RESEARCH ARTICLE
Dynamic s abili y o he inancial moni o ing
sys em: In ellec ual analysis
Olha Kuzmenko
1
, Yu iy BilanID
2
*, E genia Bonda enkoID
3
, Bea a Ga u o aID
4
,
Hanna Ya o enko
1
1Economic Cybe ne ics Depa men , Ins i u e o Business, Economics and Managemen , Sumy S a e
Uni e si y, Sumy, Uk aine, 2Facul y o Economics and Managemen , Czech Uni e si y o Li e Sciences
P ague, P ague, Czech Republic, 3Depa men o Financial Technologies and En ep eneu ship, Ins i u e o
Business, Economics and Managemen , Sumy S a e Uni e si y, Sumy, Uk aine, 4Facul y o Managemen
and Economics Tomas Ba a Uni e si y in Zlin, Zlı
´n, Czech Republic
*[email p o ec ed]o.uk
Abs ac
In oduc ion
Al hough he e is a g owing numbe o scien i ic publica ions on inancial moni o ing, com-
ba ing money launde ing, he shadow economy, and he impac o co up ion on economic
de elopmen , u he esea ch needs o de e mine he s abili y o he na ional inancial sys-
em in dynamics. The dynamic s abili y o he na ional inancial moni o ing sys em subjec s
will allow o adequa ely assess he e ec i eness o he exis ing na ional inancial moni o ing
sys em in each coun y and de e mine he in luen ial ac o s.
Ma e ials and me hods
The a icle in es iga es an app oach o iden i ying he dynamic s abili y o he na ional inancial
moni o ing sys em subjec s based on he calcula ion o he in eg a ed indica o o he coun y’s
inancial sys em p opensi y o ALM, ec o au o eg ession (VAR) model aking in o accoun
ime lag. The p oposed in eg a ed indica o allowed o adequa ely assess he exis ing inancial
moni o ing sys ems o he coun ies (15 coun ies o he Eu opean Union o 2000–2020: Aus-
ia, Belgium, Cyp us, Es onia, Finland, F ance, G eece, I eland, I aly, La ia, Mal a, Ne he -
lands, Po ugal, Slo ak Republic, Spain). In addi ion, ec o au o eg ession models (VAR) o
he dependence o he coun y’s inancial sys em p opensi y o ALM on he eg esso s Go e n-
men In eg i y, Index o economic eedom, Mone a y Sec o c edi o he p i a e sec o (%
GDP), we e buil , aking in o accoun ime lags in gene al and o each s udied coun y.
Resul s
Acco ding o he modeling esul s, he na ional inancial moni o ing sys ems in Aus ia, Bel-
gium, Es onia, Finland, F ance, I eland, Ne he lands, Slo ak Republic, Spain we e esis an
o money launde ing. I is ice e sa in Mal a, G eece, Cyp us, Po ugal, I aly, La ia. These
conclusions a e also con i med based on a bina y app oach. Such exogenous a iables as
Go e nmen In eg i y (wi h a lag o 2 yea s) and he Index o economic eedom ( aking in o
accoun he ime delays o he eg ession e lec ion unde he in luence o his eg esso o
1 and 2 yea s) ha e a s a is ically signi ican e ec on he coun y’s inancial sys em.
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OPEN ACCESS
Ci a ion: Kuzmenko O, Bilan Y, Bonda enko E,
Ga u o a B, Ya o enko H (2023) Dynamic s abili y
o he inancial moni o ing sys em: In ellec ual
analysis. PLoS ONE 18(1): e0276533. h ps://doi.
o g/10.1371/jou nal.pone.0276533
Edi o : La
´szlo
´Vasa, Szechenyi Is an Uni e si y:
Szechenyi Is an Egye em, HUNGARY
Recei ed: No embe 18, 2021
Accep ed: Oc obe 9, 2022
Published: Janua y 20, 2023
Copy igh : ©2023 Kuzmenko e al. This is an open
access a icle dis ibu ed unde he e ms o he
C ea i e Commons A ibu ion License, which
pe mi s un es ic ed use, dis ibu ion, and
ep oduc ion in any medium, p o ided he o iginal
au ho and sou ce a e c edi ed.
Da a A ailabili y S a emen : All ele an da a a e
open sou ce da a e e enced wi hin he pape .
Funding: The a icle was p epa ed based on he
esul s o a esea ch unded by he Na ional
Resea ch Fund o Uk aine "Op imiza ion and
au oma ion o inancial moni o ing p ocesses o
inc ease in o ma ion secu i y in Uk aine" o he
Minis y o Educa ion and Science o Uk aine
( egis a ion numbe : 0120U104810). This wo k is
suppo ed by he Scien i ic G an Agency o he
Minis y o Educa ion, Science, Resea ch, and
Spo o he Slo ak Republic and he Slo ak
Conclusion
The gene al ec o au o eg ession (VAR) model shows ha he cu en alue o he coun-
y’s inancial sys em p opensi y o ALM by 92.78% is de e mined by i s p e ious alue. Wi h
an inc ease o Go e nmen In eg i y by 1%, he coun y’s inancial sys em’s p opensi y o
ALM will dec ease by 0.000616 uni s wi h a lag o wo yea s. The na u e o he impac made
by he Index o economic eedom on he pe o mance ea u e was speci ic—when his indi-
ca o inc eases by 1% o a lag delay in one yea , he PFSALM alue will dec ease by
0.001997 uni s, and o a lag delay o wo yea s i will change he end and inc ease by
0.003076 uni s pe uni , espec i ely.
In oduc ion
Global ans o ma ion p ocesses, he ex ension o global economic ies, and he in oduc ion
o new echnologies in he inancial sphe e signi ican ly inc ease he o e all ulne abili y o
he in e na ional inancial sys em and, as a esul , pose a h ea o each coun y’s na ional secu-
i y. One o he majo h ea s o any na ional economy is he p oblem o An i-Money Laun-
de ing (AML).
Such in e na ional o ganiza ions as he Uni ed Na ions, he Council o Eu ope, he Wo ld
Bank, he In e na ional Mone a y Fund, he Financial Ac ion Task Fo ce (FATF) ocus hei
a en ion on AML.
The p oblem o c ea ing an in eg a ed inancial moni o ing sys em is impo an and ele-
an oday; i s solu ion can con ibu e o he coun y’s socio-economic de elopmen , e o m
he budge and social secu i y sys em, he e icien use o s a e p ope y and ensu e an adequa e
na ional secu i y.
Wi hin he amewo k o inancial ac i i y, inancial moni o ing is a inancial and legal
ins umen o an i-money launde ing and an i- e o is inancing.
The need o conside inancial moni o ing as a manda o y condi ion o ensu ing inancial
secu i y is jus i ied by he nega i e consequences ha he s a e and socie y su e om he
s a e’s money launde ing and e o is inancing. Money launde ing h ea ens he s a e’s
na ional secu i y since i a ac s o ganized c iminal g oups o he coun y, p omo es he c ea-
ion o "home-g own" illegal communi ies, inc eases he o e all c ime a e, and, as a esul ,
o ces he s a e o inc ease law en o cemen . Thus, he c ea ion o a inancial g ound o c imi-
nal ac i i y en ails an inc ease in go e nmen expendi u e on law en o cemen , which usually
has a nega i e impac on go e nmen unding o in es men p ojec s o capi al expendi u es,
slows GDP g ow h, inc eases he budge de ici .
I is impo an o no e ha money launde ing is pa icula ly de imen al o eme ging ma -
ke s since money launde ing becomes he inancial basis o o ganizing and ca ying ou
a med coups, co up ion, and es ablishing he inancial dependence o legi ima e au ho i ies
on la ge c iminal g oups wi hin he coun y.
Uncon olled mo emen o signi ican inancial esou ces—illegal (unaccoun ed) c iminal
p oceeds in a sho pe iod is also a se ious h ea o inancial secu i y.
C iminal p oceeds, which in he sho e m may bene i economies in need o in es men ,
including o eign, can unde mine he na ional economy o any s a e since i is impossible o
p edic and plan he ac i i ies o en i ies engaged in money launde ing o o he p ope y.
