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We propose the systemic risk beta as a measure for financial companiesâ€™ contribution to systemic risk given network interdependence between firmsâ€™ tail risk exposures. Conditional on statistically pre-identified network spillover effects and market and balance sheet information, we define the systemic risk beta as the time-varying marginal effect of aâ€¦ (More)

We propose a methodology for forecasting the systemic impact of financial institutions in interconnected systems. Utilizing a five-year sample including the 2008/9 financial crisis, we demonstrate how the approach can be used for timely systemic risk monitoring of large European banks and insurance companies. We predict firmsâ€™ systemic relevance as theâ€¦ (More)

We introduce a new model for time-varying spatial dependence. The model extends the wellknown static spatial lag model. All parameters can be estimated conveniently by maximum likelihood. We establish the theoretical properties of the model and show that the maximum likelihood estimator for the static parameters is consistent and asymptotically normal. Weâ€¦ (More)

In practice, multivariate dependencies between extreme risks are often only assessed in a pairwise way. We propose a test for detecting situations when such pairwise measures are inadequate and give incomplete results. This occurs when a significant portion of the multivariate dependence structure in the tails is of higher dimension than two. Our testâ€¦ (More)

- Julia Schaumburg
- 2010

This paper studies the performance of nonparametric quantile regression as a tool to predict Value at Risk (VaR). The approach is flexible as it requires no assumptions on the form of return distributions. A monotonized double kernel local linear estimator is applied to estimate moderate (1%) conditional quantiles of index return distributions. For extremeâ€¦ (More)

- Julia Schaumburg
- Computational Statistics & Data Analysis
- 2012

A framework is introduced allowing to apply nonparametric quantile regression to Value at Risk (VaR) prediction at any probability level of interest. A monotonized double kernel local linear estimator is used to estimate moderate (1%) conditional quantiles of index return distributions. For extreme (0.1%) quantiles, nonparametric quantile regression isâ€¦ (More)

We show that two alternative perspectives on how to deal with missing data in the context of the score-driven timevarying parameter models of Creal et al. (2013) and Harvey (2013) lead to precisely the same dynamic transition equations. As score-driven models encompass a wide variety of time-varying parameter models (including generalized autoregressiveâ€¦ (More)

We propose a novel observation-driven dynamic finite mixture model for the study of banking data. The model accommodates time-varying component means and covariance matrices, normal and Studentâ€™s t distributed mixtures, and economic determinants of time-varying parameters. Monte Carlo experiments suggest that banks can be classified reliably into distinctâ€¦ (More)

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