal zero means that all changes in the observed se- seven phases dynamic model for the develop- ries should be explained by trend developments. A ment of financial crises (Grytten & Hunnes, 2016, high smoothing parameter implies that the cycle pp. 45-52), described as in Figure 1. is an important component in the time series. One can calculate the cycle component by deducting 4.2. Methodology the trend component from the observed series: To map booms and busts the paper seeks to meas- c= t xt − gt . (6) ure cycles within time series. It uses a structural time series analysis separating an observed time High smoothing parameters give trends with mi- series ( xt ) into a trend component ( gt ) , a cycle nor fluctuations, and significant cycles, when low component ( ct ) a seasonal component ( st ) and smoothing parameters give trends with large fluc- an irregular component ( it ) : tuations and minor cycles. Rules of thumb suggest a smoothing parameter with λ = 100 for annu- xt = f ( gt , ct , st , it ) . (1) al figures, λ = 1, 600 for quarterly figure, and λ = 14, 400 for monthly figures. An arithmetic approach to this function gives the following relationship: 4.3. Data xt = gt + ct + st + it , (2) Key macroeconomic indicators serve as the most important source of data in this work. They are where it is natural to consider it as the residual: basically taken from the World Bank database on macro indicators1. They were first assembled and it = xt − ( gt + ct + st ) . (3) calculated by national statistical authorities and then processed according to common standards In this analysis both it and st can be seen as part by the World Bank. of both ct . This implies a reduced form of equa- tion (2) as: It would have been preferable to use quarterly or even monthly data for the analysis, and to include x=t gt + ct . (4) additional parameters such as interest rates and asset prices. However, lack of valid and reliable se- It is feasible using a Hodrick-Prescott filter to iden- ries does not allow that. Hence, we use annual data 1 https://data.worldbank.org/ 170 http://dx.doi.org/10.21511/imfi.16(3).2019.16 Investment Management and Financial Innovations, Volume 16, Issue 3, 2019 for the EE11. Since these are summing up the an- Thirdly, shortsighted policies of governments try- nual development in macroeconomic and finan- ing to gain benefits at the expense of other states cial indicators, they still serve as valid variables causes delay in the integration and moderniza- for the purpose of this paper. tion process. Despite declarations on the need to reduce customs barriers, governments operate in Nevertheless, the data series might be somewhat the opposite direction. The economies have be- biased, in particular towards the beginning of the come unstable, dependent on external factors and series. This is due to noise during the transition resource-intensive. period from communism to market based econ- omies and lack of reliable data. In order to avoid 5.2. Reorganization and privatization this noise, the analysis starts in 1996, when the markets more or less were stabilized. The data col- It was assumed that the transfer of enterprises to lection basically stops with 2017, which makes it private ownership would increase their efficien- possible to include the aftermaths of the crisis2. cy, competitiveness and lead to the entering of international markets. In practice, old principles of regulation were maintained, which led to ineffi- 5. INSTITUTIONAL cient use of growth potentials. FRAMEWORK There was a sharp decline in production during The institutional framework of economies is im- the transition period in the 1990s as GDP dropped perative for their development and ability to around 50 percent. At the initial stage of privatiza- handle crises (Riaz, 2009, pp. 26-35). It decides tion, new owners of enterprises were not ready to their level of integration and modernization into manage the market principles, strategic develop- a global economy. Until the 1990s the EE11 had ment planning and business activities. Thus, they similar socialist economic systems and mecha- directed their efforts to obtain “fast” profits from nisms, and thereafter started transformation of privatized property leading to inefficiency, exces- their systems towards market economies (Harris, sive exploitation of natural resources and environ- 1999, pp. 125-158). mental pollution (Roaf, 2014, pp. 10-28). The tran- sition is still not completed. 