Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...

This paper provides empirical evidence on the ability of consensus prices to reduce valuation uncertainty in the over-the-counter market for financial derivatives. The analysis is based on a proprietary data set of price estimates for S&P500 index options provided by major broker-dealers to a...

This paper tests for the transmission of the 2007-2010 financial and sovereign debt crises to fifteen EMU countries. We use daily data from 2003 to 2010 on country financial and non-financial stock market indexes. First, we find strong evidence of crisis transmission to European non-financials...

This paper tests for the transmission of the 2007-2010 financial and sovereign debt crises to fifteen EMU countries. We use daily data from 2003 to 2010 on country financial and non-financial stock market indexes. First, we find strong evidence of crisis transmission to European non-financials...

order to analyze the pricing of portfolio credit risk as revealed by tranche spreads of a popular credit default swap (CDS) index we extract risk-neutral probabilities of default (PDs) and physical asset return correlations from single-name CDS spreads. The time profile and overall level of...

Can banks trade credit default swaps (CDSs) referenced on their current corporate clients at competitive prices, or are banks penalized for potentially holding private information? To answer this question we merge CDS trades reported under the European Market Infrastructure Regulation (EMIR)...