“Hands On – Hands Off”: Centralized vs. Decentralized Management of Economic Systems

by Nonlinear Time Series Analysis

Professor Eugen I. Scarlat, Ph.D. Dept. of Physics

University “Politehnica” Bucharest, Romania

egen@physics.pub.ro

Professor Cezar Scarlat, Ph.D Dept. of Management

University “Politehnica” Bucharest, Romania

cezarscarlat@yahoo.com Abstract—This study is proposing a semi-quantitative, unbiased “hands on – hands off” partition of the economic systems on the bases of whether they are or are not exhibiting structure as revealed by non linear time series analysis. The analysis is performed on the corresponding exchange rate time series with respect to the United States Dollar. The structure is related to the impact of the ongoing economic crisis upon several economic systems. By assessing the effect of the financial hit as “recession / no recession”, the analysis of 26 economic systems has revealed that almost all systems suffered financial shock but not all financial shocks were followed by economic recession. The evidence of the financial shock is addressed in terms of the specific quantities of time series analysis i.e. flatness and Hölder exponent. Under the assumption that the “hands on” economies are more robust against the crisis, the crossing analysis emphasizes that systems that do have such a structure could be assigned to a certain degree of centralization imprinted by an unspecified influential entity in the decision making process.

Keywords-recession; financial shock; hands on – hands off; correlation dimension; flatness; Hölder exponent; time series

I. INTRODUCTION The American financial crisis of 2007-2008 triggered the

global ongoing crisis; however, the global character itself is subject to discussion: global does not mean all, a significant number of states managing the difficulties such as avoiding the recession. The crisis was equally impacting on economic systems like national economies, federations, and monetary unions, regardless the geographic region or size. The economic crisis mechanism is intimately associated with the theory of free-market and therefore “hands off” economy [1]. The current global crisis gave credit to Minsky’s model [2]: the free-market financial system swings between robustness and fragility. The governmental interventions in the private sector via strict recovery plans and/or large scale financial infusions like the newly established European semester for budgetary policies, the United States guarantee agreement with Citigroup, and the tens of billions of Euros channelled to Greece and Ireland are relevant examples supporting the assumption that “hands on” economies are less sensitive to the turbulences.

This study is twofold: on one side it is focused onto what extent the hitting of the financial devaluations upon systems subjected to study (SSS) triggered economic recession; on the other side, the nonlinear properties of the economic systems are analyzed via the read out function of the daily exchange rates.

Both approaches are dichotomously evaluated and then correlated in order to relate each other. Depending on the characteristics of the underlying non linear structure and using the additional instrument of Index of Economic Freedom (IEF) ranking, a partition in two groups i.e. “hands on” - ”hands off” is proposed and then compared to the tableau of the affected economies. “Hands on” - ”hands off” partition is related to the decision type that drives the markets i.e. centralized / decentralized (distributed) decision making. The acronyms of the SSS are given in the Annex (BRA stands for Brazil; CHI stands for Peoples’ Republic of China, etc).

The effect of the shock is addressing the topic in two steps: the first, if there is any evidence of the financial shock in the corresponding SSS exchange rate (ER) series; and the second, if the financial shock turned into economic recession. The presence of the financial shocks embedded in time series are investigated by using two complementary instruments: (i) the presence of intermittencies; and (ii) the existence of irregularities in the Hölder map. The quantities associated with these instruments are the intermittency factor and the pointwise Hölder exponent. Intermittency is characterized by abrupt changes of the system activity with alternating periods of quiescent low-level fluctuations and bursting high-level fluctuations. There is widely accepted that intermittency is a fundamental feature of complex economic and financial systems ([3], [4]). Besides, it is considered a typical ‘fingerprint’ of crises [5] especially when complementarily considered with the pointwise Hölder exponent ([6], [7]). Considering the previous Asian crisis as benchmark, the thresholds values were empirically set for both.

The structure of the trace of the corresponding exchange rate series with respect to the US Dollar (USD) is revealed by non linear analysis (NLA) in the phase space [8]. The nonlinear dynamic in economic data is accepted as compromise between deterministic and stochastic processes. Neglecting the unavoidable contamination of data with noise, some authors interpret the time evolution as exclusively random – as Nakagawa [9], Wang and Tong [10], Mantegna and Stanley [11] – while others try to explain the behavior in terms of nonlinear theory, implying chaotic dynamics of the exchange rates – as Dieci and Westerhoff [12], Scarlat et al. [13], Barkoulas [14], Chen [15], Chian et al. [5]. More sophisticated models involve mixed models ([16], [17]).

