The lognormal-gamma distribution, being a heavy-tailed distribution, is very attractive from the operational risk modeling perspective because historical operational losses also exhibit heavy tails. Unfortunately, fitting this model requires two severe challenges to be properly addressed. First,...

The paper compares the performance of four estimation methods, including the maximum likelihood estimation, which can be used in fitting operational risk models to historically available loss data. The other competing methods are based on minimizing different types of measure of the distance...

We model the U.S. banking system as a thermodynamic system of interacting elements with individual banking firms representing those elements. Firms with similar asset and liability structures interact in the sense that they pursue similar objectives. These objectives include specialization in...

This paper addresses challenges of estimating operational risk regulatory capital when a loss sample is truncated from below at a data collection threshold. Recent operational risk literature reports that the attempts to estimate loss distributions by the maximum likelihood method are not always...

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...

In this paper, we present a compellingly simple yet innovative approach to capturing the buildup of systemic risk associated with commonalities in banks' asset holdings. We draw on a growing strand of theoretical literature that studies the systemic externalities of banks' balance sheet asset...

In this paper, I introduce a theoretically justified framework that incorporates scenario analysis into operational risk modeling. The basis for the framework is the idea that only worst-case scenarios contain valuable information about the tail behavior of operational losses. In addition,...

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,...