We show that the saddle-point approximation method to quantify the impact of undiversified idiosyncratic risk in a credit portfolio is inappropriate in the presence of double default effects. Specifically, we prove that there does not exist an equivalent formula to the granularity adjustment,...

Despite intense criticism, agency credit ratings are still widely used in regulation and risk management. One possible alternative is to replace them with quantitative default risk measures. For US data, I find that systemically relevant losses from corporate defaults are mostly smaller if...

Choosing a proper external risk measure is of great regulatory importance, as exemplified in the Basel II and Basel III Accord which use Value-at-Risk (VaR) with scenario analysis as the risk measures for setting capital requirements. We argue a good external risk measure should be robust with...

A model-based assessment of credit risk is subject to both specification and calibration errors. Focusing on a well known credit risk model, we propose a methodology for quantifying the relative importance of alternative sources of such errors and apply this methodology to a large data set. We...

We develop a measure of systemic importance that accounts for the extent to which a bank propagates shocks across the banking system and is vulnerable to propagated shocks. Based on Shapley values, this measure gauges the contribution of interconnected banks to systemic risk, in contrast to...

This article proposes several simple financial market reforms that can help regulators both identify systemically risky institutions and mitigate the systemic risk associated with derivative trading, especially trading in credit derivatives such as credit default swaps.The Federal Reserve (or...

A model-based assessment of credit risk is subject to both specification and calibration errors. Focusing on a well known credit risk model, we propose a methodology for quantifying the relative importance of alternative sources of such errors and apply this methodology to a large data set. We...