The unp edic abili y o money launde e s in inancial ma ke s leads o sha p and signi i-
can luc ua ions in demand o inancial esou ces, changes in exchange and in e es a es,
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Academy Sciences as a pa o he esea ch p ojec
VEGA 1/0797/20. The unde s suppo au ho s wi h
da a collec ion.”
Compe ing in e es s: The au ho s ha e decla ed
ha no compe ing in e es s exis .
and as a esul , po en ial omissions in he inancial policy o public inancial au ho i ies ha
assess capi al ma ke s, which can lead o inancial di icul ies in he s a e and economic en i-
ies, up o he bank up cy o he la e .
Tha is why he s a e au ho i ies in mos coun ies make signi ican e o s o c ea e and
ensu e he e ec i e unc ioning o he na ional inancial moni o ing sys ems o hei na ional
economies. An impo an peculia i y o he inancial moni o ing sys em is i s esilience o a i-
ous economic en i ies’ ac ions (including c iminal ones) and o he ex e nal ac o s. The inan-
cial moni o ing sys em is also conside ed s able, e ec i ely coun e ing money launde ing e en
wi h a signi ican change in he ex e nal en i onmen pa ame e s. The necessi y o de e mine
he sus ainabili y o inancial moni o ing sys ems in Eu opean coun ies and he ac o s ha
ha e a key impac on such sus ainabili y iden i ied he chosen esea ch opic.
Theo e ical amewo k
In ecen decades, he issue o inancial moni o ing in each coun y has been e y ele an .
The g owing a en ion o he p oblem o imp o ing he inancial moni o ing e ec i eness
among scien is s and p ac i ione s is la gely caused by i s impo ance o ensu ing he s a e’s
economic secu i y. In addi ion, he u gency o enhancing inancial moni o ing sys ems
inc eases wi h he u he de elopmen o inancial and paymen echnologies. Thus, i leads
o an inc ease in he numbe o scien i ic pape s in his ield a ound he wo ld.
I is ad isable o analyze exis ing scien i ic pape s and p ac ical ecommenda ions on inan-
cial moni o ing and ela ed indus ies o unde s and he esea ch p oblem be e .
In hei s udy, scien is s Leono , S., Ya o enko, H., Boiko, A., & Do senko, T. [1] de eloped
an in o ma ion sys em p o o ype o in abank moni o ing o money launde ing ansac ions.
Since banks a e he key subjec s o inancial moni o ing in many coun ies, i is easonable o
pay conside able a en ion o he au oma ion o hese sys ems in banking ins i u ions when
de eloping an e ec i e an i-money launde ing sys em. Acco ding o scien is s, his au oma-
ion will inc ease bank e iciency by s udying all banking ope a ions wi hou excep ion, le el-
ing he human ac o , maximizing he de ec ion speed o suspicious ansac ions and
minimizing losses ela ed o he paymen o penal ies imposed by egula o y au ho i ies. I is
es ablished ha he in o ma ion sys em p o o ype o moni o ing ope a ions ela ed o money
launde ing h ough banks should consis o a model o moni o ing business p ocesses in an
au oma ed sys em en i onmen , DFD-model o au oma ic moni o ing o banking ope a ions,
s uc u al da abase model, use in e ace o ms and include he logic o checking business
ules.
Subeh, M.A., Boiko, A. [2] p esen ed a s udy on building a scien i ic and me hodological
app oach o assessing he S a e Financial Moni o ing Se ice e ec i eness as pa o a na ional
an i-money launde ing o an i- e o ism inancing sys em in Uk aine. The scien i ic and
me hodological app oach p oposed by he au ho s allowed us o conclude ha he S a e Finan-
cial Moni o ing Se ice in Uk aine wo ks ine icien ly. In addi ion, he e iciency o i s wo k
has a nega i e endency o dec ease. Scien is s iden i ied ha he easons a e low in eg a ed
index, low e iciency o s a e au ho i ies’ ac i i ies (desc ibed by he inpu low), low e iciency
o aken measu es ( ep esen ed by he se ices), and ine iciency o esul s ob ained om
applied s eps (desc ibed by he low o ou pu ). One should also no e ha he de eloped scien-
i ic and me hodological app oach is uni e sal and can be used o assess he e ec i eness o
any go e nmen agency o comme cial en i y he ac i i ies o which a e ela ed o h ee com-
ponen s: inpu in o ma ion, i s se ice, and conclusions in he o m o a low o esul s.
In he p ocess o s udying inancial moni o ing, Alibeki H., Samsono M. [3] paid conside -
able a en ion o he s udy o banking supe ision "o -si e", namely, emo e moni o ing o
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banks. The au ho s ha e iden i ied consolida ed supe ision and sys ema ic s ess es ing by
banks as e ec i e me hods o emo e banking moni o ing. Acco ding o scien is s, an e ec i e
s ess es ing sys em will p o ide a comp ehensi e, in eg a ed, and p omising se o measu es
o a banking o ganiza ion o help iden i y and measu e i s ma e ial isks and ulne abili ies.
The s ess es ing me hodology o each speci ic banking ins i u ion should be de eloped in
p opo ion o i s size, complexi y, business ac i i y, and o e all isk p o ile. In his con ex
Belas e al. [4] emphasize bank models a e no pe ec and gi e qui e un eliable esul s, espec-
i ely; hey con ibu e o he p ocyclical endencies o he inancial sys em.
In hei wo k, Bukh ia o a e al. [5] in es iga ed e alua ing he e ec i eness o inancial
moni o ing measu es in Uk aine. This p oblem is especially ele an o Uk aine since he
coun y has a bank-cen ic inancial ma ke model (abou 90% o asse s pass h ough he bank-
ing sys em). Acco ding o o icial da a, 50% o economic ac i i y in Uk aine ends wi h money
launde ing. The a icle p esen s an imp o ed me hod ha quan i ies he e iciency o he
inancial moni o ing sys em in comme cial banks o Uk aine based on he calcula ions o he
in eg a ed index. The index indica es he p o ec ion deg ee dynamics o he inancial sys em
om he h ea o money launde ing based on he easibili y and e ec i eness o inancial
moni o ing in he banking sys em. The me hod p oposed by he au ho s can be used o assess
he e ec i eness o he inancial moni o ing sys em in any coun y o imp o e he an i-money
launde ing sys em h ough he banking sys em.
Mode n scien is s in he s udy o inancial moni o ing pay conside able a en ion o he
shadow economy as one o he mos impo an ac o s, which leads o an inc ease in he num-
be o money launde ing cases. Acco ding Gine icius, e al. [6] he highe he le el o na ional
economic de elopmen , he lowe he size o he shadow economy. The long- un analysis
e ealed ha shadow economies nega i ely a ec ed o eign di ec in es men in lows (Baya
e al. 2020).
Fo example, Zolko e , A., Te zie , V. [7] analyzed esea ch a eas ela ed o he shadow
economy. The au ho s used VOS iewe , Scopus, and Web o Science (WoS) o he analysis.
The au ho s conduc ed a s udy based on 5361 wo k o he Scopus da abase and 3773 a icles o
he Web o Science. Time analysis has shown ha in 2014–2015, he numbe o a icles on he
shadow economy began o inc ease. A he same ime, he esea ch ocal poin has mo ed
om gene al issues (shadow sec o assessmen , impac on he labo ma ke , e c.) o he p ob-
lem o ansi ion om he in o mal o he o mal economy. In 2019, he numbe o wo ks on
he shadow economy inc eased by 95% compa ed o 2014, acco ding o he Scopus da abase—
by 29%. Mos a icles wi h he keywo d “shadow” (in o mal, hidden, e c.) economy we e pub-
lished in he ollowing subjec a ea, acco ding o Scopus: social sciences; economics, econo-
me ics, and inance; business, managemen and accoun ing; ecology; a s and humani ies,
and acco ding o WoS: business economics; sociology; go e nance; s a e law; de elopmen
esea ch; o he opics o social sciences; en i onmen al sciences; e i o ial esea ch. Mos a i-
cles on he shadow economy ha e been published by scien is s om he Uni ed S a es, B i ain,
India, Ge many, and Sou h A ica. These esul s p o e ha he in o mal economy and i s an-
si ion o a o mal one co esponds o he cu en ends o mode n egula ion.