Acknowledgment: This work was supported by ANCS/UEFISCDI-Ro Research Grant ID # 1556/ No.836/2009.

978-1-4244-6581-1/11/$26.00 ©2011 IEEE

Here a set of 26 economic systems are investigated against the economic crisis installed by fall of 2007. The economic SSSs are of national type (e.g. Fiji, Korea), federations as Mexico, or monetary unions like Euro Zone (EZ). All of them exhibited stable scores according to the Index of Economic Freedom [18] for the last two decades. The findings show that most of the SSSs exhibited measurable financial shocks, but not all financial shocks could be correlated with subsequent economic recessions.

The structure of the remaining paper is as follows: II. The impact of crisis; III. Detecting the shock; IV. “Hands on - Hands off” partition; V. Discussion; VI. Conclusions.

II. THE IMPACT OF CRISIS The term “financial crisis” is largely applied to many

situations in which financial institutions lose a significant part of their value. When occurring abruptly and unexpectedly, it is a shock. Over the last two centuries the shocks were merely associated with banking panics, crashes of stock markets, and the bursting of other financial bubbles, unilateral currency shortages and sovereign defaults – as presented by Kindleberger et al. [19] or Laeven and Valencia [20].

The effect of the economic crisis i.e. the decision recession/no recession is assessed here using a two criteria decision: the first (C1) is defined according to the accepted econometric criterion of diminishing of at least two consecutive quarterly GDPs – real values, seasonally adjusted - between Q4 2007 and Q4 2009, while the second (C2) is considering the total result of the year 2009, according to information available at 31 January 2011 provided by the International Monetary Fund site [21] and the corresponding national sites [22]. Since the present study is focused onto the financial trigger localized in the autumn of 2007 and considering a certain delay time of spreading out of the effects, only the year 2009 was considered for the second criterion. The decision is of “and” type i.e. one has recession if both criteria are fulfilled. C1 is marked with negative sign “−” when one can find at least two consecutive quarters of diminishing GDP with respect to the previous quarter, and with positive sign “+” on the contrary. C2 is marked with negative sign when the overall yearly growth of GDP is negative in 2009. Zero value is set when the result is in the margin of plus/minus 0.1% i.e. it remained uncertain for the time being of the availability of data. The decision on experiencing recession is marked with “y” when both signs are negative (Table I). Otherwise the decision is against and marked with “n”. Excepting Brazil, Korea and Israel, where C1 and C2 indicate split decision (edge of recession), for the remaining SSS the criteria are complying with each other (unanimous decision for, or against the recession). As one can easily see, 15 economic systems out of 26 included in this study are labelled as experiencing recession, Other 11 were showing reluctance at least during the inception stage of the crisis.

III. DETECTING THE SHOCK In order to highlight the financial shock generated by the

US turbulences, the daily exchange rates with respect to USD of all above mentioned countries are investigated during the

interval 1 January 1996 – 31 December 2010, while the Euro is considered from its establishment (i.e. 1 January 1998) until 31 December 2010. The first interval consists of 5480 data points while the second counts only 4380 samples.

The ER evolutions depend on the characteristics of each particular economy. However, some of them exhibit a weak visible response (e.g. the Iranian Real), or no response (e.g. Chinese Yuan). For this reason additional instruments are used like detecting the intermittencies in the ER series, and the irregularities in the corresponding Hölder map.

The intermittency is evaluated by computing the flatness or its equivalent intermittency factor of a sliding sequence of 90 days length according to the formula [23]:

t

t

y

y

4

223=γ (1)

where the angular brackets < >t stand for averaging over time t i.e. 90 days, and y are the returns of the exchange rate x at discrete time n:

( ) ( ) ( )( )nx

nxnxny −+= 1 (2)

γ=1 corresponds to an entirely active signal while γ=0 corresponds to an entirely quiescent signal.

