The interconnection of stock markets offers valuable insights into the broader dynamics of global financial markets. This study uses the Diebold and Yilmaz index model to analyze and measure volatility spillovers and interconnectedness among APEC stock markets. The objective is to identify major...

We propose to pool alternative systemic risk rankings for financial institutions using the method of principal components. The resulting overall ranking is less affected by estimation uncertainty and model risk. We apply our methodology to disentangle the common signal and the idiosyncratic...

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...

This article analyzes the manifold situations in which the efficient-market hypothesis (EMH) has influenced — or has failed to influence — federal securities regulation and state corporate law, and the prospective roles for the EMH in these contexts. In federal securities regulation, the EMH...

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....

We investigate regulations intended to stop managers from privately disclosing corporate information to analysts in a setting with enhanced potential to isolate regulatory effects: the European Union (EU) Market Abuse Directive (MAD), a common regulation implemented by member states with varying...

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...

We use machine learning techniques to conduct out-of-sample predictions of the underpricing of U.S. initial public offerings (IPOs) from 1990 to 2019. Using predicted underpricing based on ex ante information to sort the IPOs into 10 groups, we find that the underpricing averages for the top and...

Using non-parametric and parametric models, we show that the bivariate distribution of an Asian portfolio is not stable along all the period under study. We suggest several dynamic models to compute two market risk measures, the Value at Risk and the Expected Shortfall: the RiskMetrics...