Narrow Results

Financial institutions’ heterogeneity, a high degree of dissimilarity across multiple dimensions, including business focuses, correlated asset holdings, capital structures, and funding sources, reduces systemic risk. We empirically test this hypothesis using a bank holding company (BHC) level heterogeneity index based on granular balance sheet, income statements, cash flow statements, and off-balance-sheet information for U.S. bank holding companies over a sample period spanning the second quarter of 2000 to the fourth quarter of 2021. We find that the BHC-level heterogeneity negatively correlates with BHC-level systemic risk, SRISK, measured both at the global and local levels. We also construct a sector-heterogeneity index and demonstrate that a reduction of heterogeneity occurred in the U.S. financial sector prior to both the Great Recession (2007–2009) and the COVID-19 Recession, especially for the largest BHCs. As such, a declining level of financial sector heterogeneity may exacerbate the consequences of systemic shocks.

In this chapter, we propose the structural model in terms of the Stair Tree model and barrier option to evaluate the fair deposit insurance premium in accordance with the constraints of the deposit insurance contracts and the consideration of bankruptcy costs. First, we show that the deposit insurance model in Brockman and Turle (2003) is a special case of our model. Second, the simulation results suggest that insurers should adopt a forbearance policy instead of a strict policy for closure regulation to avoid losses from bankruptcy costs. An appropriate deposit insurance premium can alleviate potential moral hazard problems caused by a forbearance policy. Our simulation results can be used as reference in risk management for individual banks and for the Federal Deposit Insurance Corporation (FDIC).

Stress testing of economic policies, regulations, financial risk evaluations, and statistical inference have lately become a “requirement.” This chapter highlights extant econometric tools for stress testing with an emphasis on: (a) maximum entropy bootstraps, recently implemented in a new version of the R software package called “meboot,” and (b) creation of stress scenarios by considering time heterogeneous nonstationary time series. We use published simulation designs of other authors to report the superiority of meboot over the moving block bootstrap (mbb) and the blocking external bootstrap (BEB) in the context of many types of time-heterogeneity. For illustration, we apply meboot tools to stress test inference regarding Granger-causality between asset prices and world savings rates, and also to the “Value at Risk” used in finance. We indicate potential uses in stress testing of banks.