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...

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....

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...

Our study explores the impact financial crisis has on performance of microfinance institutions in Zimbabwe employing the Vector Autoregression using annual time series data from 1990 to 2018. The findings from our study revealed a positive impact of financial crisis on performance of...

We analyze the determinants of individual bank failures arising from solvency and liquidity shortages in a stylized banking system following Krause and Giansante (2012) where banks are characterized by the amount of capital, cash reserves and their exposure to the interbank loan market as...

We study the time-varying dependence of sovereign credit default swap (CDS) spreads on real-time, country-specific macro indicators during the eurozone sovereign debt crisis. Macro fundamentals explain 66% of the time-series variance of CDS spreads, but the time variation in macro sensitivities...

We develop early warning models for financial crisis prediction using machine learning techniques on macrofinancial data for 17 countries over 1870–2016. Machine learning models mostly outperform logistic regression in out-of-sample predictions and forecasting. We identify economic drivers of...

We develop early warning models for financial crisis prediction by applying machine learning techniques to macrofinancial data for 17 countries over 1870–2016. Most nonlin-ear machine learning models outperform logistic regression in out-of-sample predictions and forecasting. We identify...