The second instrument is the map of the pointwise Hölder exponents provided by MatLab-FracLab software package. A pointwise Hölder exponent H=1 indicates a smooth curve with differentiable underlying functional in the corresponding point, while H<1 is the sign of irregularity; the lower the value of the exponent, the greater the irregularity. Hereafter these criteria are denoted C3 – the criterion regarding the intermittency factor, and C4 – the criterion related to the pointwise Hőlder map. In the vicinity of the shock both quantities exhibits low values. How low could they be, is questionable, and, besides the amplitude of the variation of the USD exchange rate, they also depend on the interval of time averaging – the intermittency factor, and on the parameters taken into account when computing the Hölder exponent. Therefore the thresholds for C3 and C4 were empirically set using a bootstrap evaluation of the previous Asian turbulences in the ‘90s. The regional crisis originated in South-East Asian economies in the spring of 1997 and then spread to other Asian countries notably to Indonesia, Korea, Singapore, Japan, Taiwan, Hong Kong and Thailand. Under the constraint of deciding in favour of the existence of the financial shock in the range spring 1997 - autumn 1998 for all seven currencies mentioned above, the thresholds for C3 and C4 were chosen as follows:

γ > γth = 0.61 for C3, and H < Hth = 0.49 for C4 (3)

For evaluating the presence of financial shocks on SSSs in the case of the current global crisis, the reciprocal version of bootstrap was used: focusing on abscissa between July 2007 – October 2008, the attainableness of the thresholds (3) was investigated. Results are displayed in Table I. The thresholds are not relevant for all SSS (e.g. in the case of DEN the Hölder exponent remains well below the threshold in the selected time range) – these cases are marked with “X”.

TABLE I. DECISIONS ON RECESSION AND ON THE EVIDENCE OF THE FINANCIAL SHOCKS

SSS Criterion

C1 Criterion

C2 Decision on

recession Criterion

C3 Criterion

C4 Decision on shock

BRA - 0 n y y y CHI + + n y X y DEN - - y y X y EGY + + n n y y EZ - - y X y y FIJ - - y y n y HK - - y X y y IND + + n y y y IDO + + n y y y IRA + + n n n n ISR - + n y y y JAP - - y X y y KOR - + n y y y KUW - - y y n y MEX - - y y y y NIG + + n y n y NOR - - y y y y PHI + + n y y y POL + + n y X y SAA - - y y n y SAF - - y y y y SIN - - y y y y TAI - - y y y y TUR - - y n y y UK - - y y X y VEN - - y n y y

Such irrelevances also occurred for EZ, HK and JAP in the case of C3, and for CHI, DEN, POL, and UK in the case of C4. The final decision on evidence, and consequently on the presence of the financial shocks is of “or” type, i.e. it is ”yes” when at least one of C3 or C4 is ”yes”. According to the method, the print of the shock is detectable in the exchange rates of all systems, excepting the Iranian Real.

IV. “HANDS ON – HANDS OFF” PARTITION The underlying non linear structure is investigated in order

to partition the SSSs in two groups: the “hands off” systems with no detectable short run structure, and the “hands on” systems with persistent structure at small time scales. Since the economic plans cover at least one quarter, hereafter long run means intervals larger than the quarter, while short run means shorter intervals. The following hypotheses stand:

1. The exchange rate series emerging from efficient markets where the hypothetical perfect competition prevails (i.e. with no government or other influential entity) is characterized by Brownian motion and consequently, its returns and residuals time series are approaching the white Gaussian noise [24]. “No rules” means “equally likely” events and therefore white noise.

2. The behaviour of any economic agent is of deterministic type (rationale), therefore they are making plans in order to maximize the profit. When the powerful competitor (if any)

exerts the influence on the exchange rate, then it imprints a certain deterministic structure on the data samples. The bigger the company, the longer the time horizon of its plans, and the higher the impact of the managerial decisions; therefore such structure could be detected in the long run. If the influence is a more intricate one (e.g. is not only of economic type but also of behavioural type), then the structure could be detected at smaller time scales. For example, the constant values of the totally controlled ERs - like the North Korean case, not investigated here - have the image of a fixed point in the phase space, while a periodic cycle has the image of an ellipse. The measure to assessing the existence of the structure is the correlation dimension DC; it indicates the spatial correlation of the ER data in the embedding phase space [25].