In hei s udy, Bilan, e al. [8] conside ed how he size o he shadow economy o Eu opean
coun ies a ec s he GDP ca bon in ensi y o he economy. As a esul o calcula ions (con i -
ma ion o s a iona i y (Dickey-Fulle es ), coin eg a ion o da a se ies (Johansen es ), equa-
ion o coin eg a ion o dependence o GDP ca bon in ensi y on he shadow economy, i was
con i med ha Eu opean coun ies a e cha ac e ized by inc easing GDP ca bon in ensi y o
economy wi h inc easing he shadow economy.
Lyulyo , e al. [9] analyzed he d i ing ac o s o he shadow economy de elopmen in
ansi ion economies. I was ound ha an inc ease in GDP pe capi a in he selec ed coun ies
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wi h ansi ion economies by 10% educes he shadow economy by 1.2%; an inc ease in o -
eign di ec in es men by 10% educes he shadow economy by 0.5%; a 10% imp o emen in
ene gy e iciency co ela es wi h a 2% g ow h in he shadow economy. I was also ound ha
inc easing he ax a e by 10% aises he shadow economy by 1%. Based on he esul s, he
au ho s o med key policy ec o s o coun ies wi h ansi ion economies ha will educe he
shadow economy: s imula e economic g ow h and e o m he s uc u e o he ax sys em
owa ds a la ge sha e o indi ec axes.
In hei s udy, Zolko e , A., Geo gie , M. [10] summa ized he a gumen s and coun e a -
gumen s in he scien i ic discussion on he p oblem o comba ing shadow ac i i y in e ms o
mac oeconomic s abili y. The au ho s ocused on de e mining he allowable le el o in es -
men ansac ions wi h ic i iousness, which co espond o he balance be ween he shadowing
o he na ional economy and i s mac oeconomic s abili y. The ele ance o his scien i ic p ob-
lem is ha he shadow in es men ac i i y dis o s he ma ke mechanism and makes i impos-
sible o a ac inancial esou ces o expanded ep oduc ion in he coun y. The calcula ions
pe o med by he au ho s p o ed ha he e is a nonlinea unc ional dependence o he in es -
men ope a ions wi h ic i iousness ea u es on he le el o shadowing o he na ional economy
and i s mac oeconomic s abili y.
Shpak e al. [11] analyzed he shadow indus y in he Uk ainian egions, assessing he in e-
g a ed indica o o inancial and economic secu i y o inancial and economic secu i y he
indus y. The s udy’s key esul s we e: 1) de elopmen o heo e ical and applied app oaches
o he impac o he shadow economy on public adminis a ion o he inancial and economic
secu i y o indus y in he egions; 2) imp o ing he me hodology o public policy analysis
ega ding he shadow economy in his a ea. Recommenda ions on s a e policy measu es o
educe he shadow indus y le el in he egions we e also p esen ed. In addi ion, based on he
analysis, a ma ix o s a egic zones "The le el o he shadow economy— he le el o inancial
and economic secu i y", which can be used o public adminis a ion decisions depending on
he s a egic a ea in which he egion is o med.
In addi ion o he shadow economy, in e ms o he s udy o inancial moni o ing, schola s
wo ldwide also pay a en ion o co up ion, gi en ha co up ion con ibu es o he de elop-
men o he shadow economy and inc eases he numbe o money launde ing ansac ions.
The s udy o Nguyen, T.A.N., Luong, T.T.H. [12], whe e he au ho s examined how co up ion
and he shadow economy in e ac wi h economic g ow h in 17 selec ed Asian coun ies, is pa -
icula ly in e es ing. This pape analyzes he annual da a o he Wo ld Bank, T anspa ency
In e na ional, and he In e na ional Mone a y Fund o he pe iod 2000–2015 o assess
whe he co up ion and he shadow economy a ec economic g ow h. The au ho s’ calcula-
ions show ha he co up ion index has a s a is ically signi ican and posi i e impac on eco-
nomic g ow h, while he shadow economy has a signi ican nega i e e ec . In addi ion,
educing he size o he shadow economy may be mo e bene icial o eme ging ma ke s in
e ms o economic de elopmen oppo uni ies. The esul s also indica e ha in hese coun ies,
o eign di ec in es men , go e nmen spending, ax e enues and in la ion ha e a posi i e
e ec on g ow h, while emi ances do no ha e a signi ican impac on he economy.
Simo ic M. [13] conduc ed a simila s udy o Eas e n Eu opean coun ies (Mon eneg o,
Bosnia and He zego ina and Se bia). The subjec o his s udy is he ela ionship be ween co -
up ion and economic g ow h in he h ee selec ed coun ies. The s a ing poin o he s udy
was he hypo hesis ha co up ion has a signi ican nega i e impac on economic g ow h in
Sou heas Eu ope. The au ho used he Co up ion Pe cep ions Index and he g oss domes ic
p oduc (GDP) pe capi a o he analysis. The s udy pe iod s a s om he beginning o 2003
o he end o 2018. The esul s o he s udy con i med he ini ial hypo hesis o explaining he
co up ion impac on economic g ow h in he coun ies o Sou h-Eas e n Eu ope.
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B ychko e al.(2021) [14] conduc ed a s udy o he media ing impac o he c isis o con i-
dence in he inancial sec o on mac oeconomic s abili y indica o s due o he expec ed impac
o he de elopmen o inancial in e media ion and he mone a y policy ansmission mecha-
nism. Among o he hings, he nega i e impac o he c isis o con idence in he inancial sec-
o on he illegal ac i i ies o inancial in e media ies is conside ed. I has been empi ically
con i med ha he in e es a e, c edi and o eign exchange channels o he mone a y policy
ansmission mechanism can be used o o e come he c isis o e osion o he inancial sec o ’s
con idence in mac oeconomic s abili y.
A icle o Kuzmenko e al.(2020) [15] explains how o use da a mining and bi u ca ion anal-
ysis based on limi ed in o ma ion abou a coun y’s o e all pe o mance o assess a coun y’s
esilience o he in ol emen o i s inancial ins i u ions in money launde ing. Empi ical calcu-
la ions ha e shown ha o he g oup o coun ies o which Uk aine belongs, he dynamic sys-
em is in a non-equilib ium s a e and is desc ibed as a "saddle" phase po ai . The e o e, he
isk o using inancial ins i u ions o money launde ing in Uk aine is high, al hough i is
unde ce ain s a e con ol.
Ano he g oup o au ho s (Lyeono e al. (2020) [16] has de eloped a scien i ic and me h-
odological app oach o assessing he isk o inancial moni o ing in e ms o he use o inan-
cial o ganiza ions o money launde ing. This app oach is based on he me hods o
mul idimensional s a ic analysis, desc ip i e, clus e and a iance da a analysis, g a i y heo y,
non-linea econome ic modeling, di e en ial and bi u ca ion modeling, analysis o dynamic
nonlinea sys ems. The s udy esul ed in a de eloped model o a comp ehensi e assessmen
o he isks o inancial o ganiza ions o coun ies in ela ion o money launde ing, which p o-
ides o g ouping coun ies acco ding o he le el o isk o money launde ing, iden i ying
whe he he clus e belongs o he s a e; o ma ion o an in eg a ed index o bo h a a ing
assessmen o he isk o money launde ing and a isk assessmen based on a g a i y model;
cons uc ion o a phase po ai o a dynamic isk sys em o he use o inancial ins i u ions o
coun ies based on a non-linea econome ic model.
An impo an a ea o esea ch is esea ch aimed a s udying he g owing ole o in ech in
paymen ela ions and i s impac on money launde ing oppo uni ies. The s udy by Pe ush-
enko e al.(2018) [17] examines he cos and s uc u e o in es men lows as he mos ob ious
indica o s o in ech and desc ibes he ypes o paymen ela ions in i . The esul s o he s udy
show he high po en ial o FinTech o p ocessing c oss-bo de paymen s and comba ing
money launde ing.
