





































Azerbaijan State University of Economics UNEC


AGORA International Journal of Economical Sciences, http://univagora.ro/jour/index.php/aijes 

ISSN 2067-3310, E-ISSN 2067-7669 

Vol. 17, No. 2 (2023), pp. 54-67 

 

54 

 

GREY FLOWLESS, OR PREDICTING CAPITAL OUTFLOWS IN 

CRYPTOCURRENCY 
 

G. J. AMRAHOVA 
 

Gunel Jannataly Amrahova 
Azerbaijan State University of Economics (UNEC), Azerbaijan 

https://orcid.org/0000-0002-6710-5961, E-mail: gunel.amrahova.c@sabah.edu.az  

 

Abstract: Globalization and irrational capital distribution have fuelled global financial 

crises. Illicit transactions on a global scale worsen financial challenges, limiting government 

spending on public services. Amid these challenges, blockchain technology and 

cryptocurrencies have emerged as potential solutions. Due to anonymity public identification 

through “keys”, some scholars argue that cryptocurrencies create an opportunity for misuse 

as an easy tool for money laundering, tax evasion and illegal activities. This study investigates 

the relationship between illicit finance flows and cryptocurrency markets, utilizing grey 

systems theory and grey relational analysis. Drawing samples from 41 states with the highest 

cryptocurrency trade volumes, the research reveals nuanced dynamics within the 

cryptocurrency market, shedding light on the connections between cryptocurrencies, shadow 

activities, and capital outflows. The findings contribute valuable insights to the ongoing 

discourse on the impact of cryptocurrencies on global financial stability. The intricate 

exploration of these interconnections underscores the need for a comprehensive understanding 

of the role cryptocurrencies play in shaping the contemporary financial landscape.  

Keywords: Globalization, Illicit finance flows, Cryptocurrency markets, Grey systems 

theory, Financial stability 

 

 

INTRODUCTION  

Even though relevant national policies and international regulation are in place, lack of 

transparency and corruption, especially in developing countries, reduce effectiveness of these 

practices in stimulating financial stability, resulting in tax evasion and growth of criminal 

activities (Fanusie & Robinson, 2018). This means, that centralized financial systems and state 

power have proven insufficiency in combating illicit activities. Therefore, the need in searching 

for new systems and tools which could better deal with existing challenges greatly increases. 

One of such innovative systems was brought to economic world through invention of 

blockchain systems and its application in trade transactions through cryptocurrencies, offering 

higher efficiency and transparency for financial transactions. According to European 

Commission, cryptocurrencies contribute to the establishment of ‘trust between two mutually 

unknown and unrelated parties to such extent that sensitive and secure transactions can be 

performed with full confidence over an open environment, such as the Internet’. This solution 

has introduced a new economy, namely ‘crypto economy’.  

In the early 1990s physicists Stuart Haber and W Scott Stornetta published the first 

paper to outline the use of a chain of cryptographically secured blocks to preserve the integrity 

of past information to protect it. This paper was followed by the subsequent seminal whitepaper 

“Bitcoin: A Peer-To-Peer Electronic Cash System” in 2008 by Satoshi Nakamoto (a group of 

people), where the scholars described the financial transactions that can be made through the 

use of blockchain systems, adding practical value to previous research in this area. 

https://orcid.org/0000-0002-6710-5961
mailto:gunel.amrahova.c@sabah.edu.az


Gunel Jannataly AMRAHOVA 

 

55 

 

Cryptocurrency is the largest primary application of blockchain technology and is recently 

experiencing an unprecedented rise with market capitalization surpassing in 2018 $335b 

(Bright, Koskinen, & Malm, 2019). 

In academic literature, opinions on the long-term existence of cryptocurrencies are 

separated, however several key applications have been identified by several sources. For 

instance, report of the UK Government (Lerner, 2003) states that blockchains have the capacity 

to ‘reform our financial markets, supply chains, consumer and business-to-business services, 

and publicly-held registers’. 

One of the key scientific opinions in this area is the analysis of Babbitt and Dietz 

(Babbitt and Dietz, 2014), which define cryptoeconomy regardless of ‘geographic location, 

political structure or legal system, but using cryptographic techniques to constrain behavior in 

place of using trusted third parties’. Foundational principles of the blockchain technology are 

cryptography, smart contracts, distributed consensus and system architectures. 

As a novel financial concept cryptocurrency is anticipated to remove inefficiencies 

identified in the financial industry, such as overcentralisation, too many intermediaries, 

unwillingness to delete or change historic records, tracking issues and others, adding more 

practical value of the current research due to the need to get a more informed perspective of 

how well cryptocurrency is performing with regard to transparency of financial transactions. 

