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...

In this paper, we investigate whether credit spread curve information helps forecast the government bond yield curve and whether the joint dynamics of the government bond yields and credit spreads have structural changes. For this purpose, we use a joint dynamic Nelson-Siegel (DNS) model of the...

We enhance the method of integrating scenarios proposed in Ergashev (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, which is originally proposed for...

Operational risk is a substantial source of risk for US banks. Improving the performance of operational risk models' allows banks' management to make better risk decisions by better matching economic capital and risk appetite, and allows regulators to better understand the risk of banks. We show...