Benefits of Machine Learning and Artificial Intelligence -- Machine Learning -- Best Practices -- Setting Up the R Development Environment -- Fundamentals of the R Programming Language -- Machine Learning with R -- Application of Machine Learning with R -- Outlook.

The recent financial crisis has raised numerous questions about the accuracy of value-at-risk (VaR) as a tool to quantify extreme losses. In this paper we develop data-driven VaR approaches that are based on the principle of optimal combination and that provide robust and precise VaR forecasts...

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...

This study is motivated by the continuing popularity of the Altman Z-score as a measure of distress risk. Altman first introduced the ‘Z' score in 1968 and 50 years later it is still going strong as a means to predicting bankruptcy. During these 50 years, academicians have studied the...

In this paper, we simulate and analyze the impact of financial regulations concerning the collateralization of derivative trades on systemic risk - a topic that has been vigorously discussed since the financial crisis in 2007/08. Experts often disagree on the efficacy of these regulations....

Accurate Value at Risk (VaR) estimations are crucial for the robustness and stability of a financial system. Even though significant advances have been made in the field of risk modelling, many crises have emerged during the same period, and an explanation for this is that the advanced models...

Dealing with a recurring low level of data quality in private equity, we propose a novel approach of understanding the behavior of private equity funds (PEFs): we use PEFs' illiquidity as a factor of analysis. To do so, we measure the distance between PEF cash-flows (“J-Curves”) and return...

This paper shows how the role of Financial Soundness Indicators (FSIs) in financial surveillance can be usefully enhanced. Drawing from different statistical techniques, the paper illustrates that FSIs generate signals that can accurately detect, with 4 to 12 quarters lead, emerging financial...

The parameter loss given default (LGD) of loans plays a crucial role for risk-based decision making of banks including risk-adjusted pricing. Depending on the quality of the estimation of LGDs, banks can gain significant competitive advantage. For bank loans, the estimation is usually based on...

This study investigates how well weekly Google search volumes track and predict bank failures in the United States between 2007 and 2012, contributing to the expanding literature that exploits internet data for the prediction of events. Different duration models with time-varying covariates are...