The au ho s (Paske icius, A., & Keliuo y e-S aniuleniene, G. (2018) [18] paid conside able
a en ion o he isky na u e o inancial inno a ions and he need o s udy hei impac on
capi al ma ke s, also om he poin o iew o he possibili y o hei use in illegal schemes.
The au ho s ha e de eloped a panel model o he impac on capi al ma ke s in he coun ies o
Cen al and Eas e n Eu ope. The model p oposed by he au ho s explains almos ou - i hs o
he changes in he capi al ma ke , exp essed in e ms o capi aliza ion.
The au ho s Bilan, Y e al.(2019) [19] s udied he ela ionship be ween he d i e s o he
shadow economy and he le el o demand in he in es men ma ke . Based on he Shapi o-
Wilk es , he no mali y o he dis ibu ion o capi al in es men s and he le el o he shadow
economy o he EU coun ies and Uk aine we e assessed. As a esul , he ollowing conclusions
we e ob ained: he g ow h o he shadow economy, as well as he inc ease in he olume o
money launde ing ope a ions, has a nega i e impac on he olume o capi al in es men s in
he coun y.
Based on he explica ion o s uc u al and unc ional ela ionships, B ychko e al.2021) [20]
de eloped a scien i ic and me hodological app oach o modeling he ela ionship be ween he
illegal ac i i ies o inancial in e media ies, a c isis o con idence in he inancial sec o and i s
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de o ma ions. Using s uc u al equa ion modeling ools, a media o analysis was ca ied ou ,
he esul s o which con i med he hypo hesis ha he c isis o con idence in he inancial sec-
o s leads o an inc ease in he numbe o shadow ansac ions in ol ing inancial
in e media ies.
Also, he issues o inancial moni o ing a e conside ed om he poin o iew o hei
impac on he inancial secu i y o he coun y. Vasilye a e al.(2020) [21] de eloped a me hod-
ology o calcula ing a coun y’s inancial sec o go e nance quali y index as a weigh ed a e -
age o a coun y’s o e all indica o . compliance wi h he main in e na ional s anda ds, ules
and p inciples in he indus y (including he ules and p inciples o inancial moni o ing).
Analysis o he esul s shows ha he le el o economic de elopmen o coun ies does no play
a key ole in de e mining he le el o inancial secu i y, and o he de e minan s a e mo e
impo an , such as he ules and p inciples o inancial moni o ing.
Al hough he e is a cons an ly g owing numbe o scien i ic publica ions on inancial moni-
o ing, an i-money launde ing, he shadow economy, and he impac o co up ion on eco-
nomic de elopmen , u he esea ch needs o de e mine he s abili y o he na ional inancial
moni o ing sys em in dynamics. The dynamic s abili y o he na ional inancial moni o ing
sys em subjec s will allow o adequa ely assess he e ec i eness o he exis ing na ional inan-
cial moni o ing sys em in each coun y and de e mine he ac o s ha ha e a signi ican
impac on his s abili y.
Ma e ials and me hods
S udy design
The s udy in ol es he ollowing s ages:
1. s age. Collec ion and sys ema iza ion o s a is ical da a o in ellec ual analysis o dynamic
s abili y o na ional inancial moni o ing sys em subjec s o ac ions on money launde ing.
2. s age. Classi ica ion o inpu indica o s in o eg esso s and eg essan s and based on he
selec ed eg esso s, he o ma ion o an in eg a ed indica o o he coun y’s inancial sys-
em p opensi y o ALM. Among he many indica o s o he s a is ical inpu base o med in
he i s s age, i is p oposed o conside as eg esso s Index o economic eedom, Go e n-
men In eg i y, Mone a y Sec o c edi o he p i a e sec o (% GDP). A he same ime,
eg essan s a e Financial F eedom, Cu ency in Ci cula ion (% GDP). The inpu indica o s
a e dis ibu ed in such a way since he Financial F eedom indica o de e mines he banking
sys em e iciency and indica es i s go e nmen egula ion independence, de e mining he
s a e in e en ion deg ee in he inancial sec o . The index assesses he s a e egula ion
in ensi y o he inancial se ice sec o , he s a e in e en ion deg ee in banks and o he
inancial ins i u ions h ough di ec o indi ec owne ship, he s a e’s in luence on lending,
he inancial and capi al ma ke de elopmen deg ee, and openness o o eign compe i ion.
I means ha he Financial F eedom indica o is a key indica o o assessing he e ec i e-
ness o he inancial moni o ing sys em o each coun y and can be ela ed o he
eg essan .
The Cu ency in Ci cula ion (% GDP) indica o is he cu ency ha is physically used o
ansac ions be ween consume s and businesses and is no s o ed in a bank, inancial ins i u ion
o cen al bank. Since mos o he illegally ob ained income is ecei ed in cash, his indica o indi-
ec ly desc ibe he exis ing inancial moni o ing sys em, so i was also chosen as a eg essan .
The Index o economic eedom, Go e nmen In eg i y, Mone a y Sec o c edi o he p i-
a e sec o (% GDP) we e chosen as eg esso s because hey desc ibe he ex e nal condi ions
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in which he na ional inancial moni o ing sys em ope a es: s a e egula ion e iciency, he ule
o law in he coun y, limi ed s a e powe and openness o ma ke s (Index o economic ee-
dom), he le el o pe cep ion o co up ion in he public sec o (Go e nmen In eg i y) and
he amoun o inancial suppo o he p i a e sec o o s a e and c edi o s a e en e p ises
(Mone a y Sec o c edi o he p i a e sec o ) (% GDP) ha e he mos signi ican impac on
he e ec i eness o he na ional inancial moni o ing sys em in he coun y and i s dynamic
s abili y.
Based on he selec ed eg esso s, he o ma ion o he in eg a ed indica o o he coun y’s
inancial sys em p opensi y o ALM in ol es a numbe o in e media e s eps:
2.1. No maliza ion o eg esso s Financial F eedom (deno e FF) and Cu ency in Ci cula ion
(% GDP) o b ing o a compa able o m ia a nonlinea me hod using he logis ics
unc ion:
n
ij ¼1
ek�ð ij aÞ
a¼maxij ij þminij ij
2
ð1Þ
whe e n
ij –no malized alue o he eg ession Financial F eedom in e ms o he j-coun y o
he j-yea
ij
– he ac ual alue o he eg ession Financial F eedom in e ms o he i-coun y o he j-
yea ;
k–adjus men ac o ;
maxij ij ðminij ijÞ– he maximum ( espec i ely, minimum) alue o he eg ession Finan-
cial F eedom on he se o alues o he 15 conside ed coun ies om 2000 o 2021.
ccn
ij ¼1
ek�ðccijccaÞ
cca¼
max
ij ccij þmin
ij ccij
2
ð2Þ
whe e ccn
ij–no malized alue o he eg esan Cu ency in Ci cula ion (% GDP) in e ms o he
i-coun y o he j-yea ;
cc
ij
–ac ual alue o eg esan Cu ency in Ci cula ion (% GDP) in e ms o he i-coun y
o he j-yea ;
maxij ccijðminij ccijÞ– he maximum ( espec i ely, minimum) alue o he eg esan Cu -
ency in Ci cula ion (% GDP) on he se o alues o he 15 conside ed coun ies om 2000 o
2020.
2.2. Calcula ion o he in eg a ed indica o o he cha ac e is ic o he coun y’s inancial sys-
em p opensi y o ALM based on he alue in e se o he a i hme ic mean o he no malized
le els o eg esan Financial F eedom and Cu ency in Ci cula ion (% GDP):
PFSALMij ¼1 n
ij þccn
ij
2ð3Þ
whe e PFSALM
ij
—in eg a ed indica o o he cha ac e is ics o he coun y’s inancial sys em
p opensi y o ALM in e ms o he i-coun y o he j-yea .