Cryptocurrency has demonstrated tremendous growth over last years, and the volume 

of trade transactions in cryptocurrencies multiplies every quarter (Broséus et al., 2016). The 

key advantages of cryptocurrency comparing to its precedents is its decentralized, or distributed 

nature. Unlike other forms of money, crypto money is not a promissory note issued by states 

or their central banks, rather it is a speculative code issued by individual miners (Brown, 2016; 

Glaser et al., 2014). Use of cryptographic signatures and smart contracts and peer-to-peer 

networks, which form the heart of cryptocurrency distributed consensus systems allows from 

the one hand, confidentiality for the financial transactions, while on the other hand, 

transparency and credibility in financial interactions and operations. 

Cryptocurrency is a revolutionary form of money, and by its functions and nature 

significantly differ from all other forms of money ever existed. Cryptocurrency deliver key 

monetary functions deriving from its nature as described by the main relevant theories of 

commodity, debt (credit) and fiat theories of money.  

Cryptocurrencies proved successfully delivering store of value function of money, 

ensuring transparency of transactions made in this currency and parties of these transactions 

accountable for their content. However, on the other hand, even though trade transaction both 

on national and international level continue to expand by means of cryptocurrencies which can 

be used for provisioning trade supply, the integrated distributed ledger still fails with carrying 

mean of exchange function, compared to its alternatives, namely national fiat currencies. 

Although, crypto market continues to grow and saturate both from technical and 

economic viewpoints with progressively increasing number of currencies mined over the world 

(Mukhopadhyay et al., 2016; Corbet et al., 2020), number of arguments and misinterpretations 

around the concept of cryptocurrencies also boosts in the academic literature (Lee et al., 2019; 

Farrugia et al., 2020; Alvarez, 2018). Majority of debates around the impact of cryptocurrency 

are posessed on the state of shadow and illegal activities and capital outflows from one country 

to another. Further development and expansion of trade transactions using cryptocurrency 

creates the concerns around the nature and governance issues associated with this currency.  



GREY FLOWLESS, OR PREDICTING CAPITAL OUTFLOWS IN CRYPTOCURRENCY 

 

56 

 

Existing debates among economists on governance and regulation of cryptocurrency 

can be resolved by describing the technical nature of this phenomenon (Irwin & Turner, 2018; 

(Yermack, 2013; Brière, Oosterlinck, & Szafarz, 2013; Brito, Shadab, & Castillo, 2014; 

Buchholz et al., 2012). Unlike the centralized or decentralized systems, shared and distributed 

data structures or ledgers, or blockchains, can securely store digital transactions without using 

a central point of authority, blockchain platforms instead of a single trusted center management, 

allow each individual network member within a distributed network of digital users to hold a 

copy of the records’ chain and partner on the valid state of the ledger with consensus, 

safeguarding integrity of the ledger. New transactions are linked to previous transactions by 

cryptography which makes blockchain networks resilient and secure.  

Despite overarching interest of international agencies towards revealing and measuring 

trade using cryptocurrencies, assessment of cryptomarket maturity by states remains 

challenging task for global cryptocurrency researchers and scholars (Lee et al., 2019, Farrugia 

et al., 2020; Alvarez, 2018; Hileman & Rauchs, 2017). Very few methodologies consider 

cryptocurrency markets maturity in a complex context, paying attention to mainly volume and 

number of transactions made in these endogenous currencies. However, while internet users 

continue safely to transact on the internet, international organizations should ensure crime 

finance, including three-dimensional rendering (source of funds, ways of transferring the funds 

and use of funds) is not flowing through this channel (Jacquez, 2016).  

According to existing views on cryptocurrencies (Brezo and Bringas, 2012), due to 

anonymity public identification through “keys”, cryptocurrencies create an opportunity for 

misuse as an easy tool for money laundering, tax evasion and illegal activities. In the broadest 

form, illicit financial flow cover illicit activities, in particular the provision of services or the 

production, sale, possession or use of goods forbidden by law, including the illicit production 

and trafficking of drugs, the illicit manufacturing of and trafficking in firearms, trafficking in 

persons, and money laundering, as defined in the relevant international treaties. (C.Williams, 

2018). Many researchers (Farrugia et al., 2020; Brown, 2016; Bichler, Malm, & Cooper, 2017) 

define international financial flows in four main categories in accordance with the activities, 

generating these transactions, and point out inefficiencies of current financial systems and need 

of decentralized systems. According to analysis of Decker et al, financial institutions establish 

trust through audit process (Möser & Narayanan, 2019), proposing a software-based audit of 

bitcoin exchanges. This way financial institutions eliminate state and private intermediary 

actors, such as auditors, replacing latter with software relying on trusted computing.  

