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1 – 4 of 4The purpose of this study is to exploration potential money laundering crimes with virtual currency facilities in Indonesia. Money laundering using crypto is the process of…
Abstract
Purpose
The purpose of this study is to exploration potential money laundering crimes with virtual currency facilities in Indonesia. Money laundering using crypto is the process of disguising the origin of money obtained illegally. Then, the perpetrator transfers it to a legitimate business. Virtual money then started to become a phenomenon in society since the emergence of cryptocurrencies as a form of technology development of e-commerce activities.
Design/methodology/approach
This research method is normative law which is prescriptive. The data collection technique used is document study or literature study by collecting primary and secondary legal materials.
Findings
The results of this study show that the bitcoin virtual currency has the potential to act as a means of money laundering. There are technologies and online platforms that are moving with more sophisticated methods. Through bitcoin exchanges, it has the greatest potential for money laundering. The usage of virtual currency (cryptocurrency) by those who commit money laundering offenses is responsible for the actions’ severe negative effects on the State of Indonesia.
Originality/value
To the best of the author’s knowledge, this is the first study conducted in Indonesia that explores potential money-laundering crimes using virtual currency facilities.
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Aadil Amin, Asif Tariq and Masroor Ahmad
The principal aim of this study is to examine the relationship between financial development and income inequality in India using the financial Kuznets curve (FKC) hypothesis.
Abstract
Purpose
The principal aim of this study is to examine the relationship between financial development and income inequality in India using the financial Kuznets curve (FKC) hypothesis.
Design/methodology/approach
This study uses the autoregressive distributed lag (ARDL) model and the Toda–-Yamamoto causality test to investigate the long-run and short-run relationship and causality between financial development and income inequality. In addition, this study employs a principal component analysis (PCA) to construct a comprehensive financial development index.
Findings
The study found a long-run relationship between financial development and income inequality in India for the period under consideration. Trade is found to improve the income distribution, while inflation worsens income distribution. Moreover, the empirical results revealed a feedback causality between financial development and income inequality. The study results confirm an inverted U-shaped relationship between financial sector development indicators and income inequality, thus validating the FKC hypothesis for the Indian economy.
Research limitations/implications
The study draws attention of the government and policymakers, urging them to focus on building a strong financial sector by improving its efficiency. This, in turn, will lead to enhanced financial stability and a reduction in income inequality. They should prioritise the development of high-quality and sustainable financial products and services to ensure the robust growth of the financial sector.
Originality/value
To the best of our knowledge, this study is the latest of its kind to empirically test the financial development on income inequality and the FKC hypothesis simultaneously for the Indian economy using financial proxy variables from financial institutions (FIs) and financial markets (FMs) for the measurement of financial depth.
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Z. Göknur Büyükkara, İsmail Cem Özgüler and Ali Hepsen
The purpose of this study is to explore the intricate relationship between oil prices, house prices in the UK and Norway, and the mediating role of gold and stock prices in both…
Abstract
Purpose
The purpose of this study is to explore the intricate relationship between oil prices, house prices in the UK and Norway, and the mediating role of gold and stock prices in both the short- and long-term, unraveling these complex linkages by employing an empirical approach.
Design/methodology/approach
This study benefits from a comprehensive set of econometric tools, including a multiequation vector autoregressive (VAR) system, Granger causality test, impulse response function, variance decomposition and a single-equation autoregressive distributed lag (ARDL) system. This rigorous approach enables to identify both short- and long-run dynamics to unravel the intricate linkages between Brent oil prices, housing prices, gold prices and stock prices in the UK and Norway over the period from 2005:Q1 to 2022:Q2.
Findings
The findings indicate that rising oil prices negatively impact house prices, whereas the positive influence of stock market performance on housing is more pronounced. A two-way causal relationship exists between stock market indices and house prices, whereas a one-way causal relationship exists from crude oil prices to house prices in both countries. The VAR model reveals that past housing prices, stock market indices in each country and Brent oil prices are the primary determinants of current housing prices. The single-equation ARDL results for housing prices demonstrate the existence of a long-run cointegrating relationship between real estate and stock prices. The variance decomposition analysis indicates that oil prices have a more pronounced impact on housing prices compared with stock prices. The findings reveal that shocks in stock markets have a greater influence on housing market prices than those in oil or gold prices. Consequently, house prices exhibit a stronger reaction to general financial market indicators than to commodity prices.
Research limitations/implications
This study may have several limitations. First, the model does not include all relevant macroeconomic variables, such as interest rates, unemployment rates and gross domestic product growth. This omission may affect the accuracy of the model’s predictions and lead to inefficiencies in the real estate market. Second, this study does not consider alternative explanations for market inefficiencies, such as behavioral finance factors, information asymmetry or market microstructure effects. Third, the models have limitations in revealing how predictors react to positive and negative shocks. Therefore, the results of this study should be interpreted with caution.
Practical implications
These findings hold significant implications for formulating dynamic policies aimed at stabilizing the housing markets of these two oil-producing nations. The practical implications of this study extend to academics, investors and policymakers, particularly in light of the volatility characterizing both housing and commodity markets. The findings reveal that shocks in stock markets have a more profound impact on housing market prices compared with those in oil or gold prices. Consequently, house prices exhibit a stronger reaction to general financial market indicators than to commodity prices.
Social implications
These findings could also serve as valuable insights for future research endeavors aimed at constructing models that link real estate market dynamics to macroeconomic indicators.
Originality/value
Using a variety of econometric approaches, this paper presents an innovative empirical analysis of the intricate relationship between euro property prices, stock prices, gold prices and oil prices in the UK and Norway from 2005:Q1 to 2022:Q2. Expanding upon the existing literature on housing market price determinants, this study delves into the role of gold and oil prices, considering their impact on industrial production and overall economic growth. This paper provides valuable policy insights for effectively managing the impact of oil price shocks on the housing market.
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Ibrahim Cutcu, Guven Atay and Selcuk Gokhan Gerlikhan
This study aims to analyze the relationship between the consequences of the pandemic and the housing sector with econometric tests that allow for structural breaks.
Abstract
Purpose
This study aims to analyze the relationship between the consequences of the pandemic and the housing sector with econometric tests that allow for structural breaks.
Design/methodology/approach
Study data were collected weekly between March 9, 2020, and February 4, 2022, and analyzed for Turkey. In the model of the study, housing loans were used as a housing market indicator, and the number of new deaths and new cases were used as data related to the pandemic. The exchange rate, which affects the use of housing loans, was added to the model as a control variable. This study was analyzed to examine the relationship between the pandemic and the housing sector, time series analysis techniques that allow structural breaks were used.
Findings
Based on the result of the analyses, it was concluded that there is a long-run relationship between the pandemic stages and housing markets along with structural breaks. As a result of the time-varying causality test developed to determine the causality relationship between the variables and its direction, a bidirectional causality relationship was identified between all variables at certain dates.
Research limitations/implications
Study data were collected weekly between March 9, 2020, and February 4, 2022, and analyzed in the case of Turkey.
Practical implications
Based on results of the study, it is recommended that policy makers and market actors take into account extraordinary situations such as pandemics and create a budget allocation that is always ready to use for this purpose.
Originality/value
The empirical examination of the relationship between the pandemic and the housing sector in Turkey provides originality to this study in terms of its topic, sample, methodology, contribution to the literature and potential policy recommendations.
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