3 s age. Analysis o he dynamic s abili y o he na ional inancial moni o ing sys em o
money launde ing based on a bina y app oach. A his s age, he au ho s p oposed o analyze
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he s abili y o he subjec s o na ional inancial moni o ing sys ems in di e en coun ies o
money launde ing and s udy he s abili y dynamics. When exceeding he alue o he in e-
g a ed indica o o he cha ac e is ics o he coun y’s inancial sys em p opensi y o he ALM
le el o 0.5, we can say abou he ins abili y, and o he wise—abou he s abili y.
I PFSALMij �0:5;uns able
I PFSALMij <0:5;s able ð4Þ
(
4 s age. Iden i ica ion o ele an ac o s o dynamic s abili y/ins abili y causali y o na ional
inancial moni o ing sys em subjec s o money launde ing based on he bina y app oach con-
s uc ing a ec o au o eg ession model (VAR) o dependence o he inancial sys em p open-
si y o ALM on eg esso s Index o economic eedom, Go e nmen In eg i y, Mone a y
Sec o c edi o he p i a e sec o (% GDP) conside ing ime lags o 15 conside ed coun ies.
To implemen his s age, au ho s p oposed o use he EViews p og am Quick / Es ima e VAR
/ VAR Type—Un es ic ed VAR / Endogenous Va iables—GI, IEF, MSCPS, PFSALM / Lag
In e al o Endogenous—1 and 2. Vec o au o eg ession model, he easibili y o which
occu s when i is necessa y o o malize in e connec ed ime-se ies sys ems and analyze he
dynamic e ec o andom pe u ba ions on he sys em o a iables, is as ollows:
y ¼A1�y 1þA2�y 2þ. . . þAp�y pþC�x þε ð5Þ
whe e y y ¼ ðy1 ;y2 ;. . . ;yn ÞT ec o o endogenous dimensional a iables n×1;
x x ¼ ðx1 ;x2 ;. . . ;xk ÞT ec o o exogenous dimensional a iables k×1;
A
1
, A
2
,. . .A
p
–cons an s, cons an coe icien s be o e lag endogenous a iables;
C–dimension ma ix n×ko coe icien s be o e exogenous dimensional a iables;
ε ε ¼ ðε1 ;ε2 ;. . . ;εn ÞTwhi e noise, dimension ec o n×1.
Conside ing an in eg al indica o o he coun y’s inancial sys em p opensi y o ALM
(PFSALM_ ) as an endogenous a iable wi h accoun o lag delays o one and wo yea s, and
Index o economic eedom, Go e nmen In eg i y, Mone a y Sec o c edi o p i a e sec o
(% GDP) as exogenous a iables wi h accoun o ime lags in one and wo yea s ( espec i ely,
GI_ , IEF_ , MSCPS_ ), he ec o au o eg ession (VAR) model (4) is as ollows:
PFSALM ¼a1�GI 1þa2�GI 2þa3�IEF 1þa4�IEF 2þa5�MSCPS 1þa6�MSCPS 2
þa7�PFSALM 1þa8�PFSALM 2þε ð6Þ
5 s age. De e mina ion o speci ic ea u es o dynamic s abili y/ins abili y o na ional inan-
cial moni o ing sys em subjec s o money launde ing based on he o maliza ion o depen-
dence o he coun y’s inancial sys em p opensi y o ALM on eg esso s Index o economic
eedom, Go e nmen In eg i y, Mone a y Sec o c edi o he p i a e sec o (% GDP) consid-
e ing ime lags o each o he 15 conside ed coun ies based on ec o au o eg ession (VAR).
In his s age, using ec o au o eg ession ools, we will de e mine o each coun y which exog-
enous a iables a e signi ican and quan i y hei impac , as well as which lag delays explain
he e lec ion o he coun y’s inancial sys em p opensi y o ALM. Like he p e ious s ep, he
EViews oolki Quick / Es ima e VAR / VAR Type—Un es ic ed VAR / Endogenous Va i-
ables—GI, IEF, MSCPS, PFSALM / Lag In e al o Endogenous—1 and 2 is used o imple-
men his s ep.
Sampling and da a collec ion
A s a is ical a ay o inpu indica o s was c ea ed in he o m o panel da a o he pe iod om
2000 o 2021 in e ms o 15 Eu opean Union coun ies: Aus ia, Belgium, Cyp us, Es onia,
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Analyzing he isualiza ion o he coun y’s inancial sys em p opensi y o ALM unde he
in luence o eg esso s GI , IEF , MSCPS p esen ed in Fig 3 depending on he ime lag; we
can s a e he ollowing: wi h inc easing ime lag, he in luence o i s p e ious alues o
Table 5. Vec o au o eg ession es ima es.
GI IEF MSCPS PFSALM
GI(-1) 0.971899 0.046996 0.078202 3.27E-05
(0.05875) (0.02048) (0.09593) (0.00053)
[16.5417] [2.29436] [0.81523] [0.06180]
GI(-2) -0.004621 -0.037300 -0.072046 -0.000616
(0.05857) (0.02042) (0.09562) (0.00053)
[-0.07890] [-1.82686] [-0.75347] [-1.16848]
IEF(-1) 0.086672 0.904273 -0.072932 -0.001997
(0.17654) (0.06155) (0.28823) (0.00159)
[0.49095] [14.6928] [-0.25303] [-1.25703]
IEF(-2) -0.051198 0.060806 0.050863 0.003076
(0.17851) (0.06223) (0.29146) (0.00161)
[-0.28680] [0.97704] [0.17451] [1.91486]
MSCPS(-1) 0.020937 0.003869 1.592687 1.98E-06
(0.02539) (0.00885) (0.04145) (0.00023)
[0.82472] [0.43720] [38.4260] [0.00867]
MSCPS(-2) -0.026693 -0.007654 -0.625140 5.60E-05
(0.02517) (0.00877) (0.04109) (0.00023)
[-1.06055] [-0.87233] [-15.2132] [0.24727]
PFSALM(-1) -4.798004 -0.561652 -4.823649 0.927782
(6.50876) (2.26911) (10.6267) (0.05857)
[-0.73716] [-0.24752] [-0.45392] [15.8393]
PFSALM(-2) 2.433461 1.210013 -0.465160 0.039683
(6.50896) (2.26918) (10.6270) (0.05858)
[0.37386] [0.53324] [-0.04377] [0.67745]
C 1.368914 1.819832 6.891387 -0.025273
(4.11889) (1.43594) (6.72480) (0.03707)
[0.33235] [1.26734] [1.02477] [-0.68181]
R-squa ed 0.943992 0.954360 0.977543 0.935332
Adj. R-squa ed 0.942398 0.953060 0.976904 0.933491
Sum sq. esids 3906.997 474.8520 10414.58 0.316423
S.E. equa ion 3.728794 1.299948 6.087904 0.033557
F-s a is ic 592.0197 734.4824 1528.999 508.0332
Log likelihood -788.5855 -482.9949 -930.7490 577.4886
Akaike AIC 5.500589 3.393068 6.481027 -3.920611
Schwa z SC 5.614482 3.506961 6.594920 -3.806718
Mean dependen 66.65517 69.28138 95.78273 0.559727
S.D. dependen 15.53633 6.000075 40.05894 0.130119
De e minan esid co a iance (do adj.) 0.819882
De e minan esid co a iance 0.722744
Log likelihood -1598.887
Akaike in o ma ion c i e ion 11.27508
Schwa z c i e ion 11.73066
No e: S anda d e o s in () & -s a is ics in []
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PFSALM g adually dec eases; wi h inc easing lag delay, he impac o he indica o Mone a y
Sec o c edi o he p i a e sec o (% GDP) on he esul an ea u e g adually inc eases, bu
wi h a p obabili y o 0.95 i is no s a is ically con i med; in he sho e m (wi h a lag o one
yea ) indica o s GI , IEF ac as a s imula o and des imula o , espec i ely, o he coun y’s
inancial sys em p opensi y o ALM, o u he ime lags, s a ing om 2 yea s, ends o
hese indica o s change d ama ically o he opposi e.
