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...

Many studies find that yields for government bonds predict real economic activity. Most of these studies use the yield spread, defined as the difference between two yields of specific maturities, to predict output. In this paper, I propose a different approach that makes use of information...

Using supervisory operational loss data of the U.S. banking industry, we analyze correlations among operational losses within banks and across banks. We find evidence of relatively high correlations among tail losses of different operational risk types within banks. The median of these...

This study documents the association between the quality of risk management practices and operational loss realizations at large financial institutions in the United States. Using detailed supervisory data, we find that companies with weak risk management practices experience higher and more...

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 investigate how the entire term structure of interest rates is influenced by changes in monetary policy regimes. To do so, we develop and estimate an arbitrage-free dynamic term-structure model which accounts for regime shifts in monetary policy, volatility, and the price of risk. Our results...

In this paper, abrupt and large changes in volatility of financial variables representing dynamics of the US financial sector are modeled with a joint regime-switching process, distinguishing "low" and "high" volatility regimes. I find that the joint "high" volatility regime for the TED spread,...

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...