5.1. Development of eastern emerging economies 5.3. Liberalization Transformation to market-oriented economies de- The transition to free prices under a regime of manded economic integration (Moghadam, 2014, higher demand than supply led to rapid inflation pp. 8-13). However, the levels of integration are dis- and low investment activity, and export of sav- similar. Bulgaria and Romania are already mem- ings (Njemcevic, 2017, pp. 15-22). Imported goods bers of the EU, when most of the others are looking dominate in high-tech and high-skilled markets. for ways of cooperation within the Commonwealth Thus, the EE11 have been even more dependent on of Independent States (CIS) and the EU. traditional industries, like mining and manufac- turing, when in order to buy modern consump- Important decisions on strengthening the inte- tion goods the countries run trade deficits, giving gration remain on paper or are being implement- way to further exports of capital. This has been ed at different paces (Cerqueira, 2018, pp. 329- fuelled by foreign credits with high interest rates 333). In the first place, it is a consequence of the to consumers in order to buy foreign goods. deep decline of their economies, the breakdown of economic ties between the states of the former 5.4. Institutional changes USSR and the difficulties of the transition to mar- ket economy. Secondly, this situation is also due to Legislative reform and institutional changes also lack of political will. played an important role for the failures of market 2 Some alterations had to be made for Azerbaijan, Kyrgyz Republic and Tajikistan. http://dx.doi.org/10.21511/imfi.16(3).2019.16 171 Investment Management and Financial Innovations, Volume 16, Issue 3, 2019 economy implementation. The ill-conceived liber- is the residual, which gives this relationship: alization of the economy led to dominant shadow T −1 economies, with increase in crime and corruption. ∑ ( gt +1 − gt ) − ( gt − gt −1 )  . 2 As a result, risks in business increased. The legal ct = xt − λ (8) t =2 system was not ready for the changes and neces- sary reforms have been hold back by agents bene- Cycles are found by deducting smoothed parame- fiting from the mismatch (Turk, 2014, pp. 199-208). ters from their respective observed series. All pa- rameters, except unemployment and government Hence, institutional shortcomings seem to be debt are supposed to be pro-cyclical. As for those an important obstacle for economic growth and two, they should move counter-cyclically, con- a fragile framework for integration, economic tracting in good times and expanding in bad times. growth and financial crisis management. The peak values of the cycles previous to the fi- nancial crisis for the parameters are reported in 6. CYCLE ANALYSIS Table 1 and the troughs or minimums in Table 2, where the numeric results are presented as natural By using structural time series analysis in order logarithms, to express percentage deviations from to separate trend and cycle components one can trends: find out if financial stability indicators possibly paved way for the financial crisis. Rapid increase log = ( ct ) log ( xt ) − log ( gt ) . (9) in money and credits could have caused demand driven booms and overheated economy. This can Precise parameters can be listed as follows: be seen in positive deviations from trend, i.e. pos- itive cycles. • Y – gross domestic product in fixed prices, na- tional currencies; 