3. It is assumed that reliability of the conjecture according to which the dynamics of a system with correlation dimension larger than five is essentially implying the prevalence of noise [26]. Accordingly, values below the upper limit

DC < DC,th = 5 (4)

are considered that trajectory and consequently ERs have structure. In other words, the extreme cases are those of the free market economy of “hands off” type (i.e. decentralized management) with no detectable structure, and the economy of command of “hands on” type (i.e. centralized management) with small scale structure to the limit of a single point. It is clear that in any real exchange rate time series the free market dynamics is intimately intertwined with the effects of state influence. During the recent history, the existence of economic systems where the dynamics of financial time series are driven by local long run market forces – of economic or political type – are well known: the Saudi Arabia Riyal is a good example for the first case, while the Chinese Yuan for the second. However, several authors [27] are considering the Chinese currency as a very special case in the sense that the clumpiness test of its returns time series shows a very good approximation of a Sierpinski gasket. This is due to the very high value of the corresponding slimness of the experimental distribution of the samples [28].

Every series is prepared in that sense it is stationarized by dropping out the coarse trends. This prerequisite allows for making the hypothetical structures more visible in the phase space because of dropping out the shifts that are shadowing the trajectory. Despite certain arbitrary in establishing the limit between what is and what is not structure, here the “coarse” part of the trends is consisting in the components that can be removed in order to obtain stationarity according to the augmented Dickey-Fuller (ADF) test [29]. For every time series there is a degree GSTAT of the polynomial that fit the series using the least square error (LSE) method such as the ADF indicates stationarity at 99% confidence level:

x(n) = xSTAT(n) ∑=

+STAT

0

G

k

kknb , n = 1, …, N (5)

where: xSTAT are the samples of the stationary series, bk are the corresponding coefficients of the polynomial, and N represents the number of points in the series. The remaining long run

influences are further eliminated with polynomials of progressively increasing orders:

r(n) = xSTAT(n) ∑=

−MAXG

Gk

kk nb

STAT

, n = 1, …, N (6)

where r are the residuals. The residuals are checked for the correlation dimension DC in the phase space with Chaos Data Analyzer. The chosen time lag for computing DC is the one based on the mutual information; here the Akaike criterion was preferred [30].

There are two cases: (i) the threshold (4) could not be reached whatever the order of the polynomial; and (ii) the threshold (4) is reached for a certain degree G, GSTAT ≤ G≤ 85. The partition of SSSs according to the non-linear analysis is indicated in Table II.

The members of the first group generally belong to the developed markets category – excepting Fiji, and to some extent Poland – where the political role of government or central bank remains in the regulatory area and does influence only the long run correlations. In the light of the present NLA the evolution toward the coloured noise is interpreted in the sense of the existence of a biasing free competition market where a large number of autonomous entities (e.g. small businesses and individuals) are deciding on their own economic activities over which the planning of the more powerful companies are superimposed in a manner that does not hinder the competition nor influence the accurate economic behaviour of any undertaker. For this reason they are of the type of “hands off” systems. On the contrary, the presence of self similar structures in shorter run as effect of the influence on ERs of an unspecified entity like government, central bank, transnational corporation, etc. whose political role overlaps the economic and psychological market forces via more intricate channels is an argument to include them in the category of “hands on” systems. Such channels could be interventions in the stock market, explicit price controls or property rights regulations in the economic area, or of behavioural nature. The partition is supported when linking the results with the IEF scores that can be at best related to the degree of centralization of the decision making, namely the property right regulation (PRR) score [18]. Excepting Taiwan and Hong Kong, all “hands on” systems have small values for PRR [31].

V. DISCUSSION The natural tendency is to correlate the findings from the

Tables I and II. According to Table I, all SSSs were financially affected by the turbulences spilled over US banking system, excepting Iran. Seven out of eleven “hands on” SSSs did not suffer economic recession while ten out of fifteen “hands off” SSSs were affected; a joint partition is given in Table III.

