This paper assesses the probability method for quantifying EU consumer survey data on perceived and expected inflation. Based on household level data from the Swedish consumer survey that asks for both qualitative and quantitative responses, it is found that the theoretical assumptions of the...

The year 2020 witnessed the introduction of new rules and regulations by the United Kingdom for virtual asset service providers, and for the first time, a Bitcoin “Mixer” was Penalized by the United States Department of the Treasury’s Financial Crimes Enforcement Network (FinCEN) for...

This article predicts the likelihood of a country being sanctioned by Basel for not adhering to anti-money laundering (AML) standards. Multiple machine learning algorithms were used to identify the strongest risk predictors, resulting in Basel sanctions against a country. In terms of sanction...

This project focuses on using predictive modeling techniques to combat money laundering and improve Anti-Money Laundering (AML) compliance efforts. The IBM Synthetic Financial Data Money Laundering dataset is used to explore the effectiveness of different machine learning algorithms in...

The asymmetric moving average model (asMA) is extended to allow forasymmetric quadratic conditional heteroskedasticity (asQGARCH). Theasymmetric parametrization of the conditional variance encompassesthe quadratic GARCH model of Sentana (1995). We introduce a framework fortesting asymmetries in...