6.1. Booms and busts • MP – manufacturing production in fixed pric- By using World Bank data presented by the Federal es, national currencies; Reserve Bank of St Louis, we trace such devel- opments (FRED, 2019)3. The paper looks at seven • U – unemployment rate; key macroeconomic indicators. Firstly, productive measures: domestic product, manufacturing output • M3 – money supply as broad definition in cur- and unemployment. How did these behave before rent prices, national currencies; and during the crises? Secondly, financial indica- tors: money, credits, government debt and inflation. • C – domestic credits; Using the HP-filter as described in equation (1) – • P – inflation rates, measured by increase in (3) one is able to map cycles from trend: consumer price indices; T GD – government debt as percentages of gross min ∑ ( xt − gt ) = 2 • gt (7) domestic product in current prices, national t =1 T −1 currencies. =xt − λ ∑ ( gt +1 − gt ) − ( gt − gt −1 )  , 2 t =2 Furthermore, p denotes peak moment during a boom, when t denotes trough, as the bottom of a where the cycle component burst or recession. T min ∑ ( xt − gt ) 2 Table 1 reveals that all the EE11 GDP peaked in gt t =1 2007 or 2008, when the picture is very similar 3 https://fred.stlouisfed.org/ 172 http://dx.doi.org/10.21511/imfi.16(3).2019.16 Investment Management and Financial Innovations, Volume 16, Issue 3, 2019 Table 1. Cycle peaks before financial crises of 2008–2010 as natural logarithms Real economy indicators Financial indicators Country Yp MPp Up M3p Cp Pp GDp 0.159 0.233 ID 0.213 0.356 0.694 –0.580 Armenia (2008) (2008) ID (2007) (2008) (2008) (2008) 0.120 0.158 –0.060 0.023 0.200 1.010 –0.577 Azerbaijan (2007) (2007) (2008) (2008) (2008) 2008 (2008) 0.086 0.116 –0.587 0.175 0.215 0.876 –0.328 Bulgaria (2008) (2008) (2008) (2008) (2008) 2008 (2005) 0.058 0.068 –0.026 0.138 0.241 0.996 –0.311 Belarus (2008) (2008) (2005) (2008) (2008) 2011 (2008) 0.073 0.043 –0.070 0.097 0.209 0.646 –0.430 Georgia (2007) (2007) (2007) (2008) (2008) 2008 (2007) 0.059 0.063 –0.011 0.187 0.103 0.763 –0.483 Kazakhstan (2007) (2007) (2007) (2008) (2008) 2008 (2007) 0.043 0.035 –0.082 0.118 0.185 1.048 –0.284 Kyrgyz Republic (2008) (2008) (2007) (2007) (2008) 2008 (2008) 0.056 0.062 –0.435 0.342 0.345 0.363 –0.464 Moldova (2008) (2008) (2008) (2008) (2008) 2008 (2008) 0.121 0.185 –0.189 0.118 0.209 0.348 –0.572 Romania (2008) (2008) (2008) (2008) (2008) 2008 (2008) 0.019 0.147 –0.008 0.149 0.122 0.681 –0.250 Tajikistan (2008) (2006) (2008) (2007) (2008) 2008 (2008) 0.109 0.150 –0.210 0.372 0.348 0.917 –0.780 Ukraine (2008) (2007) (2007) (2008) (2008) (2008) (2007) Note: ID – indecisive. for manufacturing output and unemployment, of symptom relief during the financial crisis. i.e. manufacturing peaked almost simultane- ously, when unemployment was at a temporary The calculations reveal considerable expansion in minimum. money and credits for all EE11 countries previ- ous to the financial crisis. For all, but Azerbaijan, As for the financial indicators, we find that money the positive cycle value reached between 9.7 supply peaked in 2007–2008 for all eleven coun- (Georgia) and 37.2 (Ukraine) percent. The cred- tries, when credits peaked in 2008. The same did it cycle peaked between 12.2 (Armenia) and 35.6 inflation, apart from in Belarus. Public debt also (Azerbaijan). This shows that domestic monetary reached a minimum in the years leading up to and expansion came prior to the crisis, and it happened including 2008. after attempts of cautious monetary policy in most of these countries. Money and credit expansion Table 2 reports troughs during the financial cri- made the inflation cycle step up to between 34.8 sis. The pace and the depth of the contraction were (Romania) and 104.8 (Kyrgyz Republic) over the far less uniform than the upswing before the cri- smoothed line, and the economies lost financial sis. However, most real value indicators reached stability. the