Using supervisory data from large U.S. bank holding companies (BHCs), we document a significant association between BHCs' operational losses and the U.S. macroeconomic environment. In adverse conditions, BHCs face higher and more volatile operational losses. The frequency of loss events...

One of the main challenges that banks face in modeling operational risk is the instability of risk estimates caused by heavy-tailed and insufficient loss data. To address these issues, we propose a loss scaling method to combine a bank's internal loss data with external loss data of other banks....

The literature proposes several alternatives for estimating compound distributions, which are widely used for risk quantification in the banking and insurance industries. In this paper, we evaluate the accuracy and time-efficiency of different approaches for estimating quantiles of compound...

We enhance the method of integrating scenarios proposed in Ergashev (J Financ Serv Res 41(3):145–161, 2012) into risk models. In particular, we provide additional theoretical insights of the method with focus on stress testing Value-at-Risk models. We extend the application of the method,...

In the current low interest rate environment, the possibility of a sudden increase in rates is a potentially serious threat to financial stability. As a result, analyzing interest rate risk (IRR) is critical for financial institutions and supervisory agencies. We propose a new method for...

In this paper, I propose an approach to measuring systemic financial stress. In particular, abrupt and large changes in the volatility of financial variables that represent the dynamics of the US financial sector are modeled with a joint regime-switching process, distinguishing “low” and...