I is impo an o u he use o he cons uc ed ec o au o eg ession model (VAR) esul s
o he dependence o he coun y’s inancial sys em p opensi y o ALM on eg esso s Index o
economic eedom, Go e nmen In eg i y, Mone a y Sec o c edi o p i a e sec o (% GDP)
o p o e he accu acy and adequacy o s a e he alues gi en in Table 5: R-squa ed a he le el
o 0.9353, i.e., he a ia ion o he e ec i e ea u e o PFSALM by 93.53% is explained by he
a ia ion o ac o GI
, IEF
, MSCPS
conside ing he ime lags o e lec ion; F-s a is ic a he
le el o 508.03, which is signi ican ly highe han he c i ically accep able le el, and indica es
he s a is ical signi icance o he ob ained model (7); Akaike in o ma ion c i e ion (11.28) and
Schwa z c i e ion (11.73), which indica e a ai ly good i o s a is ics o he model; dis ibu ion
o esiduals o bo h eg ession and eg esso s (Fig 4).
The ob ained esul s a e sys ema ized in abula o m (Table 6).
Based on he da a in Table 6, we w i e au o eg ession models o he dependence o he
coun y’s inancial sys em p opensi y o ALM on eg esso s Index o economic eedom, Go -
e nmen In eg i y, Mone a y Sec o c edi o p i a e sec o (% GDP) conside ing ime lags:
–Aus ia:
PFSALM ¼ ð5:651056e05Þ�GI 1þ0:000719 �GI 20:001440 �IEF 10:003552
�IEF 20:000591 �MSCPS 1þ0:000733 �MSCPS 20:212796
�PFSALM 1þ0:3635116 �PFSALM 2þ0:653299 ð8Þ
Fig 3. Visualiza ion o he e lec ion o he coun y’s inancial sys em p opensi y o ALM unde he in luence o
eg esso s GI
, IEF
, MSCPS
depending on he ime lag.
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–Belgium:-
PFSALM ¼0:000638 �GI 10:003335 �GI 20:007812 �IEF 10:000621 �IEF 2
0:000639 �MSCPS 10:003145 �MSCPS 20:087483 �PFSALM 1
0:108417 �PFSALM 2þ1:523398 ð9Þ
–Cyp us:
PFSALM ¼0:004733 �GI 10:001314 �GI 20:025313 �IEF 1þ0:0153970108373
�IEF 20:001136 �MSCPS 1þ0:001357 �MSCPS 2þ0:497073
�PFSALM 10:176545 �PFSALM 2þ0:791541 ð10Þ
Fig 4. Dis ibu ion o he coun y’s inancial sys em p opensi y balances o ALM, o eg esso s Index o economic eedom, Go e nmen
In eg i y, Mone a y Sec o c edi o p i a e sec o (% GDP).
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Table 6. Vec o au o eg ession es ima es.
PFSALM PFSALM PFSALM PFSALM PFSALM PFSALM PFSALM PFSALM
Aus ia Belgium Cyp us Es onia Finland F ance G eece I eland
GI(-1) 5.65E-05 0.000638 0.004734 0.004220 -0.000385 -0.006810 -0.002407 0.003271
(0.00075) (0.00364) (0.00440) (0.00638) (0.00349) (0.00369) (0.00524) (0.00200)
[0.07536] [0.17548] [1.07487] [0.66108] [-0.11025] [-1.84654] [-0.45980] [1.63872]
GI(-2) 0.000719 -0.003335 -0.001314 0.003726 0.000145 -0.005014 -0.002820 0.000828
(0.00085) (0.00240) (0.00383) (0.00847) (0.00333) (0.00342) (0.00506) (0.00181)
[0.84678] [-1.39095] [-0.34293] [0.44013] [0.04357] [-1.46708] [-0.55697] [0.45726]
IEF(-1) -0.001440 -0.007813 -0.025314 0.007269 -0.004605 -0.000369 -0.018789 -0.001471
(0.00281) (0.00628) (0.02097) (0.00891) (0.00809) (0.00798) (0.01123) (0.00382)
[-0.51195] [-1.24391] [-1.20721] [0.81585] [-0.56913] [-0.04627] [-1.67366] [-0.38550]
IEF(-2) -0.003553 -0.000621 0.015397 -0.009360 0.010524 -0.003863 0.002680 -0.005749
(0.00233) (0.00755) (0.02185) (0.01989) (0.00801) (0.00847) (0.00741) (0.00602)
[-1.52436] [-0.08231] [0.70479] [-0.47055] [1.31396] [-0.45582] [0.36143] [-0.95579]
MSCPS(-1) -0.000591 -0.000639 -0.001137 0.003549 -0.005005 -0.002919 0.001440 -0.001424
(0.00088) (0.00438) (0.00104) (0.00217) (0.00462) (0.00353) (0.00274) (0.00042)
[-0.67403] [-0.14585] [-1.09702] [1.63297] [-1.08231] [-0.82775] [0.52620] [-3.38847]
MSCPS(-2) 0.000733 -0.003146 0.001358 -0.004624 0.002978 -0.009013 -0.003482 0.001166
(0.00076) (0.00464) (0.00117) (0.00363) (0.00394) (0.00543) (0.00250) (0.00038)
[0.96750] [-0.67744] [1.15660] [-1.27284] [0.75622] [-1.65913] [-1.39423] [3.06521]
PFSALM(-1) -0.212797 -0.087484 0.497074 0.580349 0.293588 0.164283 0.157634 0.071502
(0.30363) (0.32980) (0.57333) (0.84337) (0.46855) (0.35593) (0.33464) (0.30613)
[-0.70085] [-0.26526] [0.86700] [0.68813] [0.62659] [0.46157] [0.47106] [0.23357]
PFSALM(-2) 0.363512 -0.108417 -0.176545 0.457266 0.141163 -0.311606 0.403917 -0.047945
(0.29461) (0.30503) (0.45034) (0.58523) (0.16635) (0.29701) (0.44918) (0.33391)
[1.23386] [-0.35543] [-0.39203] [0.78134] [0.84861] [-1.04915] [0.89923] [-0.14359]
C 0.653299 1.523398 0.791542 -0.295793 -0.023503 2.735708 1.597071 0.691092
(0.37993) (0.54955) (2.67818) (0.90372) (1.35445) (0.76360) (0.61508) (0.56856)
[1.71951] [2.77207] [0.29555] [-0.32731] [-0.01735] [3.58264] [2.59654] [1.21551]
R-squa ed 0.868002 0.752176 0.830119 0.768069 0.913797 0.979090 0.760286 0.934204
F-s a is ic 6.575861 3.035123 4.886482 2.069771 10.60055 46.82515 3.171645 14.19845
Akaike AIC -7.045885 -4.521473 -3.093927 -3.604539 -4.832001 -4.368160 -2.929851 -5.030452
Schwa z SC -6.604772 -4.080361 -2.652815 -3.193716 -4.390888 -3.927047 -2.488738 -4.589339
PFSALM PFSALM PFSALM PFSALM PFSALM PFSALM PFSALM
I aly La ia Mal a Ne he lands Po ugal Slo ak Republic Spain
GI(-1) 0.000896 0.001836 0.009100 0.001407 -0.006925 -0.000699 -0.000189
(0.00784) (0.00257) (0.00695) (0.00216) (0.00552) (0.00049) (0.00503)
[0.11436] [0.71497] [1.31003] [0.65170] [-1.25554] [-1.41678] [-0.03754]
GI(-2) 0.000444 0.001129 -0.001670 0.001794 0.000148 -1.60E-05 0.000406
(0.00758) (0.00501) (0.00212) (0.00219) (0.00574) (0.00077) (0.00439)
[0.05865] [0.22528] [-0.78901] [0.81945] [0.02570] [-0.02088] [0.09246]
IEF(-1) 0.030836 0.002391 0.006787 0.001091 -0.003155 0.009353 7.49E-05