bottom during 2009 and 2010, with some late- comers. Looking at the financial indicators, both In consequence of overheating, the financial cri- money and credit tend to reach their minimum sis hit hard. Ukraine’s and Armenia’s annual GDP before or simultaneously with the real economy fell by 14.4 and 13.4 percent, respectively, against indicators, when inflation and government debt 7.2 and 5.9 percent in Romania and Moldova. seem to lag compared to the other variables. The Manufacturing output contracted even more, latter confirms that government debt was a mean while unemployment increased. http://dx.doi.org/10.21511/imfi.16(3).2019.16 173 Investment Management and Financial Innovations, Volume 16, Issue 3, 2019 Table 2. Cycle troughs during financial crises of 2008 as natural logarithms Real economy indicators Financial indicators Country Yt MPt Ut M3t Ct Pt GDt –0.047 –0.125 0.064 –0.054 –0.020 –0.300 0.213 Armenia (2010) (2009) (2010) (2010) (2009) (2009) (2009) –0.042 –0.042 0.030 –0.128 –0.020 –1.666 0.304 Azerbaijan (2012) (2011) (2011) (2008) (2009) (2009) (2016) –0.033 –0.031 0.323 –0.010 0.022 –0.509 0.454 Bulgaria (2014) (2010) (2013) (2008) (2010) (2009) (2011) –0.011 0.009 0.380 –0.077 –0.052 –0.003 0.253 Belarus (2009) (2009) (2010) (2009) (2009) (2013) (2014) –0.043 –0.062 0.144 –0.105 –0.054 –1.051 0.159 Georgia (2009) (2009) (2009) (2009) (2010) (2009) (2010) –0.038 –0.022 0.060 0.037 –0.161 –0.086 0.020 Kazakhstan (2009) (2009) (2009) (2009) (2009) (2009) (2009) –0.045 –0.182 0.046 –0.034 –0.082 –0.223 0.047 Kyrgyz Republic (2010) (2012) (2010) (2009) (2010) (2009) (2010) –0.047 –0.170 0.263 –0.411 –0.024 –0.063* 0.015 Moldova (2009) (2009) (2010) (2010) (2010) (2009) (2012) –0.039 –0.117 0.053 0.005 –0.100 –0.288** 0.179 Romania (2012) (2012) (2011) (2009) (2010) (2009) (2012) –0.057 –0.159 0.055 –0.081 –0.340 –0.384 0.086 Tajikistan (2010) (2010) (2010) (2010) (2009) (2009) (2011) –0.064 –0.118 0.137 –0.145 –0.135 –0.239* 0.149 Ukraine (2009) (2009) (2009) (2009) (2008) (2013) (2010) Note: * – deflation rate, ** – fall in inflation rate. 6.2. Crisis anatomy drastically, paving way for imports of even more inflation and lack of trust (Åslund, 2010). After the transition crisis from communist to mar- ket liberalism in the 1990s, most Eastern European During spring and summer 2008, it became evi- economies gained economic growth prior to the dent that these economies were overheated. Real crisis. This went on for almost a decade and lasted estate prices were out of control due to high de- more or less until autumn 2008. During this peri- mand caused by monetary expansion and low sup- od of growth emerging economies benefited from ply. Wages had increased dramatically for skilled underutilized capital. Additionally, they took part workers and the booming stock markets begun to in the international boom from the early 2000s. fall down. The growth was not sustainable. The countries ran After the bank crises hit the US during the ear- huge current account deficits and developed high ly fall 2008 liquidity became extremely scarce. foreign debts along with dubious currency exchange During a few weeks Eastern Europe saw rapid de- cline in international finance and a liquidity crisis rates. Belarus, Bulgaria and Ukraine had fixed rates, which attracted massive inflows of short-term cred- evolved rapidly, soon revealing a solidity crisis in its, fuelling monetary expansion, loans and high the private sector due to the high gearing with for- inflation. eign and domestic capital. Financial panic made capital flee the Eastern economies rapidly, and Foreign credit institutions granted Ukrainian citi- their currencies were sold for gold, dollars, euros, zens consumer credits to interest rates of amazing pounds and Swiss francs (Mihalijek, 2010) mak- 50 percent (Stroe, 2011, pp. 47-52). Thus, foreign ing them diving further and even more dubious exchange inflows accelerated imports and balance means