There are studies that have been already carried out about illicit financial flows within 

crypto market (Liu & Tsyvinski, 2018; Liu, Tsyvinski, & Wu, 2019; Brezo & Bringas, 2012), 

however, majority of them are based on application of mathematical and statistic techniques to 

estimate cryptocurrency market maturity. Furthermore, the empirical results of research carried 

up to date lacks consideration of many supplementary factors, impacting market maturity and 

development. Therefore, the purpose of the research is to provide scientifically based and 

empirically proven evidence to hypothesis on existence of relation between the two concepts, 

one of which reflects an economic power (Liu & Tsyvinski, 2018), while another one impedes 

international equality and sustainability.  

The current research is grounded on two fundamental theories. First, quantity theory of 

money defines the key factors, impacting scale and maturity of cryptocurrency as a broad term, 

introduced as money and successfully delivering key money functions, such as store of value, 



Gunel Jannataly AMRAHOVA 

 

57 

 

and ideally medium of exchange. Quantity theory of money describes the value of money using 

its key parts, namely money supply, its velocity and demand for money. According to the 

quantity theory of money, cryptocurrency market maturity was identified based on the 

cryptocurrency market cap (money supply), cryptocurrency velocity (transaction volume 

divided by cryptocurrency market cap) and demand for cryptocurrency.  

To analyse cryptocurrency components in the most inclusive way, we used a grey 

system theory approach, which is broadly applied to decentralized systems’ research (Broséus 

et al., 2016; Bichler & Malm, 2013; Farell, 2015), such as cryptocurrency. Being 

interdisciplinary, grey system approach can explain the complex relationships between the 

elements of cryptocurrency as a multifaceted system of different elements, since decentralized 

systems need "a whole systems perspective, including levels, spheres, sectors and functions 

and seeing the community level as the entry point at which holistic definitions of development 

goals are from the people themselves and where it is most practical to support them. It involves 

seeing multi-level frameworks and continuous, synergistic processes of interaction and 

iteration of cycles as critical for achieving wholeness in a decentralized system and for 

sustaining its development" (UNDP, 2021).  

As major part of grey system theory, grey relational analysis was applied during the 

research since the cryptocurrency system is comprised from the elements with novel features 

and limited information available. Application of grey relational analysis is driven by several 

reasons. First, the current research deals with huge amount of uncertain information, especially 

the one, related to illicit capital flows (Kuo, Yang, & Huang, 2008). Second, being very 

complex and uncertain, illicit finance flows require examination of its elements before making 

assessment and predictions of the system as a whole. Through application of grey relational 

analysis, a complex umbrella series with observable and latent variables in face of 

cryptocurrency was produced and analysed and further examined to have a link with the level 

of illicit financial flows (Holz et al., 2020), constructed by GFI (GFI, 2018). After grey 

relational research is completed and grades are obtained, we are comparing the grey 

coefficients against globally applied illicit finance flow estimations in order to reveal the 

possible correlation between these two concepts. 

Finally, we conducted relational analysis of different social, political and economic 

exposure and development criteria of sustainable development of nations in order to answer the 

fundamental question of the research and examine the relationship between cryptomarket 

maturity of the countries and the state of their illicit finance flows. 

 

RESEARCH METHODOLOGY 

The current study, based on grey systems theory application, aims to scientifically 

reveal the grey relational coefficients of different criteria, which describe the maturity of the 

cryptocurrency, and examine impact of cryptocurrencies on the state of illicit trade with the 

existing information gaps. Selection of criteria of cryptocurrency system is grounded on the 

key components of currency system in accordance with the quantitative theory of money.  

The theory of grey relational analysis is nowadays broadly used in many different areas, 

including finance markets performance analysis, money markets and economic analysis. It was 

first introduced (Deng, 1982; Deng, 1989) as a response to gaps and incompleteness of research 

data related to the studies of actual problems, including illicit and illegal economic activities. 

The scholar introduced grey elements and relations to express the level of trust to the source of 



GREY FLOWLESS, OR PREDICTING CAPITAL OUTFLOWS IN CRYPTOCURRENCY 

 

58 

 

research information in any area and explained the behavior of mechanisms using grey 

clustering. Linking social and natural science through framing mathematical models for 

quantitative analysis with uncertain and incomplete information, grey relational analysis aims 

to help with decision making using multiple variables and factors. Unlike the other 

methodologies, applied to measure and estimate cryptomarket maturity, our methodology 

involves grey system theory and grey system analysis, which is not a statistical method. The 

reason for using grey system is a significant complexity of statistical methods and quite a large 

number of causes of illicit financial flows, which obscures the calculations and at high risk of 

creating chaotic datasets with huge discrepancies. 

