Gregor N.F. Weiß

Systemic Risk and Bank Consolidation: International Evidence

This paper analyzes the systemic risk effects of bank mergers to test the “concentration-fragility” hypothesis. We use the marginal expected shortfall as well as the lower tail dependence between a bank’s stock returns and a relevant bank sector index to capture the merger-related change in an acquirer’s contribution to systemic risk. In our empirical analysis of a dataset of international domestic and cross-border mergers, we find clear evidence for a significant increase in the merging banks’, the combined banks’ as well as their competitors’ contribution to systemic risk following mergers, thus confirming the “concentration-fragility” hypothesis.

Forecasting Liquidity-Adjusted Intraday Value-at-Risk with Vine Copulas

We propose to model the joint distribution of bid-ask spreads and log returns of a stock portfolio by using Autoregressive Conditional Double Poisson and GARCH processes for the marginals and vine copulas for the dependence structure. By estimating the joint multivariate distribution of both returns and bid-ask spreads from intraday data, we incorporate the measurement of commonalities in liquidity and comovements of stocks and bid-ask spreads into the forecasting of three types of liquidity-adjusted intraday Value-at-Risk (L-IVaR). In a preliminary analysis, we document strong extreme comovements in liquidity and strong tail dependence between bid-ask spreads and log returns across the firms in our sample thus motivating our use of a vine copula model. Furthermore, the backtesting results for the L-IVaR of a portfolio consisting of five stocks listed on the NASDAQ show that the proposed models perform well in forecasting liquidity-adjusted intraday portfolio profits and losses.

Why Do Some Insurers become Systemically Relevant

Are some insurers relevant for the stability of the financial system? And if yes, what firm fundamentals and aspects of insurers’ business models cause them to destabilize an entire financial sector? We find that several insurers did indeed contribute significantly to the instability of the U.S. financial sector during the recent financial crisis. We empirically confirm that insurers that were most exposed to systemic risk were on average larger, relied more heavily on non-policyholder liabilities and had higher ratios of investment income to net revenues. Contrary to current conjectures of insurance regulators, we find that the contribution of insurers to systemic risk is only driven by insurer size.

What factors drive systemic risk during international financial crises

We analyze the determinants of the contribution of international banks to both global and local systemic risk during prominent financial crises. We find no empirical evidence supporting conjectures that bank size, leverage, non-interest income or the quality of the bank’s credit portfolio are persistent determinants of systemic risk across financial crises. In contrast, our results show that global systemic risk in particular is predominantly driven by characteristics of the regulatory regime. We also confirm for the subprime crisis that the banks’ contribution to moderately bad tail events in the past predicts the financial sector’s crash risk.

A New Set of Improved Value-at-Risk Backtests

We propose a new set of formal backtests for VaR-forecasts that significantly improve upon existing backtesting procedures. Our new test of unconditional coverage can be used for both one-sided and two-sided testing, which leads to a significantly increased power. Second, we stress the importance of testing the property of independent and identically distributed (i.i.d.) VaR-exceedances and propose a simple approach that explicitly tests for the presence of clusters in VaR-violation processes. Results from a simulation study indicate that our tests significantly outperform competing backtests in several distinct settings.

Is Tail Risk Priced in Credit Default Swap Premia

We show that the propensity of a bank to experience extreme co-movements in its credit default swap (CDS) premia together with the market is priced in the bank’s default swap spread during the financial crisis. We measure a bank’s CDS tail beta by estimating the upper tail dependence between its default swap spreads and a CDS market index. Our study shows that protection sellers receive a premium for bearing the risk of extreme upward co-movements in default risk. The economic significance of this effect is large yet limited to the recent financial crisis. Banks in the upper quintile of CDS tail beta have spreads that are on average 140 basis points higher than those of banks in the lower CDS tail beta quintile.

Testing for Structural Breaks in Correlations: Does it Improve Value-at-Risk Forecasting?

In this paper, we modify the Constant Conditional Correlation (CCC) model and its dynamic counterpart, the Dynamic Conditional Correlation (DCC) model by combining them with a pairwise test for constant correlations, a test for a constant correlation matrix, and a test for a constant covariance matrix. We compare these models to their plain counterparts with respect to the accuracy for forecasting the Value-at-Risk of financial portfolios by a set of distinct backtests. In an empirical horse race of these models based on multivariate portfolios, our study shows that correlation models can be improved by approaches modified by tests for structural breaks in co-movements in several settings.

Evaluating Value-at-Risk Forecasts: A New Set of Multivariate Backtests

We propose two new tests for detecting clustering in multivariate Value-at-Risk (VaR) forecasts. First, we consider CUSUM-tests to detect non-constant expectations in the matrix of VaR-violations. Second, we propose χ2-tests for detecting cross-sectional and serial dependence in the VaR-forecasts. Moreover, we combine our new backtests with a test of unconditional coverage to yield two new backtests of multivariate conditional coverage. Results from a simulation study underline the usefulness of our new backtests for controlling portfolio risks across a bank’s business lines. In an empirical study, we show how our multivariate backtests can be employed by regulators to backtest a banking system.

Bank stock performance and bank regulation around the globe

ABSTRACT We analyze the effect of bank capital, regulation, and supervision on the annual stock performance of global banks during the period of 1999–2012. We study a large comprehensive panel of international banks and find that higher Tier 1 capital decreases a banks stock performance over the whole sample period. However, during turbulent times stocks of more highly capitalized banks perform significantly better. Additionally, we find strong evidence that banks that are more likely to receive government bailout during financial distress realize smaller stock performance. In contrast, we find no convincing evidence that banks that generate higher non-interest income have a higher performance.

Liquidity tail risk and credit default swap spreads

We show that liquidity tail risk in credit default swap (CDS) spreads is time-varying and explains variation in CDS spreads. We capture the liquidity tail risk of a CDS contract written on a firm by estimating the tail dependence, i.e., the asymptotic probability of a joint surge in the bid-ask spread of the firm’s CDS and the illiquidity of a CDS market index. Our results show that protection sellers earn a statistically and economically significant premium for bearing the risk of joint extreme downwards movements in the liquidity of individual CDS contracts and the CDS market. This effect holds in various robustness checks such as instrumental variable regressions and alternative liquidity measures and is particularly pronounced during the financial crisis.