(0.02575) (0.01591) (0.01162) (0.00355) (0.01073) (0.00322) (0.00654)
[1.19762] [0.15026] [0.58399] [0.30714] [-0.29395] [2.90858] [0.01144]
IEF(-2) 0.013819 0.006496 -0.001369 -0.002872 -0.005807 0.004201 0.010227
(0.02746) (0.01834) (0.01388) (0.00323) (0.01023) (0.00296) (0.00752)
[0.50316] [0.35410] [-0.09863] [-0.88884] [-0.56769] [1.41935] [1.36021]
MSCPS(-1) -0.006412 -0.002225 0.004277 -8.03E-05 -0.004423 -0.005064 -0.000760
(Con inued)
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–Es onia:
PFSALM ¼0:004219 �GI 1þ0:003726 �GI 2þ0:007268 �IEF 10:009360 �IEF 2
þ0:003549 �MSCPS 10:004624 �MSCPS 2þ0:580348 �PFSALM 1
þ0:457266 �PFSALM 20:295792 ð11Þ
–Finland:
PFSALM ¼  0:000385 �GI 1þ0:000145 �GI 20:004604 �IEF 1þ0:010524 �IEF 2
0:005004 �MSCPS 1þ0:002977 �MSCPS 2þ0:293587 �PFSALM 1
þ0:141162 �PFSALM 20:023503 ð12Þ
–F ance:
PFSALM ¼  0:00681 �GI 10:005013 �GI 20:000369 �IEF 10:003862 �IEF 2
0:002918 �MSCPS 10:009013 �MSCPS 2þ0:164283 �PFSALM 1
0:311605 �PFSALM 2þ2:735707 ð13Þ
–G eece:
PFSALM ¼  0:002407 �GI 10:002820 �GI 20:018789 �IEF 1þ0:002679 �IEF 2
þ0:001439 �MSCPS 10:003481 �MSCPS 2þ0:157633 �PFSALM 1
þ0:403917 �PFSALM 2þ1:597070 ð14Þ
Table 6. (Con inued)
PFSALM PFSALM PFSALM PFSALM PFSALM PFSALM PFSALM PFSALM
Aus ia Belgium Cyp us Es onia Finland F ance G eece I eland
(0.01059) (0.00377) (0.00241) (0.00113) (0.00273) (0.00137) (0.00133)
[-0.60545] [-0.59083] [1.77343] [-0.07133] [-1.61919] [-3.69149] [-0.56986]
MSCPS(-2) 0.011610 0.005397 -0.004413 0.001762 0.004174 0.004707 -0.000106
(0.00977) (0.00404) (0.00243) (0.00130) (0.00297) (0.00134) (0.00142)
[1.18809] [1.33625] [-1.81738] [1.35481] [1.40455] [3.52293] [-0.07485]
PFSALM(-1) 0.324963 0.538334 0.410129 -0.247120 0.501151 -0.625538 0.397570
(0.34737) (0.33048) (0.42438) (0.28628) (0.29570) (0.13211) (0.40756)
[0.93549] [1.62895] [0.96643] [-0.86321] [1.69480] [-4.73515] [0.97549]
PFSALM(-2) 0.097402 0.242120 0.629759 -0.361859 0.825162 0.067340 0.091902
(0.35663) (0.36110) (0.27331) (0.26677) (0.39750) (0.07415) (0.44241)
[0.27312] [0.67051] [2.30423] [-1.35646] [2.07589] [0.90820] [0.20773]
C -2.929223 -0.871021 -0.748631 0.247175 0.817181 -0.267648 -0.399508
(2.51439) (1.79355) (1.77579) (0.42726) (0.75813) (0.38033) (0.71240)
[-1.16498] [-0.48564] [-0.42158] [0.57851] [1.07790] [-0.70372] [-0.56079]
R-squa ed 0.459917 0.838112 0.945691 0.520799 0.905987 0.982328 0.794934
F-s a is ic 0.851566 5.824247 8.706500 1.086808 9.636870 20.84468 3.876475
Akaike AIC -2.337655 -2.965258 -4.668220 -5.808951 -3.486648 -7.687374 -4.211487
Schwa z SC -1.896542 -2.520073 -4.277101 -5.367838 -3.045535 -7.323694 -3.770374
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–I eland:
PFSALM ¼0:003270 �GI 1þ0:000827 �GI 20:001471 �IEF 10:005749 �IEF 2
0:001423 �MSCPS 1þ0:001165 �MSCPS 2þ0:071502 �PFSALM 1
0:047944 �PFSALM 2þ0:691092 ð15Þ
–I aly:
PFSALM ¼0:000896 �GI 1þ0:000444 �GI 2þ0:030836 �IEF 1þ0:013818 �IEF 2
0:006411 �MSCPS 1þ0:011610 �MSCPS 2þ0:324963 �PFSALM 1
þ0:097402 �PFSALM 22:929222 ð16Þ
–La ia:
PFSALM ¼0:001836 �GI 1þ0:001129 �GI 2þ0:002391 �IEF 1þ0:006496 �IEF 2
0:002225 �MSCPS 1þ0:005397 �MSCPS 2þ0:538334 �PFSALM 1
þ0:242120 �PFSALM 20:871021 ð17Þ
–Mal a:
PFSALM ¼0:0091 �GI 10:001669 �GI 2þ0:006787 �IEF 10:001369 �IEF 2
þ0:004277 �MSCPS 10:004413 �MSCPS 2þ0:410129 �PFSALM 1
þ0:629758 �PFSALM 20:74863 ð18Þ
–Ne he lands:
PFSALM ¼0:001407 �GI 1þ0:001793 �GI 2þ0:00109 �IEF 10:002872 �IEF 2
 ð8:031951e05Þ�MSCPS 1þ0:001762 �MSCPS 20:247119
�PFSALM 10:361858 �PFSALM 2þ0:247174 ð19Þ
–Po ugal:
PFSALM ¼  0:006924 �GI 1þ0:000147 �GI 20:003155 �IEF 10:005807 �IEF 2
0:004423 �MSCPS 1þ0:004173 �MSCPS 2þ0:50115 �PFSALM 1
þ0:825161 �PFSALM 2þ0:81718 ð20Þ
–Slo ak Republic:
PFSALM ¼  0:000699 �GI 1 ð1:60076e05Þ�GI 2þ0:009352 �IEF 1þ0:004201
�IEF 20:005064 �MSCPS 1þ0:004706 �MSCPS 20:625538
�PFSALM 1þ0:067339 �PFSALM 20:267647 ð21Þ
–Spain:
PFSALM ¼  0:000188 �GI 1þ0:000405 �GI 2þð7:486275e05Þ�IEF 1þ0:010227
�IEF 20:000759 �MSCPS 10:000105 �MSCPS 2þ0:397569
�PFSALM 1þ0:091901 �PFSALM 20:399508 ð22Þ
S age 5. De e mina ion o speci ic ea u es o dynamic s abili y / ins abili y o na ional
inancial moni o ing sys em subjec s o money launde ing based on dependence o coun y’s
inancial sys em p opensi y o ALM on eg esso s Index o economic eedom, Go e nmen
In eg i y, Mone a y Sec o c edi o p i a e sec o (% GDP) conside ing ime lags o each o
he 15 coun ies based on au o eg ession ec o (VAR). We will de ine o each coun y which
exogenous a iables a e signi ican and quan i y hei impac , as well as which lag delays
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explain he e lec ion o he coun y’s inancial sys em p opensi y o ALM, based on Tables 6
and 7.
The analysis o Table 7 allows us o conclude ha o each coun y, di e en exogenous a -
iables a e signi ican . A he same ime, despi e i , we can iden i y some common ends. In
pa icula , he in luen ial exogenous a iable o mos s udied coun ies ( o ele en o he i -
een coun ies) was he Mone a y Sec o c edi o he p i a e sec o (% GDP) wi h a lag o 2
yea s (MSCPS (-2)). I means ha o ele en coun ies, changes in he olume o MSCPS lead
o a change in he s abili y o he inancial moni o ing sys em in 2 yea s.
Fo se en coun ies om he selec ed lis , he exogenous a iable MSCPS (-1) has a signi i-
can impac , i.e., he change in he olume o MSCPS leads o a change in he inancial moni-
o ing sys em s abili y in one yea .
