Money Laundering Risk in Developing and Transitive Economies: Analysis of Cyclic Component of Time Series

Abstract

Money laundering has become a global threat to the international stability and security, leading both to economic and social upheavals, and to an increase in terrorist threats. Therefore, an objective necessity arises for a more detailed study of the money laundering within the scope of its developmental patterns and time-dependent behaviour. The study mission is the development of a theoretical framework and methodological support for modelling the cyclic component of the money laundering risk. The correlation and regression are used for isolating the cyclic component. In turn, the Fourier harmonic analysis allows specifying the cyclic component. Additionally, we carried out a decomposition of time series, analysis of its volatility and persistence using the Hurst exponent. We determined the peaks, downturns and duration of the money laundering cycles in the developed economies and economies in transition, and established the possibility of predicting this process in the medium term. We proved the internationalization of the money laundering and the similarity of behaviour of trends that characterize it both for developed economies among themselves and between groups of countries. The further scientific research is needed within the framework of the imposition of trends in the development of the money laundering processes of some countries on others and the formation of international medium-term anti-fraud strategies.

Gravitational and intellectual data analysis to assess the money laundering risk of financial institutions

“…The econometric model of nonlinear multifactor regression dependence of the money laundering risk of financial institutions, on relevant factors of its formation is investigated by means of MS Excel toolkit, Data Analysis/Regression package (Levchenko et al, 2019;Zheng et al, 2019) (Table 3). -0,0054 0,0077 -0,7048 0,4826 -0,0207 0,0098 (K6) 3 0,0000 0,0000 0,0688 0,9453 0,0000 0,0000 sqrt K9 0,0317 0,0118 2,6738 0,0088 0,0082 0,0552 sqrt K10 0,0234 0,0059 3,9721 0,0001 0,0117 0,0350 K4 K6 K9 K10 0,0000 0,0000 3,5595 0,0006 0,0000 0,0000…”

Efficient use of the insurance sector potential adjusted for its shadowing: Case of Ukraine

“…In particular, Shumska and Nezhyvenko (2013) considered methodological approaches to determining the size of the shadow economy in Ukraine and the world and assessing its level by various methods. Vinnychuk and Ziukov (2013) and Levchenko et al (2019) built an economic and mathematical model to identify patterns of the shadow economy. Remeikiene et al (2017) studied the influence of the main factors on the shadowing of Ukraine's economy using regression analysis.…”

Social safety and behavioral aspects of populations financial inclusion: A multicountry analysis

“…Garcia (2016) investigates the relationship between financial security and affordability by the correlation model based on data from 2008 -2016 in more than 150 countries. Such scientists as (Grybaitė & Stankevičienė, 2018), (Khan et al, 2017), (Levchenko et al, 2019), (Mishchuk et al, 2019), (Nocoń & Pyka, 2019), (Sisodia, 2019), (Vasileva & Lasukova, 2013), , (Grenčíková et al, 2019) used regression-correlation analysis and other methods as the way to process large amounts of financial data in the works.…”