of foreign investment. of payments deficits rocketed. It became impos- sible to maintain fixed exchange rates. Thus, they A fundamental problem for the crisis was exces- had to give up the policy and exchange rates fell sive inflows of short-term bank credits, enticed by 174 http://dx.doi.org/10.21511/imfi.16(3).2019.16 Investment Management and Financial Innovations, Volume 16, Issue 3, 2019 fixed exchange rates. Hence, foreign private debt sures, and when pressures are outweighing rocketed. Public finances, however, seemed to be the capacity to balance states. Daily, FFP col- under control, with an exception for Romania and lects global information on social, economic Hungary. However, public debt increased during and political pressures faced in 178 countries. the crisis due to reduced tax income and need for It uses 12 parameters, graded on a scale from government counter cyclical efforts (Dudas, 2013, 0 to 10, and then an index constructed on a pp. 184-193). 0-120 scale (Fund for Peace, 2018). Thus, the analysis confirms that the financial in- • Political Stability Index (PSI). The index re- stability hypothesis contributes significantly to flects the possibility of conflict situations and understand the financial crisis of the EE11. The fi- violence in the region. It uses –2.5 as the weak- nancial crisis of emerging Eastern European econ- est measure, and 2.5 as the strongest (The omies doesn’t seem very different from traditional Global Economy, 2017). financial crises. 2. Environment: 7. INSTITUTIONAL • Environmental Performance Index (EPI). This presents issues covering environmental health DEVELOPMENT and ecosystem vitality for 180 countries, based It is of importance to present the institutional de- on 24 parameters, on a scale of 0 to 100. The velopment of the EE11 under investigation here. last years EPI give weights of approximate- This might help us to the possible strengths of in- ly 40 percent to environmental health and 60 stitutions in order to defend the economy from percent to ecosystem vitality (Yale Centre for financial crises. The paper offers both an institu- Environmental Law and Policy, 2018). tional development matrix (IDM) and an integrat- ed institutional development index (IIDI). • Environmental Health Index (EHI). This reflects economic growth and prosperity. 7.1. Institutional development matrix The index is constructed on a 0-100 point scale. Approximately 65 percent is attribut- The matrix is made up of six categories each con- able to air quality, 30 percent to water and taining two parameters or variables. The indica- sanitation, and five percent to lead exposure tors reflect different aspects of institutional devel- (Yale Centre for Environmental Law and opment of each country. These would be of impor- Policy, 2018). tance when it comes to institutional framework related to the handling of crises. Each category 3. Freedoms and rights: has two parameters presented as sub-indices. The indices rest on different sources4: • Index of Human Freedom (IHF). This provides snapshots of the human freedom, based on 1. Fragility and instability: civil, personal, and economic indicators. The parameters are expressed into 79 indices in 12 • Fragile States Index (FSI). This is done by the areas on a scale of 0 to 10, where 10 represents Fund for Peace (FFP). It identifies normal pres- freedom (Cato Institute, 2018). 