We propose a novel approach to calibrate the conditional value-at-risk (CoVaR) of financial institutions based on neural network quantile regression. Building on the estimation results, we model systemic risk spillover effects in a network context across banks by considering the marginal effects of the quantile regression procedure. An out-of-sample analysis shows great performance compared to a linear baseline specification, signifying the importance that nonlinearity plays for modelling systemic risk. We then propose three network-based measures from our fitted results. First, we use the Systemic Network Risk Index (SNRI) as a measure for total systemic risk. A comparison to the existing network-based risk measures reveals that our approach offers a new perspective on systemic risk due to the focus on the lower tail and to the allowance for nonlinear effects. We also introduce the Systemic Fragility Index (SFI) and the Systemic Hazard Index (SHI) as firm-specific measures, which allow us to identify systemically relevant firms during the financial crisis.

This paper aims to model the joint dynamics of cryptocurrencies in a nonstationary setting. In particular, we analyze the role of cointegration relationships within a large system of cryptocurrencies in a vector error correction model (VECM) framework. To enable analysis in a dynamic setting, we propose the COINtensity VECM, a nonlinear VECM specification accounting for a varying systemwide cointegration exposure. Our results show that cryptocurrencies are indeed cointegrated with a cointegration rank of four. We also find that all currencies are affected by these long term equilibrium relations. The nonlinearity in the error adjustment turned out to be stronger during the height of the cryptocurrency bubble. A simple statistical arbitrage trading strategy is proposed showing a great in-sample performance, whereas an out-of-sample analysis gives reason to treat the strategy with caution.

We propose a novel approach to estimate the conditional value at risk (CoVaR) of nancial institutions. Our approach is based on neural network quantile regression. Building on the estimation results we model systemic risk spillover eects across banks by considering the marginal eects of the quantile regression procedure. We obtain a time-varying risk network represented by an adjacency matrix. We then propose three measures for systemic risk. The Systemic Fragility Index and the Systemic Hazard Index are measures to identify the most vulnerable and most critical rms in the nancial system, respectively. As a third risk measure we propose the Systemic Network Risk Index which represents the overall level of systemic risk. We apply our methodology to the global systemically relevant banks from the United States in a time period from 2007 until 2018. Our results are similar to previous studies about systemic risk. We nd that systemic risk increased sharply during the height of the nancial crisis in 2008 and again after a short period of easing in 2011 and 2015.

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