By accepting the simple “majority rule” according to which “hands off” SSSs are more vulnerable to shocks and therefore should suffer economic crisis and “hands on” SSSs should not, there are two groups of exceptions, also shown in Table III. The two groups of exceptions are the following:

TABLE II. PARTITION OF THE SSS ACCORDING TO NLA

“Hands on” systems

Brazil, China, Egypt, Hong Kong, India, Indonesia, Iran, Nigeria, South Africa, Taiwan, Venezuela

“Hands off” systems

Denmark, Euro Zone, Fiji, Israel, Japan, Korea, Kuweit, Mexico, Norway, Philippines, Poland, Saudi Arabia, Singapore, Turkey, United Kingdom

TABLE III. JOINT PARTITION OF THE SSS

“Hands on” – “Hands off” “Hands on” “Hands off”

Recession Hong Kong, South Africa, Taiwan, Venezuela

Denmark, Euro Zone, Fiji, Japan, Kuwait, Mexico, Norway, Saudi Arabia, Singapore, Turkey, UK

No recession Brazil, China, Egypt, India, Indonesia, Iran, Nigeria

Israel, Korea, Philippines, Poland

(i) Hong Kong, Taiwan, South Africa and Venezuela belong to the “hands on” group and experienced economic recession, while (ii) Israel, Korea, Philippines and Poland belong to the “hands off” group and did not have economic recession – at least until December 2009, when the main crisis shock would have been vanished.In a simplistic manner, “hands off” economies react to exogenous shocks in a balanced positive (predictive) feed-forward - negative (corrective) feedback way with adjustable characteristic delay time allowing to shorten it up to appropriate values that make possible just-in-time decisions to counterbalance the threats of the crises; if the decisions were correct, then the crisis might be avoided or its effects minimized. From this perspective, the crises appear rather as normal socio-economic phenomena than misfortunes or tragedies. On the other side, “hands on” economies are based on dominant negative feedback based actions; it means the corrective side is enhanced against the predictive one i.e. the psychological short run market force is strongly distorted; when too strong, the lag time increases and the system may become insensitive to exogenous stimuli, and this is the case of Iran (another case is North Korea, not discussed here).

A validity test for the “majority rule” is the argument that Euro Zone falls in the “hands off” category. Despite the large number of directives emerging from the EU government, their influence is of regulatory type and the decision making is merely decentralized. The case of the Greek financial crisis is significant for the limited power of the European Central Bank that has yet a lot to do in order to become a true central bank in terms of decisions and instruments over entire EZ.

VI. CONCLUSIONS Depending on the attainableness of the threshold of the

correlation dimension of residuals of the exchange rate series, a semi-quantitative, unbiased criterion for partitioning several economic systems in two bins “hands on – hands off” is presented. This might be an auxiliary tool supporting the decisions on whether and how much to invest in different markets.

According to the IEF aggregated score 26 stable economic systems were chosen. Fifteen systems that revealed only a long run, removable structure, were associated with “hands off” economies and distributed decision making, while the other 11 that exhibited persistent structure inside a quarter time length were associated with “hands on” economies and centralized decision making.

The tableau is compared to the one made according to the criterion of suffering or not economic recession in the interval 2008-2009. The financial shock is revealed by a sliding window study of the intermittency factor and the pointwise Hölder exponent. 25 systems exhibited at least one of the signs of the shock, and 15 economic systems suffered economic recession during the crisis.

The compliance between the two pictures is as follows: 11 out of 15 “hands off” systems suffered economic recession, and 7 out of 11 “hands on” systems did not. Therefore the overall compliance is 18 out of 26 systems that are fitting the categories.

The study is aiming to provide an alternative way to characterize the features of the economic systems that are likely to allow the financial turbulence to turn into recession. Euro Zone is illustrative for distributed decision and control, and Iran is expressive for centralized decision, insensitiveness to shock and recession; they are good arguments for validating the method. Despite of limitations of such an attempt, it is an unbiased instrument to coarsely characterize the world economies and possibly the robustness against the crises [32].

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ANNEX: THE ACRONYMS FOR ECONOMIC SYSTEMS

Brazil (BRA), Peoples’ Republic of China (CHI), Denmark (DEN), Egypt (EGY), Euro Zone (EZ), Fiji (FIJ), Hong Kong (HK), Israel (ISR), India (IND), Indonesia (IDO), Iran (IRA), Japan (JAP), South Korea (KOR),

Kuwait (KUW), Mexico (MEX), Nigeria (NIG), Norway (NOR), Philippines (PHI), Poland (POL), Saudi Arabia (SAA), Singapore (SIN),

South Africa (SAF), Taiwan (TAI), Thailand (THA), Turkey (TUR), United Kingdom (UK), Venezuela (VEN).