4 https://fragilestatesindex.org/ https://www.theglobaleconomy.com/rankings/wb_political_stability/ https://epi.envirocenter.yale.edu/2018/report/category/hlt https://epi.envirocenter.yale.edu/epi-topline https://www.cato.org/human-freedom-index-new https://www.heritage.org/index/ http://www.doingbusiness.org/content/dam/doingBusiness/media/Annual-Reports/English/DB2018-Full-Report.pdf http://hdr.undp.org/en/composite/trends http://www3.weforum.org/docs/WEF_GGGR_2018.pdf http://hdr.undp.org/en/composite/GII https://pages.eiu.com/rs/753-RIQ-438/images/Democracy_Index_2017.pdf https://www.transparency.org/news/feature/corruption_perceptions_index_2017 http://dx.doi.org/10.21511/imfi.16(3).2019.16 175 Investment Management and Financial Innovations, Volume 16, Issue 3, 2019 • Index of Economic Freedom (IEF). This index • Corruption Perceptions Index (CPI). is published by the Heritage Foundation and Transparency International presents this in- ranking is based on 12 parameters – from dex for 180 countries as a measurement of property rights to financial freedom. Each pa- public sector corruption. It applies a scale rameter is measured on a 0-100 scale for 186 from 0 (corrupt) to 100 (clean) (Transparency countries (The Heritage Foundation, 2018). International, 2017). 4. Socio-economics: The series are made comparable by moderation. This is done by transforming scores into indices • Doing Business Index (DBI). The World Bank where the scores of each nations parameter, ai , Group Flagship gives annual reports on reg- are placed in the interval 0 < ai < 1. Thus, one ar- ulations constraining business activities (in- rives at an IDM, as shown in Figure 2. cluding parameters of protection of the prop- erty rights and indicators of business regula- The EE11 present a mixed picture. However, the tion). 190 countries are ranged from 1 to 190, very important parameters of fragile state, politi- where 1 represents the best performance (A cal stability democracy and corruption perception World Bank Group Flagship, 2018). score alarmingly badly. Figure 3 reports the eleven economies scores according to the twelve different • Human Development Index (HDI). This re- indices. flects development of countries in three aspects, standard of leaving, access to education and life It proves huge difference in development within expectancy. It ranges from 0 to 1, where 1 means the EE11, despite of similar initial conditions at the highest level of development (United Nations the beginning of independence. Thus, there is a Development Programme, 2017). significant need to analyze the financial crisis in these countries in light of institutional integration. 5. Gender: 7.2. Integrated institutional • Global Gender Gap Index (GGI). The World development index Economic Forum first introduced this in 2006 as a framework for mapping gender-based dis- It is now possible to construct an integrated insti- parities. GGI ranks 149 countries on a scale tutional development index (IIDI). In line with the from 0 (disparity) to 1 (parity) across four Human Development Index by the United Nations, thematic dimensions (The World Economic this paper offers a geometric approach. The depar- Forum, 2018). ture can be explained by a general equation: 1 • Gender Inequality Index (GII). This index  n n  ∏ ai  = ai ⋅ ai +1 ⋅ ai + 2 ⋅ ai +3 ⋅ ⋅ an , is a summary measure of gender disadvan- n (10) tage, based on three dimension. The indica-  i =1  tors are expressed into indices on a scale of 0 to 1, where less values means fare equality be- where Π is the geometric average of different pa- tween genders (United Nations Development rameters, a, numbered from i to n. In our case Programme, 2017). these parameters are taken from the structural de- velopment matrix: 6. Governance: • FSI – Fragile States Index; • Democracy Index (DI). This index demon- • PSI – Political Stability Index; strates the democracy situation in each coun- • EPI – Environmental Performance Index; try. It is based on five political categories in 167 • EHI – Environmental Health Index; states, and ranked on a scale of 0 to 10, based • IHF – Index of Human Freedom; on 60 indicators (The Economist Intelligence • IEF – Index of Economic Freedom; Unit, 2017). • DBI – Doing Business Index; 176 http://dx.doi.org/10.21511/imfi.16(3).2019.16 Investment Management and Financial Innovations, Volume 16, Issue 3, 2019 Source: See footnote 1. 