To successfully implement the proposed research, we used the following 

methodological approach: 

1. Based on quantity theory of money we defined the key components of cryptocurrency and 

developed a list of key criteria, that contribute to market maturity and saturation: 

cryptocurrency market cap (money supply), cryptocurrency velocity (transaction volume 

divided by cryptocurrency market cap) and demand for cryptocurrency. Our data was 

constructed upon eight key features, which broadly reflect key currency market indicators, 

applied to crypto type of money. Based on the criteria, the cryptocurrency dataset was created 

for 41 countries with the largest number of cryptocurrency traders in the world.  

2. Using grey system theory approach, which is broadly applied to decentralized systems’ 

research (Broséus et al., 2016; Bichler, Malm, & Cooper, 2017; Farell, 2015), such as 

cryptocurrency, we adopted grey relational analysis, since the cryptocurrency system embraces 

elements with novel features and limited information available, e.g. this is a system with grey 

parts. Interdisciplinary grey system methodology successfully explains complex relationships 

between the elements of multidimensional decentralized cryptomoney system. We adopted the 

grey relational analysis to our cryptocurrency dataset with multiple variables describing 

cryptocurrency market environment in order to identify the maturity of cryptocurrency markets. 

Within this framework we construct the decision matrix, grey relational coefficients and 

identify the ranking of the countries by cryptomoney market maturity.  

3. We have completed multiple regression analysis in order to answer the fundamental question 

of the research and examine the relationship between cryptomarket maturity of the countries 

and the state of their illicit finance flows in a more sophisticated way using latent variables, 

such as level of political and economic exposure. Therefore, after grey relational research is 

completed and grades are obtained, relational analysis was conducted by comparing the grey 

coefficients against globally applied illicit finance flow estimations in order to reveal the 

possible correlation between these two concepts. We then matched the result of grey relational 

coefficients to the list of countries with the level of illicit finance flows to reveal the correlation 

possible. To study the relationship between crypto market capitalization and illicit financial 

flows data collection approach is based on data points obtained from Global Finance Institution, 

World Bank, UN Comtrade, CoinMarketCap and DataWorld databases. We selected countries 

and ranked them by following criteria:  

i. Crypto market maturity identified for the country X  

ii. Illicit Financial Flow of the country X identified as per the UN data using trade misinvoicing 

tools and techniques 

iii. Social, political and economic exposure and development criteria of sustainable development 

of nations.  



Gunel Jannataly AMRAHOVA 

 

59 

 

 

The following steps were conducted during the grey relational analysis:  

I. The evaluation matrix was produced based on the information collected on the key criteria, 

describing the maturity of cryptomarkets.  

II. We then develop our standard series with the target values of selection criteria in our decision 

making model. This series will identify our reference points in the decision making problem.  

III. After creating our matrix with 41 countries and 8 attributes of crypto markets we perform grey 

normalization in order to be able to compare the country series for each attribute. The values 

in the normalized series will be in the interval from 0 to 1. We apply three traditional methods 

of normalization to our data. For attributes, where higher is better for a selection criterion, or 

attribute, we apply the following calculation:  

𝑥𝑖∗(k) =
𝑥𝑖(k) − 𝑚𝑖𝑛

𝑘
𝑥𝑖∗(k)

⁡

𝑚𝑎𝑥
𝑘𝑥𝑖∗(k)

−𝑚𝑖𝑛
𝑘𝑥𝑖∗(k)

⁡
 

where, 𝑚𝑖𝑛
𝑘
𝑥𝑖∗(k)

⁡is a minimum value as per selection criteria I in the decision matrix, 

𝑚𝑎𝑥
𝑘
𝑥𝑖∗(k)

⁡is a maximum value as per selection criteria I in the decision matrix 

For attributes, where lower value is more desired for selection criteria, we used the following 

procedure:  

𝑥𝑖∗(k) =
𝑚𝑎𝑥

𝑘
𝑥𝑖∗

(k) − 𝑥𝑖(k)

𝑚𝑎𝑥
𝑘𝑥𝑖∗(k)

−𝑚𝑖𝑛
𝑘𝑥𝑖∗(k)

⁡
 

For selection criteria with a given desired value within the evaluation matrix, we perform the 

following method:  

𝑥𝑖∗(k) = 1 −⁡
|𝑥𝑖(k) − 𝑥0(k)|

𝑚𝑎𝑥 {𝑚𝑎𝑥𝑘𝑥𝑖(k)−𝑥0(k);⁡𝑥0(k)⁡–⁡𝑚𝑖𝑛𝑘𝑥𝑖(k)}⁡⁡⁡⁡
 

where 𝑥0(k) is a desired value of alternative (country) k 

IV. Once the normalized sequence is ready, we calculate the deviation sequence as an absolute 

difference between reference sequence and comparable sequences. This action is performed for 

further obtainment of Grey Relational coefficient.  

