This article reviews two leading measures of financial risk and an emerging alternative. Embraced by the Basel accords, value-at-risk and expected shortfall are the leading measures of financial risk. Expectiles offset the weaknesses of value-at-risk (VaR) and expected shortfall. Indeed,...

Each of the most recent accords of the Basel Committee on Banking Regulation, known as Basel II, 2.5, and II, has embraced a different primary measure of market risk in global banking regulation: traditional value-at-risk (VaR), stressed VaR, and expected shortfall. After introducing the...

Neural networks can forecast economic data with accuracy matching that of conventional autoregressive methods such as SARIMA and VAR. This study uses dense, recurrent, convolutional, and convnet/RNN hybrids to conduct time-series analysis of interest rates, consumer and producer prices, and...

Models for predicting business bankruptcies have evolved rapidly. Machine learning is displacing traditional statistical methodologies. Three distinct techniques for approaching the classification problem in bankruptcy prediction have emerged: single classification, hybrid classifiers, and...

Demand forecasting relies heavily on traditional methods with well known limitations. Improved accuracy in predicting demand for mortgages, whether for purposes of purchase or refinance, is critical to profitability in home lending. To overcome obstacles to prediction using nonlinear...

This study extends previous work applying unsupervised machine learning to commodity markets. "Clustering Commodity Markets in Space and Time" [DOI: 10.1016/j.resourpol.2021.102162] examined returns and volatility in commodity markets. That paper supported the conventional ontology of commodity...