1. FRAGILITY AND 3. FREEDOM 4. SOCIO- 2. ENVIRONMENT 5. GENDER 6. GOVERNANCE INSTABILITY AND RIGHS ECONOMICS 1,000 0,900 0,800 0,700 0,600 Score 0,500 0,400 0,300 0,200 0,100 0,000 FSI PSI EPI EH IHF IEF DB HDI GGG GII DI CPI 2018 2017 2018 2018 2016 2016 2018 2017 2018 2017 2017 2017 Indexes Armenia Azerbaijan Belarus Bulgaria Georgia Kazakhstan Kyrgyz Republic Moldova Romania Tajikistan Ukraine Note: FSI – Fragile States Index, PSI – Political Stability Index, EPI – Environmental Performance Index, EHI – Environmental Health Index, IHF – Index of Human Freedom, IEF – Index of Economic Freedom, DB – Doing Business Index; HDI – Human Development Index; GGG – Global Gender Gap Index, GII – Gender Inequality Index, DMI – Democracy Index, CPI – Corruption Perceptions Index. Figure 2. Institutional development matrix Source: See footnote 1. 1. FRAGILITY 2. ENVIRONMENT 3. FREEDOMS AND INSTABILITY AND RIGHTS 0,70 0,80 1,00 0,60 0,60 0,80 0,50 0,60 0,40 0,40 0,40 0,30 0,20 0,20 0,20 0,00 0,10 0,00 Armenia Georgia Kazakhstan Moldova Romania Ukraine Tajikistan Kyrgyz Republic Azerbaijan Belarus Bulgaria Armenia Georgia Kazakhstan Moldova Kyrgyz Republic Romania Tajikistan Azerbaijan Belarus Bulgaria Ukraine 0,00 Kazakhstan Georgia Moldova Armenia Romania Tajikistan Kyrgyz Republic Azerbaijan Belarus Bulgaria Ukraine Environmental Performance Index Fragile States Index (2018) Index of Human Freedom (2016) (2018) Political Stability Index (2017) Environmental Health Index (2018) Index of Economic Freedom (2016) 4. SOCIO-ECONOMICS 5. GENDER 6. GOVERNANCE 0,90 1,00 0,80 0,80 0,90 0,70 0,70 0,80 0,60 0,60 0,70 0,60 0,50 0,50 0,50 0,40 0,40 0,40 0,30 0,30 0,30 0,20 0,20 0,20 0,10 0,10 0,10 0,00 0,00 0,00 Georgia Georgia Moldova Moldova Armenia Bulgaria Kazakhstan Romania Armenia Bulgaria Tajikistan Kazakhstan Romania Tajikistan Kyrgyz Republic Kyrgyz Republic Azerbaijan Azerbaijan Belarus Belarus Ukraine Ukraine Georgia Moldova Armenia Kazakhstan Kyrgyz Republic Romania Tajikistan Azerbaijan Belarus Bulgaria Ukraine Doing Business (2018) Global Gender Gap index (2018) Democracy Index (2017) Human Development Index (2017) Gender Inequality Index (2017) Corruption Perceptions Index (2017) Figure 3. Institutional development charts http://dx.doi.org/10.21511/imfi.16(3).2019.16 177 Investment Management and Financial Innovations, Volume 16, Issue 3, 2019 • HDI – Human Development Index; Table 1 shows huge differences between the EE11 • GGG – Global Gender Gap Index; countries, with Bulgaria and Romania at the top • GII – Gender Inequality Index; and Tajikistan at the bottom of the list. One also • DMI – Democracy Index; finds that they score significantly lower than both • CPI – Corruption Perceptions Index. the PIIGS countries and the EU10. Moreover, the standard deviations of the institutional develop- By applying these parameters in equation (10) one ment parameters are highest for the EE11 group, arrives at a more specified equation: followed by the PIIGS countries, and lowest among the EU10. This gives evidence of institu- 1 tional stability as tool for financial stability.  n n IIDI =  ∏ ai  = 12 FSI ⋅ PSI ⋅ EPI ⋅ EHI ×  i =1  (11) Figure 4 clearly illustrates the lack of institu- ×12 IHF ⋅ IEF ⋅ DBI ⋅ HDI ⋅ GGG ⋅ GII × tional development in the EE11 countries. Even the best of them score significantly lower than ×12 DMI ⋅ CPI the EU countries with the worst crises and low- est institutional development. In order to compare with other states, the chart also presents EU numbers (Roth & Jonung, 2019). Combining the troughs of the cycles in Table 2 The first group is the PIIGS countries, i.e. Portugal, and the IIDI from Table 3 it is possible to draw Ireland, Italy, Greece Spain, which experienced a plot diagram indicating between the two pa- a severe contraction of 2008–2010. The second rameters, as done in Figure 5. Here we also in- group is EU10, which experienced a moderate clude PIIGS and EU10. The estimated regres- crisis, i.e. Austria, Belgium, Denmark, Finland, sion line indicates that weak institutional devel- France, Germany, Luxembourg, Netherlands, opment