So, we subtract normalized decision matrix values and standard series values and get an 

absolute value of this subtraction equation, as indicated below:   

 

∆0𝑖(k) = |𝑥𝑖∗(k) − 𝑥0∗(k)| 

 

⌊

∆01(1) ∆01(2) ∆01(5)

∆02(1) ⁡⁡⁡⁡∆02(2)⋯ ∆02(5)
⋮ ⋮ ⋮

∆12(1) ⋯ ∆12(5)

⌋ 

 

V. Once the concluding matrix is ready, we identify grey relational coefficients of attribute k for 

alternative I (country) 

𝑦(𝑥0(k), 𝑥𝑖(k)) =
∆𝑚𝑖𝑛⁡ +⁡ζ ∗ ∆𝑚𝑎𝑥⁡

∆0𝑗⁡−ζ ∗ ∆𝑚𝑎𝑥⁡⁡⁡⁡⁡
 

 

Where ∆𝑚𝑎𝑥⁡refers to the highest value in the deviation sequence, ∆𝑚𝑖𝑛⁡represents the lowest 

value in the difference sequence 



GREY FLOWLESS, OR PREDICTING CAPITAL OUTFLOWS IN CRYPTOCURRENCY 

 

60 

 

y is the grey relational coefficient, and ζ is an adjustment coefficient between ∆0𝑗⁡ and ∗ ∆𝑚𝑎𝑥⁡, 

which belongs to an interval from 0 to 1.  

For the purposes of the current research we used ζ equal to 0.5.  

 

VI. Based on grey relational coefficients we construct the grey factor matrix:  

 

⌊

y01(1) 𝑦01(2) 𝑦01(5)

y02(1) ⁡⁡⁡⁡𝑦02(2)⋯ 𝑦02(5)
⋮ ⋮ ⋮

𝑦12(1) ⋯ 𝑦12(5)

⌋ 

 

VII. and finally, we identify our grey relational grades. Grey relational grade displays the similarity 

between the normalized decision matrix and the standard series. Similarity increases as the grey 

relational grade increases, and therefore the highest similarity gives the best alternative in the 

decision making challenge. If the importance levels of the selection criteria in the decision 

making problem are equal, the grey relational grade is calculated as 

τ(𝑋0, 𝑋𝑖) =
1

𝑚
+∑𝑦(𝑋0(k), 𝑋𝑖(k)1)

12

𝑘=1

 

However, with different importance levels of attributes in the decision making problem, a 

different equation is used:  

τ(𝑋0, 𝑋𝑖) = ∑𝑦(𝑋0(k), 𝑋𝑖(k)) ∗ 𝑊𝑖(k)

12

𝑘=1

 

where 𝑊𝑖(k) is a weighted value of the selection criteria i.  

 For the purposes of the current research, we have proportionally distributed the value among 

all the criteria. 

 

RESULTS 

As mentioned above, traditional grey relational analysis procedure was adopted to 

assess the level of cryptocurrency market maturity. Within this framework we construct the 

decision matrix.  

First, using grey relational analysis we evaluate maturity and performance of 

cryptocurrencies in the countries and then compare this to the state of IFF. Selection criteria of 

our evaluation matrix is built upon a set of attributes for selected countries with the largest 

share of cryptotraders in the total amount across the globe. As a result, 41 economies were 

selected, namely China, United_States, Japan, United_Kingdom, India, Canada, Hong_Kong, 

Australia, Brazil, Switzerland, Russia, Mexico, Saudi_Arabia, Singapore, Sweden, Poland, 

Malaysia, United_Arab_Emirates, Vietnam, Turkey, Chile, Norway, Philippines, 

South_Africa, Denmark, Czech_Republic, New_Zealand, Pakistan, Morocco, Colombia, 

Romania, Hungary, Nigeria, Argentina, Peru, Ukraine, Kazakhstan, Kenya, 

Dominican_Republic, Croatia, Tanzania.  

The following criteria was selected for alternatives (scenarios):  

 Cryptocurrency trading volume in 2020, mln USD  

 Share of trading volume in M2 broad money supply  

 Average trading value per user  



Gunel Jannataly AMRAHOVA 

 

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 Number of cryptoowners  

 Share of cryptoowners in total population  

 Legal Status of Cryptocurrencies  

 Regulatory Framework for Cryptocurrencies: (Application of Tax Laws, Anti-Money 

Laundering/Anti-Terrorism Financing Laws, or Both)  

 Countries that have or are in the process of issuing their own national or regional 

cryptocurrency 

 We then develop our standard series with the target values of selection criteria in our 

decision making model. This series will identify our reference points in the decision making 

problem.  We have applied normalization to our data due to measurement differences of the 

criteria selected.  