Exogenous a iables GI (-1) and PFSALM (-2) we e in luen ial o he six coun ies on he
lis . I means ha a change in he alue o he Go e nmen In eg i y indica o o a pa icula
coun y leads o a change in he s abili y o i s inancial moni o ing sys em wi h a lag o one
yea . The a iable PFSALM (-2) means a signi ican impac o he s abili y indica o in he
inancial moni o ing sys em o he pe iod −2 (wi h a lag o 2 yea s) on he cu en s abili y
indica o .
Fo nine o he i een su eyed coun ies, only wo o hese ac o s a ec he inancial mon-
i o ing sys em s abili y, and hese ac o s a e di e en o each coun y. Such coun ies include
Aus ia (0.41), Belgium (0.4), Es onia (0.37), Finland (0.33), G eece (0.52), I aly (0.6), La ia
(0,43), he Ne he lands (0.34) and Spain (0.36). Nine o hese coun ies a e cha ac e ized by a
high inancial moni o ing sys em s abili y, excep o G eece and I aly.
The la ges numbe o exogenous a iables ha ha e a signi ican impac on he inancial
moni o ing sys em s abili y is peculia o Po ugal (0.46) ( i e ac o s) and Slo ak Republic
(0.33) (6 ac o s).
Thus, we can conclude ha each coun y has i s own unique se o exogenous ac o s ha
signi ican ly impac he inancial moni o ing sys em s abili y. Thei iden i ica ion is an impo -
an aspec since i will allow public au ho i ies o unde s and he in luen ial mechanism o
ce ain decisions on he exis ing inancial moni o ing sys em s abili y in he coun y, and hus
will inc ease i s e iciency.
Table 7. S a is ically signi ican coe icien s be o e he in luen ial indica o s o he coun y’s inancial sys em p opensi y o ALM.
Aus ia Belgium Cyp us Es onia Finland F ance G eece I eland
GI(-1) 0.004734 -0.006810 0.003271
GI(-2) -0.003335 -0.005014
IEF(-1) -0.007813 -0.025314 -0.018789
IEF(-2) -0.003553 0.010524
MSCPS(-1) -0.001137 0.003549 -0.005005 -0.001424
MSCPS(-2) 0.001358 -0.004624 -0.009013 -0.003482 0.001166
PFSALM(-2) 0.363512 -0.311606
I aly La ia Mal a Ne he lands Po ugal Slo ak Republic Spain
GI(-1) 0.009100 -0.006925 -0.000699
IEF(-1) 0.030836 0.009353
IEF(-2) 0.004201 0.010227
MSCPS(-1) 0.004277 -0.004423 -0.005064
MSCPS(-2) 0.011610 0.005397 -0.004413 0.001762 0.004174 0.004707
PFSALM(-1) 0.538334 0.501151 -0.625538
PFSALM(-2) 0.629759 -0.361859 0.825162 0.091902
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Conclusion
As a esul o he s udy, au ho s p oposed a me hod o assess he in eg a ed indica o o he
cha ac e is ics o he coun y’s inancial sys em p opensi y o ALM. I allowed o adequa ely
assess he exis ing inancial moni o ing sys ems o selec ed coun ies o he Eu opean Union.
The lis o i een selec ed coun ies was di ided in o coun ies cha ac e ized by he na ional
inancial moni o ing sys em s abili y o money launde ing (Aus ia, Belgium, Es onia, Finland,
F ance, I eland, Ne he lands, Slo ak Republic, Spain) and coun ies o which such s abili y is
uncha ac e is ic (Mal a, G eece, Cyp us, Po ugal, I aly, La ia). Simila da a we e ob ained
a e analyzing he dynamic s abili y o he na ional inancial moni o ing sys em subjec s o
money launde ing using a bina y app oach.
De e mining he coe icien s o he ec o au o eg ession model (VAR) o he dependence
o he coun y’s inancial sys em p opensi y o ALM on eg essions GI_ , IEF_ , MSCPS_
wi h ime lags allowed o s a e ha s a is ically signi ican in luence on he coun y’s inancial
sys em p opensi y o ALM is exe ed by exogenous a iables such as GI_( -2), IEF_ ( -1), IEF_
( -2), i.e., Go e nmen In eg i y wi h a lag o 2 yea s and he Index o economic eedom wi h
he ime delays o he eg ession’s e lec ion unde he in luence o his eg esso o 1 and 2
yea s. In addi ion o hese exogenous a iables, a signi ican impac on he coun y’s inancial
sys em p opensi y o ALM has and PFSALM_ ( -1), i.e., he p e ious alue. The calcula ed
alue o he S uden ’s c i e ion con i med he s a is ical signi icance o hese ac o s.
In addi ion, ec o au o eg ession models (VAR) o he dependence o he coun y’s inan-
cial sys ems p opensi y o ALM on he eg esso s GI_ , IEF_ , MSCPS_ , we e buil , wi h ime
lags in gene al and o each s udied coun y sepa a ely. The gene al ec o au o eg ession
(VAR) model shows ha he cu en alue o he coun y’s inancial sys em p opensi y o
ALM by 92.78% is de e mined by i s p e ious alue. The Go e nmen In eg i y indica o ac s
as a des imula o o he PFSALM pe o mance indica o , i.e., when he le el o Go e nmen
In eg i y inc eases by 1%, he coun y’s inancial sys em p opensi y o ALM will dec ease by
0.000616 sha es pe uni wi h a lag o wo yea s. The impac o indica o —Index o economic
eedom on he pe o mance is speci ic—when his indica o inc eases by 1% o lag delay in
one yea , he alue o PFSALM will dec ease by 0.001997 uni s, and o lag delay in wo yea s
will change he end and inc ease by 0.003076 sha es pe uni , espec i ely. I gene ally indi-
ca es he na u e o his indica o as a s imula o bu wi h he exis ing in lec ion poin o e lec-
ion. The accu acy and adequacy o he ob ained model was con i med by he ollowing: R-
squa ed a he le el o 0.9353, i.e., he e ec i e ea u e a ia ion o PFSALM by 93.53% is
explained by he a ia ion o ac o ea u es GI_ , IEF_ , MSCPS_ wi h ime lags o e lec ion;
F-s a is ic a he le el o 508.03, ha is signi ican ly highe han he c i ically accep able le el
and indica es he s a is ical signi icance o he ob ained model (7); Akaike in o ma ion c i e-
ion (11.28) and Schwa z c i e ion (11.73), which indica e a ai ly good i o s a is ics o he
model; dis ibu ion o esiduals o bo h eg esso and eg esso s.
Signi ican exogenous a iables and hei quan i a i e impac on he in eg a ed indica o o
he cha ac e is ic o he coun y’s inancial sys em p opensi y o ALM o each o he s udied
coun ies we e also iden i ied. Since each coun y has i s unique se o exogenous ac o s ha
ha e a signi ican impac on he sus ainabili y o i s inancial moni o ing sys em, iden i ying
and assessing hei impac will help go e nmen s o conside such exogenous ac o s in an i-
money launde ing policies and ensu e i s e ec i eness g ow h in he u u e.
The a icle was p epa ed based on he esul s o a esea ch unded by he Na ional Resea ch
Fund o Uk aine "Op imiza ion and au oma ion o inancial moni o ing p ocesses o inc ease
in o ma ion secu i y in Uk aine." ( egis a ion numbe : 0120U104810).
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Au ho Con ibu ions
Concep ualiza ion: Yu iy Bilan.
Da a cu a ion: Olha Kuzmenko.
In es iga ion: E genia Bonda enko.
Me hodology: Bea a Ga u o a.
Resou ces: E genia Bonda enko.
So wa e: Olha Kuzmenko.
Valida ion: Hanna Ya o enko.
Visualiza ion: Olha Kuzmenko.
W i ing – o iginal d a : Yu iy Bilan, E genia Bonda enko, Hanna Ya o enko.
W i ing – e iew & edi ing: Olha Kuzmenko, Bea a Ga u o a.
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