Melanie Schienle

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In this paper, we analyze the properties of nonparametric estimators of a regression function when some covariates are not directly observed, but have only been estimated by some nonparametric procedure. We provide general results that can be used to establish rates of consistency or asymptotic normality in numerous econometric applications, including(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 firms’ systemic relevance as the(More)
We propose a novel approach to model serially dependent positive-valued variables which realize a non-trivial proportion of zero outcomes. This is a typical phenomenon in financial time series observed at high frequencies, such as cumulated trading volumes. We introduce a flexible point-mass mixture distribution and develop a semiparametric specification(More)
Standard fixed symmetric kernel type density estimators are known to encounter problems for positive random variables with a large probability mass close to zero. We show that in such settings, alternatives of asymmetric gamma kernel estimators are superior but also differ in asymptotic and finite sample performance conditional on the shape of the density(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 test(More)
∗ Frank Betz, European Investment Bank, 98-100 Boulevard Konrad Adenauer, 2950 Luxembourg, Luxemburg, email: Nikolaus Hautsch, University of Vienna, Department of Statistics and Operations Research, Oskar-Morgenstern-Platz 1, A-1090, Vienna, Austria, and Center for Financial Studies, Frankfurt, Germany, email:
In many applications, covariates are not observed but have to be estimated from data. We outline some regression-type models where such a situation occurs and discuss estimation of the regression function in this context. We review theoretical results on how asymptotic properties of nonparametric estimators differ in the presence of generated covariates(More)