 Since there is no standard of what should be the value of our preferred reference 𝑥0(k), 

when creating a normalized comparable sequences, the reference sequence was chosen from 

given original values of comparable sequences. Since all criteria have “the higher – the better” 

characteristic, target values for each of our criteria/sub criteria is selected as max values among 

all countries observed.  

Since as mentioned the values in the evaluation matrix had “the higher – the better” 

characteristic, the normalized values were calculated using the following formula from Part 2. 

Research Data and Methodology: 

𝑥𝑖∗(k) =
𝑥𝑖(k) − 𝑚𝑖𝑛

𝑘
𝑥𝑖∗(k)

⁡

𝑚𝑎𝑥
𝑘𝑥𝑖∗(k)

−𝑚𝑖𝑛
𝑘𝑥𝑖∗(k)

⁡
 

For example, the attribute “Cryptousers_Share_Of_Population” for United_States was 

obtained as follows:  

0.489429 = (6,46-MIN(RANGE” Cryptousers_Share_Of_Population”))/(MAX RANGE” 

Cryptousers_Share_Of_Population”-MIN RANGE” Cryptousers_Share_Of_Population”) 

 

The other values have also been calculated in the similar way.  

After normalized table is ready, deviation sequence can be calculated as a difference 

between reference (target) value and comparable sequence.  

To calculate the absolute value of the difference between the target, or reference, value 

and comparable sequence, we applied the following formula  

 

∆0𝑖(k) = |𝑥𝑖∗(k) − 𝑥0∗(k)| 

For example, “legal status of cryptocurrencies” for Japan was calculated as follows:  

 

∆03(3) = |𝑥3∗(3) − 𝑥0∗(3)| ⁡=⁡| 1 - 0.06104752| = 0.93895248 

We applied the similar calculation for all countries and for each criterion to produce the 

table shown above. This formula shows how far the values of comparable sequences are from 

the values of target sequences. If the difference values are close to 0, it means that the 

comparable sequence values are close to the reference sequences, or target values. And vice 

versa, if the difference values near to 1, it means that the comparable values are far from the 

desired target value.  

After difference sequences are ready, we used these values to calculate grey relational 

coefficients. Our adjustment coefficient ζ between ∆0𝑗⁡ and∆𝑚𝑎𝑥⁡was chosen as 0.5.  



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62 

 

𝑦(𝑥0(k), 𝑥𝑖(k)) =
∆𝑚𝑖𝑛⁡ +⁡ζ ∗ ∆𝑚𝑎𝑥⁡

∆0𝑗⁡−ζ ∗ ∆𝑚𝑎𝑥⁡⁡⁡⁡⁡
 

For instance, to calculate the grey relational coefficient average transaction value per 

user for Vietnam, we applied the following formula:  

𝑦(𝑥0(5), 𝑥12(5)) =
∆𝑚𝑖𝑛⁡ +⁡0.5 ∗ ∆𝑚𝑎𝑥⁡

∆0⁡12⁡−0.5 ∗ ∆𝑚𝑎𝑥⁡⁡⁡⁡⁡
 

To obtain the respective grey relational coefficient, we need to obtain first the highest 

and the lowest deviation values in the range of country values for criteria average transaction 

value per user:  

∆𝑚𝑖𝑛⁡ = ⁡0 

∆𝑚𝑎𝑥⁡= 1 

Once the minimum and maximum deviation values are known, grey relational 

coefficient average transaction value per user for Vietnam is calculated as follows:  

 

𝑦(𝑥0(5), 𝑥12(5))⁡= (0+0.5*1)/(0.989898+0.5*1) = 0.335593 

 

Similarly, we obtain the other grey relational coefficients.  

After grey relational coefficients are calculated, we can obtain grey relational grades by 

using the following equation:  

τ(𝑋0, 𝑋𝑖) = ∑𝑦(𝑋0(k), 𝑋𝑖(k)) ∗ 𝑊𝑖(k)

12

𝑘=1

 

For the purposes of the current research, the importance of each criteria was selected 

equal.  Grey relational grades represent the correlation between target sequence and comparable 

sequence and are equal to weighted sum of the values in Table 4. Therefore, the best 

performance of cryptomarket is selected for the alternative (country) with the highest 

correlation.  

For instance, grey relational grade for the United Kingdom is calculated as follows:  

τ(𝑋0, 𝑋4) = ∑𝑦(𝑋0(5), 𝑋4(5)) ∗ 0.2

5

𝑘=1

 

 

As explained above, the weighted share was selected equal for each alternative, 

therefore it is equal to 0.2.  