was correlated with significant contrac- Sweden and the United Kingdom. tion of the business cycle during the financial Table 3. Integrated institutional development index Fragility and Environment Freedoms and Socio- Gender Governance Country instability rights economics IIDI FSI PSI EPI EHI IHF IEF DBI HDI GGI GII DMI CPI Armenia 0.305 0.358 0.621 0.569 0.724 0.757 0.725 0.755 0.678 0.738 0.411 0.350 0.554 Azerbaijan 0.254 0.348 0.623 0.486 0.608 0.649 0.702 0.757 0.680 0.682 0.265 0.310 0.494 Belarus 0.295 0.506 0.650 0.696 0.614 0.623 0.751 0.808 0.740 0.870 0.313 0.440 0.578 Bulgaria 0.483 0.574 0.679 0.696 0.778 0.741 0.719 0.813 0.756 0.783 0.703 0.430 0.668 Georgia 0.260 0.426 0.557 0.571 0.780 0.802 0.820 0.780 0.677 0.650 0.593 0.560 0.597 Kazakhstan 0.366 0.504 0.546 0.667 0.674 0.711 0.754 0.800 0.741 0.803 0.306 0.310 0.567 Kyrgyz 0.214 0.414 0.549 0.548 0.659 0.693 0.657 0.649 0.691 0.608 0.511 0.290 0.512 Republic Moldova 0.305 0.452 0.520 0.603 0.685 0.664 0.730 0.700 0.785 0.774 0.594 0.310 0.568 Romania 0.506 0.512 0.648 0.587 0.817 0.769 0.729 0.811 0.711 0.689 0.644 0.480 0.649 Tajikistan 0.205 0.366 0.479 0.263 0.619 0.672 0.569 0.650 0.638 0.683 0.193 0.210 0.414 Ukraine 0.274 0.122 0.529 0.644 0.628 0.598 0.658 0.751 0.708 0.715 0.569 0.300 0.488 EE!! 0.315 0.417 0.582 0.575 0.690 0.698 0.710 0.752 0.710 0.727 0.464 0.363 0.561 Stdev 0.099 0.122 0.065 0.122 0.075 0.064 0.065 0.061 0.043 0.076 0.173 0.102 0.073 PIIGS 0.623 0.599 0.759 0.915 0.802 0.737 0.748 0.885 0.735 0.903 0.807 0.584 0.749 Stdev 0.138 0.106 0.030 0.039 0.050 0.059 0.045 0.034 0.039 0.017 0.068 0.105 0.058 EU10 0.746 0.654 0.794 0.935 0.839 0.760 0.780 0.920 0.767 0.932 0.863 0.804 0.811 Stdev 0.055 0.076 0.023 0.043 0.018 0.022 0.048 0.013 0.038 0.027 0.052 0.054 0.026 Note: FSI – Fragile States Index, PSI – Political Stability Index, EPI – Environmental Performance Index, EHI – Environmental Health Index, IHF – Index of Human Freedom, IEF – Index of Economic Freedom, DBI – Doing Business Index, HDI – Human Development Index, GGG – Global Gender Gap Index, GII – Gender Inequality Index, DMI – Democracy Index, CPI – Corruption Perceptions Index. 178 http://dx.doi.org/10.21511/imfi.16(3).2019.16 Investment Management and Financial Innovations, Volume 16, Issue 3, 2019 Source: See footnote 1. EU10 0,811 PIIGS 0,749 Romania 0,649 Bulgaria 0,668 Belarus 0,578 Georgia 0,597 Moldova 0,568 Kazakhstan 0,567 EE11 0,561 Armenia 0,554 Ukraine 0,529 Kyrgyz Republic 0,512 Azerbajan 0,494 Tajikistan 0,414 Figure 4. Integrated institutional development index 0,85 y = 8,3845x + 0.9411 0,80 R² = 0.6775 0,75 0,70 0,65 IIDI 0,60 0,55 0,50 0,45 0,40 -0,070 -0,065 -0,060 -0,055 -0,050 -0,045 -0,040 -0,035 -0,030 -0,025 -0,020 Cycle Figure 5. Plot diagram IIDI and GDP contraction during financial crisis crisis, with satisfactory explanatory degree modernization and integration into the glob- (R 2 = 67.7%). al economy is limited. Thus, these economies frameworks were not the best for meeting a fi- In sum, institutional developments of the EE11 nancial crisis. under investigation seem fragile, meaning that CONCLUSION The present paper investigates the financial crisis of 2008–2010 in eleven emerging Eastern European economies with departure in the financial instability hypothesis and institutional development. The research follows three key time series for the real economy and four for financial markets in eleven countries. http://dx.doi.org/10.21511/imfi.16(3).2019.16 179 Investment Management and Financial Innovations, Volume 16, Issue 3, 2019 Using a structural time series analyzes the paper isolates cycles from other time series components. The analysis reveals substantial overheating in the economy mirrored in huge expansion in financial and real economy indicators prior to the crisis, when the same variables contracted correspondingly during the crisis. 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