We have further ranked the grey relational grades of the countries from the greatest to 

the lowest cryptomarket performance comparison.  

The next part of the data analysis includes comparison of grey relational grades against 

illicit finance flows, obtained from GFI database (GFI, 2018).  

 
Country Grey Relational Grade Grey Grade Ranking IFF IFF Ranking 

China 0.448831432 19 19.63 9 

United_States 0.686291986 1 11.4 28 

Japan 0.505328468 9 9.8 29 

United_Kingdom 0.477219137 15 8.5 30 

India 0.397895939 35 19.5 10 

Brazil 0.443099532 23 16.5 23 

Russia 0.541745724 3 19.3 11 

Mexico 0.426950805 29 14.46 25 

Saudi_Arabia 0.35338034 39 22.44 5 



Gunel Jannataly AMRAHOVA 

 

63 

 

Poland 0.44396952 22 17.21 21 

Malaysia 0.42871459 28 23.63 3 

United_Arab_Emirates 0.351959316 40 25.16 1 

Vietnam 0.440385873 25 16.68 22 

Turkey 0.425403132 30 20.85 6 

Chile 0.438397895 26 12.12 27 

Philippines 0.433298612 27 24.24 2 

Pakistan 0.354855142 38 17.65 17 

Morocco 0.341208134 41 19.26 12 

Colombia 0.392753582 36 16.4 24 

Romania 0.444768525 21 19.94 8 

Hungary 0.418705121 34 18.13 15 

Nigeria 0.503408115 10 17.88 16 

Argentina 0.459646585 17 13.61 26 

Peru 0.44188191 24 18.87 14 

Ukraine 0.489868532 13 19.96 7 

Kazakhstan 0.423399111 31 17.35 19 

Kenya 0.494064616 12 22.62 4 

Dominican_Republic 0.4448156 20 17.37 18 

Croatia 0.419351586 33 17.3 20 

Tanzania 0.373909933 37 19.11 13 

 

We have applied color coding to the rankings of the respective alternative both in grey 

relation grades and IFF ranking to visualize the results of the correlation analysis.  

The first part of the research is completed successfully by obtaining the grey relational 

coefficients, however, when comparing to the IFF level in the selected states, direct neither 

positive nor negative relationship between maturity of cryptocurrency market and illicit 

financial trade can be concluded.  

For this reason, we have included analysis of latent variables, such as political and 

economic exposure and culture index into our research. We have selected political freedom 

scores issued on annual basis by the Freedom House agency (Freedom House, 2021), which is 

an updated and most comprehensive dataset from all researched broken down by countries. 

Another advantage of freedom scores dataset of Freedom House Inc. is that this index includes 

not only political but also social integration of the states, based on its methodology (Freedom 

House, 2021). At the same time, appreciating cultural differences of the selected states, we 

have chosen a culture index dimensions, namely, power distance, individualism, masculinity 

and uncertainty avoidance indexes into our research. Cultural dimensions are based on 6D 

Model of national culture (Hofstede, 1984; (Hofstede Insights, 2020) introduced in Hofstede's 

Cultural Dimensions Theory by Hofstede G. et al in 1980. Subsequently, Gross Domestic 

Product (GDP) per capita was chosen to demonstrate economic development of the states. The 

data was obtained from World Bank on the latest year available in the World Bank Databank 

(The World Bank, 2019).  

We further applied the multiple linear regression analysis to our dataset to identify the 

relationship between illicit financial flows and set of independent variables, including crypto 

grey relational grades and selected above freedom scores, national cultural dimensions and 

GDP per capita. Our independent variables include observed variable of grey relational grades 

as well as latent variables of GDP per capita, political and social integration level and selected 

cultural dimensions, namely power distance index, masculinity index, uncertainty avoidance 

index and individualism index. Mentioned cultural dimensions are considered to be the most 

powerful when choosing the mechanisms of financial management by economic agents.  

 The summary output of our research returned the following statistical results. 

 

  



GREY FLOWLESS, OR PREDICTING CAPITAL OUTFLOWS IN CRYPTOCURRENCY 

 

64 

 

Regression Statistics 

Multiple R 0.802753 

R Square 0.644412 

Adjusted R Square 0.531271 

Standard Error 2.740324 

Observations 30 

 

The result of the research shows statistical significance as per the 30 values for 7 

variables observed:  

  Coefficients 

Standard 

Error t Stat P-value 

Lower 

95% 

Upper 

95% 

Lower 

95.0% 

Upper 

95.0% 

Intercept 19.87653 5.785131 3.435797 0.002361 7.878906 31.87416 7.878906 31.87416 

Grey Coefficient, CryptoMaturity -13.5859 9.022759 -1.50573 0.146359 -32.2979 5.126177 -32.2979 5.126177 

Freedom Score -0.04505 0.023592 -1.90965 0.069304 -0.09398 0.003874 -0.09398 0.003874 

GDP per capita 4.77E-06 3.97E-05 0.120137 0.905465 -7.8E-05 8.72E-05 -7.8E-05 8.72E-05 

IDV (INDIVIDUALISM) 0.009488 0.048773 0.194534 0.847543 -0.09166 0.110637 -0.09166 0.110637 

PDI (POWER DISTANCE INDEX) 0.119966 0.040255 2.980165 0.006905 0.036483 0.203449 0.036483 0.203449 

MAS (Masculinity) -0.02005 0.038262 -0.52407 0.605469 -0.0994 0.059298 -0.0994 0.059298 

UAI (Uncertainty Avoidance Index) -0.02014 0.026632 -0.75631 0.457484 -0.07537 0.035089 -0.07537 0.035089 

 

Our data fits the model by 81%. The regression model result of our ANOVA test is 

given below:  
ANOVA      

  df SS MS F Significance F 

Regression 7 299.3942 42.7706 5.695625 0.000749 

Residual 22 165.2063 7.509378   

Total 29 464.6005       

 

As shown above, our regression model returns strong F significance score, proving the 

relevance of our hypothesis on the correlation between illicit finance flows and cryptomarket 

maturity. As stated in the beginning of our research, we also used our proxy indicators, which 

strengthened our research.  

 

DISCUSSIONS 

The current study was conducted to determine if development of novel endogenous 

cryptocurrencies can influence countries’ likelihood to impact capital outflows.  According to 

existing views on cryptocurrencies (Brezo and Bringas, 2012), due to anonymity public 

identification through “keys”, cryptocurrencies create an opportunity for misuse as an easy tool 

for money laundering, tax evasion and illegal activities. 

It was hypothesized that anonymity of public identification keys creates a space for 

violence, increasing scale of money laundering, tax evasion and illegal activities, predicting 

high level of capital outflows. 

To test the hypothesis, research methodology including grey relational and multiple 

regression analysis was used. We first applied grey relational analysis to evaluate maturity of 

cryptotrade at 41 states with the highest number of cryptotraders and returned their grey 

relational coefficients in cryptocurrency maturity. Based on the grey relational coefficients 

obtained for cryptocurrency maturity, we developed a new dataset comprising cryptocurrency, 

socio-economic and illicit trade factors across the 30 states, with available data on research 



Gunel Jannataly AMRAHOVA 

 

65 

 

criteria. Multiple regression analysis was adopted to determine the relationship between these 

factors.  Our data fit the multiple regression model by 81%, which is quite strong evidence of 

the relationship between mentioned factors. Result shows that 53.12% of the variance in illicit 

finance flows can be accounted by the set of cryptocurrency maturity and socio-economic 

predictors, collectively, F(7,22) = 5.6956 p<0.001.  

Looking at the unique individual contributions of predictors, the result shows that 

economic indicator, such as GDP per capita, and cultural indexes, namely Individualism and 

Power Distance Index, positively predict illicit financial flow level.  

Furthermore, result also reveals that those countries which have high rate of transactions in 

cryptocurrency, freedom scope, masculinity and uncertainty avoidance are more likely to report 

low level of illicit finance flows.   

This suggests that use of  blockchain systems in monetary transactions can be 

considered a more transparent system, adding value to the state of global trade relationships 

not only by less bureaucracy and intermediaries involved, but also higher transparency and 

simplicity. 

Having central idea of decentralization, these distributed systems however provide total 

security and confidentiality, being transparent. Without a central authority within this network, 

blockchains establish trust through consensus and cryptography. Cryptography is used to shift 

the burden of trust from intermediaries (such as banks, financial and govern institutions) to 

cryptographic algorithms, and constructing and analyzing protocols that prevent third parties 

or the public from reading private messages.  Cryptography provides techniques for keeping 

information secret, for determining that information has not been tampered with, and for 

defining who authored process of information. It is based on techniques related to aspects of 

information security such as confidentiality, data integrity, entity authentication, and data origin 

authentication. Cryptography is not the only means of providing information security, but 

rather one set of techniques. From the other hand, cryptocurrency development can positively 

impact the quality of international financial transactions, reducing not only transaction costs, 

but also reducing the illicit financial flows, as revealed by the research results. However, use 

of AML/KYC and other regulations relevant to transactions with cryptocurrency has an 

important role to bridge the gaps within trade operations with cryptocurrency that might 

potentially cause illicit financial outflows.  

 

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